Technical Report: Appendices Report... · 2014-06-11 · This report has been divided into two parts: 1. Main report and 2. Appendices Citation: DPIW. (2008). Conservation of Freshwater

Conservation of Freshwater Ecosystem Values (CFEV) Project

Technical Report: Appendices

‘A strategic framework for statewide management and conservation of Tasmania’s freshwater ecosystem values’

Conservation of Freshwater Ecosystems Values Project

Water Assessment Branch

Water Resources Division

Department of Primary Industries and Water

Novemeber 2008

© Department of Primary Industries and Water, November 2008

Published by:

Water Resources Division

Department of Primary Industries and Water

GPO Box 44

Hobart Tas 7001

Telephone: (03) 6233 6328

Facsimile: (03) 6233 8749

Email: [email protected]

Website: www.dpiw.tas.gov.au/cfev

This report accompanies the Conservation of Freshwater Ecosystems Values database and outlines the methodology of conducting a statewide audit of Tasmania‟s freshwater-dependent ecosystem values, including the adopted assessment framework and data limitations.

Financial support contributed by the Tasmanian State Government via the Department of Primary Industries and Water and the Australian Government through the National Action Plan for Salinity and Water Quality (NAP) is gratefully acknowledged.

This report has been divided into two parts: 1. Main report and 2. Appendices

Citation: DPIW. (2008). Conservation of Freshwater Ecosystem Values (CFEV) Project Technical Report: Appendices. Conservation of Freshwater Ecosystem Values Project. Department of Primary Industries and Water, Hobart, Tasmania.

ISBN: 978-07246-6466-5

Cover photograph: Freshwater tarns, Mount Field National Park.

Scott Hardie 2003

Copyright

All material published in the report by the Department of Primary Industries and Water, as an agent of the Crown, is protected by the provisions of the Copyright Act 1968 (Cwlth). Other than in accordance with the provisions of the Act, or as otherwise expressly provided, a person must not reproduce, store in a retrieval system, or transmit any such material without first obtaining the written permission of the Department of Primary Industries and Water.

Disclaimer

Whilst the Department of Primary Industries and Water makes every attempt to ensure the accuracy and reliability of information published in this report, it should not be relied upon as a substitute for formal advice from the originating bodies or Departments. DPIW, its employees and other agents of the Crown will not be responsible for any loss, however arising, from the use of, or reliance on this information.

Contents

Contents

Contents .................................................................................................................. iii

1 Key contributors to the Conservation of Freshwater Ecosystem Value (CFEV) Project ................................................................................................. 1

2 Summary of data sources considered for use in the Conservation of Freshwater Ecosystem Values (CFEV) assessment ..................................... 8

3 Introduction to expert rule systems .............................................................. 18

3.1 Expert rule systems: a worked example .......................................................23

3.2 The data integration programs......................................................................26

4 Condition assessment expert rule systems ................................................. 32

4.1 Rivers ...........................................................................................................32

4.1.1 Sediment input ......................................................................................32

4.1.2 Flow change .........................................................................................32

4.1.3 Geomorphic condition ...........................................................................33

4.1.4 Macroinvertebrate condition ..................................................................33

4.1.5 Biological condition ...............................................................................34

4.1.6 Naturalness score .................................................................................35

4.2 Waterbodies .................................................................................................35

4.2.1 Hydrology ..............................................................................................35

4.2.2 Fish .......................................................................................................36

4.2.3 Sediment input ......................................................................................36


4.3 Wetlands ......................................................................................................38

4.3.1 Hydrology ..............................................................................................38

4.3.2 Native vegetation ..................................................................................39

4.3.3 Water quality .........................................................................................39


4.4 Saltmarshes .................................................................................................40

4.4.1 Land disturbance within the saltmarsh ..................................................40

4.4.2 Impacts within the saltmarsh .................................................................40

4.4.3 Land disturbance adjacent to the saltmarsh ..........................................41

4.4.4 Backing vegetation condition .................................................................41

4.4.5 Adjacent vegetation condition ...............................................................42

4.4.6 Impacts adjacent to the saltmarsh .........................................................42


4.5 Karst.............................................................................................................43

Contents

4.5.1 Hydrology ..............................................................................................43


5 Spatial selection algorithm ............................................................................ 45

5.1 Conservation inventory .................................................................................45

5.2 Conservation evaluation ...............................................................................45

5.2.1 The Conservation Priority Index (CPI) ...................................................45

5.2.2 Reverse-ranked rarity ............................................................................48

5.2.3 Quality of representation .......................................................................49

5.3 Spatial selection ...........................................................................................49

5.3.1 River clusters ........................................................................................51

5.4 Evaluation of results .....................................................................................53

6 Development of data sets .............................................................................. 58

6.1 Glossary of metadata elements ....................................................................58

6.2 Spatial data layers ........................................................................................60

6.2.1 Acid drainage ........................................................................................60

6.2.2 Buffer zone ...........................................................................................60

6.2.3 Burrowing crayfish regions ....................................................................62

6.2.4 Catchment disturbance .........................................................................62

6.2.5 Crayfish regions ....................................................................................63

6.2.6 Digital elevation model ..........................................................................65

6.2.7 Estuaries ...............................................................................................66

6.2.8 Fluvial geomorphic mosaics ..................................................................66

6.2.9 Fluvial geomorphic river types ............................................................. 112

6.2.10 Frog assemblages .............................................................................. 126

6.2.11 Geomorphic responsiveness ............................................................... 128

6.2.12 Groundwater Dependent Ecosystems ................................................. 131

6.2.13 Hydrological regions............................................................................ 132

6.2.14 Karst ................................................................................................... 136

6.2.15 Land Tenure Security .......................................................................... 136

6.2.16 Land use (nutrients) ............................................................................ 138

6.2.17 Macroinvertebrate assemblages ......................................................... 141

6.2.18 Major drainage catchments ................................................................. 151

6.2.19 Mining sedimentation .......................................................................... 151

6.2.20 Modified TASVEG ............................................................................... 152

6.2.21 Native fish assemblages ..................................................................... 153

6.2.22 Platypus condition ............................................................................... 168

6.2.23 Riparian vegetation condition .............................................................. 170

Contents

6.2.24 Rivers (drainage network) ................................................................... 171

6.2.25 River Section Catchments ................................................................... 173

6.2.26 Saltmarshes ........................................................................................ 176

6.2.27 Sub-catchments .................................................................................. 177

6.2.28 Tidal/wave energy regime ................................................................... 178

6.2.29 Tree assemblages .............................................................................. 180

6.2.30 Tyler corridor ....................................................................................... 190

6.2.31 Urbanisation ........................................................................................ 191

6.2.32 Waterbodies ........................................................................................ 192

6.2.33 Waterbody catchments ....................................................................... 194

6.2.34 Wetlands ............................................................................................. 195

6.2.35 Willows................................................................................................ 197

6.3 Attribute data .............................................................................................. 199

6.3.1 Abstraction index ................................................................................ 199

6.3.2 Acid drainage ...................................................................................... 204

6.3.3 Area .................................................................................................... 204

6.3.4 Burrowing crayfish .............................................................................. 205

6.3.5 Catchment disturbance ....................................................................... 205

6.3.6 Conservation Management Priority – Immediate ................................. 209

6.3.7 Conservation Management Priority – Potential .................................... 213

6.3.8 Crayfish regions .................................................................................. 216

6.3.9 Elevation ............................................................................................. 218

6.3.10 Estuaries biophysical classification ..................................................... 219

6.3.11 Exotic fish impact ................................................................................ 221

6.3.12 Flow variability index ........................................................................... 229

6.3.13 Fluvial geomorphic mosaics ................................................................ 231

6.3.14 Fluvial geomorphic river types ............................................................. 232

6.3.15 Frog assemblages .............................................................................. 232

6.3.16 Geomorphic responsiveness ............................................................... 233

6.3.17 Hydrological regions............................................................................ 233

6.3.18 Integrated Conservation Value ............................................................ 233

6.3.19 Karst catchments ................................................................................ 235

6.3.20 Karst catchment size ........................................................................... 237

6.3.21 Karst physical classification ................................................................. 237

6.3.22 Karst physical sensitivity ..................................................................... 243

6.3.23 Lake level manipulation ....................................................................... 244

6.3.24 Land Tenure Security .......................................................................... 245

Contents

6.3.25 Land use (nutrients) ............................................................................ 246

6.3.26 Lateral extent of backing vegetation .................................................... 248

6.3.27 Long axis orientation ........................................................................... 249

6.3.28 Macroinvertebrate assemblages ......................................................... 250

6.3.29 Macroinvertebrate O/E rank abundance .............................................. 251

6.3.30 Macrophyte assemblages ................................................................... 255

6.3.31 Mean annual run-off ............................................................................ 259

6.3.32 Mining sedimentation .......................................................................... 263

6.3.33 Native fish assemblages ..................................................................... 264

6.3.34 Native fish condition ............................................................................ 266

6.3.35 Naturalness-Representativeness Class............................................... 268

6.3.36 Platypus condition ............................................................................... 270

6.3.37 Regulation index ................................................................................. 271

6.3.38 Representative Conservation Value .................................................... 276

6.3.39 Riparian vegetation condition .............................................................. 281

6.3.40 River section numbering ..................................................................... 282

6.3.41 Saltmarsh biophysical classification .................................................... 284

6.3.42 Saltmarsh location .............................................................................. 285

6.3.43 Saltmarsh vegetation .......................................................................... 286

6.3.44 Shoreline complexity ........................................................................... 286

6.3.45 Spartina anglica (rice grass) ................................................................ 288

6.3.46 Special values ..................................................................................... 289

6.3.47 Stream order (position in drainage) ..................................................... 327

6.3.48 Tidal/wave energy regime ................................................................... 328

6.3.49 Tree assemblages .............................................................................. 329

6.3.50 ‘Tyler’ biogeochemical classification .................................................... 329

6.3.51 Tyler corridor ....................................................................................... 332

6.3.52 Urbanisation ........................................................................................ 333

6.3.53 Waterbody artificiality .......................................................................... 333

6.3.54 Waterbody depth ................................................................................. 334

6.3.55 Waterbodies physical classification ..................................................... 335

6.3.56 Wetland catchments............................................................................ 338

6.3.57 Wetlands physical classification .......................................................... 339

6.3.58 Wetland vegetation ............................................................................. 342

6.3.59 Wetland vegetation condition .............................................................. 344

6.3.60 Width of backing vegetation ................................................................ 345

6.3.61 Willows................................................................................................ 346

Contents

7 Data validation layers ................................................................................... 348

8 Limnological publications by Professor Peter Tyler and associates ....... 350

9 Ramsar and Directory of Important Wetlands in Australia (DIWA) Wetlands ...................................................................................................................... 355

10 Conservation assessment of estuaries by Edgar et al. (1999a) ................ 358

11 Karst areas priortised by the Karst Atlas .................................................... 361

12 Vegetation communities (Tasmanian Vegetation Map (TASVEG)) ............ 368

13 In-lake impact scores ................................................................................... 374

14 Major dams and waterbodies flow variability ratings ................................ 389

15 References .................................................................................................... 393

Appendix 1 – Contributors

Conservation of Freshwater Ecosystem Values Project Technical Report -Appendices 1

1 Key contributors to the Conservation of Freshwater Ecosystem Value (CFEV) Project

The following tables provide lists of people who contributed to various aspects of the CFEV Project. Membership is current as at the finalisation of the CFEV assessment and CFEV database (December 2005) and the organisation stated is the one they represented at the time of their input.

Water Resources Steering Committee

Alistair Brooks* Department of Primary Industries, Water and Environment

Wes Ford Department of Primary Industries, Water and Environment

Alan Harradine Department of Primary Industries, Water and Environment

Warren Jones Department of Primary Industries, Water and Environment

John Pauley* Department of Primary Industries, Water and Environment

Alex Schaap* Department of Primary Industries, Water and Environment

Mike Temple-Smith* Department of Primary Industries, Water and Environment

John Whittington Department of Primary Industries, Water and Environment

* ex-member of committee

CFEV Project team

John Gooderham Department of Primary Industries, Water and Environment

Danielle Hardie Department of Primary Industries, Water and Environment

Tristan Harradine* Department of Primary Industries, Water and Environment

Kate Hoyle* Department of Primary Industries, Water and Environment

Jessemy Stone* Department of Primary Industries, Water and Environment

John Whittington* Department of Primary Industries, Water and Environment

* ex-member of team

Technical Management Group

Chris Bobbi Department of Primary Industries, Water and Environment

Mick Brown Private consultant

Peter Davies Freshwater Systems

Helen Dunn Landmark Consulting


Kate Hoyle* Department of Primary Industries, Water and Environment

Scott Marston* Department of Primary Industries, Water and Environment

Martin Read Department of Primary Industries, Water and Environment

Jessemy Stone* Department of Primary Industries, Water and Environment



Project Reference Group


2 Conservation of Freshwater Ecosystem Values Project Technical Report-Appendices

Mike Askey-Doran Department of Primary Industries, Water and Environment

Peter Bosworth* Department of Primary Industries, Water and Environment


Troy Collie Hydro Tasmania


John Diggle* Inland Fisheries Service


Colin Dyke Tasmanian Fishing Industry Council and Tasmanian Aquaculture Council

Jeff Gilmore* Department of Primary Industries, Water and Environment


Mick Howland* Hydro Tasmania

David Jarvis Inland Fisheries Service

Scott Marston* Department of Primary Industries, Water and Environment

Sarah Munks Forest Practices Board

Jessemy Stone Department of Primary Industries, Water and Environment

Penny Wells* Forestry Tasmania


Ian Whyte Tasmanian Farmers and Graziers Association

Craig Woodfield Tasmanian Conservation Trust

Allison Woolley* Forestry Tasmania

Marie Yee Forestry Tasmania


Environmental consultants


Steve Carter Environmental Dynamics



Rod Knight Rod Knight GIS Services

Lois Koehnken Technical Advice on Water

Frances Mowling

Scientific Working Groups

Conservation objectives

Peter Bosworth Department of Primary Industries, Water and Environment






Scott Marston Department of Primary Industries, Water and Environment

Penny Wells Department of Primary Industries, Water and Environment


Estuaries

Neville Barrett Tasmanian Aquaculture and Fisheries Institute

Christine Crawford Tasmanian Aquaculture and Fisheries Institute


Graham Edgar Tasmanian Aquaculture and Fisheries Institute

Alan Jordan Tasmanian Aquaculture and Fisheries Institute

Ray Murphy Department of Primary Industries, Water and Environment

Colin Shepherd Department of Primary Industries, Water and Environment

Fish distribution


John Diggle Inland Fisheries Service

Mick Howland Hydro Tasmania

Jean Jackson Inland Fisheries Service


Fluvial geomorphology



Grant Dixon Department of Tourism, Parks, Heritage and the Arts


Joanna Ellison University of Tasmania

John Foster Department of Primary Industries, Water and Environment

Ian Houshold Department of Primary Industries, Water and Environment

Kathryn Jerie Department of Primary Industries, Water and Environment

Kevin Kiernan University of Tasmania

Lois Koehnken Technical Advice on Water

Helen Locher Hydro Tasmania


Michael Pemberton Department of Primary Industries, Water and Environment

Chris Sharples Department of Primary Industries, Water and Environment

Peter Stronach Department of Primary Industries, Water and Environment


Groundwater Dependant Ecosystems

Michael Askey-Doran Department of Primary Industries, Water and Environment



Leon Barmuta University of Tasmania

Richard Barnes Department of Primary Industries, Water and Environment

Stewart Blackhall Department of Primary Industries, Water and Environment

Jason Bradbury Department of Primary Industries, Water and Environment


Arthur Clarke University of Tasmania

Sib Corbett Department of Primary Industries, Water and Environment

Gary Davidson University of Tasmania


Jenny Deakin Department of Primary Industries, Water and Environment

David Dettrick Department of Primary Industries, Water and Environment

Grant Dixon Parks and Wildlife Service

Niall Doran Department of Primary Industries, Water and Environment

Nathan Duhig Forest Practices Board


Rolan Eberhard Department of Primary Industries, Water and Environment

Louise Gilfedder Department of Primary Industries, Water and Environment



Anne Kitchener Department of Primary Industries, Water and Environment

Miladin Latinovic Mineral Resources Tasmania

David Leaman Private consultant

Loyd Matthews

Peter McIntosh Forestry Tasmania

Bob Mesibov Queen Victoria Museum and Art Galley

Lindsay Millard Department of Primary Industries, Water and Environment


Alistair Richardson University of Tasmania


Chris Sharples Geoconservation consultant

Sarah Tassell University of Tasmania

Danielle Warfe Department of Primary Industries, Water and Environment

Jennie Whinam Department of Primary Industries, Water and Environment

Geomorphic condition assessment

Steven Carter Environmental Dynamics


Peter Stronach Department of Primary Industries, Water and Environment





Guy Lampert Department of Primary Industries, Water and Environment

Helen Locher Hydro Tasmania

Henry Maxwell Department of Primary Industries, Water and Environment


Karst


Jenny Deakin Department of Primary Industries, Water and Environment




Alastair Richardson University of Tasmania

Lakes and Wetlands


Chris Bobbi Department of Primary Industries, Water and Environment



Mick Howland Hydro Tasmania


Janet Smith University of Tasmania

Peter Tyler Deakin University

Adam Uytendaal Inland Fisheries Service

Micah Visoiu Department of Primary Industries, Water and Environment

Macrophytes

Michael Askey-Doran Department of Primary Industries, Water and Environment

Kate Chappell


Micah Visoiu Department of Primary Industries, Water and Environment

Frogs

John Ashworth




Karyl Michaels World Wide Fund for Nature

David Peters Department of Primary Industries, Water and Environment



Birds


Ralph Cooper Birds Tasmania


Els Hayward Birds Tasmania

Priscilla Park Birds Tasmania

Bill Wakefield Birds Tasmania

Eric Woehler Birds Tasmania

Special Values

Richard Ashby Birds Tasmania

John Ashworth Birds Tasmania

Michael Askey-Doran** Department of Primary Industries, Water and Environment


Peter Bosworth Department of Primary Industries, Water and Environment

Sally Bryant Department of Primary Industries, Water and Environment

Mick Brown** Private consultant

Alex Buchanan Tasmanian Herbarium

Ralph Cooper Birds Tasmania

Peter Davies** Freshwater Systems

Richard Donahey Birds Tasmania

Niall Doran** Department of Primary Industries, Water and Environment

Michael Driessen Department of Primary Industries, Water and Environment

Peter Duckworth Birds Tasmania

Fred Duncan Forest Practices Board

Helen Dunn** Landmark Consulting


Graham Edgar Tasmanian Aquaculture and Fisheries Institute

Rae Glazik Department of Primary Industries, Water and Environment

Jane Gudde

Michael Hammer University of Adelaide

John Hawking Murray Darling Freshwater Research Centre

Danielle Hardie** Department of Primary Industries, Water and Environment

Els Hayward Birds Tasmania

Jim Hunter Birds Tasmania


Paul Hydes Birds Tasmania

Jean Jackson Inland Fisheries Service

David Jarvis Inland Fisheries Service

Peter Last Commonwealth Scientific and Industrial Research Organisation



Sarah Lloyd Birds Tasmania

Jessemy Long** Department of Primary Industries, Water and Environment


Peter McQuillan University of Tasmania

Karyl Michaels Worldwide Fund for Nature

Sarah Munks Department of Infrastructure, Energy and Resources

Priscilla Park Birds Tasmania

Wendy Potts Department of Primary Industries, Water and Environment

Karen Richards** Forest Practices Board

Alastair Richardson** University of Tasmania

Brian Smith Queen Victoria Museum and Art Gallery

Jim Spinks Birds Tasmania

Bill Wakefield Birds Tasmania

Robert Walsh CSIRO

Mark Wapstra** Forest Practices Board

Penny Wells Department of Primary Industries, Water and Environment


George „Buz‟ Wilson Australian Museum

Eric Woehler Birds Tasmania

Allison Woolley** Forestry Tasmania

** contributed to establishing criteria

Input to other data layers

Shivaraj Gurung Department of Primary Industries, Water and Environment

John Pemberton Mineral Resources Tasmania

Geographic Information System

Mark Brown Department of Primary Industries, Water and Environment

Chris Collins Department of Primary Industries, Water and Environment

John Corbett Department of Primary Industries, Water and Environment

Ruiping Gao Department of Primary Industries, Water and Environment

Felicity Hargraves Department of Primary Industries, Water and Environment

David Peters Department of Primary Industries, Water and Environment

Simon Pigot Department of Primary Industries, Water and Environment

Colin Reed Department of Primary Industries, Water and Environment

Wengui Su Department of Primary Industries, Water and Environment

Michael Varney Department of Primary Industries, Water and Environment

Appendix 2 – Datasets considered for the CFEV assessment


2 Summary of data sources considered for use in the Conservation of Freshwater Ecosystem Values (CFEV) assessment

Table 1 This appendix presents a table of data sources which were considered for use in the CFEV assessment. The table indicates if and how the data was used and a brief summary of the rationale for inclusion or exclusion.

Data Custodian Ecosystem theme Used Justification

Acid drainage Mineral Resources Tasmania (MRT)

Rivers Y River condition assessment:

Used to develop mining sedimentation layer.

Input into the modelling of macroinvertebrate Observed/Expected (O/E) rank abundance.

Input into native fish condition rules.

Aerial photos Service Tasmania – Department of Primary Industries and Water (DPIW)

Saltmarshes Y Saltmarsh condition assessment:

Used to assess various condition variables.

Algae Water Resources Division (WRD) – DPIW

Rivers

Waterbodies

Wetlands

Estuaries

N No consistent statewide dataset on either benthic or planktonic algae in any ecosystem type. Distributions or measures could not be mapped or reliably modelled.

Astacopsis gouldi distribution

Inland Fisheries Service (IFS)

Rivers

Waterbodies

N Not statewide coverage.

Astacopsis sp. distributions

School of Zoology – University of Tasmania (UTas)

Forestry Tasmania

Rivers

Waterbodies

Y River and waterbody classification:

Used to develop crayfish (Astacopsis genus) regionalisation.

Australian River Assessment System (AUSRIVAS) – benthic macroinvertebrates

WRD – DPIW

Freshwater Systems

Rivers Y River classification:

Family level data plus species data for some insect orders (Ephemeroptera, Plecoptera and Trichoptera) derived from original samples. Used to develop macroinvertebrate assemblages.

River condition assessment:

Input into the modelling of macroinvertebrate O/E rank abundance.




Biogeochemical/ limnological data

Professor Peter Tyler (data and publications)

Rivers

Waterbodies

Wetlands

Y Tasmanian limnological publications by Tyler and colleagues were reviewed.

Special Values (SVs) assessment:

Assessed in the nomination of priority limnological sites.


Used in the statistical analyses prior to modelling the O/E rank abundance.

Used to develop the Tyler corridor, which was used as a modifier for the assessment of sediment input.

River classification:

Input into the macrophyte assemblage rules.

Waterbodies classification:

Input into the Tyler classification rules.

Used to set thresholds for waterbody depth.

Wetlands classification:

Input into the Tyler classification rules.

Input into the physical classification rules.

Biophysical Naturalness (BPN) Layer

Information and Land Services (ILS) Division – DPIW

Rivers (primarily)

Y River condition assessment :

Used as an input into the catchment disturbance data layer and assigned through the River Section Catchments (RSCs) to other ecosystem themes.

Bird distribution Birds Australia Waterbodies

Wetlands

Saltmarshes

Estuaries

N Distribution data analysed and mapped. Preliminary classifications of regional assemblages conducted, but deemed unreliable. Distributions could not be reliably modelled.

Burrowing crayfish School of Zoology – UTas

(Assoc. Prof. Alistair Richardson)

Wetlands Y Special Values (SVs) assessment:

Used to assign burrowing crayfish distributions for threatened fauna species assessment.

Wetland classification:

Used to develop burrowing crayfish regionalisation.

Catchment clearance Forestry Tasmania

Private forest companies

Rivers

Waterbodies

Estuaries

Wetlands

Karst

N Data not publicly available




Climate data (rainfall and evaporation)

Bureau of Meteorology Rivers (primarily)

Y Condition :

Effective precipitation layer used to develop Mean Annual Run-off (MAR) data layer which was assigned through the River Section Catchments (RSCs) to other ecosystem themes..

River classification:

Used to model the tree assemblages

Dams databases DPIW (Water Information Management System (WIMS)) – farm dams

Hydro Tasmania – Hydro dams (see also Hydro infrastructure and discharge data set below)

Rivers

Waterbodies

Wetlands

Karst

Y River condition assessment:

Used in the rules for calculating the flow variation, abstraction and regulation indices.

Used in the native fish and exotic fish condition rules.

Waterbody condition assessment:

Used in the rules for calculating the abstraction and regulation indices and waterbody condition.

Wetland condition assessment:

Used in the rules for calculating the abstraction index.

Karst condition assessment:

Used in the rules for calculating the abstraction and regulation indices.

Diatoms Deakin University Professor Peter Tyler

DPIW

Freshwater Systems

Waterbodies

Rivers

N High quality data restricted to waterbodies. Insufficient coverage to characterise all waterbodies and rivers across state.

Directory of Important Wetlands in Australia (DIWA) inventory

Resource Management and Conservation (RMC) Division – DPIW

Rivers

Waterbodies

Wetlands

Saltmarshes

Estuaries

N Not based on systematic assessment for all values or wetland types. Only includes a selection of (high value) wetlands.

Environmental flows WRD – DPIW Rivers N No consistent statewide coverage of environmental flow provisions or implementation.

Environmental Monitoring of Marine farms

Primary Industries Division – DPIW

Estuaries N Data not consistent across estuaries statewide.




Estuarine fish Commonwealth Scientific and Industrial Research Organisation (CSIRO)

DPIW

Estuaries Y Estuary classification:

Used as input into Edgar et al. (1999a)‟s estuary biophysical classification.

Estuarine health Tasmanian Aquaculture and Fisheries Institute (TAFI)

Estuaries N Not a statewide coverage. Only conducted on 22 estuaries.

Estuary bioregions TAFI Estuaries Y Estuary classification:


Estuary biota TAFI/UTas Estuaries Y Estuary classification:


Estuary catchment boundaries

Hydro Tasmania Estuaries N CFEV generated own catchment boundaries using 1:25 000 drainage network to be consistent with rivers mapping.

Data Custodian Ecosystem theme Used by the CFEV Project

Justification

Estuary physical classification

TAFI Estuaries Y Estuary classification:


Exotic fish presence and biomass

Freshwater Systems

IFS

Rivers

Waterbodies

Y River condition assessment:

Distribution data collated and used with and biomass-environmental relationship from Davies (1989) to develop exotic fish impact data layer.

Waterbody condition assessment:

Distribution data used to develop the exotic fish impact data layer.

Floodplain vegetation communities/species

DPIW Rivers

Wetlands

N No statewide coverage.




Frog distribution World Wildlife Fund (WWF)

Tasmanian Conservation Trust (TCT)

RMC – DPIW

Waterbodies

Wetlands

N Waterbody classification:

Regional frog assemblages developed by experts and mapped.


Regional frog assemblages developed by experts and mapped.

Geoconservation database

RMC – DPIW All ecosystem themes Y Special Values (SVs) assessment:

Selected sites used to as an input into the priority geomorphic features assessment.

Geomorphic river characterisation

RMC – DPIW Rivers N No statewide coverage.

Geomorphic river condition

RMC – DPIW Rivers N No statewide data available.

Geomorphic river regionalisation

RMC – DPIW Rivers

Waterbodies

Wetlands

Y River classification:

Used to develop the fluvial geomorphic mosaics, which were in turn used to identify river types.

River condition assessment

Geomorphic mosaics used to assess geomorphic responsiveness, which was used to set a context for the geomorphic condition assessment.

Waterbodies classification

Used to develop fluvial geomorphic mosaics, which were then grouped and input into the physical classification.

Wetland classification

Geomorphic mosaics used to assess geomorphic responsiveness, which was used as an input into the physical classification.

Geo Temporal Species Point Observations Tasamania (GTSpot)


Location data of freshwater dependent species were used in the assessment of threatened flora and fauna species, priority flora and fauna species and fauna species of phylogenetically distinct species.

Human population density

TAFI Estuaries Y Estuary condition assessment:

Used as input into Edgar et al. (1999a)‟s estuary condition assessment.




Hydro infrastructure discharge data

Hydro Tasmania Rivers Y Condition (general):

Used to develop the „Current‟ MAR data layer.


Used in the rules for calculating the flow variation, abstraction and regulation indices.

Hydrological classification

Dr Jocelyne Hughes Rivers Y River classification:

Hydrological classification used to derive broad regions. Used in river classification.

Hydrological regionalisation

WRD – DPIW Rivers N No statewide coverage of gauging stations.

Interim Biogeographic Regionalisation of Australia (IBRA)

ILS – DPIW All Ecosystem themes

N Bioregionalisation based on terrestrial biotic information. Deemed not directly relevant – confirmed by cross-comparison.

IBRA tree assemblages ILS – DPIW Rivers

Waterbodies

Wetlands

Y Classification (general):

Used as the basis for the development of the reference tree assemblages for rivers, waterbodies and wetlands.

Index of Stream Condition (ISC)

WRD – DPIW Rivers N No a statewide coverage.

Karst Atlas RMC – DPIW Karst Y Karst spatial units:

Used to as the basis of development of karst spatial units.

Karst classification:

Selected variables used as input into the karst physical classification.

Lake Sorell and Crescent Rehabilitation Project

IFS Waterbodies

Wetlands

N Limited to only these lakes/wetlands. Could be used as a reference on a case by case basis as required.

Land Information Systems Tasmania (LIST) Coastline

ILS – DPIW All ecosystem themes Y Catchments:

Used in the development of the nested set of catchments – catchments, sub-catchment and River Section Catchments (RSCs).

LIST Contours ILS – DPIW All ecosystem themes Y Catchments:

Used in the development of the nested set of catchments – catchments, sub-catchment and RSCs.

Digital Elevation Model (DEM):

Used in the development of the DEM, which in turn was used to identify the elevation of waterbodies and wetlands.




LIST Floodplain form and character

ILS – DPIW Rivers

Wetlands

N No consistent statewide coverage or consistent hydrological basis.

LIST Hydrographic theme

ILS – DPIW All ecosystem themes Y Spatial units:

Used in the development of the rivers, waterbodies, wetlands and estuaries base data layers.

Catchments:

Used in the development of the nested set of catchments – catchments, sub-catchment and RSCs.

Condition assessment (general):

Used in the development of the buffer zone data layer.

LIST Land Tenure ILS – DPIW All ecosystem themes Y Land Tenure Security (LTS)

Used in the development of the LTS data layer.

LIST Land Use ILS – DPIW All ecosystem themes Y Condition assessment (general):

Used in the development of the catchment disturbance data layer used in the river, waterbody, wetland and karst condition assessment.

Used in the development of the land use (nutrients) data layer for waterbodies and wetlands.

LIST Roads ILS – DPIW Rivers Y River condition assessment:

Input into the modelling of macroinvertebrate O/E rank abundance.

LIST Topographic data layer

ILS – DPIW Rivers (primarily) Y Spatial units:

Used in the development of the rivers base data layer (drainage network).

LIST Waterbodies ILS – DPIW Waterbodies Y Spatial units:

Used in the development of the waterbodies base data layer.


Used on the development of the farm dams data layer which was used in the development of attributes for assessing regulation for rivers and abstraction in the river, waterbody and wetland condition assessment. The farms dams data layer was also a consideration in the exotic fish index for the river condition assessment.

LIST Wetlands ILS – DPIW Wetlands Y Spatial units:

Used in conjunction with the Tasmanian Vegetation Map (TASVEG) data layer in the development of the wetlands base data layer.

Macroinvertebrates – soft sediment assemblages

TAFI/UTas Estuaries Y Estuary classification:

used as input into Edgar et al. (1999a)‟s estuary biophysical classification?

Marine Farms TAFI Estuaries N No uniform data on the impacts of marine farms on estuary condition.




Monitoring River Health Initiative (MRHI) macrophytes

WRD – DPIW Rivers

Waterbodies

Wetlands

Estuaries

N Data limited to visual % cover estimates.

National Estate palaeobotanical sites

School of Plant Science – UTas

All ecosystem themes Y Special Values (SVs) assessment:

Selected sites used as an input into the palaeobotanical sites assessment.

Native fish distribution (Regional Forest Agreement (RFA) Fish Database)

IFS

Freshwater Systems

Rivers

Waterbodies

N River classification:

Used to assist in checking mapping rules for selected species, which formed basis for fish assemblage classes.

Non-forest covenants (Non-forest Vegetation Program (NFVP))

RMC – DPIW All ecosystem themes Y Land Tenure Security (LTS)


Protected areas (Protected Areas on Private Land (PAPL))



Private Forests (Private Forest Reserves Program (PFRP))



Platypus distribution Forest Practices Authority (FPA)

All ecosystem themes Y Special Values (SVs) assessment:

Sites used as an input into the fauna species of phylogenetic distinctiveness assessment.

Platypus Mucor disease Forest Practices Authority

Rivers Y River condition assessment:

Used to develop the platypus condition data layer.

Ramsar wetlands RMC – DPIW Waterbodies

Wetlands

Estuaries

N Not based on systematic assessment for all values or wetland types. Only includes a selection of (high value) wetlands.

Regional Forestry Agreement (RFA) Reserves

ILS – DPIW All ecosystem themes Y Land Tenure Security (LTS)


Riparian vegetation clearance

DPIW Rivers N No consistent data available.

Riparian vegetation communities/species

School of Geography and Environment – UTas (Dr Elizabeth Daley

Rivers

Waterbodies

Estuaries

Wetlands

N Not a statewide coverage. Not publicly available.




Riparian vegetation abundance

School of Geography and Environment – UTas (Dr Elizabeth Daley)

Rivers

Waterbodies

Wetlands

N Not a statewide coverage. Not publicly available.

Saltmarsh fauna School of Zoology – UTas

Saltmarshes N Not enough statewide coverage. Data only collected from 52 saltmarshes around the state whereas CFEV included 335 saltmarshes.

Saltmarsh vegetation communities

School of Geography and Environment – UTas

Saltmarshes N Not enough statewide coverage.

Seagrass TAFI Estuaries N No statewide coverage at the time of assessment.

SEAMAP Tasmania TAFI Estuaries N Patchy coverage – not statewide.

Spartina distribution Primary Industries Division – DPIW

Saltmarshes Y Saltmarsh condition assessment:

Used in the assessment of the Spartina adjacent to saltmarshes.

Tasmanian Catchment Mapping

Parks and Wildlife Service – DTAE

All ecosystem themes N Maps boundaries of catchments of all rivers and grouped into regions. Regions are based on local government boundaries so the data layer is essentially devised for management planning (Dunn 2002).

TASVEG RMC – DPIW Rivers

Waterbodies

Wetlands

Saltmarshes

Karst

Y Spatial units:

Selected vegetation communities used in conjunction with the LIST wetlands to develop the wetlands base data layer.

Selected vegetation communities used to develop the saltmarshes base data layer.


Used in the development of the dominant wetland vegetation data layer.

Saltmarsh classification:

Used in the development of the dominant saltmarsh vegetation data layer, which was input into the saltmarsh biophysical classification.


Used in the development of the urbanisation data layer.


Used in the development of the catchment disturbance data layer for rivers, waterbodies, wetlands and karst.

Used in assessment of riparian vegetation condition for rivers, waterbodies and wetlands.

SV assessment:

Used in the development of distribution data for the threatened and priority vegetation community assessment.




Threatened flora and fauna species distribution


Location data of freshwater dependent species were used in the assessment of threatened flora and fauna species, priority flora and fauna species and fauna species of phylogenetically distinct species.

Water Assessment and Planning (WAP) Stream Gauging Data

WRD – DPIW Rivers (primarily) Y Condition (general):

Used to validate Mean Annual Runoff data.

Water quality WRD – DPIW Rivers N No consistent data sets for statewide mapping or modelling.

Waterway health Hydro Tasmania Rivers N Only Hydro storages and rivers. Not statewide coverage.

Wave/tidal energy TAFI Saltmarshes Y Saltmarsh classification:

Regionalisation developed from data in Edgar et al. (1999a) used as an input into

the saltmarsh biophysical classification.

Wetlands audit RMC – DPIW Wetlands N Not based on systematic assessment for all values or wetland types. Only includes a selection of (high value) wetlands.

Wetlands of Tasmania RMC – DPIW Wetlands N Not based on systematic assessment for all values or wetland types. Only includes a selection of (high value) wetlands.

Wild Rivers data RMC – DPIW Rivers N Used different criteria and developed only for identifying „wild‟ rivers. Not consistent with requirements for the CFEV naturalness scoring and data out of date. Not mapped at 1: 25 000 scale and only included high condition rivers.

Willows data TCT Rivers Y River condition assessment:

Used as in the development of the willows data layer then as a modifier for riparian vegetation condition assessment.

Water Information Management System (WIMS)

WRD – DPIW Rivers

Waterbodies

Wetlands

Karst

Y Condition assessment (general):

Dam and licensed abstraction data used in the development of the regulation index for rivers and the abstraction index for rivers, waterbodies, wetlands and karst.

Zooplankton IFS

School of Zoology – UTas

Waterbodies N No consistent statewide data available.

Appendix 3 – Introduction to expert rule systems


3 Introduction to expert rule systems

In the Conservation of Freshwater Ecosystem Values (CFEV) assessment, the Naturalness score (N-score) of each ecosystem theme (except estuaries) was derived by combining a set of condition variables using expert rule systems. This method allows a number of relevant condition variables to be incorporated to form an overall assessment of condition or N-score that can incorporate expert opinion on the relative importance of each of the contributing condition variables.

This appendix provides some technical background information to introduce the concept of expert rules systems (including an example) and its application to the CFEV Project.

The expert rule systems (fuzzy logic) approach was considered the preferred method for integration of condition variables, by the CFEV Project‟s Technical Management Group (TMG), because it:

can integrate disparate types of inputs

allows more sophisticated integration than mathematical averaging methods

incorporates expert opinion about combinations of input condition variables and interactions

could be encoded in scripts and run as an executable file

can deal with different data ranges

implicitly handles data with „fuzzy‟ relationships i.e. ones in which sharp boundaries are known not to exist within data ranges.

Many other data integration methods are available, such as weighted averages and scoring systems. The choice of method depends on the specific integration needs. An expert rule system is a suitable tool if:

relating the input variables to the output is difficult using ordinary mathematics, but experts are available who can put the relationship into words, or

the values of the input variables are not known with certainty.

Expert rule systems use a series of logical statements or rules, to reason about data. They are useful as they can handle problems with imprecise or incomplete data. The rules in an expert rule system are usually in the form similar to:

if x is low and y is high then z = medium

where x and y are the input variables (known data values), z is an output variable (data value to be computed), low and high are the membership functions defined for the input variables x and y, and medium is the membership function defined for the output variable, z. Appendix 3.1goes through a worked example to explain this concept further.

For each ecosystem theme listed in Table 2, a number of condition variables were developed using expert rule systems. In some cases, a sub-index was developed by integrating multiple condition variables, before finally being integrated with other condition variables to generate an overall, single index – the N-score. The number of condition variables and the proportion of these that were produced using expert rule systems are presented in Table 2.



Table 2. Number and proportion of condition variables developed using expert rule systems, for each ecosystem theme.

Ecosystem theme

Number or variables produced using expert rule systems

Proportion of variables produced using expert rule systems

Karst 2 0.2

Saltmarshes 7 0.3

Rivers 6 0.3

Waterbodies 4 0.3

Wetlands 4 0.4

In brief, the development of an expert rule system can be viewed as a five-step process:

1. Define the input and output variables.

2. Define suitable „membership functions‟ for each variable.

3. Construct a definition table

4. Refine the expert rule system until its „decision surface‟ is acceptable.

These steps were applied to the CFEV Project as follows:

Step 1. Defining the input data and output variables

A series of expert workshops were used to identify the key drivers for each ecosystem theme, and their associated variables and data sets. The approach to integrating the condition variables into an N-score was agreed, and in each case the approach was to use a suite of expert rule systems to integrate sub-groups of variables into intermediate indices, before using a final expert rule system to integrate the intermediate indices into an N-score.

To simplify the exercise, most condition variables were converted to a 0-1 scale prior to their integration. The 0-1 scale was developed so that a low value (e.g. approaching 0) described poor condition, and a high value (e.g. approaching 1) described good condition.

The conversion of each variable to the 0-1 scale was done by a pre-processing step agreed on by the TMG. Similarly, all the expert rule systems calculate their output indices on the 0-1 scale.

Step 2. Define suitable „membership functions‟ for each variable

A membership function is a curve that defines how each value of the input variable data satisfies the concept, in this case, of being in High or Low condition.

They do this by assigning a value (or degree of membership) between 0 and 1 (The Mathsworks, 2008). for each possible variable value, according to the degree to which each value satisfies the concept.

An example membership function for both High and Low is shown in Figure 1. In this case, the variable completely satisfies the concept of High value (degree of membership = 1) when the variable value = 1. At lower variable values, the degree of membership decreases, so that if the variable value is 0.25 (moderate to poor condition), the degree of membership is low (0.1). Similarly for the concept of Low



condition, the variable completely satisfies the concept of Low condition (degree of membership = 0) when the variable value = 0, but still closely matches the concept at a variable value of 0.25 (so that the degree of membership is high at 0.9).

Figure 1. A simple example of membership functions for High and Low condition.

The „degree of membership‟ scale and the membership function is said to „fuzzify‟ the concept of High or Low condition, since a degree of membership that lies between 0 and 1 denotes a value of the variable that only partly satisfies the concept (The Mathsworks, 2008). The membership functions were designed to facilitate the definition table approach to prescribing expert rule systems, described in Step 3.

Membership functions are specific for each input variable and output index combination. The same variable may have a different membership function when it is used to develop a different output index depending on how experts consider it to behave in each context. Figure 4 shows a more complicated membership function where the value of the variable has a stepped relationship (above 0.5 is close to High condition, but below 0.4 is closer to Low condition), reflecting a different relationship between High and Low condition.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Variable Value

Low

High

Degre

e o

f m

em

bers

hip



Figure 2. An example of stepped membership functions for High and Low condition.

Step 3. Construct a definition table

For each expert rule system, the environmental experts used a workshop process to establish a definition table. The table is a matrix that shows the relationship between the extreme values of the input variables (High and Low) and the output Index values (see Table 2 for a two variable example).

Table 3. A two variable example of a definition table.

Variable A Variable B Output Index

H H 1

H L 0.4

L H 0.2

L L 0

Experts determined weightings for each of the input variables that described any differential influence each variable has on the output index. This was done firstly in ranks, and then as scores of the output index. These definition tables were then

encoded in MatLab®

Where different weightings were applied, rows two and three

do not always produce an output of 0.5, as might be expected if the variables were equally weighted. In Table 3, Variable A has a higher weighting than Variable B. With the appropriate specification of the membership functions for each variable, this definition table translates directly into the required expert rule system.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Variable Value

Low

High

Degre

e o

f m

em

bers

hip



Some definition tables can incorporate context variables that modify the output scores under defined sets of circumstances. These are referred to as „modifiers‟ or „context‟ variables. For an example see Appendix 4.1.3 where geomorphic responsiveness modifies geomorphic condition score outputs in instances where the three input variables have strongly contrasting membership (i.e. one of them is High and the other two are Low).

Step 4. Refine the expert rule system until its decision surface is acceptable

Expert rule systems were specified that show the relationship between the input variables and the outputs over the whole range of variable values, visualised as a decision surface (see Figure 3).

By design, the CFEV expert rule systems should have decision surfaces with the values specified in the decision table (e.g. Table 2), for the corresponding (extreme) values of the input variables (i.e. the points in Table 2 are “pinned”). The decision surface should vary smoothly between its „pinned points‟, and in a way that reflects the membership function as the variables move from High to Low condition. The decision surface was deemed to be satisfactory to the CFEV experts when, it reflected their understanding of the input variables and how they influence the output, with respect to freshwater-dependent ecosystems.

Figure 3. A decision surface as used for the worked example in Section 3.1.

The specified outputs for each combination of extreme input variable values (i.e. Highs and Lows) were checked against other outputs for consistency using pair-wise comparisons.



3.1 Expert rule systems: a worked example

This section presents a worked example, illustrating the development of an expert rule system using the four step procedure described above. The example includes how CFEV variables may be integrated using this method.

In this exercise, two variables, A and B, are to be integrated. In the CFEV example below, the integration of the roads/tracks within saltmarsh (Variable A) and landfill within saltmarsh (Variable B) variables for assessing the land disturbance within the saltmarsh condition variable (Output). The environmental experts provide the following definition table (Table 4) to underpin the expert rule system that is to carry out the integration calculation. They believe that landfill within the saltmarsh has more of an affect on land disturbance within the saltmarsh than the presence of roads/tracks within the saltmarsh.

Table 4. An example of a definition table for the land disturbance within the saltmarsh variable.

Variable A

e.g. Roads/tracks within saltmarsh (SM_RDWIN)

Variable B

e.g. Landfill within saltmarsh (SM_LFWIN)

Output

e.g. Land disturbance within the saltmarsh

(SM_LDWIN)

H H 1.0

L H 0.4

H L 0.2

L L 0.0

The first row of this table sets out the requirement that if the input variables have the extreme values A=1 (roads/tracks within saltmarsh are in good condition) and B=1 (landfill within saltmarsh is in good condition), then the expert rule system should calculate an output value of 1 (land disturbance within the saltmarsh is in good condition). The second row sets out the requirement that if the input variables have the extreme values A=0 (roads/tracks within saltmarsh are in poor condition) and B=1 (landfill within saltmarsh is in good condition), then the expert rule system should calculate an output value of 0.4 (land disturbance within the saltmarsh is in moderate condition), and so on. The experts determined the weightings of each of the input variables and as such, rows two and three do not always produce an output of 0.5, as might be expected. In this CFEV example, the experts considered landfill within a saltmarsh to have a greater influence on overall land disturbance within the saltmarsh than roading and hence, the outputs are reflected in this way.

The definition table - Table 4 translates directly into the following expert rule system, which consists of a series of logical statements, e.g.:

If (A is High) AND (B is High) THEN (Output is One)

If (A is Low) AND (B is High) THEN (Output is Point Four)

If (A is High) AND (B is Low) THEN (Output is Point Two)

If (A is Low) AND (B is Low) THEN (Output is Zero)

The above expert rule system assumes appropriate specification of the membership functions for each variable. Membership functions show how concepts (e.g. high (or good)) relate to some variable (e.g. landfill condition), and they do this by mapping the variable‟s value to a scale of 0 to 1, according to the degree to which the value satisfies the concept.



The scale is known as the „degree of membership‟ scale, and the membership function is said to „fuzzify‟ the concept, since a degree of membership that lies between 0 and 1 denotes a value of the variable (e.g. landfill within the saltmarsh = 0.25 (moderate)) that only partly satisfies the concept.

In the saltmarsh example, High (good condition) and Low (poor condition) are the membership functions for variable A (roads/tracks within saltmarsh), and also for variable B (landfill within saltmarsh). These membership functions are illustrated, graphically, in Figure 4.

Figure 4. Input membership functions for the worked example, where variable A is Roads/tracks within saltmarsh and variable B is Landfill within saltmarsh. Both have the same High / Low membership functions.

Zero, Point Two, Point Four, and One are the membership functions for variable Output (land disturbance within the saltmarsh). These membership functions are shown in Figure 5. The expert rule system‟s output may need to be rescaled if the centroid defuzzification method is used. Defuzzification is the process of converting a fuzzy output to a crisp number. Using the centroid method, the crisp value of the output variable is computed by finding the value of the centroid of the membership function for the fuzzy value (CMU, 1993). In this case, the triangular membership functions for the output variable avoid the need for rescaling.

Figure 5. Output membership functions for the worked example, the Output variable represents Land disturbance within the saltmarsh.

Figure 6 (Line 1) shows the graphical form of the calculation for input variable values A=1 (roads/tracks within saltmarsh are in good condition) and B=1 (landfill within saltmarsh is in good condition). This is effectively a graphical representation of Table 4.



Figure 6. Expert rule system calculation of the output (1.0) for input variable values A=1(High) and B=1(High).

Although all four rules are evaluated, only the first rule (row 1) contributes to this particular calculation . The integrated membership function in the bottom right panel takes a particularly simple form, and its centroid defuzzification gives the value Output=1. Similarly, the input variable values A=0 (roads/tracks within saltmarsh are in poor condition) and B=0 (landfill within saltmarsh is in poor condition) produce the value Output=0 (land disturbance within the saltmarsh is in poor condition). Rows 2 and 3 show the calculations that result in the output variables 0.4 and 0.2 respectively. No rescaling of the output variable is needed in order to achieve the 0-1 scale.

The final step checks the performance of the expert rule system by examining its output for all possible combinations of inputs. Figure 3 shows the decision surface produced by the expert rule system for this example. This simple expert rule system has only two input variables, and hence, there are no additional input variables being held at fixed values, and no decision surfaces for other combinations of input variables. If an expert rule system has more than two input variables, then a decision surface should be produced for each pair of input variables, and in each case the effect of fixing the remaining input variables at different values should be examined.

The decision surface in Figure 3 shows the correct output values of 0, 0.2, 0.4 and 1 for the four sets of extreme input variable values, (0, 0), (0, 1), (1, 0), and (1, 1). It also varies smoothly between these „pinned points‟, with no kinks or bumps. The decision surface tends to flatten out a little when either input variable has an extreme value, forming small plateaus in the vicinity of the input values (0, 0) and (1, 1). The features are deliberately introduced by using sigmoidal functions for the membership functions, and reflects the opinion of the CFEV environmental experts that if both of the input variables have very low (or high) values, then the output should be close to zero (or one), irrespective of the precise input values. It also shows the experts‟ definition that Variable B (Landfill within saltmarshes) is more important than Variable A (Roads/tracks within saltmarshes).



3.2 The data integration programs

MatLab, a well known scientific computing software package, was used to develop the computer programs needed to calculate the N-score for each ecosystem theme. The MatLab software was used in conjunction with the Fuzzy Logic “toolbox” of specialist routines for developing expert rules systems.

The programs used in the CFEV assessment can be fully accessed, and modified if necessary to accommodate future changes.

A separate program was developed for each ecosystem theme, as follows:

CFEV_Saltmarshes

CFEV_Rivers

CFEV_Wetlands

CFEV_Waterbodies

CFEV_Karst

Note: the estuaries condition assessment was undertaken without the use of an expert rule system. This was because there were a limited number of data inputs available, and a simple rule set was adequate for the estuaries condition assessment. This is outlined in the main report – Section 9.4.

These programs can be run either separately from the MatLab command window, or by using a Graphical User Interface, CFEVGUI.

Figure 7 shows the main menu of CFEVGUI. The data integration programs can be run by clicking on the appropriate button on the left hand side of the screen. Each program prompts the user to specify an input data file, as described below, and after reading in the data the program provides the user with the option of returning to the main menu or processing the data.

The drop-down lists on the right hand side of the CFEVGUI screen enable the user to examine the expert rules systems and/or the data pre-processing mappings for each data integration program.



Figure 7. The CFEVGUI main menu, with a drop-down list of expert rules systems.

Each program prompts the user to select an input data file. The data file for each ecosystem type must be prepared ahead of time according to the specifications expected by the appropriate MatLab program. All the input data files are required to be comma delimited text files (“txt” file extension), such as Saltmarshes_data.txt.

Each program reminds the user that the first record of the data file should NOT contain headers, and lists the data that the program expects to find in each record. As an example, the program CFEV_Saltmarshes expects each record to contain data in the following order (refer Table 5).

Each program applies data checking routines before calculating the N-score for a given record. The routine Datacheck is used to check those variables whose values are expected to lie somewhere between upper and lower limits specified for each variable. The routine Datacheck_discrete is used to check those variables whose values are expected to be one of a specified list.

Missing or bad data are indicated by the number –9. If a value of a variable in a given record either has the value –9 in the input data file, or is assigned this value by the data checking routines, then the N-score for that record cannot be calculated. Records containing missing or bad data are included in the output file, with the –9 values indicating where the problem lies.

In very few instances, data values of variables are checked using specific code. For example, values of –9 for the native fish condition variable RS_FISHCON in the rivers ecosystem theme mean “no fish” and not “bad data”.

Many variables require some form of pre-processing ahead of being integrated, such as the transformation to a 0-1 scale, as discussed in the previous section. Most of this work is done ahead of preparing the input data files, but some is carried out by the data integration programs.



Table 5. Example of input data file.

Variable Column Description

FID 1 Feature identifier

SM_GRWIN 2 Grazing within saltmarsh

SM_GRADJ 3 Grazing adjacent to saltmarsh

SM_UDADJ 4 Urban development adjacent to saltmarsh

SM_SPADJ 5 Spartina adjacent to saltmarsh

SM_LFWIN 6 Landfill within saltmarsh

SM_LFADJ 7 Landfill adjacent to saltmarsh

SM_DRWIN 8 Drainage disturbance within saltmarsh

SM_DRADJ 9 Drainage disturbance adjacent to saltmarsh

SM_RDWIN 10 Roads and tracks within saltmarsh

SM_RDADJ 11 Roads and tracks adjacent to saltmarsh

SM_LEVEG 12 Lateral extent of backing vegetation

SM_VEGCON 13 Native vegetation condition

SM_WIDVEG 14 Width of native backing vegetation

SM_AREA 15 Area of saltmarsh

Each pre-processing exercise takes the form of a mapping that transforms the value of some variable to a different value. CFEVGUI provides a drop-down list of the data pre-processing mappings, and shows the user the mapping in graphical form. Figure 8 shows an example of three pre-processing exercises, used by the CFEV_Rivers program.



Figure 8. Data pre-processing for three variables in the rivers ecosystem type.

CFEVGUI provides a drop-down list of the expert rules systems used in each data integration program, and uses the main expert rule system development GUI to enable the user to examine the selected expert rule system. The entire architecture of the expert rule system can be viewed, including the membership functions, rules, and decision surface(s). All the definition tables that underpin the expert rules systems are presented in Appendix 4.

Ahead of returning the user to the CFEVGUI main menu, the expert rule system GUI prompts the user to save the expert rule system, irrespective of whether the user has made changes. The user is warned to NOT save the file unless certain of the action, to avoid the possibility of introducing unwanted changes to the selected expert rule system.

Table 6 summarises the expert rule systems used by the data integration programs.



Table 6. Summary of the expert rule systems used in the condition assessments.

Saltmarshes Rivers Wetlands Waterbodies Karst

SMLANWIN RSSEDIN WLHYDRO WBHYDRO KTHYDRO

SMIMPWIN RSSEDIN_abs

WLNATVEG WBSEDIN KTNSCORE_small

SMLANADJ RSFLOW WLWATERQ WBNSCORE KTNSCORE_big

SMBACKVEG RSGEOM WLNSCORE WBNSCORE_abs

SMLANWIN RSBUGSCON

SMVEGADJ RSBIOL

SMIMPADJ RSBIOL_abs

SMNSCORE RSNSCORE

In Table 6, the name of each expert rule system also denotes the variable that it is used to calculate. For example, CFEV_Saltmarshes uses the expert rule system contained in the MatLab file SMLANWIN to calculate the value of SMLANWIN, which is land disturbance within a saltmarsh.

The exact usage of the various expert rules systems can quickly be determined by a MatLab programmer (or anyone familiar with scientific programming) by examination of the data integration programs.

In several data integration exercises, the scientific experts require the result of the expert rule system‟s calculation to be modified according to the value of a separate “context” variable. For example, the result of the SMNSCORE expert rule system, which calculates the N-score for the saltmarsh ecosystem type, is modified by the value of SMAREA, the area of the saltmarsh.

Such modifications are carried out by the data integration programs using ordinary scientific programming, and details are easily reviewed by examination of the programs.

Each data integration program reports on its progress in calculating the N-score for each record. Some of the CFEV ecosystem data files are quite large, notably the river data file which contains approximately 350 000 records.

All five data integration programs were written in an explicit programming style. However, CFEV_Rivers was re-written in the more compact style to avoid program run times of many hours on the 3 GHz computers available at the time of writing, in 2004. The re-written program processes the rivers data file in less than an hour.

After a data integration program has completed processing all the records in the input data file, the program offers to produce a set of histograms that summarise the results of the integration. The histograms set out the distribution of all the input variable values, together with the distribution of all the intermediate variables and the final N-score. Specific examples for each of the ecosystem themes (excluding estuaries) are shown in their respective sections (5-10). The histograms can be modified as needed, either by applying the appropriate MatLab commands in the command window, or by using the MatLab figure editing GUI. The histograms can then be exported as picture files, using the “save as” command.

At this point, all the input and output variables are held in the MatLab workspace memory. They can be analysed using MatLab, which provides many sophisticated



high level routines for this purpose, together with a full range of graphics (in addition to simple histograms) for displaying the results.

Each program prompts the user to specify the name of an output data file, which MatLab then produces.

The output data files contain all the records of the input data file, with the results of the data integration appended to each record. The files are comma separated value (“csv” file extension) files, which are basically the same as the comma delimited text files (“txt” file extension) used for the input data files.

The program then prompts the reader to use Microsoft Word to edit the output file and insert a header line, if desired. Microsoft Word is recommended, since some of the output files are too large to be handled by Microsoft Excel. An appropriate header line is provided in the code near the end of each program, and can be cut-and-paste into the output file.

Finally, the program terminates, and returns the user to the CFEVGUI main menu if the program has been run using this GUI.

Appendix 4 – Expert rule systems


4 Condition assessment expert rule systems

This appendix presents information on each of the expert rules systems used in integrating data for the condition assessment of the CFEV framework. This information relates to results presented in the condition assessment sections of the main report for each of the ecosystem themes.

Inputs, weightings, membership functions, pre-processing and definition tables are included in the following section when appropriate. Membership functions are provided where the input variable is continuous, but where pre-processing is used to convert to a High-Low categorical variable, or where the variable is measured in terms of absolute High or Low condition, no membership function is required.

Further information on expert rule systems can be found in Section 3.4.1 of the main report and Appendix 3. It should be noted that expert rule systems are more complicated than they appear from the definition tables, and interpretation should not be attempted without a general understanding of fuzzy logic and expert rule systems.

4.1 Rivers

4.1.1 Sediment input

Inputs: Catchment disturbance (RS_CATDI), Urbanisation (RS_URBAN), Mining sedimentation RS_MINES (context), where H = high (good condition), L = low (poor condition)

Membership Functions: RS_CATDI: H = >0.95, L = 0-0.05

Weightings (in order of influence): Urbanisation, Catchment disturbance

Expert rules definition table:

RS_URBAN RS_CATDI Score

RS_MINES=1 RS_MINES=0

H H 1 0.4

H L 0.4 0.2

L H 0.2 0.1

L L 0 0

Where 0 = poor condition and 1 = good condition

4.1.2 Flow change

Inputs: Flow variability index (RS_FLOVI), Abstraction index (RS_ABSTI), Regulation index (RS_REGI), where H = high (good condition), L = low (poor condition)

Pre-Processing:

RS_FLOVI: H = >0.5, L = 0-0.1

RS_ABSTI: H = 0-0.1 (absolute value), M = 0.1- 0.4 (absolute value), L = >0.4 (absolute value)

RS_REGI: H = 0-0.05, M = 0.05-0.15, L = 0.15

Weightings (in order of influence): Flow variability index, Abstraction index, Regulation index




RS_REGI RS_ABSTI RS_FLOVI Score

H H H 1

H H L 0.2

H L H 0.4

L H H 0.3

H L L 0.1

L H L 0.5

L L H 0.2

L L L 0


4.1.3 Geomorphic condition

Inputs: Sediment input (RS_SEDIN), Flow change (RS_FLOW), Sediment capture (RS_SEDCA, where Regulation index (RS_REGI) is used as a substitute), Geomorphic responsiveness (RS_GEORESP) (context), where H = high (good condition), L = low (poor condition)

Pre-Processing:

RS_SEDCA (RS_REGI): H = RS_REGI = 0-0.1, M = RS_REGI = 0.1-0.8, L = RS_REGI = >0.8

Weightings (in order of influence): Flow change, Sediment input, Sediment capture


Score

RS_SEDIN RS_FLOW RS_SEDCA RS_GEORESP

L M H

H H H 1 1 1

H H L 0.8 0.8 0.8

H L H 0.4 0.35 0.3

L H H 0.6 0.6 0.6

H L L 0.2 0.15 0.1

L H L 0.3 0.3 0.3

L L H 0.1 0.08 0.05

L L L 0 0 0


4.1.4 Macroinvertebrate condition

Inputs: Flow variability index (RS_FLOVI), Abstraction index (RS_ABSTI), Macroinvertebrate Observed/Expected (O/E) rank abundance (RS_BUGSOE), where H = high (good condition), L = low (poor condition)

Interpretation:

RS_BUGSOE: A = 1, AB = 0.8, B = 0.6, BC = 0.5, BCD = 0.3, CD = 0.2

Pre-Processing:

RS_BUGSOE: H = 0.8-1.0, M = 0.4-0.8, L = 0-0.4

RS_FLOVI: H = 0.9-1.0, M = 0.5-0.9, L = 0-0.5

RS_ABSTI: H = -0.05-0.05, M = 0.05-0.15 (absolute value), L = 0.15 (absolute value)



Weightings (in order of influence): Abstraction index, Macroinvertebrate O/E rank abundance, Flow variability index


RS_BUGSOE RS_ABSTI RS_FLOVI Score

H H H 1

H H L 0.6

H L H 0.3

H L L 0.2

L H H 0.5

L L H 0.1

L H L 0.4

L L L 0


4.1.5 Biological condition

Inputs: Platypus condition (RS_PLATYP) (context), Native fish condition (RS_FISHCON), Exotic fish impact (RS_EXOTICF), Macroinvertebrate condition (RS_BUGSCO), Native riparian vegetation (RS_NRIPV), Willows (RS_WILLOWS), where H = high (good condition), L = low (poor condition)

Pre-Processing: RS_NRIPV: Decrease score by 0.2 if willows present (RS_WILLOWS = 0) down to a minimum of 0.

Membership Functions:

RS_BUGSCO: H = 0.8-1.0, M = 0.5-0.8, L = 0-0.5

RS_EXOTICF: H = 0.8 and 1.0, M = 0.32 and 0.65, L = 0 and 0.4

RS_NRIPV: H = 0.75-1.0, L = 0-0.25

Weightings (in order of influence): Macroinvertebrate condition, Native riparian vegetation, and Native fish condition and Exotic fish impact (equally weighted)

Expert rules definition Table A: for when Native Fish are Absent (RS_FISHCON = -9)

RS_BUGSCO RS_EXOTICF RS_NRIPV Score*

H H H 1

H H L 0.6

H L H 0.7

L H H 0.4

H L L 0.5

L H L 0.2

L L H 0.3

L L L 0

* Context: Reduce score by 0.1 if RS_PLATYP = 0, down to a minimum of 0.




Expert rules definition Table B: for when Native Fish are Present (RS_FISHCON -9)

RS_BUGSCO RS_EXOTICF RS_NRIPV RS_FISHCON Score*

H H H H 1

H H H L 0.83

H H L H 0.7

H H L L 0.57

H L H H 0.83

H L H L 0.7

L H H H 0.7

L H H L 0.57

H L L H 0.43

H L L L 0.3

L H L H 0.3

L H L L 0.2

L L H H 0.3

L L H L 0.2

L L L H 0.1

L L L L 0

*Context: Reduce score by 0.1 if RS_PLATYP = 0, down to a minimum of 0.


4.1.6 Naturalness score

Inputs: Geomorphic condition (RS_GEOM), Biological condition (RS_BIOL), where H = high (good condition), L = low (poor condition)

Weightings (in order of influence): Geomorphic condition and Biological condition (equally weighted)


RS_GEOM RS_BIOL Score

H H 1

H L 0.3

L H 0.3

L L 0


4.2 Waterbodies

4.2.1 Hydrology

Inputs: Catchment disturbance (WB_CATDI), Regulation index (WB_REGI), Abstraction index (WB_ABSTI), Lake level manipulation (WB_LLEVELM) (context), where H = high (good condition), L = low (poor condition)


WB_CATDI: H = >0.95, L = <0.05

WB_REGI: H = 0-0.5, L = >0.15

WB_ABSTI: H = 0-0.1 (absolute value), M = 0.1-0.4 (absolute value), L = >0.4 (absolute value)



Weightings (in order of influence): Abstraction index, Catchment disturbance, Regulation index


WB_CATDI WB_REGI WB_ABSTI Score

H H H 1

H H L 0.4

H L H 0.9

L H H 0.6

L L H 0.5

H L L 0.3

L H L 0.1

L L L 0


Context: Lake level manipulation (WB_LLEVELM)

WB_LLEVELM Score x by Index (Factor)

1 1

0.8 0.9

0.6 0.6

0.4 0.5

0.2 0.4

0 0.2

4.2.2 Fish

Inputs: Exotic fish impact (WB_EXOTICF) (modifier), Native fish condition (WB_FISHCON), where H = high (good condition), L = low (poor condition)


If WB_FISHCON = 0 then WB_FISH = 0

If WB_FISHCON = -9 (no fish) then WB_FISH = -9

If WB_FISHCON ≠ 0 and ≠ -9 then WB_FISH score is modified by WB_EXOTICF, as follows:

WB_EXOTICF Score

1 or 0.8 = WB_FISHCON score

0.32 or 0.65 0.33

0 or 0.04 0


4.2.3 Sediment input

Inputs: Catchment disturbance (WB_CATDI), Exotic riparian vegetation (WB_CRIVE), Sediment Quality (where Land use (nutrients)) (WB_NUTRI) is used as a substitute), Tyler corridor (WB_TYLER) (modifier), where H = high (good condition), L = low (poor condition)


WB_CATDI: H = >0.95, L = <0.05

WB_TYLER: 1 = East, 0.5 = Corridor, 0 = West



Weightings (in order of influence): Catchment disturbance, Sediment capture (Land use (Nutrients)), Exotic riparian vegetation


If WB_TYLER is East (=1):

WB_CATDI WB_CRIVE WB_NUTRI Score*

H H H 1

H H L 0.7

H L H 0.8

L H H 0.5

L L H 0.3

H L L 0.4

L H L 0.2

L L L 0


*Modify score if WB_TYLER = West (0) or Corridor (0.5):

if interim WB_SEDIN score >0.7, then final WB_SEDIN score = 1.0, else

if interim WB_SEDIN score ≤0.7, then *10/7 to calculate final WB_SEDIN score.


Inputs: Hydrology (WB_HYDRO), Fish (WB_FISHC) (modifier), Sediment input (WB_SEDIN), Native riparian vegetation (WB_NRIVE), Artificiality (WB_ARTIF) (modifier), where H = high (good condition), L = low (poor condition)

Weightings (in order of influence): Hydrology, Sediment input, Fish condition, Native riparian vegetation

Definition Table A: for when Native Fish are Absent (WB_FISHC = -9)

If WB_ARTIF = 1 (not artificial):

WB_HYDRO WB_SEDIN WB_NRIVE Score*

H H H 1

H H L 0.75

H L H 0.6

L H H 0.5

H L L 0.45

L H L 0.3

L L H 0.2

L L L 0


* Modify score if WB_ARTIF = 0 (artificial) then WB_NSCORE = 0.5*Score



Definition Table B: for when Native Fish are Present (WB_FISHC ≠ -9)

If WB_ARTIF = 1 (not artificial):

WB_HYDRO WB_SEDIN WB_FISHC WB_NRIVE Score*

H H H H 1

H H H L 0.9

H H L H 0.8

H L H H 0.7

L H H H 0.65

H L H L 0.5

H L L H 0.45

H L L L 0.25

H H L L 0.6

L H H L 0.4

L H L H 0.35

L H L L 0.2

L L H H 0.3

L L H L 0.15

L L L H 0.1

L L L L 0


* Modify score if WB_ARTIF = 0 (artificial) then WB_NSCORE = 0.5*Score

4.3 Wetlands

4.3.1 Hydrology

Inputs: Catchment disturbance (WL_CATDI), Abstraction index (WL_ABSTI), where H = high (good condition), L = low (poor condition)


WL_CATDI: H = >0.95, L = <0.05

WB_ABSTI: H = 0-0.1 (absolute value), M = 0.1-0.4 (absolute value), L = >0.4 (absolute value)

Weightings (in order of influence): Catchment disturbance, Abstraction index


WL_ABSTI WL_CATDI Score

H H 1

H L 0.2

L H 0.3

L L 0




4.3.2 Native vegetation

Inputs: Native riparian vegetation (WL_NRIVE), Native wetland vegetation (WL_NVEG), where H = high (good condition), L = low (poor condition)

Weightings (in order of influence): Native wetland vegetation, Native riparian vegetation


WL_NRIVE WL_NVEG Score

H H 1

H L 0.2

L H 0.4

L L 0


4.3.3 Water quality

Inputs: Land use (nutrients) (WL_NUTRI), Catchment disturbance (WL_CATDI), where H = high (good condition), L = low (poor condition)


WL_CATDI: H = >0.95, L = <0.05

Weightings (in order of influence): Land use (nutrients), Catchment disturbance


WL_NUTRI WL_CATDI Score

H H 1

H L 0.3

L H 0.1

L L 0



Inputs: Hydrology (WL_HYDRO), Native vegetation (WL_NATVE), Sediment input (WL_SEDIN, where Catchment disturbance (WL_CATDI) is used as a substitute), Water quality (WL_WATER), where H = high (good condition), L = low (poor condition)


WL_SEDIN = WL_CATDI: H = >0.95, L = <0.05

Weightings (in order of influence): Native vegetation, Hydrology, Water quality, Sediment input




WL_NATVE WL_HYDRO WL_SEDIN WL_WATER Score

H H H H 1

H H H L 0.8

H H L H 0.9

H H L L 0.7

H L H H 0.65

H L H L 0.5

H L L H 0.55

H L L L 0.45

L H H H 0.4

L H H L 0.3

L H L H 0.35

L H L L 0.25

L L H H 0.2

L L H L 0.1

L L L H 0.15

L L L L 0


4.4 Saltmarshes

4.4.1 Land disturbance within the saltmarsh

Inputs: Landfill within saltmarsh (SM_LFWIN), Roads/tracks within saltmarsh (SM_RDWIN), where H = high (good condition), L = low (poor condition)


SM_LFWIN: Use discrete values as index 0-1 (L-H)

SM_RDWIN: Use discrete values as index 0-1 (L-H)

Weightings (in order of influence): Landfill within saltmarsh, Roads/tracks within saltmarsh


SM_RDWIN SM_LFWIN Score

H H 1

L H 0.4

H L 0.2

L L 0


4.4.2 Impacts within the saltmarsh

Inputs: Land disturbance within saltmarsh (SM_LDWIN), Grazing within saltmarsh (SM_GRWIN), Drainage within saltmarsh (SM_DRWIN), where H = high (good condition), L = low (poor condition)


SM_GRWIN: H = 1 (absent), L = 0 (present)

SM_DRWIN: Use discrete values as index 0-1 (L-H)



Weightings (in order of influence): Land disturbance within saltmarsh, Drainage within saltmarsh, Grazing within saltmarsh


SM_LDWIN SM_GRWIN SM_DRWIN Score

H H H 1

H H L 0.5

H L H 0.6

H L L 0.4

L H H 0.2

L H L 0.1

L L H 0.15

L L L 0


4.4.3 Land disturbance adjacent to the saltmarsh

Inputs: Roads/tracks adjacent to saltmarsh (SM_RDADJ), Urban development adjacent to saltmarsh (SM_UDADJ), Landfill adjacent to saltmarsh (SM_LFADJ), where H = high (good condition), L = low (poor condition)


SM_RDADJ: Use discrete values as index 0-1 (L-H)

SM_UDADJ: Use discrete values as index 0-1 (L-H)

SM_LFADJ: Use discrete values as index 0-1 (L-H)

Weightings (in order of influence): Urban development adjacent to saltmarsh, Roads/tracks adjacent to saltmarsh, Landfill adjacent to saltmarsh


SM_RDADJ SM_UDADJ SM_LFADJ Score

H H H 1

H L H 0.5

L H H 0.55

H H L 0.6

H L L 0.35

L L H 0.3

L H L 0.4

L L L 0.1


4.4.4 Backing vegetation condition

Inputs: Lateral extent of adjacent vegetation (SM_LEVEG), Width of adjacent vegetation (SM_WIDVEG), Native vegetation condition (SM_VEGCON) (context), where H = high (good condition), L = low (poor condition)

Membership Functions: SM_LEVEG: H = 1 (good), L = 0 (poor)

Weightings (in order of influence): Lateral extent of adjacent vegetation, Width of adjacent vegetation




SM_LEVEG SM_WIDVEG

Score

Context: SM_VEGCON =

1 0.5 0.25 0

H H 1 0.9 0.7 0.5

H L 0.5 0.4 0.3 0.2

L H 0.3 0.3 0.2 0.1

L L 0.1 0.1 0 0


4.4.5 Adjacent vegetation condition

Inputs: Backing vegetation (SM_BKVEG), Spartina adjacent to saltmarsh (SM_SPADJ), where H = high (good condition), L = low (poor condition)

Membership Functions: SM_SPADJ: H = 1 (absent), L = 0 (present)

Weightings (in order of influence): Backing vegetation, Spartina adjacent to saltmarsh (equally weighted)


SM_BKVEG SM_SPADJ Score

H H 1

L H 0.4

H L 0.4

L L 0


4.4.6 Impacts adjacent to the saltmarsh

Inputs: Land disturbance adjacent to saltmarsh (SM_LDADJ), Grazing adjacent to saltmarsh (SM_GRADJ), Drainage adjacent to saltmarsh (SM_DRADJ), Adjacent vegetation (VGADJ), where H = high (good condition), L = low (poor condition)


SM_GRADJ: H = 1 (absent), L = 0 (present)

SM_DRADJ: Use discrete values as index 0-1 (L-H)

Weightings (in order of influence): Drainage adjacent to saltmarsh, Land disturbance adjacent to saltmarsh and Adjacent vegetation (previous two variables equally weighted), Grazing within saltmarsh




SM_DRADJ SM_GRADJ SM_LDADJ SM_VGADJ Score

H H H H 1

H H H L 0.8

H H L H 0.8

H H L L 0.6

H L H H 0.9

H L H L 0.7

H L L H 0.7

H L L L 0.5

L H H H 0.5

L H H L 0.3

L H L H 0.3

L H L L 0.1

L L H H 0.4

L L H L 0.2

L L L H 0.2

L L L L 0



Inputs: Impacts within the saltmarsh (SM_IMWIN), Impacts adjacent to the saltmarsh (SM_IMADJ), Saltmarsh area (SM_AREA) (context), where H = high (good condition), L = low (poor condition)

Membership Functions: SM_AREA: Big ≥20 ha, Small ≤10 ha

Weightings (in order of influence): Impacts within saltmarsh, Impacts adjacent to saltmarsh


SM_IMADJ SM_IMWIN Score

SM_AREA = Big SM_AREA = Small

H H 1 0

H L 0.3 0.3

L H 0.7 0.5

L L 0 0


4.5 Karst

4.5.1 Hydrology

Inputs: Abstraction index (KT_ABSTI), Regulation index (KT_REGI), where H = high (good condition), L = low (poor condition)


KT_ABSTI: H = 0-0.1 (absolute value), M = 0.1 – 0.4 (absolute value), L = >0.4 (absolute value)

KT_REGI: H = 0-0.05, M = 0.05 – 0.15, L = 0.15

Weightings (in order of influence): Abstraction index, Regulation index




KT_ABSTI KT_REGI Score

H H 1

H L 0.4

L H 0.2

L L 0


Inputs: Catchment disturbance (KT_CATDI) (substitute for Sediment flux and Water chemistry), Hydrology (KT_HYDRO), Physical sensitivity (KT_PHYSSEN) (context), Catchment size (Area ratio) (KT_CATCH) (context), where H = high (good condition), L = low (poor condition)


KT_CATDI: H = >0.95, L = 0 – 0.05

KT_CATCH: Big = 1, Small = 0 (Small catchments have MAR ≤48.2 GL, see Appendix 0)

Weightings (in order of influence): Hydrology and Catchment disturbance (Sediment flux) (equal), Catchment disturbance (Water chemistry). Note that H - L combinations of sediment flux and water chemistry in the definition table will be unpopulated as they draw from the same data and therefore cannot be both simultaneously.

Expert rules definition Table A: for when Catchment size is small (KT_CATCH = Small (0))

KT_CATDI

(Sediment flux)

KT_CATDI

(Water chemistry) KT_HYDRO

Score

KT_PHYSSEN = 0 (exposed)

KT_PHYSSEN = 1 (covered)

H H H 1 1

H H L 0.6 0.8

H L H 0.7 0.85

L H H 0.6 0.8

H L L 0.4 0.5

L H L 0.2 0.4

L L H 0.4 0.5

L L L 0 0


Expert rules definition Table B: for when Catchment size is big (KT_CATCH = Big (1))

KT_CATDI

(Sediment Flux)

KT_CATDI

(Water Chemistry) KT_HYDRO

Score

KT_PHYSSEN = 0 (exposed)

KT_PHYSSEN = 1 (covered)

H H H 1 1

H H L 0.65 0.7

H L H 0.8 0.8

L H H 0.65 0.7

H L L 0.4 0.5

L H L 0.3 0.4

L L H 0.45 0.5

L L L 0.2 0.3


Appendix 5 – Spatial selection algorithm


5 Spatial selection algorithm

The spatial selection algorithm was used for selecting and prioritising spatial units by iteratively selecting spatial units based on their relative conservation value (e.g. representativeness and condition). Each ecosystem theme is considered separately. As spatial units are selected and allotted priorities, the relative values of the remaining, unselected spatial units change and need to be recalculated so the process starts again until all the spatial units are selected. The order that these spatial units are selected reflects their ranked priorities and ultimately informs the banding within Representative Conservation Value (RCV).

5.1 Conservation inventory

The conservation inventory used for the spatial prioritisation process assigned each spatial unit with a range of data collected as part of the CFEV statewide audit (see Section 3.3.1 of the main report), from which the following were used as input variables for the spatial selection algorithm:

Biophysical classes - as assigned to each spatial unit

Extent - length (for rivers), area (for wetlands and saltmarshes) or number (for waterbodies, estuaries and karst)

Condition (N-score) - 1-10 converted from the original 0-1 output from the expert rule systems

From these data, a summary table or inventory was produced for each ecosystem indicating, for each biophysical class, its area or length and the number of spatial units in which it was represented. The spatial data and the summary tables formed the primary input to the conservation evaluation stage of the prioritisation process.

5.2 Conservation evaluation

The conservation evaluation used the conservation inventory to define a set of indices which can be assessed during the area selection stage. Three indices were generated:

Conservation Priority Index (CPI) - a rarity-based index to identify the relative conservation priority of each spatial unit of each biophysical class, suitable for use in an iterative process that accounts for previous selections

Reverse-ranked rarity - as calculated for each biophysical class relative to every other biophysical class

Quality of representation - a measure of how well each spatial unit represents the biophysical classes it is representative of. Derived from the naturalness score and size of each spatial feature.

5.2.1 The Conservation Priority Index (CPI)

The CPI is a rarity-based logarithmic formula, that produces rankings, the topmost members of which can be assigned, and then removed from the calculation, allowing it to be run iteratively which can which assists in providing for differential priorities on the basis of relative rarity and the amount of a biophysical class already selected.

The CPI recognises the importance of stochastic risk for naturally rare units and predicted risk for units which are rare due to anthropogenic factors, and that rarity is a relative concept. Relative rarity is a continuous scale from rare to common.



In order to apply these concepts it was necessary to determine the appropriate unit to measure relative rarity for each ecosystem. These are referred to as the extent of each ecosystem spatial unit and are defined as:

rivers – length in metres

saltmarshes and wetlands – area in hectares and

estuaries, karst and waterbodies – number (each spatial unit counted as one).

Using the number of features for estuaries, karst and waterbodies was considered preferable to measuring size (length, area) due to the large range in size of these ecosystems and the relatively high conservation importance of many of the smaller examples.

The formula developed is:

Class_ext

Ref_ext * PNS1logCPI_raw n

Where:

PNS: Proportion of a biophysical class that remains to be selected (1 = none selected, 0 = all selected)

Ref_ext: A reference extent determining the overall decay rate of the CPI by defining the extent of the least rare (most common) class in each ecosystem classification (i.e. the extent of the biophysical class)

Class_ext: The total length, area or number of spatial units (as applicable) of the biophysical class being measured

The CPI always has a minimum value of 0. The maximum varies according to Ref_ext and Class_ext and is standardised to a scale of 0-100 using the following transformation:

Where:

(CPI_raw)max: is the value of CPI_raw at the commencement of the selection process (i.e. when PNS = 1).

The decay rate changes according to the relative rarity of each biophysical class. Figure 9 – Figure 11 show the relationship between the CPI, extent and proportion selected for rivers, wetlands and waterbodies, respectively.



Figure 9. CPI scores for rivers (metres) against proportion of biophysical class selected. Different coloured lines illustrate the trajectories of different feature extents. The pink (upper) line shows the rarest biophysical class (562m).

Figure 10. CPI scores for wetlands (hectares) against proportion of biophysical class selected. Different coloured lines illustrate the trajectories of different feature extents. The pink (upper) line shows the rarest biophysical class (6ha).

0

10

20

30

40

50

60

70

80

90

100

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

Proportion of extent selected

CP

I

562

10,000

50,000

100,000

1,000,000

20,000,000

118,783,484

Classification

unit extent

(Feat_ext)

Total extent =

152,940,484m

Ref_ext =

118,783,484m

0

10

20

30

40

50

60

70

80

90

100

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00


CP

I

6

100

500

1,000

10,000

20,000

129,740

Classification

unit extent

(Feat_ext)

Total extent =

206,790ha

Ref_ext =

129,740ha



Figure 11. CPI scores for waterbodies (number in class) against proportion of biophysical class selected. Different coloured lines illustrate the trajectories of different feature extents. The pink (upper) line shows the rarest biophysical class (1 representative).

The plots of CPI against extent and percent selected in Figure 9 – Figure 11 all show very similar patterns despite differences in the measure used for extent (hectares, metres and number) and also in the number of spatial units involved, suggesting that using the most extensive biophysical class as the reference extent was appropriate.

5.2.2 Reverse-ranked rarity

The reverse-ranked rarity of each biophysical class in each ecosystem was determined by ordering the classification in descending order using total length, area or number as appropriate, and assigning the rarest biophysical class the greatest rarity value. For example, rivers are represented by 208 biophysical classes, the rarest class (562 m) was assigned a reverse-ranked rarity of 208 and the most extensive class (119 x 106 m) was assigned a reverse-ranked rarity of 1. This measure was derived for use in the area selection process to prioritise rarer biophysical classes when the CPI for two or more biophysical classes was equal.

0

10

20

30

40

50

60

70

80

90

100

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00


CP

I

1

5

10

50

100

200

1,091

Classification

unit extent

(Feat_ext)

Total extent =

1,346 features

Ref_ext =

1,091 features



5.2.3 Quality of representation

For all ecosystems except estuaries, the quality of representation of each spatial unit was considered to be a function of both size and naturalness, based on these factors contributing to conservation value in a complex matrix. The measure developed sought to address the conservation value, for example, of a large wetland with low naturalness compared to a small wetland with very high naturalness, and every possible other combination. This was achieved by weighting area by naturalness using the formula:

Where:

Extent: is the length or area of each spatial unit in the ecosystem. The extent was multiplied by 100 in order to bring the first two decimal places into consideration in the area selection process, after it was discovered that using no decimal places in some instances prevented two subtly different spatial units from being distinguished.

Naturalness: is the N-score for each spatial unit, converted to a scale of 0-10. The square function was used because it was considered that the magnitude of variation in naturalness between spatial units was better approximated on a non-linear scale.

In addition to measuring the quality of representation of each spatial unit, the naturalness-weighted area measure enabled the best example of each biophysical class to be identified by ranking all spatial units within a biophysical class. This was also incorporated into the spatial selection algorithm for rivers and wetlands.

The naturalness-weighted area measure was not used for estuaries, as treating each spatial unit as a single unit meant that the maximum naturalness score is automatically applied to the highest quality representation of the biophysical class.

5.3 Spatial selection

A spatial selection and prioritising procedure based on the data structures and Strategic Reserve Design (SRD) algorithm developed by the Comprehensive, Adequate, Representative Scientific Advisory Group (CARSAG, 2004) was used to iteratively prioritise all spatial units in each ecosystem on their relative conservation value.

The spatial selection algorithm initially assigns each spatial unit with concatenated strings assembled from the indices described in the conservation evaluation section. From left to right each string contains:

The maximum Conservation Priority Index (CPI) for each spatial unit (1-100), being the highest CPI from among the biophysical classes present in each spatial unit.

The reversed rank order of the rarest biophysical class represented in the spatial unit (e.g. for rivers, rarest biophysical class – ranked rarity class = 208, most extensive – ranked rarity class = 1).

The naturalness-weighted area for each spatial unit i.e.: (area or length*100)*(Naturalness2/100).



An example of the construction of a scoring string for a river section is:

CPI = 100.0 Rarity Rank = 029 Naturalness-weighted length = 1869.64 (length = 2995.74, N = 7.9)

1000 & 029 & 186964 = 1000029186964

The scoring string (1000029186964) is treated as a single numerical score for the spatial unit.

An additional element was incorporated into the scoring strings for the ecosystems which had multiple biophysical classifications (uncombined) and which used extent in the CPI (e.g. rivers and wetlands). This was required because the first selection of the spatial selection algorithm would pick the best example of one biophysical class, but may not be the best example of the other biophysical classes represented in the spatial unit. As a result, the CPI of these biophysical classes might be iteratively reduced and result in the best example not being selected until somewhat later in the re-running of the spatial selection algorithm, i.e. appear as of lower relative conservation value when in fact it is the best example of a biophysical class.

To address this issue, the spatial unit that uniquely represented the best example of each biophysical class was identified and an additional element was added to the front of the scoring string. The value added was the reverse-ranked rarity of the rarest biophysical class for which the spatial unit was the best example. Thus if the example above was the best example of multiple biophysical classes with a reverse-ranked rarity of both 29 and 16 (out of all 208) - 29 is rarer, so it is added to the string and the final scoring string would be:

029 & 1000029186964 = 291000029186964

All spatial units with a larger scoring string are considered to be of higher relative conservation value, so sorting these strings in reverse numerical order ranks higher values earlier. The spatial selection algorithm will thus first ensure that the best example of every biophysical class is selected in the set of spatial units comprising the top of the prioritisation. After this set of any of the biophysical classes is selected, multiple-sorting function and simple heuristic tree which the string imposes continues as:

first select the spatial unit with the highest value CPI of any of the biophysical classes

if more than one spatial unit has the same CPI, select the one which is the rarest

if more than one spatial unit has the same CPI and rarity class, select the one with the greatest quality of representation (naturalness-weighted length in this example)

if more than one spatial unit has the same values on all of the above, a random selection will be made, as these spatial units are considered to be of equal value.

The above process is repeated at each step in the spatial selection algorithm until all spatial units have been selected. After each selection the selected spatial unit is removed from the set and all CPI values are recalculated based on a PNS that does not include the already selected unit, the scoring strings for each spatial unit are then iteratively reconstructed to reflect changes in CPI values as spatial units are selected. The properties of the scoring strings ensure that the best examples of the spatial units with the highest CPI will be selected first and assigned a higher relative conservation value. As the spatial selection algorithm is run it continues to select



spatial units on this basis, successively placing those with lower quality of representation at appropriate places in the ranked prioritisation.

5.3.1 River clusters

For all ecosystems other than rivers, the spatial selection algorithm treated each spatial unit as a unique feature. In the case of rivers, a routine was incorporated which grouped neighbouring river sections of similar conservation value (CPI) into river clusters and also specified a maximum size for each cluster to be 30 km. This was required due to the greater connectivity between river sections and the much larger number of spatial units (n = 350 524). The criterion for adding a river section to a cluster is that the length-weighted mean CPI for the cluster did not fall by more than 20% of the CPI for the first selected river section.

The spatial selection algorithm for rivers first selected the river section with the highest value scoring string not yet belonging to a cluster, and assigned it both a simple and a full cluster number (e.g. 13 and 130000, indicating the first section in the cluster). The neighbours of this section were then assessed, in descending order on the value of the scoring string. If the neighbour with the highest value scoring string could be added (becoming 130001) then the next highest scoring neighbour was assessed (becoming 130002), and so on.

If the process of assessing all the neighbours of the initial selection results in the addition of further river sections, Clusters formed in this way are assessed and if neither the CPI limit nor the 30km total river section length has been exceeded then the next set of neighbours is assessed until one of the limits is reached, or there are no contiguous river sections available.

Once a limiting value is reached, the cluster is finalised, removed from further calculations and the extent of each biophysical class in the cluster is transferred to a data table. The CPI value for each biophysical class is then recalculated. The scoring strings for all unselected river sections of the biophysical classes are then reconstructed to reflect changed CPI values and the spatial selection algorithm proceeds to select the next cluster. A flow chart of the spatial selection algorithm is shown in Figure 12. Figure 13 and Table 7 show a worked example of the process of selecting clusters for rivers.



Figure 12. Flow-chart outlining the spatial selection process.

Figure 13. River section numbers in sample river cluster (see Table 6).

1

2

3

4

6

7

10

12

13

11

8

9

14

5

select another river section

Attribute spatial units with CPI, rarity rank,naturalness and best example values (where required) and calculate

scoring strings

Commence prioritisation algorithm by selecting spatial unit with the highest scoring string and store attributes

(CPI, area/length/number)

For rivers: Select neighbours and test in order for addition to cluster

Test if limiting parameters reached

Reselect cluster or spatial unit, transfer data on length/area/number of each biophysical class selected

and recalculate CPI

Yes

No

Reconstruct scoring strings where CPI changed and select spatial unit with next highest scoring string



Table 7. Example of river cluster selection process.

# Scoring String Order in cluster

River cluster length

CPI mean

Steps

1 211041184134 8 1582 55.3 1. Highest scoring segment selected (#4 CPI = 64.5)

2. Limits = 30km or CPI > 51.6 (51.6 = 64.5 * 0.8 i.e. 20% less)

3. Neighbours tested and added (#5, #6, #3)

4. Neighbours tested and added (#2, #7, #1, #10)

5. Neighbours tested and added (#9, #8, #12, #11)

6. Neighbours tested and

added #13 but #14 not added as limit CPI > 51.6 (51.6 = 64.5 * 0.8 i.e. 20% less) reached.

2 599023076132 5 1022 62.0

3 571020048265 4 890 62.3

4 645029080221 1 221 64.5

5 645023100062 2 283 64.5

6 645007050342 3 625 64.5

7 550018073299 6 1321 60.4

8 541077100052 10 1677 55.4

9 612028094043 9 1625 55.5

10 387001088127 7 1448 58.5

11 387001033082 12 2113 52.4

12 411002100354 11 2031 52.9

13 411002100067 13 2180 52.0

14 411002048109 na na 51.5

5.4 Evaluation of results

The main objectives that were defined for the spatial selection algorithm, were:

1. to represent all biophysical classes within the conservation management system at least once before second or subsequent selections and

2. to provide for differential priority on the basis of relative rarity and the amount of a biophysical class already selected.

A number of issues are associated with testing the first of these objectives. For the ecosystems with only a single classification, the only factor which would prevent each biophysical class being represented once before second or subsequent selections is the sensitivity of the CPI (i.e. does the CPI applied at one decimal place always reduce to less than 100.0 with the initial selection of each biophysical class?).

For the ecosystems with multiple biophysical classifications, the first spatial unit selected ensures the representation of the number of biophysical classes equal to the number of layers of classification. For example, rivers had seven ecosystem components and thus each section selected adds to the representation of seven biophysical classes. The total of 208 biophysical classes could therefore be represented by the selection of a minimum of 30 river sections (208/7 = 29.7). However, this would occur only in the absence of any spatial correlation between the distribution of the biophysical classes, with each single selected river section accounting for seven biophysical classes. The requirement to represent the best example of each biophysical class in the first round of selections of the spatial selection algorithm also potentially increases the number of selections required to include the best example. The assessment of river sections in clusters also potentially increases the number of selections required to meet the objectives.



The final spatial selection algorithms were therefore analysed to determine the results against these issues. For each ecosystem, the data generated were:

the theoretical minimum number of selections required to represent each biophysical class at least once

the number of selections required to represent each biophysical class once (by section and cluster for rivers) and

for rivers and wetlands, the number of selections required to include the best example of each biophysical class (by section and cluster for rivers).

The results are shown in Table 8.

Table 8. Summary of spatial selection algorithm data.

Estuaries Karst Rivers* Saltmarshes Waterbodies Wetlands

Number of spatial units

113 334 350 524 (21 733)

336 1346 20 597

Biophysical classes

19 85 208 23 127 170

Layers of classification

1 1 7 1 6 6

Theoretical minimum

19 85 30 23 22 34

Selections for 1st

representation 19 85 5747

(124) 23 68 123

Selections for all best examples

na na 6406 (138)

na na 124

*First figure is number of river sections, figure in parentheses is number of clusters.



Table 8 shows that the ecosystems with a single layer of classification (estuaries, karst and saltmarshes) were all selected once before any second and subsequent selections. Thus the CPI is sufficiently sensitive to achieve the objective of representing each biophysical class once first.

The ecosystems with multiple coarse filter classification (rivers, waterbodies and wetlands) were all selected at least once in about the middle third of the range between the theoretical minimum and the number of biophysical classes for the ecosystem. A similar result was obtained for selecting the best example of each biophysical class for rivers and wetlands, with little difference between the best example and first selection results (138 – 124 for rivers, 124 – 123 for wetlands). The data overall confirm that the spatial selection algorithm performed as designed in prioritising selections of all biophysical classes early in the running of the spatial selection algorithm. The differences between the theoretical minimum and the actual number of selections needed to achieve full representation reflect the actual spatial concurrence and spatial correlation of freshwater conservation values.

To test whether the spatial selection algorithm selected proportionally more of rarer biophysical classes in the early stages of its running, a sample of biophysical classes from each ecosystem type were selected. The proportion of the extent (length, area or number) of each selected was then plotted cumulatively against the order of selection. Figure 14 below shows the results of this for wetlands, which used the multiple coarse filter classification, using a rare biophysical class (but not one represented by only one spatial unit), two moderately extensive biophysical classes and the most extensive biophysical class. Figure 15 shows the same data for saltmarshes, which had only a single layer of classification.

Figure 14. Cumulative representation of wetlands by biophysical class rarity and spatial correlation

0

10

20

30

40

50

60

70

80

90

100

0 5000 10000 15000 20000

Selection order

Cu

mu

lati

ve

% s

ele

cte

d

BC1 -129,740ha

WLP2 -274ha

WLP29 -11,314ha

T22 -4,408ha

Classification unit

& extent



Figure 15. Cumulative representation for saltmarshes by biophysical class rarity

Comparison of the figures shows that the CPI and formulation of the spatial selection algorithm do select proportionally more of the rare biophysical classes early in its running when a single layer of classification is used, though this is tempered slightly by differences in size of the early selections. However, a more complex pattern is evident in the case of wetlands which, due to its multiple layers of classification, is strongly influenced by the degree of spatial correlation among the biophysical classes. The initial high rate of accumulation of the most extensive wetland biophysical class (BC1) occurs automatically as every wetland with this unit also has five other biophysical classes, all rarer than BC1. Although this biophysical class is the most extensive in the wetlands classification, its mean reverse ranked rarity is 114 on a scale of 1 (least rare) to 170 (most rare). Further evidence of the role of spatial correlation with rarer biophysical classes can be seen in the sharp change of accumulation trend for BC1, which occurs at the point in the running of the spatial selection algorithm when the best example of each biophysical class had been selected.

Thus the spatial selection algorithm ensures that rarer biophysical classes achieve high percentage representation early in the running of the spatial selection algorithm. However, the impact of spatial correlation among biophysical classes was also evident in the ecosystems using multiple biophysical classification, with some moderately extensive biophysical classes accumulating representation similarly to rare biophysical classes due to their co-occurrence with them. A similar pattern occurred with the more extensive biophysical classes and reaches its extreme with the most extensive biophysical class in each ecosystem always being spatially correlated with units that are rarer than it. Consequently, there is a potential for selection of the more extensive biophysical classes to be biased by their co-occurrence with rare units.

No consistent trends in the selection of higher quality sites earlier in the running of the spatial selection algorithm were evident among biophysical classes. It was deemed satisfactory that sufficient numbers of clusters/spatial units with higher qualities or size and naturalness were being selected earlier in running of the spatial selection algorithm. The best examples of some rare biophysical classes had only

0

10

20

30

40

50

60

70

80

90

100

0 50 100 150 200 250 300 350

Selection order

Cu

mu

lati

ve

% s

ele

cte

d

SM9 -1,006ha

SM15 -1.148ha

SM19 -338ha

SM3 -170ha

Classification

unit & extent



low naturalness or naturalness-weighted area scores (for example where a biophysical class occurs predominantly in heavily cleared catchments) and are selected early. Therefore, the other co-occurring biophysical classes may be lesser quality examples of their type. This is inevitable when running the spatial selection algorithm at the landscape scale across all biophysical classes concurrently. Where these situations occur, preselecting some priority areas early in, or before, the running of the spatial selection algorithm may be appropriate (cf. (Bedward et al., 1992)).

The methods developed were designed to be objective, transparent and repeatable. They also achieved the goal of representing all biophysical classes at least once before accumulating further representation using relative rarity. The CPI developed is robust and well suited to iterative processes.

Inevitably there are spatial correlation issues to be considered when dealing with multiple conservation values in the landscape. The methods used allow these issues to be recognised and addressed (e.g. cumulative representation versus quality of representation). In this work, priority has been given to accumulating representation but has also highlighted the need to be flexible (for example, by preselecting some clusters/spatial units) in order to achieve outcomes more consistent with expert judgement.

The development of the spatial selection algorithm and the evaluation of the results have also highlighted some areas for further work:

Investigating minimum and maximum sizes for river clusters and dealing with connectivity – although rivers were dealt with on the basis of contiguity of river sections, there was insufficient time to assess the basis of the chosen cluster size (30 km), to specifically incorporate issues associated with the directional connectivity characteristic of rivers, and to deal with the „tail‟ of the rivers prioritisation (which included many clusters of just one or a few river sections of limited manageability).

Applying the spatial selection algorithm with existing land tenures – some areas have their freshwater ecosystem values already under conservation management, which if treated as a preselection (as occurs in most published conservation planning exercises) would result in different priorities being identified and also highlight the extent to which existing managed areas are representative of the higher quality examples of freshwater conservation values.

Integrating Special Values (SVs) analysis into the spatial selection algorithm – the extent to which conservation priorities would be different if SVs were incorporated into the running of the spatial selection algorithm as continuous variables, as distinct from their assessment on a post-hoc basis using categorical variables alone, would provide further valuable information on the relative importance of SVs and their relationship to representative conservation values.

Appendix 6 – Data layer development


6 Development of data sets

A range of data sets were developed for the Conservation of Freshwater Ecosystem Values (CFEV) Project as inputs and outputs of the assessment framework. This section outlines the sources and mapping rules for developing various spatial and attribute data sets. Data can be considered to be of two forms :

Spatial data layers

Attribute data sets

Spatial data layers were created or modified from existing data to show the geographic distribution of particular data. This data can be in the form of points, lines or polygons (areas). All of the spatial data were created or converted to the Geocentric Datum of Australia (GDA) 1994 Map Grip of Australia (MGA) Zone 55 projection. Spatial data were developed as base layers for each of the ecosystem themes and catchments, as well as source data for assigning variables to the base data layers (e.g. assigning tree assemblage data to river sections).

Attribute data sets (tabular data) were also developed and exist in two ways. Data that are directly associated with the spatial data sets (e.g. CFEV Rivers) and data that were developed using a set or rules and was used as source data for assigning values to the base data sets (e.g. assigning catchment disturbance data to river sections) or can be linked to spatial data sets using a common field identifier. In many cases, column headings of the attribute data indicate which ecosystem theme it relates to by the following prefixes: ES =Estuaries, GDE = Groundwater Dependent Ecosystems, KT = Karst, RS = Rivers, SM = Saltmarshes, WB = Waterbodies and WL = Wetlands. All elevation data developed for the CFEV assessment is measured in metres Australian Height Datum (AHD).

6.1 Glossary of metadata elements

The metadata entries provided in Appendix 0 and 6.3 may consist of any of the following elements. Null entries are not included.

Title The name of the data set developed by the CFEV Project. Where relevant, these names relate to the names given in the CFEV database.

Custodian The custodian is the agency who owns the data and is responsible for its management, including data collection, storage, update, maintenance and distribution.

Creator The name of the individual or agency or individual who created the data set.

Description A brief summary of the data.

Input data Individual source data sets used in the production of the data set or the name (and document reference) of the CFEV spatial data layer(s) used to create the attribute data.

Lineage The history of the data set and the steps taken to produce the data set including mapping and modelling rules.

Appendix 6 – Data layer development


Data limitations

Known limitations of the data and constraints associated with using and/or interpreting the data.

Date created The date when the data was first produced.

Scale and coverage

Scale: The relationship between the size of an object in the developed data set and its actual size in the real world.

Coverage: Geographic extent of the data set.

References Any literature or further information relating to the production of the data set.

Column heading

Name of the column heading within a specific spatial data layer relating to the attribute data (e.g. RS_TYPE).

Type of data This details whether the attribute data is categorical or continuous.

Number of classes

The number of unique classes within the attribute data set.

Assigning values to ecosystem spatial units

The mapping rules for assigning the attribute data to specific spatial data layers.

CFEV assessment framework hierarchy

Describes where the attribute data fits in the CFEV assessment framework and what data sets it was used to derive (e.g. Rivers>Statewide audit>Classification).

Other comments

Any additional information that is related to the data sets, particularly in the use and interpretation of the data.

Contact details All enquiries regarding the following described data sets should be directed to:

Water Resources Division Department of Primary Industries and Water 13 St Johns Avenue New Town TAS 7008 (03) 6233 6328 [email protected]

Appendix 6 – Spatial data layers - Acid drainage


6.2 Spatial data layers

6.2.1 Acid drainage

Title Acid drainage

Custodian Water Resources Division (WRD), Department of Primary Industries and Water (DPIW)

Creator Shivaraj Gurung, WRD, DPIW

Description Distribution of acid mine drainage in rivers of Tasmania.

Input data

Tasmanian Acid Drainage Reconnaissance, Mineral Resources Tasmania (MRT)

Lineage

The acid drainage spatial data layer was based on data collected and reported on during the Tasmanian Acid Drainage Reconnaissance study undertaken by MRT (Gurung, 2001). This study focussed on acid drainage from abandoned mines. Data on water chemistry and geochemistry associated with abandoned mine sites was collected in the field and compiled from existing data sets. The study built an inventory of the distribution of acid producing abandoned mine sites and their impact on the surface water quality (Gurung, 2001). The data sets containing the distribution of low pH-high sulphate and low pH-high metals in impacted surface waters (Maps 1 & 2 in Gurung (2001)) were combined to produce the CFEV acid drainage spatial layer.

The acid drainage spatial data was then assigned to each of the river sections using the rules outlined in Appendix .

Data limitations

The study by Mineral Resources Tasmania is incomplete. The field investigations were done in combination with 1:10 000 and 1:25 000 scale maps and the extent of the impact was projected to 1:500 000 maps. Its reliability is largely dependent on the accuracy of the historical data used for geochemical classifications of rock types at or proximal to abandoned mine sites (Gurung, 2001).

Date created March 2004

Scale and coverage 1:25 000; Statewide

References (Gurung, 2001)

6.2.2 Buffer zone

Title Buffer zone

Custodian WRD, DPIW

Creator Geographic Information System (GIS) Unit, Information and Land Services (ILS) Division, DPIW

Description Buffer area adjacent to all saltmarshes, rivers, waterbodies and wetlands

Appendix 6 – Spatial data layers - Buffer zone


Input data

CFEV Rivers spatial data layer (Appendix 6.2.24)

CFEV Saltmarshes spatial data layer (Appendix 6.2.26)

CFEV Waterbodies spatial data layer (Appendix 6.2.32)

CFEV Wetlands spatial data layer (Appendix 6.2.34)

Lineage

The buffer zone data layer represents an area along each side of the river sections and around each of the saltmarshes, waterbodies and wetlands. A data layer depicting the buffer zone was developed for each of the rivers, saltmarshes, waterbodies and wetlands spatial data layers to enable calculation of variables associated with the riparian zone of each spatial unit.

The width of this buffer strip was fixed at 50 m each side of the line representing the river section and 100m from the edge of polygons representing saltmarshes, waterbodies and wetlands. Figure 16 shows some rivers sections and a wetland as an example. A separate buffer zone data layer was produced for each of these ecosystem themes.

For sections of river that were greater than 50 m in width (e.g. the lower Derwent River), the buffer calculation was applied to a separate data layer which more adequately reflected the true width of the river, rather than a thin line down the centre of the river. This reduced the chance of incorrect data (indicating a significant proportion as water - i.e. the river itself) being assigned to the river sections when condition data layers (e.g. riparian vegetation) were intersected with the river section drainage layer.

Figure 16. Example of river sections and a wetland, showing the buffer zone.

The buffer zone data layers for each ecosystem theme were created using the „Buffer‟ geoprocessing tool in ArcGIS version 8. The boundaries of the buffers were retained.

Appendix 6 – Spatial data layers - Burrowing crayfish regions


Data limitations

The buffer zone data layers are limited by the accuracy of the original data layers used to create the buffers (e.g. saltmarshes, rivers, etc.).

Date created June 2004


6.2.3 Burrowing crayfish regions

Title Burrowing crayfish regions

Custodian WRD, DPIW

Creator GIS Unit, ILS, DPIW

Description Presence/absence of burrowing crayfish in Tasmania.

Input data

Burrowing crayfish data, University of Tasmania (UTas)

Lineage

This data layer shows broad regions where there is a high probability of native burrowing crayfish being present across Tasmania. The data layer was based on distributional information supplied by UTas (A. Richardson, UTas, unpublished data). The burrowing crayfish data layer consists of a single hand-drawn polygon showing the overall range of freshwater burrowing crayfish within the genera Geocharax, Parastacoides and Engaeus. Some of the northern and eastern ranges of Parastacoides, where conditions are probably drier and burrows are either very isolated, or confined to the very edges of creeks, were excluded. All other areas, outside of the polygon, were considered to lack burrowing crayfish. The burrowing crayfish data was assigned to each of the wetland spatial units (Appendix 6.3.4).

Data limitations Hand-assigned boundaries (see Lineage)

Date created December 2004

Scale and coverage Undefined; Statewide

6.2.4 Catchment disturbance

Title Catchment disturbance

Custodian WRD, DPIW


Description Assessment of catchment impacts associated with land clearance and intensive land use practices.

Input data

Land Information System Tasmania (LIST) land use layer, DPIW

Regional Forest Agreement (RFA) Biophysical Naturalness (BPN) layer, DPIW

Tasmanian Vegetation Map (TASVEG) data layer (Version 0.1 May 2004 (interim data set released prior to the release of TASVEG Version 1.0)), DPIW

Appendix 6 – Spatial data layers - Crayfish regions


Lineage

The catchment disturbance data layer was developed by dividing the state into three groups, according to the codes described in Table 9, which represented a combination of land clearance and land use data.

Table 9. Development of codes used to describe catchment disturbance.

Disturbance codes

Description Comprised of: Specific rules

0 Highest level of disturbance

TASVEG: cr (cleared) codes, BPN: 1 (cleared) and 2 (multiple selection events), LIST Land use categories: 3 to 5 (agricultural and intensive uses such as urban areas)

TASVEG codes took precedence over BPN codes, which took precedence over Land use codes.

0.5 Some disturbance

TASVEG: co (cut over) codes, LIST Land use category: 2.1.0 (rough grazing)

TASVEG codes took precedence over Land use codes.

1 Minimal or no disturbance

Everything else

BPN codes 3 (single selective logging), 4 (unlogged since 1950) and 5 (unlogged) were not included because it was felt that these levels of disturbance (some of which took place over 50 years ago) were minimal.

The procedure is justified as follows: TASVEG „cr‟ and „co‟ data (highest level of precedence) provides the most recent figures of vegetation clearance, available to the CFEV Project. BPN codes 1 and 2 (next level) has more information at a consistently higher resolution.

The catchment disturbance data was assigned to River Section Catchments (RSCs) and then to each of the karst, river, waterbody and wetland spatial units (Appendix 6.3.5).

Data limitations Doesn‟t include data from forestry post-RFA (1997).

Date created September 2004


References (Tasmanian Public Land Use Commission, 1997b; DPIW, 2003; Harris and Kitchener, 2003)

6.2.5 Crayfish regions

Title Crayfish regions

Custodian WRD, DPIW


Description Regional occurrences of Astacopsis crayfish in Tasmania.

Input data

Astacopsis crayfish data, UTas

Astacopsis gouldi crayfish data, Forestry Tasmania

Appendix 6 – Spatial data layers - Crayfish regions


Lineage

Distributions of the three described species of crayfish of the genus Astacopsis (Astacopsis franklinii, A. tricornis, and A. gouldi) were developed from distributional information supplied by UTas (A. Richardson & B. Hansen, UTas, unpublished data) and from distributional records (for Astacopsis gouldi only) provided by Forestry Tasmania (unpublished data). These data sets and existing GIS distributional polygons were reviewed to produce a general distribution map for the Astacopsis genus which identifies known regions of occurrence of Astacopsis franklinii, A. tricornis, and A. gouldi (Figure 17). Descriptions of the crayfish classes are provided in Table 10.

Rules for assigning each river section and waterbody with a crayfish class are given in Appendix 0.

Figure 17. Map of crayfish regions used in crayfish attribution rules. C6and C4 are not contiguous regions but are defined by altitude rules (see Table 10.)

Appendix 6 – Spatial data layers - Digital elevation model


Table 10. Description of crayfish classes.

Class code Description

C0 All areas on King Island and Furneaux Group; astacopsid crayfish absent.

C1 Areas below 400 m and excluding first order streams (for rivers); Astacopsis gouldi present.

C2 Astacopsis tricornis present (excluding first order streams for rivers).

C3 Astacopsis franklinii present (excluding first order streams for rivers).

C4 Waterbodies only; Astacopsis gouldi and A. tricornis present; waterbodies greater than 400 m, A. gouldi unlikely to be present; waterbodies less than 400 m, A. tricornis unlikely to be present.

C5 Waterbodies only; Astacopsis gouldi and A. franklinii present; waterbodies greater than 400 m, A. gouldi unlikely to be present; waterbodies less than 400 m, A. franklinii unlikely to be present.

C6 All first order streams (for rivers) and areas greater than 400 m; astacopsid crayfish have a low probability of occurrence or are absent (mainland Tasmania)


Date created August 2006


6.2.6 Digital elevation model

Title Digital Elevation Model (DEM)

Custodian WRD, DPIW


Description 25 m DEM of Tasmania

Input data


LIST 10 m contours, DPIW

Lineage

The DEM is a drainage constrained 25 m model of elevation. It was developed using 1:25 000 scale 10 m contours from the LIST and the 1:25 000 river drainage network developed as part of the CFEV Project. The DEM was built using the Australian National University (ANU) DEM program Version 4.6.3.

Data limitations The DEM inherits all the data limitations of the input data layers and derivation processes.

Date created July 2004


References (ANU, 2004)

Other comments

This data layer was used to derived variables, such as elevation and slope, for input into other data layers.

Appendix 6 – Spatial data layers - Estuaries


6.2.7 Estuaries

Title CFEV Estuaries

Custodian WRD, DPIW LIST, ILS, DPIW


Description Estuaries of Tasmania

Input data

LIST 1: 25 000 hydrographic theme (Subsets: hydrographic closure + mean high water mark), DPIW

Lineage

The estuary spatial data layer was developed using the LIST 1: 25 000 Hydrographic theme. As the CFEV classification and condition assessment for estuaries was based on the work undertaken by Edgar et al. (1999a), initially only the estuaries from that study were selected. Two extra estuaries, Dianas Basin and Wrinklers Lagoon, were also added to the layer, as a result of special value important bird sites occurring in these areas. The polygon for the Huon River was modified slightly to more accurately reflect the site assessed by Edgar et al. (1999a).

Data limitations

The estuaries assessed by Edgar et al. (1999a) were selected on the basis that they either had catchment areas exceeding 20 km2 or had an area of open water that was greater than 0.2 km2, hence very small estuaries were not included in the overall assessment.

Date created October 2004


References (Edgar et al., 1999a)

6.2.8 Fluvial geomorphic mosaics

Title Fluvial geomorphic mosaics

Custodian WRD, DPIW

Creator Kathryn Jerie and Ian Houshold, Earth Science Section, Resource Management and Conservation (RMC) Division, DPIW

Description Fluvial geomorphic regionalisation for Tasmania

Input data

Environmental Domain Analysis, Earth Science Section, DPIW

Lineage

Fluvial geomorphic landscape mosaics were generated for the state using a methodology primarily based on the river Environmental Domain Analysis (EDA) from Jerie et al. (2003). The EDA is a spatially constrained multivariate analysis that was used to identify areas where similar controls on river behaviour exist. The data used in this analysis related to topography, geology, climate and history of geomorphic processes, which were mapped on a 200 m grid of the state. The EDA produced very small areas, which were classified into 489 environmental domains. These

Appendix 6 – Spatial data layers - Fluvial geomorphic mosaics


domains were further aggregated into 92 mosaics, whereby boundaries were drawn around visible mosaics of river environmental domains using a GIS. Table 11 provides a description of each of the individual mosaics. The method for assigning the mosaic classes to the spatial units is provided in Appendix 6.3.13.

Data limitations

Lack of availability of some data sets (e.g. vegetation, presence of raised surfaces and fine scale faulting) which were therefore not used in the EDA (see (Jerie and Houshold, 2003) for further details).

Date created November 2003


References (Jerie and Houshold, 2003; Jerie et al., 2003a, b)

Other comments

The development of the fluvial geomorphic mosaics was an important step in generating geomorphic river types (described in the Appendix 6.2.9). The scale at which the mosaics were mapped was deemed to be too fine for the purpose of the CFEV assessment. An aggregation of these mosaics was believed to be more suitable particularly as management of areas assessed using the fluvial geomorphic data is at a broader scale.



Table 11. Descriptions of fluvial geomorphic mosaic classes.

Class code

Subregion name Landscape description Landscape components

Local controls Likely geomorphic river character, internal streams

Likely geomorphic river character, transitional streams

MO0 No mosaic attribute

MO1 Central and eastern dolerite plateaus

Context: Residual high level erosion surfaces in the Eastern Tiers, Snug Tiers, Wellington Range and the south-east Central Plateau, with margins defined by nickpoints in stream courses.

Physiography: Dominantly rolling hills and basins, although some locally steep escarpments and valley sides are found along structural lineaments. Tectonic and erosional disruption of the rounded plateau crest has led to predominantly north-south drainage of a gentle gradient on the crest remnants, progressively disrupted through the headward incision of high energy east-west flowing streams. Geology dominated by dolerite in combination with Parmeener Supergroup sediments with some Quaternary alluvium and low angle coarse slope deposits.

Climate: Moderately dry, with effective precipitation generally between 200 and 300 mm.

Geomorphic process history: Likely to have been periglaciated during the Quaternary glaciations. Aeolian deposits in the south-east lee of deflation hollows.

Broad rolling bedrock hills

Broad alluvial basins

Short, steep valley segments downstream of basins

Shallow lakes and marshes

Lunettes and minor sand sheets

Bedrock bars control alluvial reaches upstream of valley controlled reaches.

Lakes and wetlands developed through deflation and potentially damming by aeolian deposits or organic deposits.

Highland lakes and marshes (e.g. Lakes Sorell, Crescent, Hobbs Lagoon) fed by low gradient streams in broad valleys - fine sediment load, dominantly depositional (e.g. Macquarie River headwaters, Tumbledown Creek).

Short lengths of small, valley confined bedrock streams carrying coarse sediment load, dominantly erosional (e.g. Mountain Creek, Snowy River, Macquarie River above Longmarsh).

„Den sequences‟ on streams in highland basins (e.g. Longmarsh).

Low gradient, sinuous depositional streams on high plains (e.g. Clyde River below Lake Crescent).



Class code




MO2 Central East alluvial basins

Context: Alluvial reaches in Tertiary/Quaternary sediments in the South Esk, Coal and Apsley valleys.

Physiography: Flat valley floors fringed by relict alluvial fans.

Climate: Moderate to high daily rainfall maxima (50–100 mm) with lowest maxima in the Coal and highest in the upper South Esk and tributaries. Low effective precipitation, although most areas are in the 50-100 mm range. High run-off values in the upper South Esk and Break O‟Day valleys.

Geomorphic process history: Predominantly fluvial. Widespread aeolian deposits in all areas.

Wide, alluvial valleys confined by steep valley walls, steep catchments

Relict Pleistocene terraces composed of large calibre materials, sometimes partially cemented constrain streams laterally.

Tertiary sediments contain hardpan layers and sometimes basalt, constraining streams laterally and vertically.

Very small intermittent streams locally incising soft sediments.

Large powerful trunk streams with anastomosing/meandering platforms (Upper South, Esk, Break O‟ Day, St Pauls, Apsley, Swan, Lower Coal Rivers.

Broadwater reaches (on all above streams) where soft sediments contact harder strata or bedrock outcrops.

Alluvial fan reaches in major tributary streams (e.g. Tower Rivulet, lower Wye River.).

MO3 Central Plateau glacial till and outwash plains

Context: Alluviated section of the Central Plateau to the east and south of Lake St Clair, including the headwaters of the Ouse, Derwent and Gordon catchments.

Physiography: Rolling plateau country, till deposits and outwash plains with low to moderate residual hills. Alluvial fans have overridden outwash deposits in some areas, other deposits have been reworked by Holocene fluvial processes.

Climate: Annual run-off is moderate to high, between 1200 and 1500 mm. Storm events are moderate, between 50 and 55 mm daily maximum rainfall.

Geomorphic process history: Parts of the area have been glaciated in the early Pleistocene, widespread periglacial activity and some aeolian deposits are found. Peat is common in poorly drained alpine/subalpine basins.



Class code




MO4 Dissected north-west granite

Context: Steep granite hill country around Grassy on King island and the flanks of the Meredith Range.

Physiography: Steeply dissected plateau remnants on King Island and the Meredith Range, Strongly rectangular drainage systems have developed, particularly at the latter site.

Climate: These two areas could potentially be separated according to climate, as King Island receives only 200 mm run-off yearly, as compared with 1600 mm in the Meredith Range. Daily maxima are reasonably similar, however, from 50–60 mm. A strong winter maximum occurs at King Island; this is less pronounced at Meredith Range.

Geomorphic process history: Predominantly fluvial, although some peat is present on the Meredith Range.

MO5 Dry, low slope eastern granite hills

Context: Low rolling hills and alluvial basins in granite in the Ansons, Boobyalla, George and Great Musselroe catchments.

Physiography: Low rolling hills on granite, strongly parallel NNE/SSW drainage patterns. Relict erosion surface at 80-100 m dissected towards the coast.

Climate: Generally dry, with effective precipitation between 120 and 400 mm, although up to 500 mm in the upper Great Musselroe.

Geomorphic process history: predominantly fluvial.

Erosion surface relicts form flat-topped interfluves

Rolling hills and ridges

Alluvial basins bounded by low hills

Strong joint control on stream courses

Abundant granite sands produce distinct sandy bedforms

Highly erodable sediments in alluvial basins

Ephemeral and intermittent headwater streams, often highly entrenched into alluvial sands and weathered granite bedrock.

Meandering alluvial reaches in the Great Musselroe alluvial plains, the mid George and Ansons Rivers.

Distinctive pool and sandy run reaches in valley controlled reaches in all streams.

MO6 Eastern dolerite rolling hills

Context: Foothills flanking the steep escarpment of the Eastern Tiers

Physiography: Broad, gently rolling ridge tops dissected by relatively steep valleys, although the waterfalls and gorges of mosaic 7 (Eastern granite hills and coastal sediments) are absent. Relief is also considerably less. The rivers traversing this mosaic are considerably larger than 7 and hence valley floors are comparatively wider, and alluvial deposits more common. The area is dominated by dolerite, although Parmeener rocks crop out in basins and wider valley reaches.

Climate: Effective precipitation is low - moderate with 200-300 mm annually, fairly reliable from year to year, with a moderate daily maximum.

Geomorphic process history: Predominantly fluvial, although some areas of periglacially deposited slope materials are present on higher valley sides.

Broad, rolling interfluves

Moderately steep valley walls

Rare, semi-confined alluvial basins

Cobble bedded pool and riffle sequences within the larger bedrock controlled valleys.

Rare alluvial reaches in confined basins on larger streams.

Moderately steep, bedrock controlled tributaries, sometimes transporting a mobile cobble - gravel bedload.



Class code




MO7 Eastern granite hills and coastal sediments

Context: Low granite hills along the north-east coast of Tasmania including Flinders Island.

Physiography: Rolling, highly joint controlled hills in granite. Pleistocene valley infills including coastal dune systems and bayhead spits, lagoon systems and infilled lagoons, particularly on Flinders Island.

Climate: Dry coastal country, with effective precipitation of 100-150 mm. Daily rainfall maxima highly variable inter-annually, and low to moderate intensity, 50–70 mm.

Geomorphic process history: Aeolian and marine processes have been active in the past and continue.

Rolling, joint controlled granite hills

Flat to gently undulating infilled valleys and coastal lagoons

Coastal dunes and bayhead spits dam rivers, forming coastal lagoons and estuarine systems.

Strong joint control on streams – parallel drainage networks.

Small, ephemeral headwater streams on granite and granite sands, sometimes deeply entrenched.

Major estuarine and lagoon system (Scamander estuary, Douglas, Ansons, George River estuaries and lagoons.

MO8 Eastern granite hills and relict surfaces

Context: Predominantly relict erosion surfaces, part of the Mathinna surface and lower, unnamed surface to the west.

Physiography: Rolling hills and low, marshy valleys in the headwaters of north-east river systems. Sandy, erodable regolith. Lower level rolling hills in the St Patricks River valley.

Climate: Moderately wet with 400–800 mm effective precipitation, concentrated on the higher eastern surfaces. Daily rainfall maxima are very high, over 100 mm, in the eastern areas, down to 50 in the St Patricks River valley.

Geomorphic process history: Predominantly fluvial, some periglacial deposits, particularly where surfaces surround dolerite residual hills.

Rolling, high level relict erosion surfaces

Lower level rolling hills and alluvial basins

Granite terrain produces distinctive sandy regolith, which subsequently dominates bedload in streams. Generally highly erodable.

Low gradient, marshy headwater streams.

Upland marshes.

Bedrock controlled moderately steep streams in headwater hills.

Partly confined, single channel low sinuosity streams in rolling topography (Camden Rivulet).

Partly confined, single channel low sinuosity streams in rolling topography (St Patricks River).

Alluvial fan reaches downstream of steep dolerite slope deposit reaches.

Anastomosing, marshy reaches on high plains (Buffalo Brook).

MO9 Eastern Tiers basalt flats

Context: Enclosed by inland slopes subregion in the headwaters of Glen Morriston Rivulet, a moderate sized alluvial basin complex in the midst of more highly dissected terrain.

Physiography: Flat, broad topographic basins dominated by Cainozoic sediments and basalt flows. A small area of Parmeener sediments crops out, indicating that preferential erosion of this lithology is likely to have initiated the development of the basins, since infilled with sediments and basalt.

Climate: Low and unreliable effective precipitation, although high daily rainfall and high yearly variability indicates the potential for large rainfall events.

Geomorphic process history: Predominantly fluvial, minor periglacial.

Broad alluvial basins

Broad basalt interfluves

Potential for hardpans to control local base levels.

Highland marshes fed by intermittent streams - fine sediment load.

Small, low gradient streams with low-moderate sediment load (e.g. Glen Morriston Rivulet headwaters).



Class code




MO10 Eldon strike ridges

Context: Distinctive (but generally lower than quartzite strike ridges) in the West Coast Range, Goodwins Peak, Tiger Range, Mt Ramsay and Mt Dundas.

Physiography: Steep strike ridges and v-shaped valleys, although on a smaller scale than quartzite ridges. Thick slope deposits containing small, platey clasts are common, providing bedload to tributary streams.

Climate: High run-off areas with 1500–2 000 mm in western ranges, dropping to 1000 mm along the Tiger Range. Daily rainfall maxima between 50 and 60 mm with a winter bias. Low rainfall variability in the west increases on the Tiger Range.

Geomorphic process history: Predominantly fluvial, although strong periglacial activity has produced extensive fine grained slope deposits. Blanket bogs stabilise slope deposits in south-west mountain flanks.

MO11 Finely dissected northern surface and coastal sediments.

Context: Low rolling or generally flat topography in the foothills of the northern plateau remnants between Devonport and the Rocky Cape hinterland.

Physiography: Generally flat to rolling surfaces on mixed lithologies, finely dissected by small streams and occasional major rivers. Coastal sediments of generally Pleistocene age include Aeolian sands and estuarine deposits, although some minor outcrops of Tertiary sediments are present.

Climate: Moderate annual run-off throughout the range of these mosaics, generally in the 500–700 mm range. Daily storm rainfall is moderate – between 50 and 60 mm. Seasonality and inter annual consistency of rainfall are both moderate for this area on the mid north coast.

Geomorphic process history: Predominantly fluvial, although streams have been affected by aeolian sands near the coast.



Class code




MO12 Finely dissected north eastern surface on Mathinna, Parmeener and basalt

Context: Predominantly relict erosion surfaces, part of the Mathinna surface.

Physiography: Rolling plateau surface with small alluvial basins, developed on Mathinna beds, Parmeener sediments and remnant basalt flows.

Climate: Moderate effective precipitation ranging from 300 mm in more westerly areas to approximately 800 mm in eastern areas. Storm run-off is high to very high, with daily rainfall maxima between 70 and 90 mm.

Geomorphic process history: Predominantly fluvial, periglacial deposits common around boundaries with dolerite remnants. Some peat in the Mathinna Plains area.

Rolling hills

Marshy alluvial depressions, sometimes with blanket bog peats

Peat areas produce distinctive deep, meandering channels with low width-depth ratios.

Highland marshes, sometimes containing peats.

Marshy, sometimes meandering, deep channelled streams.

Slightly entrenched headwater streams.

MO13 Finely dissected western granite relict surfaces.

Context: Relict surfaces on granite from the west coast of King island south as far as the Pieman Heads.

Physiography: Low, flat to rolling surfaces shallowly (but sometimes steeply) dissected by small east/west streams.

Climate: Annual run-off increases southwards, from approximately 200 mm on King Island, to 800 mm south of the Pieman River. Maximum daily events are similar throughout the range, at around 45 mm. There is a strong winter base to rainfall, but yearly totals are reasonably consistent.

Geomorphic process history: Predominantly fluvial, although Aeolian processes have been active. Peat influences bank stability of smaller streams in some areas.



Class code




MO14 Glacially dissected dolerite and Parmeener plateau

Context: Found in three distinct areas surrounding the upper Picton River, the Eldon Range and the Upper Fury River.

Physiography: Rolling glaciated plateau on dolerite and Parmeener sediments. Glacial lakes, tarns and cirques are common as are significant till and outwash deposits. Remnant hills, usually of dolerite, protrude from plateau surface.

Climate: High run-off areas with much precipitation as winter snowfall. In the upper Picton 1500–1800 mm run-off, with over 2 000 mm in the Eldons and Fury headwaters. Daily rainfall maxima are high, between 70 and 80 mm. A distinct winter maximum rainfall in the Eldons, however some summer rain is experienced in the Southern Ranges. Rainfall variability is generally low.

Geomorphic process history: Glacial and fluvial. Strongly periglacial in parts, with extensive slope deposits derived from dolerite. Some alpine peatlands influence headwater streams.

MO15 Glacially dissected quartzite plateau

Context: Heavily glaciated plateaus on quartzite and quartz conglomerate in the West Coast Range (Tyndall Range) and the Cradle Mountain area.

Physiography: Deeply glacially dissected plateau landscape with significant cirques, tarns and over deepened lake basins. Developed on Precambrian quartzites and Cambrian quartz conglomerates, some emergent peaks are composed of Jurassic dolerite. Large areas of glacial deposits are common as are glaciofluvial deposits.

Climate: Very wet climate in the Tyndall Range with over 2500 mm run-off calculated annually. Similarly, the Cradle Mountain area receives around 200 mm. High rainfall intensity in both areas with daily maxima in the order of 70 mm. A distinct winter rainfall bias is found in both areas as is a low variability between years.

Geomorphic process history: Predominantly glacial and fluvial. Widespread periglacial activity has produced significant blockstreams around emergent dolerite peaks in the Cradle Mountain area.



Class code




MO16 Glaciated dolerite and Parmeener peaks

Context: High, glaciated peaks and ridge crests in a north-south line at the boundary of the fault and fold structure provinces, including the Southern Ranges, Snowy Range, Hartz Mountains Mt Anne, Mt Field, King William Range and Cradle Mountains.

Physiography: Steep glaciated peaks and ridge crests where dolerite peaks emerge from softer surrounding Parmeener sediments. Many glacial lakes. Extensive screes have developed as a result of nearby periglaciation.

Climate: High run-off areas with between 1500 and 2 000 mm on higher peaks. Daily rainfall also high, particularly on south-east peaks where precipitation can exceed 80 mm. A westerly rainfall bias in the north-west grades into a less defined pattern in the Southern Ranges which receive some summer precipitation. Rainfall variability is generally low.

Geomorphic process history: Glacial, fluvial and periglacial landforms and deposits are all common.

MO17 Glaciated dolerite valleys

Context: U shaped glacial valleys, developed in dolerite bedrock in the headwaters of the Mersey, Derwent and Picton catchments.

Physiography: Steep walled dolerite valleys carved by Pleistocene valley glaciers. Much still covers valley floors, often masking bedrock. Glacially deepened lakes include Lake St Clair.

Climate: Annual run-off moderate to high, from 1400–1500 mm. Daily rainfall events moderate at between 60 and 70 mm. Seasonality of rainfall moderate and inter-annual variability of precipitation low to moderate.

Geomorphic process history: Heavily glaciated in early Pleistocene glacials, partly glaciated in the last glacial. Widespread periglacial activity on exposed rock at those times.



Class code




MO18 Glaciated quartzite peaks

Context: Some of the most spectacular mountain scenery in the state, comprising the heavily glaciated peaks of the south-west quartzite and quartz conglomerate strike ridges in the Arthur Range, Frankland Range, West Coast Range and Frenchmans Cap.

Physiography: Steep, highly glacially dissected mountain peaks and ridge crests containing numerous cirque lakes and tarns.

Climate: High rainfall areas with between 1800 and 2400 mm run-off annually. Daily maxima of 60–70 mm are also high. Rainfall is biased towards winter months with a low inter annual variability.

Geomorphic process history: Glacial and fluvial, although some reasonably extensive areas of slope deposits are also present.

MO19 Glaciated quartzite valleys.

Context: Highly glaciated, major valley systems in the Mersey, Forth and Surprise Rivers.

Physiography: Distinctive large scale erosional and depositional glacial landforms developed in Precambrian quartzites. Much till and fluvioglacial outwash in valley bottoms acts as a distinct control on river morphology.

Climate: High rainfall on surrounding peaks although these deep river valleys exert a rain shadow effect. Run-off ranges from 1000 to 1800 mm at the head of the Forth valley. Daily rainfall is moderate to high at 60–70 mm with a winter maximum in the southern mosaic tending to a more mixed regime in the Mersey. Rainfall variability is generally low although the Mersey catchment is a little higher.

Geomorphic process history: Glacial, fluvial, widespread periglacial deposits particularly where valley walls are rimmed with dolerite.



Class code




MO20 High altitude dolerite plateau

Context: High elevation, low gradient, surfaces of the Ben Lomond plateau, Wellington Range and eastern Central Plateau.

Physiography: Heavily periglaciated plains of complex origin, (potential combinations of relict erosion surfaces, cryoplanated surfaces or structurally controlled surfaces) generally on Jurassic dolerite with minor areas of Tertiary basalt and Parmeener supergroup sediments.

Climate:

Geomorphic process history: Heavily periglaciated and minor glaciation in Pleistocene glacial periods, ongoing minor present day periglaciation. Considerable areas of alpine peatland, some relict aeolian deposits.

Extensive, low angle blockstreams composed of dolerite boulders

Low relief emergent hills and peaks composed of dolerite bedrock

Minor glacial deposits and glacial lakes

MO21 Inland slopes Context: West draining hilly slopes of the Eastern Tiers and hilly country bounding the Derwent basin.

Physiography: Broad rolling hills and moderate to steep valleys, typical of a broad range of lithostructural units which have been planated at various times in the past. Geology predominantly dolerite and Parmeener, although minor Mathinna units in the west Tamar area. Some minor alluvial basins are found, generally on Parmeener substrates.

Climate: Moderately dry, with effective precipitation usually less than 400 mm, with little inter-annual variation and moderate daily rainfall maxima around 40-60 mm.

Geomorphic process history: Predominantly fluvial, although some evidence of uplifted erosion surfaces. Minor Aeolian deposits. Potential periglaciation on steep slopes.

Broad rolling hills

Moderate to steep valleys, structurally controlled

Some deflation basins with marshes and associated lunettes (e.g. Hobbs Lagoon)

Bedrock controlled valleys and gorges.

Smaller streams are largely bedrock controlled both vertically and laterally.

Some alluvial basins containing marshes in relict Pleistocene and Holocene alluvium (e.g. Sandstone Marsh, and Ellendale area).

Alluvial valleys contain small, single channel, low sinuosity channels, strongly structurally controlled in dolerite around the Tamar.

Large streams are predominantly controlled by bedrock valleys (e.g. Macquarie River, Nile River, Nth Esk River.

Gorges (e.g. Staircase Gorge on Elizabeth River, Cataract Gorge on the South Esk, Bluff River) are found where hard dolerite and sandstone crops out.

Moderate sized alluvial pockets with anastomosing/meandering reaches are found in low gradient reaches (e.g. on Nile River., Little Swanport River, Prosser River, Tyenna River, Serpentine Rivulet).



Class code




MO22 Lower Derwent Context: Generally low gradient, alluvial reaches of the Derwent River and proximal reaches of low gradient tributaries such as the Styx, Lachlan and Plenty Rivers and Sorrell Creek.

Physiography: Broad floodplain and terraces deposited into a valley cut into Tertiary Lake sediments and basalt flows. Minor areas of steep valley cut into Tertiary basalt and Jurassic dolerite.

Climate: Relatively low effective precipitation, generally less than 300 mm. Moderate daily maximum rainfall.

Geomorphic process history: Some relict Aeolian deposits in the vicinity of Hayes and Lawitta.

Broad basins containing fine grained Tertiary lake sediments, coarser Pleistocene terrace materials and fine Holocene floodplain deposits

Steep valley reaches confined by Jurassic dolerite and Tertiary basalt

Coarse grained alluvial fans in steep tributary valleys

Local bed control by outcropping dolerite and basalt bedrock.

Erodable and sometimes dispersive Tertiary Lake sediments and Holocene floodplain deposits.

Minor valley confinement by Parmeener sediments, dolerite, basalt and in places by coarse Pleistocene terrace and fan materials.

Small, low gradient alluvial streams on Cainozoic sediments.

Steep, bedrock streams on bedrock valley sides.

Small anastomosing streams on relict alluvial fans.

MO23 Lower Huon Context: Low gradient alluvial reaches of the Huon River.

Physiography: Floodplain and terraces surrounded by moderately steep hills in adjacent subregions.

Climate: Moderate effective precipitation, moderate daily maximum rainfall.

Geomorphic process history: Predominantly fluvial, although older terraces are likely to be composed of glacial outwash.

Large alluvial river valley

High level terrace systems

Local bed control by outcropping Parmeener sediments and Jurassic dolerite.

Lateral control by large calibre terrace materials.

Very small, low angle alluvial streams.

The Huon River is a major lowland alluvial stream, comprised of long pool and riffle sequences, with minor bedrock rapids. Partially confined by bedrock valley walls.



Class code




MO24 Moderate rainfall northern dolerite and Parmeener hills

Context: Rolling dolerite hills in the Brushy Creek and Rubicon catchments.

Physiography: Rolling, strongly structurally controlled hills and alluviated valleys. Similar topographically to the Inland slopes subregion, but climatically wetter.

Climate: 300-400 mm effective precipitation combined with daily rainfall maxima of 50-60 mm, moderately reliable, separates this subregion from the drier, flashier Inland slopes subregion.

Geomorphic process history: Predominantly fluvial.

Rolling, structurally controlled dolerite ridges and hills

Minor alleviated valleys

Smaller streams are largely bedrock controlled both vertically and laterally.

Some alluvial basins containing marshes in relict Pleistocene and Holocene alluvium (e.g. Reedy Marsh, Brushy Lagoon area).

Alluvial valleys contain small, single channel, low sinuosity channels, strongly structurally controlled in dolerite.

Steep walled bedrock valley controlled reaches of the Rubicon River.

MO25 Moderate slope eastern granite hills

Context: Moderate slopes on granite in the Furneaux Group (Flinders, Cape Barren and Clarke Islands) and Maria Island.

Physiography: Joint controlled hills and ridges in NE granite country.

Climate: Dry to moderately wet rainfall, with moderate maximum daily rainfall, from 60–80 mm. Effective precipitation ranges from 20 mm (Cape Barren Island). Highly variable inter-annually.

Geomorphic process history: Aeolian deposits common in the Furneaux Group, particularly Clarke Island.

Joint controlled, moderately steep hills and valleys.

Strong joint control creates parallel drainage networks in larger areas.

Small ephemeral streams, quite powerful during flash floods.

MO26 Moderate slope Mathinna hills

Context: Hills and ridges separating v-shaped valleys in the upper Boobyalla, Little Forester, Tomahawk and Pipers River catchments, and a small area on Flinders Island.

Physiography: Moderately steep, v-shaped hills, ridges and spurs developed on Mathinna beds sediments. Flat, alluvial valleys, generally of gentle gradient are found in many areas.

Climate: Generally fairly dry to moderate rainfall and run-off, with effective precipitation from 250-500 mm depending on elevation. Daily maximum rainfall figures indicate moderate storm intensities with 50-70 mm per day, moderately reliable inter-annually.


Moderately steep v-shaped hills, ridges and spurs

Moderately steep valleys, often containing alluvial sediments

Moderately steep bedrock controlled headwater streams in v-shaped valleys, contribute reasonably high sediment loads from relatively unstable valley slopes.

Anastomosing to single channel low sinuosity reaches at the upper margins of alluvial reaches.

Moderately sinuous, sometimes meandering reaches in low gradient alluvial valleys.

Meandering reaches on larger streams in significant alluvial valleys (e.g. Pipers River near Lower Turners Marsh, Boobyalla River at Gellibrand Plains).



Class code




Class code




MO27 North east volcano-sedimentary hills

Context: Steeply dissected hills in the Asbestos Range area.

Physiography: Distinctive, finely dissected volcano-sediments, v-shaped ridges and valleys resembling finely dissected Mathinna beds terrain in the Scamander and Avenue catchments.

Climate: Dry to moderately wet, with 100-300 mm effective precipitation, moderate daily rainfall of 45-55mm with moderate reliability.

Geomorphic process history: Predominantly fluvial, some minor periglacial deposits.

Steeply dissected v-shaped ridges and valleys

Some limited alleviation in larger valley floors

Bedrock controlled headwater streams transporting gravel bedload.

Anastomosing or single channel, low sinuosity streams in low gradient alluvial valleys.



Class code




MO28 North eastern basins with granitic sediments

Context: Lowland granite basins bordering coastal dunefields in the lower reaches of the Ringarooma, Tomahawk, Great Forester, Great and Little Musselroe Rivers.

Physiography: Low rolling hills and valleys developed on Tertiary non marine sediments, predominantly composed of granitic sands, Some granite bedrock hills protrude from basin sediments, and some low hill and plateaus are formed on relict basalt flows over the sediments.

Climate: Dry to moderately wet climate with low to moderate annual run-off, with effective precipitation ranging from 100–400 mm depending on elevation. Moderate storm events are indicated by daily rainfall maxima between 40 and 70 mm, more variable inter-annually in the east than west of this subregion.

Geomorphic process history: Predominantly fluvial, although some aeolian deposits are found as outliers of the neighbouring dune country to the north.

Low rolling hills on granitic sands

Relict basalt plateaus

Small granite hills

Large areas of unconsolidated granitic sands are highly erodible. Combined with a relatively dry climate and sparse vegetation cover these streams are prone to bank erosion and incision, and subject to large sediment loads of granitic sands.

Where bedrock or basalt crops out stream energy is focussed and local bed scouring is likely in sands.

Where stream flow over bedrock, short gorge reaches are found, particularly in granite.

Low gradient streams, often highly unstable and subject to headward incision and gullying.

Marshlands and wetlands in topographic depressions.

Large, single channel, moderate to low sinuosity reaches, often deeply incised into granite sands (Mid and lower Ringarooma River, Lower Boobyalla, Tomahawk, Great Forester and Great Musselroe Rivers).

Low gradient, single channel reaches, bedrock controlled, sometimes low gorges in granite bedrock (Ringarooma River upstream of Gladstone, Great Forester east of North Scottsdale).



Class code




MO29 North eastern coastal dunefields

Context: Relict terrestrial dunefields along the north-east coast, east of the Tamar estuary.

Physiography: Longitudinal Pleistocene dunes with relict, rounded hills composed of Mathinna beds sediments, granite and basalt. The dunes are relict terrestrial dunes, emplaced during low level Pleistocene sea level stand(s).

Climate: Relatively dry, with 75-150 mm effective precipitation annually. Moderate storm run-off derived from daily rainfall maxima between 50 and 60 mm with moderate reliability.

Geomorphic process history: Fluvial and Aeolian at present, with major relict Aeolian features.

Longitudinal dunefields

Relict hills on basalt, granite and metasediments

Bayhead spits and associated estuaries/lagoons

Interdune lakes and wetlands

Longitudinal dunes exert strong control over the drainage network and planforms of small to medium sized streams with strongly parallel drainage nets.

Bayhead spits and other coastal features influence the style of coastal lagoons and estuaries.

Ephemeral streams, with relatively straight single channels, sand bed, strongly parallel drainage networks (Tributaries of lower Tomahawk, Boobyalla, Ringarooma Rivers).

Major Rivers partially confined by major dunes (Lower Ringarooma River).

Estuaries dammed by bayhead spits (Musselroe River, Tomahawk River, Brid River).

MO30 North eastern Mathinna beds sedimentary basins

Context: Broad alluvial valleys and basins in the Pipers, Little Forester and Boobyalla Rivers.

Physiography: Rolling hills developed in Mathinna beds sediments, separated by alluvial plains and basins.

Climate: Low to moderate run-off from approximately 150 – 400 mm effective precipitation annually. Moderate daily rainfall maxima between 40 and 60 mm, moderately reliable.

Geomorphic process history: Predominantly fluvial, although some Aeolian sands are likely to be present.

Low rolling hills separated by alluvial valleys

Small, single channel alluvial streams.

Short, bedrock controlled reaches on interfluves.

Low to medium sinuosity single channel alluvial reaches (Lower Pipers River, Little Forester River).

Low gradient, anastomosing swamp forest reaches (Boobyalla River).

MO31 North-east alluvial basins

Context: Significant enclosed alluvial basins containing sediments derived from mixed lithologies in the Supply, St Patricks, Nth Esk and George River valleys.

Physiography: Low gradient, wide alluvial basins containing floodplain, terrace and alluvial fan terrains.

Climate: Moderately high run-off areas with effective precipitation ranging from 300 – 650 mm, driest in the Supply basin. Storm run-off is moderate to high, ranging from 50-80 mm in daily events.


Alluvial basins, some with terrace sequences

Some minor terrace control where Pleistocene materials are sufficiently consolidated.

Partial valley control in some reaches.

Small, low gradient single channel alluvial streams.

Large anastomosing/meandering alluvial reaches, (e.g. St Patricks, Supply, Nth Esk and George Rivers).

Partially valley controlled low sinuosity reaches (e.g. St Patricks, Supply, Nth Esk and George Rivers).



Class code




MO32 North-east ultramafics

Context: Small area in the west Tamar catchment.

Physiography: Finely dissected hills on ultramafic bedrock.

Climate: Low – moderate annual run-off of 335 mm. 50 mm daily maximum run-off. Moderate seasonality and annual variability.


MO33 Northern calcarenite karst

Context: Calcarenite relict dune systems, predominantly on the west coast of Flinders and King Islands.

Physiography: Low, rolling topography consisting of essentially parabolic dune systems, swales and closed depressions, sometimes karstic, on consolidated calcareous dune systems.

Climate: Dry to moderate, with daily rainfall maxima between 40 and 60 mm, effective precipitation 130–200 mm, higher on King Island. Inter-annual variability high on Flinders island, low to moderate on King.

Geomorphic process history: Aeolian in the past and ongoing in places.

Rolling parabolic dune topography, sometimes with karstic depressions

Drainage is predominantly sub-surface through solutionally enlarged conduits in dune limestone.

Virtually no surface streams at this scale.

Larger streams have been powerful enough to remove Aeolian sands as it has been deposited, and maintained „valleys of construction‟ particularly on the wetter west coast of King Island. Stream types include:

- Spring fed coastal streams (Pass River, Bungaree Creek, Boggy Creek).

- Ephemeral dune streams (Bungaree Creek).

- Pool and riffle streams incised into basement rocks in dune walled valleys (Ettrick River).

MO34 Northern high relief karst

Context: High relief karst areas at Mole Creek, Gunns Plains, Loongana and Mt. Cripps.

Physiography: Distinctive, well developed karst topography with highly integrated sub-surface drainage, stream sinks, springs and cave systems developed in Ordovician Gordon limestone.

Climate: Annual run-off in these areas is moderate to high, with around 1000 mm in the Mole Creek and Gunns Plains areas rising to over 1500 mm at Mt Cripps. Daily storm rainfall is around 60–70 mm, with seasonality and rainfall variability moderate.

Geomorphic process history: Dominated by karst and fluvial processes, some of the higher elevations are mantled by periglacially derived slope deposits with clasts from surrounding lithologies; commonly Jurassic dolerite and Tertiary basalt.



Class code




MO35 Northern karst basins

Context: Low relief karst in the Lobster Rivulet – Meander area, Kimberley-Railton and the Vale of Belvoir.

Physiography: Subdued karst topography, however significant subterranean drainage is present. Ordovician Gordon Group limestone is commonly covered by thick glacial and glaciofluvial deposits of Pleistocene age.

Climate: Climate is reasonably variable across these three mosaic areas, with run-off at Railton approximately 400 mm, 900 mm at Meander, and 1700 mm at Vale of Belvoir, however the effect of karst on this topography greatly outstrips that of climate. Daily rainfall maxima vary between 50 and 70 mm, with seasonality and annual variability moderate.

Geomorphic process history: Karst and fluvial processes dominate today, however glacial and fluvioglacial deposition have been important in the Pleistocene.



Class code




MO36 Northern Midlands Tertiary Basin

Context: Extensive lowland basin (Tertiary graben) defined by north-south trending fault zones, elevation 150-200 m, bounded by Eastern and Western Tiers.

Physiography: Generally low relief landscape, composed of broad valleys on Tertiary sediments. Interfluves and isolated hills composed of Parmeener sediments, basalt and dolerite separate valleys and form „islands‟ within them. Quaternary sediments form floodplains and significant terraces in river valleys, and low angle fans on the upslope margins of the mosaic. Aeolian landforms and associated deposits are widespread. Sandsheets, deflation hollows and associated lunettes are common, with associated wetlands and lakes important in their own right, and potentially affecting fluvial forms and processes.

Climate: Dry, with effective precipitation generally less than 100mm per annum, with low maximum daily rain.

Geomorphic process history: Relict aeolian activity, deflation basins, lunettes, source bordering dunes and sand sheets.

Bedrock hills

Plains, broad valleys on Cainozoic sediments, bedrock interfluves and „islands‟

Narrow valleys incised into broader plains, filled with younger floodplain and terrace deposits

Low to moderate angle fans where rivers enter the mosaic

Deflation basins with associated lunettes and dunes

Older terrace materials sufficiently cemented to laterally constrain flows

Tertiary sediments contain hardpans (silcrete, ferricrete) which control streams both laterally and vertically.

Significant Aeolian landforms (deflation hollows, lunettes, sand ridges and sheets constrain the courses of smaller streams.

Significant intermittent lakes and lagoons associated with Aeolian features

Small, low gradient intermittent streams, usually single channel, possibly modified from natural marshy or chain of ponds character.

Terrace confined, low gradient reaches, pool and riffle sequences, broadwaters where bedrock or hard Tertiary sediments are encountered (e.g. Macquarie River, Lower South Esk River).

Very low gradient, marshy, anastomosing/meandering reaches (Brumbys Creek, Lake River, Nile River).

Very Low gradient broadwater/marsh/meandering/anastomosing reaches (Upper (in this subregion) South Esk River).

MO37 Northern quartzite gorges

Context: Steep gorges where major rivers have cut across the strike of hard quartzite units.

Physiography: Deep, cliffed gorges on the Mersey, Forth and Leven Rivers.

Climate: Moderate annual run-off of between 500 and 900 mm. Daily rainfall maxima between 55 and 70 mm. Moderate seasonality and annual variability.




Class code




MO38 Northern quartzite ridges, hills and valleys

Context: Relatively steep quartzite and conglomerate ridges, spurs and valleys in the Black Bluff area and Norfolk Range, the Great Bend area of the Mersey valley, the middle Arthur valley, the Inglis and upper Detention valleys.

Physiography: This subregion incorporates relatively steep, well defined ridges and v-shaped valleys on hard Precambrian quartzose metasediments.

Climate: Daily storm events are of moderate intensity (50–70 mm) with some variation in annual run-off (5-600 mm on the north coast, up to 1700 mm in the Black Bluff area). Variation in the timing of storms seasonally and inter-annual variation in run-off are low – moderate.

Geomorphic process history: Predominantly fluvial, although periglacial slope deposits and screes are found in the Black Bluff and Norfolk Ranges.



Class code




MO39 Northern relict surfaces

Context: Plateau remnants, rolling hills and dissected margins of major interfluves from the Mersey catchment in the east to King Island. The largest area is the Surrey Hills area south of Wynyard.

Physiography: Rolling hills, plains, alluvial basins and minor dissected valleys on a range of lithologies including basalt, Parmeener sediments, volcano-sediments, lower grade Precambrian metamorphics and, very rarely high grade metamorphics (King Island).

Climate: Moderate to wet climate with effective precipitation ranging from 400-900mm in the coastal catchments, to 100-1400mm in the Surrey Hills. There may be a case to separate the Surrey Hills region from the others on climatic grounds, however this does not appear to manifest itself in sufficiently different stream types to be justified at present.

Geomorphic process history: Predominantly fluvial, although some significant periglacial deposits surround higher plateaus and relict hills on basalt.

Rolling basalt hills

Alluvial basins in all lithologies

Finely dissected surfaces in volcano-sediments, Parmeener and Precambrian metasediments

Thick, dispersive soils on basalt produce high landslip susceptibility, with mass movement and subsequent debris flows and transport of mainly fine grained sediments in streams. Significant deposition in alluvial basins.

Steep plateau margins often support significant periglacial deposits through which drainage flows

Alluvial plains often contain marshes, partly channelised, particularly in higher plateau areas such as Surrey Hills and Borradaile Plain, where peatlands are found. Distinctive swamp forest reaches on upper King Island surfaces associated with aeolian sediments (Potential to separate the King Island area from this subregion due to strong aeolian influence)

Small, bedrock controlled headwater streams, generally perennial (Upper Flowerdale River)

Moderate sized streams, steep valley and bedrock controlled reaches in dissected plateau margins (Leven, Blythe, Emu, Cam Rivers)

Small to moderate sized, rarely anastomosing, single channel streams in steeper alluvial valleys (Upper Leven River, Emu River, Wey River)

Small to moderate strongly meandering streams in low gradient alluvial valleys (e.g. upper Inglis River, Gawler River, Fossey River, Hatfield River).

This subregion contains only headwater streams.



Class code




MO40 Northern rolling basalt hills and alluvial basins

Context: Rolling hills and alluvial basins on elevated basalt surfaces in the upper Dip River, mid Meander and mid Rubicon River catchments.

Physiography: Rolling basalt plains and hills with large alluvial basins. Some dissection at surface boundaries.

Climate: Moderately wet in the eastern subregion (300-400 mm effective precipitation), wet in the Dip River area (700-900 mm). Daily maxima of 50-60 mm indicate moderate storm events in both areas.


Rolling relict basalt hills

Broad alluvial valleys

Minor bedrock valleys and dissected surface margins.

Perennial to intermittent (west to east) small, low gradient, low sinuosity alluvial streams

Moderately steep bedrock controlled headwater streams on basalt hills

Moderate sized streams in semi confined valleys are meandering, pool and riffle reaches (e.g. Dasher, Minnow Rivers, Rubicon River at Elizabeth Town)

Moderate and larger sized streams in relatively unconfined valleys: moderately to strongly meandering pool and riffle alluvial reaches (Dip River, Rubicon River at Avenue Plains, Quamby Brook above Westbury, Upper Don River, Meander River above Deloraine and below Porters Bridge)

Moderate and larger sized streams in valley controlled reaches (Dasher and Minnow in basalt valleys, Dip River in basalt valley, Meander River downstream of Deloraine).



Class code




MO41 Northern steep quartzite ridges and scree.

Context: A widespread mosaic in the north of the state, occurring where metamorphosed quartz sandstones and conglomerates crop out from the Gog Range in the east through the Loongana Range and St Valentines Peak to Baretop Ridge and the Longback in the west.

Physiography: Steep strike ridges composed of Precambrian quartzites and Cambrian sandstones. These have produced extensive slope deposits in some areas, notably the northern slopes of the Gog Range. Where rivers have breached these strike ridges major gorges have developed, as on the Mersey at Alum Cliffs, the middle Forth, and the Leven at Leven Canyon.

Climate: Annual run-off ranges from 4-500 mm in the eastern mosaics to over 1500 mm in the western areas. Daily maxima increase in the opposite direction from around 50 mm in the west to 70 mm around the Gog Range. Seasonality of rainfall is greater in the western blocs with a winter maximum, and inter annual variability increases from low to moderate in an easterly direction.

Geomorphic process history: Predominantly fluvial, although periglacial processes are likely to have been responsible for extensive slope deposits.

MO42 Northern Tertiary basins and coastal sediments

Context: The northern extension of the Midlands Tertiary basin, paralleling the Tamar estuary and Port Sorell.

Physiography: Relatively high relief and slope Tertiary deposits, with residual dolerite, Parmeener and basalt hills either protruding or capping the sediments.

Climate: Relatively dry, with 100–250 mm effective precipitation, moderate storm run-off related to daily rainfall maxima of 40-50 mm, with moderate inter-annual variability.

Geomorphic process history: Predominantly fluvial, some relict and coastal aeolian features.

Relatively steep slopes in erodable Tertiary sediments

Relict rolling hills in Basalt, dolerite and Parmeener sediments

Hardpans in Tertiary sediments impose local bed and bank controls

Short, relatively steep and deeply incised streams in steep Tertiary sediments (Rose Rivulet and tributaries)

Single channel, incised, moderately sinuous streams in Quaternary infills of the Tertiary sediments Lower Rose Rivulet, Browns, Briggs Creek, Panatana Rivulet)

Ephemeral, bedrock controlled streams on Parmeener, dolerite and basalt residuals

Major rivers with strongly meandering planforms in unconsolidated Quaternary fill, infilling Tertiary sediments (North Esk River)



Class code




MO43 Northern valleys Context: Large, steep walled valleys incising northern relict surfaces from the Mersey catchment in the east to the Arthur and Inglis in the west. Gorge reaches within these valleys are generally developed in Precambrian quartzites; a separate subregion.

Physiography: Steep walled valleys cut through Tertiary basalt caps into volcano-clastic sediments (with minor occurrences of Precambrian metasediments and quartzites and Parmeener sediments).

Climate: Moderately wet to wet with 400-900 mm effective precipitation in the coastal streams to 1000-14 000 mm in the Arthur catchment (It may be reasonable to separate this catchment from the above on climatic grounds, however river character does not appear to be sufficiently different).

Geomorphic process history: Predominantly fluvial, although significant periglacial deposits and mass movement deposits are found on valley walls, particularly below basalt caps.

Steep walled valleys below relict surfaces

Minor alluvial pockets in softer lithologies or upstream of resistant lithologies.

Gradient of valley strongly controlled by hardness of lithology.

Periglacial and mass movement deposits cause sub-surface flow, and when active provide considerable bedload material to streams (reasonably rare in the present climate, although apparently common in glacial stages).

Very steep, small bedrock controlled streams

Sub-surface streams in periglacial and slope deposits, particularly below basalt.

Major bedrock and valley controlled rivers with little stored alluvium (e.g. Flowerdale, Inglis, Cam, Emu, Blythe, Leven, Forth, Wilmot Mersey Rivers)

Alluvial reaches, partly valley controlled pool and riffle sequences (e.g. Leven, Forth, Mersey, Wilmot, Lower Flowerdale and Inglis Rivers)

MO44 North-west dissected escarpments

Context: Major escarpments separating coastal plains and surfaces with higher plateau remnants flanking the eastern side of the Duck River valley and the main eastern escarpment of King Island.

Physiography: Relatively steep escarpments on Cambrian volcano-sediments, Precambrian metasediments and various minor units. Characterised through strongly separating landscape surfaces, this mosaic is repeated on much smaller scales through out the regions as escarpments to steps in terraces and erosion surfaces. Streams are usually actively incising these areas.

Climate: Low to moderate annual run-off between 300 mm on the east coast of King Island, to around 600 mm on the Duck escarpment. Daily rainfall maxima of around 50 mm are moderate. A moderate seasonality with wider maxima more pronounced on King Island, and a low inter-annual variability of rainfall.

Geomorphic process history: Predominantly fluvial, however some Aeolian sands are present.



Class code




MO45 North-west hills, coastal sands and remnant surfaces

Context: Low relief relict erosion surfaces, residual hills and coastal plans on King, Robbins, Three Hummock and Hunter Islands, and the Cape Grim – lower Welcome River areas on the Tasmanian mainland.

Physiography: Stepped, raised coastal surfaces are common, separated by locally steep escarpments. These are covered by variable thicknesses of aeolian sands. Some small areas of basalt emerge, particularly along the Cape Grim coast. Streams are generally very low energy, low gradient systems controlled by swamp forest vegetation, apart from escarpment reaches where they are actively incising and retreating headwardly.

Climate: Mean annual run-off is low to moderate (200–300 mm) with daily storm events also moderate (40–50 mm). There is a low to moderate seasonality of run-off, although King Island is moderate, and a low inter-annual variability of run-off.

Geomorphic process history: Much of the area has been subject to Aeolian processes at various times throughout the Pleistocene. Some coastal dune systems have been active in Holocene times and remain so today.

MO46 North-west moderate relief karst

Context: Hilly karst country in the Duck, Montagu, Arthur, Savage and Pieman valleys.

Physiography: Low to moderate relief karst developed in Precambrian dolomite. This bedrock is variably silicified, leading to large variations in the intensity of karstification. Many areas have extensive doline fields, stream sinks and springs. Extensive cave systems are less common, however some significant systems exist.

Climate: Moderate to high annual run-off, varies between 500 mm in the Montagu area to over 1000 mm around Julius River to the south. Daily rainfall maxima are moderate, commonly 50–60 mm. Rainfall is concentrated in the winter months with a low annual variability.

Geomorphic process history: Predominantly fluvial and karst.



Class code




MO47 North-west Rolling Hills

Context: Rolling residual hills flanking coastal plains, surfaces and karst basins between Redpa and Forest.

Physiography: Rolling hill country developed on Tertiary basalts, Cambrian volcano sediments and Precambrian metasediments.

Climate: Low to moderate annual run-off of approximately 500 mm. Moderate storm events with maximum daily precipitation around 50mm. Moderate rainfall seasonality and low annual variability of run-off.

Geomorphic process history: Predominantly fluvial, although some marginal Aeolian has occurred during Pleistocene glacials, commonly leaving sand sheets.

MO48 North-western dissected surface on Precambrian folded sediments

Context: Moderately dissected surface (correlated with other northern Tasmanian surfaces) however developed on softer units of Precambrian sedimentary rocks. Gibson, Wedge, Dempster and Holder Plains have been moderately dissected by the Arthur, Black, Rapid, Black and Horton Rivers.

Physiography: Well defined plateaus and surface remnants, the largest at Dempster Plains, are moderately dissected by major streams. North-south streams are controlled by bedding and cleavage plains in the moderately metamorphosed sediments, however east-west stream have cut across the strike forming steep sided valleys.

Climate: Moderate maximum daily rain events (55–60 mm) with moderate to high annual run-off (1000–1300 mm).

Geomorphic process history: Some plateau remnants support buttongrass peatlands which have a strong controlling influence on bank stability of smaller streams.



Class code




MO49 North-western granite hills

Context: Rolling granite hills at the northern tip of King Island and in the headwaters of the Blythe River.

Physiography: Rolling Devonian granite topography on the present day erosion surface of King Island, and as residual hills on a moderate level relic in the Blythe catchment.

Climate: These two sites should potentially be separated on climatic grounds. King Island has low daily maxima (around 38 mm) and annual run-off (200 mm) compared with the Blythe River (70 mm, 1200 mm). King Island has a distinct winter rainfall bias whilst Blythe River is more uniform. Both have reasonably consistent rainfall patterns from year to year.

Geomorphic process history: Aeolian processes have played a role on King Island, whilst it is likely that at Blythe River processes have been predominantly fluvial.

MO50 North-western granite valley

Context: Valley of the mid reaches of Blythe River.

Physiography: Unique geomorphic feature in Tasmania – a large slope deposit in Devonian granite has buried the river channel leading to significant sub-surface flow for 6 km of the Blythe river.

Climate: Moderate annual run-off of 735 mm with moderate daily rainfall maximum of 65 mm. Moderate seasonality and annual variability of precipitation.

Geomorphic process history: Predominantly fluvial and mass movement, however slope processes may have been significantly accelerated by periglacial activity during glaciations.



Class code




MO51 North-western karst basins and plains

Context: Flat karst valleys and poljes in the Duck, Montagu and Welcome River catchments.

Physiography: Distinctive, generally flat valley floors and poljes, often containing localised areas of sinkholes developed on Precambrian dolomite. Small remnant hills often contain complex cave systems. Plains are commonly covered with aeolian sandsheets or sands derived from quartz lags – residues of karstic solution of silicified dolomite.

Climate: Moderate annual run-off of around 500 mm. Daily storm events are moderate, with around 50 mm maximum. Seasonal variability is moderate, with a winter maximum, although inter annual variability is low.

Geomorphic process history: Area is karstic, although this is mainly expressed in the subterranean hydrology. Some poljes and sinkholes are found, although the area is of very low relief. The mosaic has a very strong Aeolian component, mainly Pleistocene sand sheets. Peat is found in some areas along the Montagu and Welcome Rivers and tributaries.

MO52 North-western valleys on Precambrian folded sediments

Context: Major valley systems on lower grade Precambrian metasediments in the Arthur River and Pieman River catchments.

Physiography: Major, steep walled valleys developed in moderately erodable metasediments.

Climate: Moderate to high annual run-off, between 800 and 1000 mm. Daily rainfall maxima between 45 and 60 mm with a winter maximum. Low annual rainfall variability.

Geomorphic process history: Predominantly fluvial. Some peatland influence on bank stability from rainforest areas.



Class code




MO53 South east rolling hills and coastal sands

Context: Low, rolling dolerite and Parmeener hills, interspersed with significant areas of marine and Aeolian coastal sands in the Moulting Lagoon, Bruny Island, South Arm and Tinderbox areas.

Physiography: Low, rolling dolerite hills interspersed with valleys and basins on Parmeener rocks. These have been filled by Aeolian and marine sands, forming parallel dune systems, beach ridges, coastal lagoons and estuarine reaches.

Climate: Relatively dry, with maximum daily rainfall between 50 and 70 mm in all areas. Low yearly variation. Effective precipitation is very low at Moulting Lagoon and South Arm – Tinderbox (50-70 mm), but moderate (200 mm) at South Bruny.

Geomorphic process history: Much aeolian and marine erosion and deposition.

Large coastal dunefields and spits

Low, rolling dolerite/Parmeener hills

Dunes and coastal spits dam fluvial systems, resulting in coastal lagoons and estuaries

Small, ephemeral, low gradient streams

Coastal lagoons and estuaries

Major dune barred estuaries such as Pittwater on the Coal River and Moulting Lagoon on the Swan River.

MO54 South Eastern coastal slopes

Context: Rolling foothills of south-west mountains in the Huon region, rounded hills and ridges in the Tasman Peninsula and the Weilangta areas.

Physiography: Dominated by dolerite and Parmeener rocks, resistant dolerite forms ridge and spur tops. Minor plateaus, both structural and erosion surfaces. Parmeener valleys are relatively steep. Overall, structural control not particularly evident (due to higher stream power than drier sites?).

Climate: Moderate daily maximum rainfall events (range from 44–65 mm in the Huon area to 45–75 mm at Weilangta). Moderate effective precipitation, from 300–700 mm, higher in the western Huon and lowest on western Tasman Peninsula.

Geomorphic process history: Some areas of peat are present in topographic basins in the Huon area (Esperance Plain, Kermandie Plain, Raminea Plain).

V shaped ridge and valley topography predominates

Minor plateau remnants

Minor basalt and alluvial basins

Steep, perennial to intermittent headwater streams, often with wet forest or rainforest canopy

Rare low gradient, peaty headwater streams on plateau remnants and basins in the Huon area

Medium rivers with lowland anastomosing – meandering reaches in alluvium – pool and riffle sequences (lower Arve River, Kermandie, Esperance Rivers)

Steep, v-shaped bedrock valley confined reaches with bedrock bars, pool and riffle sequences (Prosser River, middle Arve, Kermandie, Esperance Rivers)

MO55 South eastern dolerite dry hills and basins

Context: Dry rain shadow valleys and intervening dolerite ridges in lower Coal and Jordan valleys.

Physiography: Moderately steep, structurally controlled dolerite ridges separate alleviated valleys cut into Parmeener sediments. Basalt has filled the Jordan Valley creating a very flat valley floor.

Climate: Dry with low effective precipitation (50–150 mm), quite unreliable run-off is likely inter-annually. Daily rainfall maxima are 40–55 mm, low to moderate intensity, highly variable.

Geomorphic process history: Some Aeolian activity in valley floors of the Jordan and tributaries.

Broad alluvial or basalt filled valleys in Parmeener sediments

Relatively steep, interfluves on dolerite.

Minor deflation basins and lunettes

Basalt has laterally forced streams in the Jordan valley

Steep, ephemeral to intermittent streams dissect steep dolerite and sandstone valleys

Minor alluvial fan reaches where headwater streams debouch into main valleys

Single channel, rarely meandering, intermittent streams in alluvial valleys

Locally bedrock controlled, intermittent rivers, pool and run sequences with minor broadwaters (e.g. Lower Jordan River.)

Minor anastomosing reaches in Quaternary alluvium.



Class code




MO56 South eastern karst basins

Context: Lowland basins and interfluves in the far south-east of the state – South Cape Rivulet, Catamaran, D‟Entrecasteaux, Lune Rivers and Creekton Rivulet.

Physiography: Ordovician limestone and Precambrian dolomite forms low basins between broad crested, steep-sided interfluves, generally consisting of Parmeener supergroup sediments and Jurassic dolerite.

Climate: Local effective precipitation is moderate, up to 80 mm per annum, averaging 50. A very steep climatic gradient exists to the west, with rapidly increasing effective precipitation and daily maxima.

Geomorphic process history: Extensive karst areas, generally covered with glacial outwash and some early Pleistocene glacial deposits, but exposed and actively developing on valley flanks.

Extensive karst basins

Steep sided, broad crested interfluves

Coastal lagoons and sand deposits

Moraines developed in early Pleistocene glaciations provide local base levels in areas mapped as Cainozoic sediments

Subterranean drainage

Drainage diversion under topographic divides, through karst cave systems

Coastal spits dam back streams to form lagoons

Small, low gradient headwater streams, sometimes in marshy depressions

Subterranean streams where headwater reaches encounter carbonate rocks (Local tributaries of the Exit and Hastings karst systems)

Flashy, anastomosing streams developed on large, partly relict alluvial fans in karst basins (South Cape Rivulet, Catamaran, D‟Entrecasteaux, Lune Rivers)

Pool and riffle reaches downstream of fan topography continue to well developed estuarine reaches (South Cape Rivulet, Catamaran, D‟Entrecasteaux, Lune Rivers)

Underground rivers where streams are captured underground (D‟Entrecasteaux River, Mystery Creek in Exit Cave)

MO57 South western complex karst valleys

Context: Predominantly karst landscapes in the Maxwell and Jane Rivers and tributaries.

Physiography: Low rolling valleys developed on Precambrian dolomite with various non-karst lithologies protruding as remnant hills. Variably karstified, often according to the degree of silicification present, however some significant cave systems are known. Very poorly investigated.

Climate: High run-off area with around 2000 mm annually. Daily maximum storm of around 60 mm. Distinct winter rainfall maximum with low inter annual variability.

Geomorphic process history: Predominantly karst and fluvial. Significant areas of blanket bog affect stability of smaller tributaries.



Class code




MO58 South-eastern high relief karst

Context: Extensively karstified hill flanks at Precipitous Bluff, Cracroft River, Vanishing Falls, Weld Valley, Mt Anne, Mt Field and the Florentine Valley.

Physiography: Highly karstic Ordovician Gordon limestone and Precambrian dolomite exhibit the best developed karst topography in Tasmania. Numerous long and deep cave systems house a very well developed subterranean drainage network.

Climate: Moderate to high run-off here with 1100–21 500 mm per year. Daily maxima are between 50 and 70 mm. A winter bias to rainfall in the Florentine Valley, however this becomes less distinct towards Precipitous Bluff. Low rainfall variability.

Geomorphic process history: Karst and fluvial. Significant periglacial activity has moved much dolerite slope material onto karsts, partially disrupting subterranean hydrology. Glacial deposits have also affected surface and cave systems.

MO59 South-eastern karst basins, rolling

Context: Moderate relief karst basins in the Florentine Valley, New River Lagoon, Risbys Basin areas.

Physiography: Distinctive karst topography with well developed sub-surface drainage systems, cave systems, stream sinks and springs.

Climate: Moderate to high run-off areas with 1000–1200 mm in the Florentine Valley and New River Lagoon areas. Moderate daily rainfall maxima around 50 mm with a winter rainfall bias. Low rainfall variability.

Geomorphic process history: Karst, fluvial, glacial and periglacial.



Class code




MO60 Southern Midlands foothills and drainage divides

Context: Hilly interfluves and relict surfaces dividing major southern Midlands valley systems in the Clyde, Jordan and Coal River basins.

Physiography: Generally rounded interfluves, although some minor erosional surfaces are present. Broad, high level alluvial basins (e.g. Bothwell, Kempton-Melton Mowbray, Lakes Dulverton and Tiberias) are present in association where these surfaces have not yet been dissected by major valleys. Predominantly dolerite, Parmeener and basalt substrates, with dolerite predominating on ridgetops.

Climate: Relatively dry, with annual effective precipitation between 30 and 130, although this may drop to near zero in major basins such as Bothwell.

Geomorphic process history: Predominantly fluvial, although Aeolian activity has been intense in drier basins such as Lakes Tiberias and Dulverton, Eastern slopes of the Coal valley, and potentially Bothwell and Kempton-Melton Mowbray.

Moderately steep interfluve ridges and hills

Small relict erosion surfaces

Broad, flat alluvial basins

Large deflation hollow(?) lakes

Aeolian deposits may have altered stream courses around Lake Tiberias and Dulverton

Steep, intermittent headwater streams, predominantly erosional

Low gradient, single channel intermittent streams in high level alluvial basins (Naturally chains of ponds, (e.g. Jordan below Lake Tiberias, Fordell Creek))

Relict chains of ponds

Anastomosing – Meandering reaches over high level basins (e.g. Clyde at Bothwell, Jordan at Melton Mowbray)

Broadwaters, pool and riffle reaches in alluvial pockets (Jordan at Elderslie, Coal River above Craigbourne Dam).

Steep valleys and occasional gorges where interfluves have been dissected (Upper Coal River, Jordan River above Elderslie, Lower Marshes area)

MO61 Southern Midlands foothills and valleys

Context: The hill country immediately surrounding the Midlands Basin, The lower Clyde valley and mid Jordan valley.

Physiography: Gentle to moderate slopes of low to moderate relief. As a variety of potential bedrock types are indicated in domain contents, with Parmeener rock often allowing the development of broader basins, surrounded by steeper dolerite topography. Valley floors contain some significant alluvial deposits (e.g. York Plains), low angle alluvial fan heads are found as tributary valleys broaden into main stream valleys and the Midlands basin. Basalt is found both in Tertiary basins and relict interfluves, particularly in the Clyde and Ouse catchments.

Climate: Climatically drier than the Inland Slopes, yet wetter than the Midlands. Maximum daily rainfall events are small to moderate, around 40–50 mm with moderate reliability. Effective precipitation is generally very low, around -15–50 mm, although west of Bothwell the totals increase to 120 mm.

Geomorphic process history: Predominantly fluvial, although domain contents indicate similarities with areas exposed to minor periglacial activity in the past - some slope deposits are likely. Aeolian activity has been present, although deposits are not extensive.

Heads of low angle alluvial fans in large valleys

Low hills and relatively broad valleys, although some short, steep valley segments present

Potential outcrops of hardpan strata in Tertiary sediments - local base level controls

Resistant strata or erosion surface margins separate this subregion from alluvial basins upstream at Bothwell (Clyde River) and Melton Mowbray (Jordan River).

Small., intermittent/ephemeral streams, generally erosional. Often in poor physical condition, particularly on Tertiary sediments and Parmeener Supergroup rocks.

Large streams flow through moderately steep valleys. Alluvial deposits are common on wider valley floors, although not extensive. Relict terrace gravels are common. Streams are generally pool/riffle sequences although continuous bedrock reaches are found, though uncommon (e.g. Lower Clyde River, lower Ouse River, Macquarie River at Trefusis, Elizabeth River gorge).



Class code




MO62 Southern Midlands Tertiary basin

Context: Extensive lowland basin (Tertiary graben) defined by north-south trending fault zones, elevation 150-200 m, bounded by Eastern and Western Tiers.

Physiography: Generally low relief landscape, composed of broad valleys on Tertiary sediments. Interfluves and isolated hills composed of Parmeener sediments, basalt and dolerite separate valleys and form „islands‟ within them. Quaternary sediments form floodplains and low terraces in river valleys, and low angle fans on the upslope margins of the mosaic. Aeolian landforms and associated deposits are widespread. Sandsheets, deflation hollows and associated lunettes are common, with associated wetlands and lakes important in their own right, and potentially affecting fluvial forms and processes.

Climate: Dry, with effective precipitation generally less than 100mm per annum, with low maximum daily rain.

Geomorphic process history: Relict aeolian activity, deflation basins, lunettes, source bordering dunes and sand sheets.

Bedrock hills

Plains, broad valleys on Cainozoic sediments, bedrock interfluves and „islands‟

Narrow valleys incised into broader plains, filled with younger floodplain and terrace deposits

Low to moderate angle fans where rivers enter the mosaic

Deflation basins with associated lunettes and dunes

Presence of hardpans (ferricrete, silcrete, etc.) in Tertiary sediments - initiates anastomosing

Junction of Tertiary sediments and bedrock - broadwater development

Large calibre terraces at mosaic margins - constrain planform

Aeolian sediments and landforms - streams interact with wetlands

Small, low gradient intermittent streams, usually single channel, possibly modified from natural marshy or chain of ponds character.

Low gradient, bedrock valley confined, large pool and riffle reaches (Upper Macquarie River, Macquarie near Elizabeth and Isis junction, Elizabeth River)

Low angle alluvial fans (Upper Macquarie River

Broadwater sequences (Upper Macquarie River)

Very low gradient, marshy, anastomosing/meandering reaches (Isis River)

Very Low gradient broadwater/marsh/meandering/anastomosing reaches (South Esk River)

MO63 South-west alluvial basins

Context: Large alluvial basins in the catchment of Bathurst harbour.

Physiography: Low rolling plains characterised by thick alluvial deposits. These are generally the result of fluvial reworking of periglacial slope deposits rather than fluvioglacial outwash.

Climate: High rainfall area with 1700–1800 mm run-off yearly. Daily rainfall maxima of around 50 mm with a strong winter rainfall bias. Low rainfall variability.

Geomorphic process history: Fluvial and periglacial. Extensive buttongrass moorlands cover this area strongly stabilising stream banks.



Class code




MO64 South-west karst basins, flat

Context: Distinctive flat floored karst valleys, generally between higher strike ridges in quartzite or Eldon Group sediments, in the Olga – Hardwood valleys, Giblin valley, Vale of Rasselas, and Gordon valley below Lake Gordon.

Physiography: Low, flat to rolling topography developed in Ordovician Gordon limestone (or minor Precambrian dolomite) with numerous sinkholes, sinking streams and springs. Extensive cave systems are not common due to low relief, however residual hills contain many small systems. Karst hydrology generally well developed.

Climate: Run-off ranges from 1800 mm in the western valleys to around 1100 mm in the Vale of Rasselas which experiences a distinct rain shadow effect. Daily rainfall maxima around 50 mm with a winter bias. Low variability in the west.

Geomorphic process history: Karst, fluvial. Some glacial deposits are found in these areas, as are massive alluvial fans derived from fluvioglacial outwash.

MO65 South-west quartzite ridges hills and valleys

Context: A widespread mosaic in the South-west, incorporating the less dramatic quartzite terrains flanking more spectacular quartzite strike ridges. Large areas in the Murchison catchment, south-east of Lake Burbury, west of the Denison Range, Propsting and DeWitt Ranges and the Bathurst Harbour catchments.

Physiography: Steep ridges aligned with the strike in metamorphosed Precambrian sequences. Not generally glaciated, although steep in parts, interspersed with v-shaped valleys cut into softer units of this sequence.

Climate: High to very high annual run-off, generally over 2000 mm and up to 2400 mm in the higher ranges. Daily maximum rainfall events are moderate to high, up to 70 mm. Seasonality of precipitation moderate to high with a strong winter maximum. Inter annual variability of precipitation is low.

Geomorphic process history: Not subjected to last glacial ice, however the northern elements were strongly glaciated in the early Pleistocene glaciations. Periglaciation was common then also. Peat is strongly characteristic of this mosaic, having a strong influence on the character of smaller streams.



Class code




MO66 South-west quartzite strike ridges, valleys and gorges

Context: Distinctive high, north/south trending mountain ranges and associated valleys in south-west Tasmania, including the Denison, Frankland, Arthur, Prince of Wales and D‟Aguilar Ranges.

Physiography: Steep, often freshly glaciated strike ridges forming the most spectacular mountain topography in Tasmania. These are separated by steep sided v-shaped valleys, or in some cases broad, flat floored karst valleys. Ridge crests support many glacial tarns and cirques. Deep, spectacular gorges of superimposition have formed where major east/west flowing rivers have breached the strike ridges, as at the Gordon Splits, the Denison Gorge, Gordon Gorge, etc.

Climate: These areas have the highest consistent run-off in the state, with between 2200 and 2500 mm calculated. Daily maxima are moderate to high at between 60 and 70 mm. Rainfall has a strong winter bias, particularly in the west, although it is reasonably consistent between years.

Geomorphic process history: Strongly glaciated in most areas, apart from the D‟Aguilar Range where this is equivocal. Strongly periglacial in cold periods, Peat is found on lower slopes of these ranges, both in rainforest and blanket bogs.

MO67 South-western dissected relict surface on carbonate

Context: Dissected carbonate surface in the headwaters of the Weld River.

Physiography: Rolling topography characterised by large closed depressions, stream sinks and springs. Moderately well developed subterranean hydrology, however the area is still only poorly documented.

Climate: High run-off area with 1100–1200 mm per year. Daily rainfall maxima between 50 and 60 mm with a winter bias. Low rainfall variability.

Geomorphic process history: Karst, fluvial with significant glacial and periglacial influences in parts.



Class code




MO68 South-western glacial till and outwash plains

Context: Extensive glacial and fluvioglacial deposits in major river valleys and basins at Arthur Plains, Franklin/Collingwood valleys, Denison Plains, Eldon and King valleys, Rosebery and Bulgobac valleys.

Physiography: Till and outwash plains containing numerous moraine features and fluvioglacial terraces exert a strong influence on the form of rivers.

Climate: High run-off areas with 1500 – 2000 mm per year. Daily rainfall between 50 and 70 mm with a marked winter bias. Low rainfall variability.

Geomorphic process history: Glacial, fluvial and some periglacial on emergent hills. Some blanket bog peatlands stabilise tributary streams in these areas.

MO69 South-western karst basins, rolling

Context: Moderate relief karst basins at Forest Hills, Battlement Hills Franklin River, Ghost Creek, south of Birchs Inlet, Andrew River and other minor areas.

Physiography: Well developed karst topography and drainage on Ordovician Gordon limestone and Precambrian dolomite. Some extensive cave systems, although generally not well documented. glacial and periglacial effects are pronounced in some areas.

Climate: High run-off areas with between 1500 and 1800 mm per year. Daily rainfall maxima between 50 and 60 mm with a winter rainfall bias. Low inter annual variability.

Geomorphic process history: Karst, fluvial, periglacial and minor glacial processes dominate. Some Rainforest/peatland systems stabilise stream banks.



Class code




MO70 South-western quartzite valleys

Context: Steep valleys and gorges in the catchments of the Franklin, Collingwood, Alma, North and South Eldon, Fury, Dove and Fisher Rivers.

Physiography: Steep generally fluvial gorges developed in Precambrian quartzites and similar lithologies, although some imprint of early Pleistocene glaciation remains. Much glacial outwash and some till in river valleys.

Climate: A high run-off mosaic, with the core units receiving over 2000 mm per year and peripheral areas over 1500 mm. Daily rainfall maxima moderate to high, between 60 and 80 mm. A winter rainfall maximum in western polygons becomes more mixed seasonally towards the east. Likewise rainfall variability is less in western units.

Geomorphic process history: Predominantly fluvial, relict glacial landforms and deposits, widespread periglacial activity, particularly where dolerite is found in the catchment.

MO71 South-western steep ridges hills and valleys on low grade metasediments.

Context: Highly dissected terrain in the Pieman and Eldon catchments, and flanks of the West Coast Range.

Physiography: Steeply dissected terrain on relatively erodable Precambrian metasediments.

Climate: High to very high annual run-off, between 1500 and 2400 mm on the flanks of the West Coast Range. Daily maxima also high, between 50 and 80 mm. Distinct winter rainfall maximum and low annual variability.

Geomorphic process history: Predominantly fluvial, however glaciation was widespread during the early Pleistocene maximum glacial. Some blanket bog and sub alpine peatlands control smaller streams.

MO72 Steep and dry eastern granite hills and escarpments

Context: Steep granite residual hills on Flinders and Cape Barren Islands, Mt Cameron and Freycinet Peninsula.

Physiography: Steep, high relief granite topography, strongly joint controlled with erodable granite soils and alluvium in small patches.

Climate: Relatively dry, with effective precipitation between 130 and 170 mm, daily rainfall maxima 50-70 mm, high inter-annual variability.

Geomorphic process history: Predominantly fluvial, some Aeolian deposits north of Strzelecki Peak on Flinders Island.

Steep, joint controlled granite hills.

Strong joint control on streams; parallel drainage systems, deep granite gorges.

Small, steep, ephemeral streams, although quite powerful due to strong run-off in flood. Sand to cobble bedload.



Class code




MO73 Steep dissected eastern escarpment

Context: The steep, high relief eastern fall of the Eastern Tiers, Weilangta, East Maria Island, East Tasman and Forestier, Wellington Range/Mt Dromedary, Snug Tiers and South Bruny Island. The landscape is resulting from headward incision of the remnant plateaus encompassed by the Central and Eastern remnant surfaces and High Altitude plateau subregions, and associated escarpments, formed by elevation of the Eastern mountains relative to the coast in the early Tertiary.

Physiography: Coastal streams are deeply entrenched into uplifted and faulted erosion surfaces resulting in a series of gorges and very steep v-shaped valleys. Waterfalls at major nickpoints are common.

Climate: Moderately dry, with effective precipitation of 200-300 mm, although potentially higher in places. Rainfall is moderately variable, with a moderate daily maximum fall.

Geomorphic process history: Predominantly fluvial, although significant periglacial activity occurred in the past. Slope deposits are now essentially stable, although they are a continuing supply of sediment to higher energy streams.

Very steep valley heads and tributary valleys, often with extensive clifflines, often in dolerite

Low gradient alluvial valleys and basins at the downstream extremities of major streams, often in Parmeener sediments

Deep, narrow, high relief valleys in mid reaches

Dolerite outcrops and locally hard sills

East draining waterfalls above deep gorge reaches

Moderately sloped, east-west bedrock valley controlled reaches with some large calibre alluvial pockets

MO74 Steep dolerite scree

Context: Downslope of exposed dolerite cliffs bounding high plateaus and peaks in eastern Tasmanian dolerite terrain, such as the Wellington Plateau, Been Lomond Plateau and the Western Tiers.

Physiography: Steep country, with generally high relief. Almost exclusively formed in dolerite terrain, commonly overlying Parmeener supergroup rocks or Mathinna beds. Mostly relict glacial age slope deposits, these areas are still active both through freeze-thaw and mass movement processes. Some very large landslips and fluidised flows have occurred, particularly where slope deposits overlie Triassic sandstone units.

Climate: Often high daily rainfall maxima, although only moderate effective precipitation, typical of margins of the higher peaks and plateaus in the east of the state.

Geomorphic process history: Strong periglacial activity in Pleistocene glacial periods, some relict glacial deposits may also be expected, such as in the upper Meander valley.

Dolerite clifflines and slab topples

Closed depressions in mass movement deposits, sometimes containing tarns

Massive, high angle blockstreams, screes and other slope deposits composed of boulder and larger sized clasts

Early Pleistocene moraines may form localised bed and bank controls.

Closed depressions channel surface streams underground through slope deposits

Outcropping dolerite promotes waterfalls

Subterranean headwater streams often eroding interstitial clays in slope deposits or underlying weathered bedrock

Waterfall reaches on exposed bedrock or moraine material

Steep, boulder bedded reaches with high sediment load derived from undercutting or mass movement of slope deposits (Meander, Lake, Liffey, Nile, North West Bay Rivers)

Waterfall reaches downstream of low gradient plateau reaches (Meander Falls, Wellington Falls)



Class code




MO75 Steep north-western granite

Context: Escarpment and steep hillslopes in granite flanking the Meredith Range, Granite Tor, Mt Agnew and Victoria Peak in the Murchison valley.

Physiography: Steep, erodable granite terrain with a distinctive rectangular drainage pattern. Much exposed rock, although deep sandy, erodable soils are founding depositional areas.

Climate: High run-off areas with between 1200 mm in the Agnew Range to over 2000 mm at Granite Tor. Daily rainfall maxima between 60 and 70 mm with a winter bias. Low rainfall variability.

Geomorphic process history: Predominantly fluvial, although some areas such as Mt Agnew have a stable cover of blanket bog peats.

MO76 Steep western Midlands escarpments

Context: Steep escarpments of the Great Western Tiers, south of the Lake River.

Physiography: Relict, highly stable dolerite slope deposits overlie Parmeener supergroup and dolerite substrates. Steep topography.

Climate: Moderate daily rainfall maxima ranging from 40–50 mm per daily event. Effective precipitation is low, ranging from 40–270 mm, increasing with elevation.

Geomorphic process history: Relict, essentially stable periglacial slope deposits.

Steep escarpment mantled with stable slope deposits.

Slope deposits may allow sub-surface streams to develop

Large calibre clasts prevent incision or lateral movement of surface streams

Underground reaches in headwater streams

Steep, boulder bedded reaches in surface streams

Steep, powerful bedrock and slope deposit controlled streams (Blackman River, Mill Creek)

MO77 Steep wet eastern granite hills and escarpments

Context: Granitic escarpments of the Mathinna Plains, associated ridges, valleys and alluvial basins.

Physiography: Steep rounded ridges and v-shaped valleys, strongly structurally controlled drainage networks.

Climate: Moderately wet with effective precipitation ranging from 300–800 mm in the higher areas. Intense daily rainfall events, ranging from 70-100 mm.


Strong structural control of drainage networks

Deep, unconsolidated sandy regolith, highly erodable producing distinctive sandy bedload.

Highly erodable slope materials, although dense vegetation tends to restrict erosion.

Steep bedrock controlled headwaters with distinctive boulders forming valley walls

Steep anastomosing alluvial reaches with distinctive sandy bedload (Upper Ringarooma River, Maurice River, Upper North and South George River)

Low sinuosity single channel reaches in larger alluvial floodplains (e.g. North George River).

Partly valley confined anastomosing/meandering reaches in large alluvial basins (George River at Pyengana, St Patricks River at Wombat Plain, North Esk River at Ben Nevis Marshes)



Class code




MO78 Steep, climatically extreme Mathinna hills

Context: Steep, finely dissected terrain in the Scamander basin and adjacent catchments.

Physiography: Steep v-shaped ridges and valleys in headwater streams, developed on Mathinna Group metasediments. Very dry, steep slopes produce large amounts of fine, angular slope deposits derived from weathered Mathinna rocks. Highly erodable regolith.

Climate: Extreme variability with very high maximum daily rainfall events, up to 110 mm. Moderate effective precipitation, 200 – 400 mm


Steep, dry stony ridges and valley slopes produce large quantities of gravel to cobble sized slope materials following disturbance events and storms

Some steep alluvial sections in larger valleys

Development of alluvial fans at boundary of steep slopes and flat valley floors

Granite intrusions form locally steep sided valleys and base level controls

Steep, ephemeral bedrock controlled headwater streams transporting pebble to cobble bedload.

High gradient, anastomosing or fan reaches at upstream alluvial limits

Anastomosing, or low sinuosity single channel, pool and riffle sequences in larger, lower gradient alluvial reaches

MO79 Steep, moderate rainfall Mathinna ridges and escarpments

Context: Steeply dissected valleys and ridges in the upper South Esk and upper North Esk Rivers.

Physiography: Long, rounded, steep sided ridges and spurs radiate from adjacent Mathinna surface highplains. Valleys v-shaped in erosional reaches, however alluvial deposition extends well into headwaters where flat floored alluvial valleys abut steep slopes. Drier slopes are high sediment source areas providing copious quantities of platey metasediment fragments to streams following slope disturbance and storms.

Climate: This subregion has a moderate range of effective precipitation, ranging from 150 mm in the drier, rainshadow sites near major valley floors, to 700 mm near highplain remnants. Maximum daily rainfalls are high and variable, up to 100 mm per day.


Steep ridges and spurs

Steep, narrow flat floored valleys

Abrupt transition from steep, unstable slopes to flat valleys provided optimum conditions for alluvial fan development, although this is sometimes restricted due to small sediment calibre.

Flashy, bedrock controlled headwater streams transporting gravel bedload

Anastomosing alluvial fan reaches where streams initially flow over alluvium (Tyne River, Tower Rivulet)

Anastomosing or single channel, low sinuosity streams in low gradient alluvial valleys

Similar streams to internal streams, however headwaters originate on adjacent high plains (Dans Rivulet, Evercreech Rivulet)

Meandering, pool and riffle reaches in larger, low gradient alluvial valleys (South Esk River, Nth Esk River)



Class code




MO80 Steep, wet Mathinna hills and escarpments

Context: Steep, v-shaped ridges and spurs, with some alluviated valleys in the upper Little Forester, Dorset and New River valleys.

Physiography: Steep v-shaped valleys and ridges in Mathinna beds sediments. Where granite intrusions have occurred, distinctive ridges have developed on metamorphic aureoles as at Lisle. Steep, flat floored alluvial valleys are found in places. Slope deposits are better consolidated here than in the upper South Esk due to more consistent rainfall and denser vegetation cover.

Climate: Moderately wet, with effective precipitation ranging from 400-900 mm according to elevation. Moderate to very high rainfall intensities, particularly in the Sideling area, ranging from 55–100 mm per day. These events are moderately variable inter-annually.


Steep ridges and spurs

Steep, narrow flat floored valleys

Abrupt transition from steep, moderately unstable slopes to flat valleys provides conditions for alluvial fan development, although this is sometimes restricted due to small sediment calibre.

Flashy, bedrock controlled headwater streams transporting gravel bedload

Anastomosing alluvial fan reaches where streams initially flow over alluvium (New River, Dorset River)

Anastomosing or single channel, low to medium sinuosity streams in low gradient alluvial valleys (New River, Dorset River, Little Forester River)

MO81 Strongly glaciated plateau

Context: Strongly glaciated plateau country associated with the central Tasmanian Pleistocene ice cap glaciations, on the western Central Plateau, February Plains and the Cradle Plateau. Generally dolerite bedrock, although more Parmeener rocks and Precambrian quartzites crop out on the Cradle Plateau.

Physiography: Generally rolling plateau country, however steeply dissected areas surround rock cut basins and associated lakes. Some residual peaks emerge in the walls of Jerusalem and Traveller Range. Streams connect lakes through gentle valleys, although plateau margins are steeply dissected.

Climate: Annual run-off is moderate to high, increasing from around 1500 mm on the eastern central Plateau to over 2000 mm on the Cradle Plateau. Individual storms are intense, with up to 80 mm falling in daily events. These are moderately seasonal, however there is little inter-annual variation in effective precipitation.

Geomorphic process history: The area has been strongly glaciated on at least four occasions in the Pleistocene. Some aeolian activity has occurred, particularly on the eastern Central Plateau around Lake Ada. Periglacial activity has produced many blockstreams which in places have been modified by subsequent glacial or fluvial processes. Alpine peats cover considerable areas and have influenced stream bank stability.



Class code




MO82 Tamar slopes Context: Rolling hill country bounding the Tamar estuary and to the east of the South Esk valley. An outlier is found south of Bracknell in the Mcraes Hills area. Some relict plateaus with associated alluvial plains are found in all sections.

Physiography: Strongly joint controlled, dolerite hills and ridges with well developed parallel drainage networks. Some Parmeener sediments and Mathinna sediments crop out west of the Tamar, with minor alluvial basins present.

Climate: Moderate run-off (from 150–450 mm over much of the subregion) with moderate seasonality. Storm events also moderate, between 40 and 50 mm with moderate inter-annual variation.

Geomorphic process history: Predominantly fluvial, although minor periglacial deposits are likely.

MO83 Western coastal sediments, terraces and remnant surfaces

Context: Low level surfaces hugging the west coast of Tasmania.

Physiography: The west coast exhibits a range of presently active coastal plains as well as a suite of relict uplifted surfaces developed on a range of lithologies. Significant areas of coastal aeolian sand disrupt drainage patterns, often offsetting river mouths.

Climate: Annual run-off varies considerably over these surfaces, from around 500 mm per year in the north-west to over 1500 mm in the south-west. Daily rainfall maxima between 45 and 55 mm. Rainfall is significantly concentrated in winter months with a low annual variability.

Geomorphic process history: Fluvial and aeolian.



Class code




MO84 Western dissected surfaces

Context: Moderately dissected erosion surfaces in the Pieman catchment, south of Macquarie Harbour, and high level remnants on the Jubilee Range and Gallagher Plateau.

Physiography: Moderately dissected erosion surfaces on Cambrian volcano sediments and Precambrian metasediments. V-shaped valleys, although not particularly deep, may be steep walled particularly where streams cut east-west across the strike of bedding and cleavage planes.

Climate: High annual run-off varies between 1300 mm in the northern and coastal mosaics to over 1700 mm in higher areas of the Gallagher Plateau and the West Coast Range. Seasonality is moderate with a strong winter rainfall maximum. Inter-annual variation is low.

Geomorphic process history: These mosaics are strongly characterised by the presence of blanket bog peats. Higher areas in the West Coast Range and Gallagher Plateau were affected by early Pleistocene glaciation.

MO85 Western mafic and ultramafic

Context: Distinctive stream systems in serpentinites and other mafic/ultramafic sequences at the Heazelwood and Wilson Rivers.

Physiography: Rolling hills with distinctive sparse vegetation allows surface erosion to proceed at a faster rate than in surrounding rainforest. Gully erosion common.

Climate: Run-off generally high, from 1500–1600 mm per year. Daily run-off is moderate, from 50–60 mm. A winter bias to rainfall patterns and low variability characterise these mosaics.




Class code




MO86 Western relict surfaces

Context: Relict erosion surfaces along the west coast hinterland from Arthur River in the north to Low Rocky Point in the south.

Physiography: Low relief terrain comprising uplifted coastal plains and other erosion surfaces, sometimes interspersed with relatively steep escarpments.

Climate: Moderate to high annual run-off, between 500 mm in the northern coastal plains around Arthur River to over 2000 mm in the south at Low Rocky Point and the higher level surfaces around Tullah.

Geomorphic process history: Predominantly fluvial, although some Aeolian activity on lower coastal surfaces. Peatlands are widespread, with significant local influence on bank stability in smaller streams.

MO87 Western Tasmanian terrace sequences on Tertiary sediments

Context: Distinctive terraces landscapes in the Macquarie Harbour Tertiary basin between Strahan and the Wanderer River.

Physiography: Magnificent terrace sequence from sea level to around 400 m, developed in semi-consolidated Tertiary sediments. Characteristic meandering rivers highly influenced by peatlands.

Climate: High annual run-off varies between 1500 mm at Strahan and 2000 mm in the south of the mosaic. Rainfall occurs as a strong winter maximum, with low variability between years.

Geomorphic process history: Strongly influenced by the presence of blanket bog peats, streams have developed a distinctive highly sinuous meandering planform, even on relatively steep slopes.



Class code




MO88 Western Tiers basins and fans

Context: Confined alluvial basins bordering steep, unstable slopes of the north eastern portion of the Great Western Tiers.

Physiography: Flat to rolling landscapes developed on Quaternary fan and alluvial deposits.

Climate: Moderate run-off areas, from 300 (Regents Plain) – 800 (Western Creek) mm, although daily maximum rainfall is low to moderate (40-60 mm). Moderately consistent interannular storm events and run-off seasonality.

Geomorphic process history: Predominantly fluvial, although the Meander area is underlain by karstic limestone, and significant sub-surface flows may be expected there.

Broad, moderate angle alluvial fans with associated terraces

Inset Holocene floodplains

Incipient karst landforms

Large calibre clasts in terraces restrict lateral stream movement

Some sub-surface streams in karst areas

Small, intermittent streams rising on fan margins

Perennial, incised and sometimes meandering streams downstream of karst springs on Stockers Plain

Steep, highly unstable fan reaches on all significant streams draining onto this subregion from the Western Tiers (Meander River, Jackeys Creek, Liffey River, Westons Rivulet, Brumbys Creek, Lake River)

Anastomosing/meandering reaches in middle and lower limits of confined basins Meander River, Jackeys Creek, Lake River.)

MO89 South-western volcano-sedimentary and soft Precambrian hills and valleys

Undefined

MO90 South-western glacio-karst

Undefined

MO91 South-eastern complex karst valleys

Undefined

MO92 South-eastern glacio-karst

Undefined

Appendix 6 – Spatial data layers - Fluvial geomorphic river types


6.2.9 Fluvial geomorphic river types

Title Fluvial geomorphic river types

Custodian WRD, DPIW

Creator Lois Koehnken - Technical Advice on Water, GIS Unit, ILS, DPIW

Description Fluvial geomorphic river typology for Tasmania.

Input data

CFEV Fluvial geomorphic mosaics spatial data layer (Appendix 6.2.8)


CFEV Stream order attribute data (Appendix 0)

LIST 1:100 000 catchment data layer, DPIW

Lineage

Koehnken (Technical Advice on Water, unpublished data) derived fluvial geomorphic river types from the fluvial geomorphic mosaics spatial data layer developed by Jerie & Houshold (2003). A multivariate analysis was conducted which involved a classification and ordination of data on the sequence of geomorphic mosaics and stream orders traversed by the drainage from source to the sea. This was conducted for a representative sample of the drainage for every major catchment. It was believed that fluvial geomorphic river character was differentiated by the sequence and proportion of mosaic types, and stream order (the latter as a composite surrogate for channel size and discharge volume within a particular catchment context).

The classification was then used to determine how similar these drainage sequences were to each other. These rivers/catchments were grouped according to their similarity. The output was then reviewed by fluvial geomorphologists and mapped.

Details of the analysis used to identify fluvial geomorphic river types are provided below.

Identifying drainage sequences

The LIST 1:100 000 scale catchment layer was overlaid on the fluvial geomorphic mosaics data layer. One hundred major catchment drainage sequences were identified, with 218 sub-catchments. A programming script, Spinfx, was written to output the sequence of mosaic units for a single drainage sequence of the major catchment rivers from headwater to sea. Each unit was identified by its mosaic name and length. The total length for each mosaic type was calculated for each stream order within the sub-catchment sequences to provide a data set of total lengths of each mosaic x stream order combination for every sub-catchment representative river drainage sequence (Table 12 provides an example).



Table 12. An example of a river sequence of geomorphic mosaics by stream order used as an input to derive a geomorphic river typology (G9) for the Brid River.

Mosaic type Stream order

Stream length (km)

Eastern granite hills and relict surfaces (MO8) 1 1.66

Eastern granite hills and relict surfaces (MO8) 2 1.98

Steep wet eastern granite hills and escarpments (MO77)

2 2.45


3 0.98


4 4.15

North eastern basins with granitic sediments (MO28) 4 0.11


4 0.15



Moderate slope Mathinna hills (MO26) 5 7.73

North eastern Mathinna beds sedimentary basins (MO30)

5 6.56

North eastern coastal dunefields (MO29) 5 4.76

It was also believed that differences in fluvial character between numerically adjacent stream orders were probably not substantial, and that first order streams were distinctly different from higher orders. Therefore, the final data set consisted of a matrix of total length for each mosaic for each stream order combination for every river, using the following groupings of stream orders: 1, 2 & 3, 4 & 5, 6 & 7, 8 & 9. This resulted in a data set of 450 mosaic x stream order attributes for 218 river sequences.

Classification and ordination analysis

An Unweighted Paired Group Mean Averaging (UPGMA) classification and ordination analysis (Non-metric Multi-Dimensional Scaling (NMDS)) was conducted to classify the rivers using these data. Both analyses were conducted by first deriving a Euclidean distance matrix for the set of rivers based on the standardised but untransformed lengths of each unique mosaic x stream order combination. Both analyses were conducted using the PC-Ord package(McCune and Mefford, 1999), using a flexible beta value of -0.5. For the UPGMA classification, an a priori maximum of 30 groups was set.

The resulting classification dendrogram is provided to illustrate levels of similarity between groups (Figure 18). Distinct regional groupings were apparent. After the classification and manual inspection of the resultant river types, a further division of some groups was undertaken based on expert knowledge, using the following rules:

1. The river types boundaries generally coincided with catchment boundaries, except in the following instances, where the catchments were sub-divided between regions:

Ouse: catchment split between east and west along western boundary of the Shannon River/Ouse River catchment



Coal: catchment split along main stem of river

South Esk: catchment split along main stem of river

North Esk: catchment split along main stem of river

Derwent: catchment split along main stem of river

Huon: catchment split into upstream and downstream at confluence with Picton River

Gordon: catchment split along main stem of river

Arthur: catchment split into upstream and downstream river types at confluence with Lyons River.

2. In addition to splitting large river catchments in the state into several river types, the main stems of several rivers were also treated as unique river types. This is because the main stem itself reflects processes from the entire upstream catchment, and not just the river type in the local vicinity. For example, the lower South Esk River reflects processes from river types G12, G13, G14, G16 and G17 (see Table 13 for descriptions), even though it is forming the boundary between only G16 and G17. In effect, the river types analysis group similar tributaries within large river catchments, with the main stem recognised as having different characteristics. The rivers identified as unique river types include:

Coal River from south of Stonor to mouth;

South Esk: from Tamar estuary to confluence with Break O‟Day River

North Esk: from confluence of South Esk to confluence with St Patrick‟s River

Derwent: from Derwent estuary to confluence of Ouse

Huon: from estuary to confluence with Picton River

Gordon River from Macquarie Harbour to upper catchment (north bend upstream of Lake Gordon)

Arthur River from estuary to confluence of Lyons River

This process resulted in 43 fluvial geomorphic river types. A description was given to each of the river type classes (Table 13) based on expert knowledge and the mosaic sequences. The method for assigning the fluvial river types to the river sections is provided in Appendix 6.3.14).

Data limitations

The comparison of the mosaic sequences from headwater to sea under represents the range of mosaics present in many catchments because it is based on one sequence incorporating one first order stream only.



References (Jerie and Houshold, 2003; Jerie et al., 2003a, b)



Figure 18. UPGMA cluster analysis dendrogram of river sequence mosaic and stream order data for 218 drainage sequences from all major catchments and sub-catchments in Tasmania (including the major Furneaux islands) (y axis shows the measure of similarity and the x axis the site codes).

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Table 13. Description of fluvial geomorphic river types.

Class code River typology Catchments Typical sequence/Characteristics Comments

G1 Far north-west Welcome

Montague

Duck

Deep

Grays

Ghost

Crayfish

King Island

Hilly karst country in upper catchments.

Rolling basalt hills in mid catchments.

Low relief hills and coastal plains in lower catchment.

G2 North west dissected surface

Lower Arthur River

Black Dip Creek

Donaldson

Dissected Precambrian sedimentary units.

North-west Rolling Hills in lower catchment.

Karst and Western relict surfaces in Arthur catchment.

Basalt hills and alluvial basins in Black Dip.

G3 Northern relict surfaces and northern valleys

Upper Arthur

Hellyer

Wilson

Detention

Flowerdale

Inglis

Cam

Emu

Blythe

Leven

Gawler

Northern relict surfaces in upper catchment.

Main stem rivers developed in Northern valleys incised into relict surfaces through basalt caps.

North-western granite hills in Blythe.

Leven transitional – could be divided along main stem with western catchment part of this type and eastern in Mersey Forth.

G4 Arthur main stem Arthur River from mouth to boundary of „North-west dissected surface‟




G5 Mersey Forth Forth

Mersey

Don

Headwaters in high plateaus (quartzite, dolerite) with/without glaciation.

Quartzite valleys and gorges common.

Northern relict surfaces decrease in occurrence towards east.

High relief karst in Mersey and Leven.

Finely dissected north surface and coastal sediments in lower catchments.

Lower Mersey could be separated on presence of sedimentary basins, and basalt and Parmeener hills in lower catchment and form new type with Rubicon.

G6 Northern tertiary basins and coastal sediments

Panatana

Green

Dolerite and Parmeener hills in headwaters.

Tertiary basins and coastal sediments in mid and lower catchment.

Moderate rainfall.

G7 Rubicon Rubicon

Franklin Rivulet

Rolling basalt hills and alluvial basins in headwaters.

Dolerite and Parmeener hills in lower catchment.

Similar to #6, but with basalt in headwaters in Rubicon.

G8 Tamar estuary Branches

Brown

Sheepwash

Masseys Rivulet

Anderson

Johnston

Supply

Stoney

Fourteen Mile

Dissected v-shaped ridges and valleys in volcanic sediments in upper catchment.

Tertiary basins and coastal sediment in lower catchment.




G9 North-east wet granite

Curries

Pipers River

Pipers Brook

Little Forester

Brid

Hurst

Great Forester

Headwaters wet steep Mathinna hills (west) or granite (east).

Mid catchment flowing through basin (east).

North-east coastal dunefields in lower catchment.

G10 North-east dry granite hills and basin

Tomahawk

Boobyalla

Ringarooma

Little Musselroe

Great Musselroe

Ansons

Georges River

Steep wet Mathinna or granite in upper catchment.

Dry low slope granitic hills in mid-catchment (east).

North-east basin with granitic sediments in lower catchment.

North-east coastal dunefields.

G11 Extreme Mathinna hills

Scamander

Arm

Coastal catchment

Rolling granite hills in headwaters.

Steep v-shaped ridges and valleys on Mathinna sediments.

Rolling, low granite hills and coastal sediments in lower catchments.

Extreme variability in rainfall.

G12 Eastern dolerite plateaus

Break O‟Day

St Pauls

Westward flowing from steep dolerite plateau and scree slopes through eastern escarpment and alluvial basin.

G13 Upper Esk Upper South Esk

Upper North Esk

High altitude dolerite plateau and Scree slopes.

Hilly, moderate rainfall.

G14 North Esk main stem North Esk River from confluence with South Esk to boundary of Upper Esk (G13)




G15 South Esk main stem South Esk River from Tamar estuary to confluence with Break O‟Day River

G16 Lower Esk Nile

Ben Lomond

Lower South. Esk

Lower North Esk

High altitude dolerite plateau in headwaters.

Steep dolerite scree.

Rolling dolerite hills in lower catchments.

G17 Midlands hills and basin

Elizabeth

Isis

Macquarie

Lower South Esk

Lower North Esk

Hilly, predominantly dolerite country draining into Northern and Southern Midland Tertiary basins.

G18 East coast escarpment

Templestowe

Douglas

Denison Rivulet

Apsley

Swan

Meredith

Stony

Buxton

Lisdillon

Ravensdale

Eighty Acre

Spring

Steep dissected eastern escarpment in upper catchment.

Granite hills along coast (north).

Dolerite rolling hills along coast (south).




G19 Western tiers Meander

Liffey

Brumbys

Lake River

High altitude dolerite plateau and scree slopes in headwaters.

Alluvial basin and fans where rivers exit steep Tiers.

Crossing rolling basalt hills in lower catchment before entering Northern Midlands Tertiary basin.

G20 Great Lake plateau Great Lake

G21 Upper Derwent Upper Derwent River

Nive

Dee

Western Ouse

Strongly glaciated plateau in headwater.

Glacial till and outwash plains.

Inland slopes in lower catchments.

G22 Southern Midlands Eastern Ouse (Shannon River)

Clyde

Jordan

Western Coal

Dolerite plateau in headwaters of western rivers.

Predominantly dolerite, rounded interfluves and broad alluvial valleys.

Dry hills increase in East.

Ouse split along Shannon River catchment.

G23 Florentine Florentine River South east karst basin.

G24 Western Derwent Tyenna

Plenty

Steep dolerite dissected escarpment and scree slopes.

Broad rolling hills in lower catchment.

Karst in upper Tyenna.

G25 South-east karstic Styx

Weld

Lake Pedder

Dolerite scree slopes and high relief karst in headwaters.

Dissected escarpment in lower catchment.

Glacial outwash and volcanic sediments in Lake Pedder.

Lake Pedder could be grouped with upper Huon.

G26 Derwent main stem Derwent River from estuary to confluence with Ouse River




G27 East coast Eastern Coal River

Prosser

Griffiths

Carlton

Blackman

Tasman Peninsula

Broad rolling dolerite and Parmeener hills in upper and mid catchment, reducing in relief towards coast.

Dry dolerite hills in lower Coal River.

G28 Pittwater Iron Creek

Forcett

Gilling Brook

Orielton

Sorell Rivulet

Frogmore

Duckhole

South Arm

Dry dolerite hills draining to alluvial basin near Pittwater.

Rain shadow.

G29 South-east Derwent and lower Huon

Lower Derwent

Northern lower Huon

Brown

North-west Bay

Mountain River

Margate Snug

Agnes

Nicholls

Lower Huon

Gardners Creek

Garden Island Creek

High altitude dolerite in headwaters.

Dissected eastern escarpment.




G30 South-east coastal slopes

South-east Huon

Creekton Rivulet

Esperance

Crooks

Southport

Glacially dissected dolerite plateau and Parmeener in headwaters.

Rivers flow off escarpment and through dolerite coastal slopes.

G31 South-east karst Lune

D‟Entrecasteaux

Catamaran

South Cape Rivulet

Cockle

Donnelys

Glacially dissected dolerite and Parmeener in headwaters.

Complex karst valleys in mid-catchments.

Rolling hills and coastal sands in lower catchment.

G32 Huon River main stem

Huon River from estuary to confluence with Picton

G33 Picton Picton River Glaciated dolerite valley bordered by glacially dissected peaks and plateaus.

G34 not used

G35 Upper Huon River Upper Huon River, upstream of Picton confluence




G36 South-west coast New

Old

Ray

Louisa River

Louisa Creek

Melaleuca

Horseshoe

North Spring

Crossing

Blackwater

DeWitt

Spring

Mulcahy

Giblin

Mainwaring

Wanderer

Spero

McCarthy

Hibbs

Sorell

Northern shore of Macquarie Harbour

Generally unglaciated quartzite terrains in upper catchment.

Low relief uplifted coastal plain sediments in lower catchments.

G37 Franklin Franklin River Glaciated dolerite valleys and outwash plains.

Quartzite ridges and valleys.

Complex karst valleys.




G38 North-south strike ridges and valleys

Denison

Gordon

Davey

Olga

North-west trending steep strike ridges and v-shaped valleys.

Glaciation common.

G39 Coal River main stem Coal River from estuary to near NW catchment boundary

G40 Gordon main stem Gordon River from Macquarie Harbour to upper catchment

G41 Western steep ridges King

Huskisson

Mackintosh

South-west quartzite valleys and steep ridges and valleys on low grade metasediments.

Lower King flows through Eldon Strike Ridges and relict surfaces.

2 discontinuous sections.

G42 Murchison Murchison River

G43 Western dissected surface

Savage

Whyte

Lower Pieman

Karst in western Savage.

Lower catchments dominated by low relief relict surfaces.

G44 Western coastal terraces and surfaces

Nelson Bay

Pedder

Lagoon

Coast South of Macquarie Heads

North quartzite ridges in headwaters.

Western relict surfaces form most of catchments.

Coastal sediments, terraces and remnant surfaces in lower.



Figure 19. Map of Tasmania, showing fluvial geomorphic river types described in Table 12.

Appendix 6 – Spatial data layers - Frog assemblages


6.2.10 Frog assemblages

Title Frog assemblages

Custodian WRD, DPIW

Creator John Ashworth and GIS Unit, ILS, DPIW

Description Distribution of frog assemblages in Tasmania.

Input data

Geo Temporal Species Point Observations Tasmania (GTSpot) database, Department of Primary Industries, Water and Environment (DPIWE)

Lineage

Frog assemblage distributions were determined using expert knowledge. Tasmanian frog experts (see Appendix 1) attended a workshop to discuss what frog assemblages occurred in the state and their distribution. Distribution maps for individual frog species, generated from the GTSpot database ((DPIWE, 2003)), were used to assist in deciding where assemblage boundaries were to be located. Knowledge of previous known locations was also used as a guide to pre-European distribution. Logical boundaries were hand-drawn and digitised to develop a data layer for frog assemblages. The composition of each assemblage type is provided in Table 14. The method for assigning frog assemblage classes to the waterbody and wetland spatial units is provided in Appendix 6.3.15.

Table 14. Description of frog assemblage classes.

Class code Assemblage location Species present

FR1 Midlands Limnodynastes tasmaniensis (spotted marsh frog), Limnodynastes dumerili insularis (eastern banjo frog), Litoria ewingi (brown tree frog), Litoria raniformis (green and gold frog), Crinia signifera (common froglet), Pseudophryne semimarmorata (southern toadlet), Crinia tasmaniensis (Tasmanian froglet)

FR2 King Island Limnodynastes peroni (striped marsh frog), Litoria ewingi (brown tree frog), Litoria raniformis (green and gold frog), Crinia signifera (common froglet), Geocrinia laevis (smooth froglet), Limnodynastes dumerili variegatus (eastern banjo frog)

FR3 Uplands Limnodynastes dumerili insularis (eastern banjo frog), Litoria ewingi (brown tree frog), Crinia signifera (common froglet), Geocrinia laevis (smooth froglet), Crinia tasmaniensis (Tasmanian froglet)

FR4 Tamar Limnodynastes tasmaniensis (spotted marsh frog), Limnodynastes dumerili insularis (eastern banjo frog), Litoria ewingi (brown tree frog), Litoria raniformis (green and gold frog), Crinia signifera (common froglet), Geocrinia laevis (smooth froglet), Pseudophryne semimarmorata (southern toadlet), Crinia tasmaniensis

(Tasmanian froglet)

FR5 North Limnodynastes dumerili insularis (eastern banjo frog), Litoria ewingi (brown tree frog), Crinia signifera (common froglet), Geocrinia laevis (smooth froglet), Crinia tasmaniensis (Tasmanian froglet)

Appendix 6 – Spatial data layers - Frog assemblages


Class code Assemblage location Species present

FR6 Tasman Limnodynastes tasmaniensis (spotted marsh frog), Limnodynastes dumerili insularis (eastern banjo frog), Litoria ewingi (brown tree frog), Crinia signifera (common froglet), Pseudophryne semimarmorata (southern toadlet), Crinia tasmaniensis (Tasmanian froglet)

FR7 South-east Limnodynastes tasmaniensis (spotted marsh frog), Limnodynastes dumerili insularis (eastern banjo frog), Litoria ewingi (brown tree frog), Crinia signifera (common froglet), Crinia tasmaniensis (Tasmanian froglet)

FR8 North-east Limnodynastes peroni (striped marsh frog), Limnodynastes tasmaniensis (spotted marsh frog), Limnodynastes dumerili insularis (eastern banjo frog), Litoria ewingi (brown tree frog), Litoria raniformis (green and gold frog), Crinia signifera (common froglet), Geocrinia laevis (smooth froglet), Pseudophryne semimarmorata (southern toadlet), Crinia tasmaniensis (Tasmanian froglet)

FR9 West Limnodynastes dumerili insularis (eastern banjo frog), Litoria ewingi (brown tree frog), Litoria burrowsae (Tasmanian tree frog), Crinia signifera (common froglet), Crinia tasmaniensis (Tasmanian froglet)

FR10 South Litoria ewingi (brown tree frog), Litoria burrowsae (Tasmanian tree frog), Crinia signifera (common froglet), Bryobatrachus nimbus (moss froglet), Crinia tasmaniensis (Tasmanian froglet)

FR11 Eastern Highlands Limnodynastes tasmaniensis (spotted marsh frog), Limnodynastes dumerili insularis (eastern banjo frog), Litoria ewingi (brown tree frog), Crinia signifera (common froglet), Pseudophryne semimarmorata (southern toadlet), Crinia tasmaniensis (Tasmanian froglet)

FR12 Furneaux – Flinders Limnodynastes tasmaniensis (spotted marsh frog), Limnodynastes dumerili insularis (eastern banjo frog), Litoria ewingi (brown tree frog), Litoria raniformis (green and gold frog), Crinia signifera (common froglet), Pseudophryne semimarmorata (southern toadlet)

FR13 Furneaux – Barren Limnodynastes tasmaniensis (spotted marsh frog), Limnodynastes dumerili insularis (eastern banjo frog), Litoria ewingi (brown tree frog), Crinia signifera (common froglet), Pseudophryne semimarmorata (southern toadlet)

FR14 North-west Limnodynastes peroni (striped marsh frog), Limnodynastes dumerili insularis (eastern banjo frog), Litoria ewingi (brown tree frog), Litoria raniformis (green and gold frog), Crinia signifera (common froglet), Geocrinia laevis (smooth froglet), Pseudophryne semimarmorata (southern toadlet), Crinia tasmaniensis (Tasmanian froglet)

FR15 East coast Limnodynastes tasmaniensis (spotted marsh frog), Limnodynastes dumerili insularis (eastern banjo frog), Litoria ewingi (brown tree frog), Litoria raniformis (green and gold frog), Crinia signifera (common froglet), Geocrinia laevis (smooth froglet), Pseudophryne semimarmorata (southern toadlet), Crinia tasmaniensis

(Tasmanian froglet)

Appendix 6 – Spatial data layers - Geomorphic responsiveness





6.2.11 Geomorphic responsiveness

Title Geomorphic responsiveness

Custodian WRD, DPIW


Description Susceptibility of particular fluvial geomorphic mosaics to change / impacts.

Input data


Lineage

The geomorphic responsiveness data layer was a product of grouping the fluvial geomorphic mosaics into three broad classes, based on its susceptibility to change:

1 Highly responsive – An alluvial or fine sediment system. Responsiveness of channel form to anthropogenic changes in flow and/or sediment regime is high.

0.5 Moderately responsive – A system of sedimentary character intermediate between fine and coarse. Responsiveness of channel form to anthropogenic changes in flow and/or sediment regime is moderate.

0 Unresponsive – A coarse or bedrock controlled and dominated surface water system. Responsiveness of channel form to anthropogenic changes in flow and/or sediment regime is low.

The assignment of a geomorphic responsiveness class to each of the mosaics was undertaken by expert geomorphologists (Table 15). The method for assigning the mosaic classes to the spatial units is provided in Appendix 0.

Table 15. Fluvial geomorphic responsiveness class code for each geomorphic mosaic.

Mosaic class code

Fluvial geomorphic mosaic Geomorphic responsiveness

class code

MO1 Central and eastern dolerite plateaus 0

MO2 Central East alluvial basins 0.5

MO3 Central Plateau glacial till and outwash plains 0.5

MO4 Dissected north-west granite 0.5

MO5 Dry, low slope eastern granite hills 0.5

MO6 Eastern dolerite rolling hills 0

MO7 Eastern granite hills and coastal sediments 1

MO8 Eastern granite hills and relict surfaces 1

MO9 Eastern Tiers basalt flats 0.5

MO10 Eldon strike ridges 0

MO11 Finely dissected northern surface and coastal sediments. 0.5



Mosaic class code


class code

MO12 Finely dissected north eastern surface on Mathinna, Parmeener and basalt

0.5

MO13 Finely dissected western granite relict surfaces. 1

MO14 Glacially dissected dolerite and Parmeener plateau 0

MO15 Glacially dissected quartzite plateau 0

MO16 Glaciated dolerite and Parmeener peaks 0

MO17 Glaciated dolerite valleys 0

MO18 Glaciated quartzite peaks 0

MO19 Glaciated quartzite valleys. 0

MO20 High altitude dolerite plateau 0

MO21 Inland slopes 0.5

MO22 Lower Derwent 0

MO23 Lower Huon 0

MO24 Moderate rainfall northern dolerite and Parmeener hills 0.5

MO25 Moderate slope eastern granite hills 0.5

MO26 Moderate slope Mathinna hills 0.5

MO27 North east volcano-sedimentary hills 0.5

MO28 North eastern basins with granitic sediments 1

MO29 North eastern coastal dunefields 1

MO30 North eastern Mathinna beds sedimentary basins 0.5

MO31 North-east alluvial basins 0.5

MO32 North-east ultramafics 0

MO33 Northern calcarenite karst 1

MO34 Northern high relief karst 0.5

MO35 Northern karst basins 0.5

MO36 Northern Midlands Tertiary Basin 1

MO37 Northern quartzite gorges 0

MO38 Northern quartzite ridges, hills and valleys 0

MO39 Northern relict surfaces 1

MO40 Northern rolling basalt hills and alluvial basins 0.5

MO41 Northern steep quartzite ridges and scree. 0

MO42 Northern Tertiary basins and coastal sediments 1

MO43 Northern valleys 0

MO44 North-west dissected escarpments 0

MO45 North-west hills, coastal sands and remnant surfaces 1

MO46 North-west moderate relief karst 0.5

MO47 North-west Rolling Hills 0.5

MO48 North-western dissected surface on Precambrian folded sediments 0.5

MO49 North-western granite hills 0.5



Mosaic class code


class code

MO50 North-western granite valley 0.5

MO51 North-western karst basins and plains 1

MO52 North-western valleys on Precambrian folded sediments 0.5

MO53 South east rolling hills and coastal sands 1

MO54 South Eastern coastal slopes 0

MO55 South eastern dolerite dry hills and basins 0

MO56 South Eastern karst basins 0.5

MO57 South western complex karst valleys 0.5

MO91 South-eastern complex karst valleys 0.5

MO92 South-eastern glacio-karst 0.5

MO58 South-eastern high relief karst 0

MO59 South-eastern karst basins, rolling 0.5

MO60 Southern Midlands foothills and drainage divides 0.5

MO61 Southern Midlands foothills and valleys 0.5

MO62 Southern Midlands Tertiary basin 0.5

MO63 South-west alluvial basins 0.5

MO64 South-west karst basins, flat 0.5

MO65 South-west quartzite ridges hills and valleys 0.5

MO66 South-west quartzite strike ridges, valleys and gorges 0.5

MO67 South-western dissected relict surface on carbonate 0.5

MO68 South-western glacial till and outwash plains 1

MO90 South-western glacio-karst 0.5

MO69 South-western karst basins, rolling 0.5

MO70 South-western quartzite valleys 0

MO71 South-western steep ridges hills and valleys on low grade metasediments.

0

MO89 South-western volcano-sedimentary and soft Precambrian hills and valleys

0.5

MO72 Steep and dry eastern granite hills and escarpments 0

MO73 Steep dissected eastern escarpment 0

MO74 Steep dolerite scree 0

MO75 Steep north-western granite 0

MO76 Steep western Midlands escarpments 0

MO77 Steep wet eastern granite hills and escarpments 0

MO78 Steep, climatically extreme Mathinna hills 1

MO79 Steep, moderate rainfall Mathinna ridges and escarpments 0.5

MO80 Steep, wet Mathinna hills and escarpments 0.5

MO81 Strongly glaciated plateau 0

MO82 Tamar slopes 0.5

Appendix 6 – Spatial data layers - Groundwater Dependent Ecosystems


Mosaic class code


class code

MO83 Western coastal sediments, terraces and remnant surfaces 1

MO84 Western dissected surfaces 0

MO85 Western mafic and ultramafic 0.5

MO86 Western relict surfaces 0.5

MO87 Western Tasmanian terrace sequences on Tertiary sediments 0.5

MO88 Western Tiers basins and fans 0.5

Data limitations As per fluvial geomorphic mosaics (Appendix 6.2.8).



6.2.12 Groundwater Dependent Ecosystems

Title CFEV Groundwater Dependent Ecosystems (GDEs)

Custodian WRD, DPIW

Creator Earth Science Section, RMC, DPIW

Description Point locations of GDEs in Tasmania

Input data

Various scientific studies (see References)

Lineage

The GDEs point data layer was modified from a selection of known locations of freshwater-dependent ecosystems highly dependent on groundwater which were nominated by a variety of experts in the fields of earth science, botany and zoology (Appendix 1).

In many instances, certain identified types of GDEs were addressed through the CFEV assessment of other ecosystem types (e.g. vegetation types, such as alkaline pans and sedge rush wetlands being considered in the wetlands assessment). A GDE data layer was created using points to represent the locations of GDE types not being picked up by other assessments. The point localities cover the following classes of GDEs:

Cold springs (karstic) – springs (often at cave entrances) where water temperature is unaffected by geothermal heating.

Mound springs – a tufa-depositing spring where the calcareous material is deposited by groundwater under pressure to produce a raised mound or hillock.

Sub-surface streams in talus and colluvium.

Tufa-depositing springs – spring where calcareous material (tufa) has been deposited by groundwater.

Warm springs – springs where the water temperature has been raised through geothermal heating.

Appendix 6 – Spatial data layers - Hydrological regions


Many of the springs mapped as point localities are cave entrances. In the interests of cave conservation and public safety, this information was considered sensitive and as such, the easting and northing data associated with this data was generalised so the exact location is not disclosed.

Data limitations

Not a comprehensive inventory of all GDEs. Only lists those known by experts and only includes those GDEs not considered to be assessed within other ecosystem themes. For a full list of known GDEs identified by the experts, see (Eberhard, 2004).

Date created January 2004


References (Nye et al., 1934; Jennings, 1956; Matthews, 1978; Eastoe, 1979; Middleton, 1979; Knott and Lake, 1980; Matthews, 1983; Houshold and Clarke, 1988; Clarke, 1990; Eberhard, 1991; Drysdale, 1992; Eberhard, 1994; Eberhard, 1995; Sharples, 1995; Dixon, 1996; Sharples, 1996; Houshold et al., 1999; Joyce, 2003; Eberhard, 2004)

6.2.13 Hydrological regions

Title Hydrological regions

Custodian WRD, DPIW


Description Hydrological regions of Tasmania

Input data

Hydrological data from Hughes (1987)

Lineage

Hydrological regions for Tasmania were adapted from a statewide hydrological analysis conducted by Hughes (1987a). Annual flow records, monthly flow records, peak and low flow records were used to develop a hydrological classification for 77 Tasmanian rivers (Hughes, 1987a), shown as groupings in Figure 20 and Table 16. From this classification, a map was produced to identify broad hydrological regions within Tasmania by drawing boundaries between sites in the groups identified by Hughes (Figure 21). This included defining a gradational boundary between a Group 4 dominated region in the north-west, and the central-north and north-eastern region dominated by Hughes‟ Group 1 streams. Boundaries were based on approximately 1:100 000 scale catchments with the exception of the midlands region. Descriptions of the character of the regions are provided in Table 17 and a method for assigning the hydrological class to the river section is provided in Appendix 0.



Figure 20. Map of Tasmania, showing 4 hydrological groups taken from Hughes (1987).

Table 16. Groupings of rivers classified by Hughes (1987).

Group 1 Group 2 Group 3 Group 4

Anderson‟s Creek Apsley River Arm River Arthur River

Brown River Birralee Creek Davey River Black River

Cam River Carlton River Derwent River Brid River

Claytons Rivulet Clyde River Esperance River Duck River

Don River Coal River Forth River Emu River

Gawler River Dulverton Creek Franklin River Flowerdale River

George River Iron Creek Hellyer River Inglis River

Great Musselroe River Jordan River Henty River Level River

Lake River Little Swanport River Huon River Loudwater River

Meredith River Maclaines Creek King River Meander River

Mersey River Orielton Rivulet Meander River Mountain River



Group 1 Group 2 Group 3 Group 4

Montague River Prosser River Nive River North Esk River

North West Bay River Swan River Pieman River Pet River

Pipers River Que River Ringarooma River

Rubicon River Florentine River South Esk River

Seabrook Creek Tyenna River Sulphur Creek

Snug River Whyte River

South Esk River Gordon River

South Pats River Franklin River

Supply River Pine Tree Rivulet

Tomahawk River

Meander River

Figure 21. Map of hydrological regions derived from Hughes (1987).



Table 17. Description of the hydrological character of rivers in the CFEV hydrological regions.

Class code

Mean Annual run-off

Low flow run-off

Co-efficient of variance for annual

flows

Skew annual flows

Co-efficient of variance for monthly

flows

Peak run-off

Peak flow variability

Description Hughes (1987) classification

H1 Medium Medium Medium High High Medium Medium Streams intermediate in magnitude and variability of annual, monthly and peak flows, with a skewed annual flow distribution.

Mainly Group 1, some Group 4

H2 Lowest Low High High High Medium High The state‟s driest, most variable stream systems; medium and highly skewed annual flows; highly variable peak and low flows and strong seasonality.

Group 2

H3 Highest High Low Medium

Low High Low The state‟s most perennial, high volume and least variable streams (in annual and monthly flows). High base flows; High peak run-off with relatively low variability.

Group 3

H4 High High Low Low High Medium Low As for Region 3, but with high variability in monthly of annual, monthly and peak flows, with a skewed flows and lower annual and peak run-off.

Mainly Group 4, some Group 1

Appendix 6 – Spatial data layers - Karst


Data limitations Hand-drawn boundaries.

Date created April 2004


References (Hughes, 1987a)

6.2.14 Karst

Title CFEV Karst

Custodian WRD, DPIW

Creator Earth Science Section, RMC, DPIW

Description Karst of Tasmania

Input data

Karst Atlas (Version 3), DPIWE and Forestry Tasmania

Lineage

The karst spatial data layer was derived from the Karst Atlas (Version 3, 2003). The original polygons were reviewed and some karst areas, including Riveaux, Mole Creek and Mt Cripps were updated where new information had become available. Many of the karst areas were made up of many smaller polygons all having the same name. Where polygons of the same name abutted each other or were within one another, they were dissolved to create one larger polygon. The resultant layer had some other very large (>13 000 ha) polygons which were subsequently split. This was, based on the assumption that if very large karst areas cover an area of varying condition then it is unlikely that degrading impacts influencing one end of the karst would be affecting the other end. These large karst polygons were split into approximate equal parts attempting to relate them to their catchment boundaries as far as possible. Exceptions were those polygons situated within the World Heritage Area (WHA), which weren‟t split, as it is likely that the catchments surrounding these karst areas would not vary much in condition.

Data limitations The karst spatial data layer inherits all the data limitations of the input data (see reference below) and derivation processes.



References (Kiernan, 1995)

6.2.15 Land Tenure Security

Title Land Tenure Security (LTS)

Custodian WRD, DPIW

Creator Rod Knight, GIS Services

Description The degree to which land tenure may be considered to have the potential for protecting Tasmania‟s freshwater-dependent ecosystem values.

Appendix 6 – Spatial data layers - Land Tenure Security


Input data

LIST 1:25 000 Cadastral Parcels (version February 2005), DPIW

LIST Land Tenure (version April 2005), DPIW

Non-forest Vegetation Conservation Program CAR (Comprehensive, Adequate and Representative) Reserves data layer (version January 2005), DPIW

Private Forest Reserves Program (PFRP) private forest reserves data layer (version February 2005), DPIW

RFA land tenure (version 2002), DPIW

Lineage

The LTS data layer was developed to provide a classification of the land tenure of Tasmania in terms of the security of land tenure for conservation management. The categories high, medium and low were used, although the CFEV Project‟s Technical Management Group (TMG) (Appendix 1) noted there is a substantial gap between the high and medium security categories.

The input data layers were examined for their suitability for the task. The data sets were examined for computability of classification and also for consistency of polygon boundaries (i.e. the same boundary matched in all layers reporting it). It was determined that there was some inconsistency and logical rules would therefore be required to interpret the data. Where possible, conformity with the ILS cadastre was enforced.

The data were combined by converting each layer (accounting for known overlaps e.g. strata titles) to a 10 m grid on the basis of their tenure classes. Each grid was then combined using a mathematical calculation (see below) to preserve each class in the resultant combination layer. The calculation below produces a numeric value of 19082315, which is then converted to a string of the same characters and each of the contributing classes extracted and attribute values reattached to the attribute table. The resulting layer was then reconverted to a polygon layer.

Cadastre layer - class 15; plus

CAR reserves - class 23 * 100; plus

RFA Review - class 08 * 10000; plus

Tenure - class 9 * 100000; plus

PFRP - class 1 * 1000000

Each unique combination of tenure classes was examined for logical consistency and, where possible, assigned to a land tenure class. In some instances, a single determination could not be made, so the class options were examined to determine which of the tenure security categories they would be placed in. Table 18 shows the land tenure classes and the security assigned to them for the CFEV Project.

Appendix 6 – Spatial data layers - Land use (nutrients)


Table 18. CFEV land tenure classes and tenure security.

Land tenure class LTS category

Formal Reserve (CAR) High

Informal Reserves (CAR) Medium

State Forest Medium

Commonwealth Land (not CAR) Low

Freehold Low

Hydro and other water authorities Low

Other Crown Low

Unknown (60 ha) Low

LTS categories were assigned to estuary, karst, saltmarsh, river, waterbody and wetland spatial units using rules which generally assigned the lowest LTS type according to agreed thresholds (by the TMG) for what constitutes a potential impact to the protection of freshwater values (see Appendix 6.3.24).

Data limitations

The assigning of LTS categories to each land tenure class (shown in Table 18) assumes knowledge of the way a particular area is managed. A range of land and vegetation management may occur across individual Land Tenure Security types.


Scale and coverage 1: 25 000; Statewide

6.2.16 Land use (nutrients)

Title Land use (nutrients)

Custodian WRD, DPIW


Description Estimation of excess nutrient input to freshwater-dependent ecosystems.

Input data

LIST Land use data layer (version May 2004), DPIW

Lineage

The land use (nutrients) data layer was developed by selecting LIST land use categories which are likely to result in excess nutrient input to freshwater-dependent ecosystems, particularly waterbodies and wetlands. Each land use category from the LIST data was given a score based on the relative impact it is likely to have on nutrient yield to freshwater-dependent ecosystems, either as 0.5 (moderate impact) or 0 (high to severe impact). A list of the selected land use categories and their assigned scores is given in Table 19. Areas of the state containing none of the LIST land use categories shown in Table 19 were assigned a nutrient score of 1 (no or very little impact). The land use (nutrients) data was attributed to each of the waterbody and wetland spatial units using the rules outlined in Appendix 6.3.25.



Table 19. Land use categories and their land use (nutrient) impact score.

Primary Secondary Tertiary Nutrient score

Production from dryland Grazing modified pastures Pasture legumes 0.5

Production from dryland Grazing modified pastures Pasture legume/grass mixtures

0.5

Production from dryland Grazing modified pastures Sown grasses 0.5

Production from dryland Cropping Cropping 0

Production from dryland Cropping Cereals 0.5

Production from dryland Cropping Beverage and spice crops 0.5

Production from dryland Cropping Hay and silage 0.5

Production from dryland Cropping Oil seeds 0.5

Production from dryland Cropping Sugar 0

Production from dryland Cropping Cotton 0.5

Production from dryland Cropping Tobacco 0

Production from dryland Cropping Legumes 0.5

Production from dryland Perennial horticulture Perennial horticulture 0.5

Production from dryland Perennial horticulture Tree fruits 0.5

Production from dryland Perennial horticulture Oleaginous fruits 0.5

Production from dryland Perennial horticulture Tree nuts 0.5

Production from dryland Perennial horticulture Vine fruits 0.5

Production from dryland Perennial horticulture Shrub nuts fruits and berries 0

Production from dryland Perennial horticulture Flowers and bulbs 0

Production from dryland Perennial horticulture Vegetables and herbs 0

Production from dryland Seasonal horticulture Seasonal horticulture 0

Production from dryland Seasonal horticulture Fruits 0

Production from dryland Seasonal horticulture Nuts 0

Production from dryland Seasonal horticulture Flowers and bulbs 0

Production from dryland Seasonal horticulture Vegetables and herbs 0

Production from irrigation Irrigated modified pastures Irrigated modified pastures 0.5

Production from irrigation Irrigated modified pastures Irrigated woody fodder plants

0.5

Production from irrigation Irrigated modified pastures Irrigated pasture legumes 0.5

Production from irrigation Irrigated modified pastures Irrigated legume/grass mixtures

0.5

Production from irrigation Irrigated modified pastures Irrigated sown grasses 0.5

Production from irrigation Irrigated cropping Irrigated cropping 0

Production from irrigation Irrigated cropping Irrigated cereals 0.5

Production from irrigation Irrigated cropping Irrigated beverage and spice crops

0.5

Production from irrigation Irrigated cropping Irrigated hay and silage 0.5

Production from irrigation Irrigated cropping Irrigated oil seeds 0.5

Production from irrigation Irrigated cropping Irrigated sugar 0

Production from irrigation Irrigated cropping Irrigated cotton 0

Production from irrigation Irrigated cropping Irrigated tobacco 0

Production from irrigation Irrigated cropping Irrigated legumes 0

Production from irrigation Irrigated perennial horticulture Irrigated perennial horticulture

0.5

Production from irrigation Irrigated perennial horticulture Irrigated tree fruits 0.5

Production from irrigation Irrigated perennial horticulture Irrigated oleaginous fruits 0.5




Production from irrigation Irrigated perennial horticulture Irrigated tree nuts 0.5

Production from irrigation Irrigated perennial horticulture Irrigated vine fruits 0.5

Production from irrigation Irrigated perennial horticulture Irrigated shrub nuts fruits and berries

0.5

Production from irrigation Irrigated perennial horticulture Irrigated flowers and bulbs 0

Production from irrigation Irrigated perennial horticulture Irrigated vegetables and herbs

0

Production from irrigation Irrigated perennial horticulture Irrigated seasonal horticulture

0

Production from irrigation Irrigated perennial horticulture Irrigated fruits 0.5

Production from irrigation Irrigated perennial horticulture Irrigated nuts 0.5

Production from irrigation Irrigated perennial horticulture Irrigated flowers and bulbs 0

Production from irrigation Irrigated perennial horticulture Irrigated vegetables and herbs

0

Intensive uses Intensive horticulture Intensive horticulture 0

Intensive uses Intensive horticulture Shadehouses 0.5

Intensive uses Intensive horticulture Glasshouses 0.5

Intensive uses Intensive horticulture Glasshouses (hydroponic) 0.5

Intensive uses Intensive animal production Intensive animal production 0

Intensive uses Intensive animal production Dairy 0

Intensive uses Intensive animal production Cattle 0

Intensive uses Intensive animal production Sheep 0

Intensive uses Intensive animal production Poultry 0

Intensive uses Intensive animal production Pigs 0

Intensive uses Intensive animal production Aquaculture 0

Intensive uses Manufacturing and industrial Manufacturing and industrial 0.5

Intensive uses Residential Residential 0.5

Intensive uses Residential Urban residential 0

Intensive uses Residential Rural residential 0.5

Intensive uses Services Services 0.5

Intensive uses Services Commercial services 0.5

Intensive uses Services Public services 0.5

Intensive uses Services Recreation and culture 0.5

Intensive uses Transport and communication Airports/aerodromes 0.5

Intensive uses Transport and communication Roads 0.5

Intensive uses Transport and communication Railways 0.5

Intensive uses Transport and communication Ports and water transport 0.5

Intensive uses Transport and communication Navigation and communication

0.5

Intensive uses Waste treatment and disposal Waste treatment and disposal

0

Intensive uses Waste treatment and disposal Stormwater 0.5

Intensive uses Waste treatment and disposal Landfill 0

Intensive uses Waste treatment and disposal Solid garbage 0

Intensive uses Waste treatment and disposal Incinerators 0

Intensive uses Waste treatment and disposal Sewage 0

Water Reservoir/dam Evaporation basin 0.5

Water Reservoir/dam Effluent pond 0

Appendix 6 – Spatial data layers - Macroinvertebrate assemblages



Water River River - intensive use 0.5

Water Marsh/wetlands Marsh/wetland - intensive use

0.5

Water Estuaries/coastal waters Estuary/coastal water - intensive use

0.5

Data limitations The land use (nutrients) data inherits all the limitations of the input data, and also relies heavily upon the expert assessment of the relative impacts of each of the land uses.


Scale and coverage 1: 25 000, Statewide

6.2.17 Macroinvertebrate assemblages

Title Macroinvertebrate assemblages

Custodian WRD, DPIW


Description Distribution of macroinvertebrate assemblages in Tasmania.

Input data

Australian River Assessment System (AUSRIVAS) Macroinvertebrate data, DPIWE

AUSRIVAS Macroinvertebrate data, Freshwater Systems

Lineage

The CFEV macroinvertebrate assemblage spatial data layer was developed in two stages. Firstly, distinct assemblages were identified by analysing two key macroinvertebrate data sets and then a map was developed to show the distribution of the assemblage groups.

Assemblage identification

As part of the National River Health Program, AUSRIVAS was established to assess the ecological health of rivers based on macroinvertebrate monitoring and habitat assessments (Schofield and Davies, 1996). Macroinvertebrate samples were collected by DPIWE staff using standard AUSRIVAS sampling techniques during the NRHP national assessment in 1994-1998 (Krasnicki et al., 2001) from Tasmanian stream sites selected as being in or close to pre-European reference condition. A number of samples had also been collected by Freshwater Systems (P. Davies, Freshwater Systems, pers. comm.) for a range of studies during that period, especially in locations not visited by DPIWE such as western Tasmanian and King Island.

Spring-season samples rated with an Observed/Expected (O/E) value >0.9 were selected from these two sample sets and all mayfly, stonefly and caddis fly larvae (orders Ephemeroptera, Plecoptera and Trichoptera, or EPT) were identified to lowest taxonomic (genus/species) level. UPGMA cluster and multi-dimensional scaling (MDS) analyses were conducted using presence/absence data for all taxa in the samples, along with self-organising map (SOM) neural network classification and



multivariate analysis of similarities (ANOSIM in the Primer-E package) to classify river site groups by macroinvertebrate assemblage composition and to identify distinct assemblages.

The above analyses were conducted on two data sets:

A data set termed „combined‟, for which two samples had been collected from a given site, one in a riffle and one in an edge habitat. This data set, comprising 189 sites and 144 taxa, was assumed to represent the most comprehensive available collection of the dominant taxa at a river site.

A second data set, comprising 239 sites and 165 taxa, was also derived based only on data from samples collected in riffle habitats. This data set was deemed to contain a less comprehensive taxon list per site, but had significantly greater spatial coverage due to the collection of samples only at riffle habitats from a number of catchments, particularly in western Tasmania.

The data analyses were initially conducted on the combined data set, and a set of macroinvertebrate assemblage classes identified. This resulted in a set of 11 distinctive (by ANOSIM at p <0.05) assemblage classes.

A second round of analyses was then conducted using the riffle data set only. Within this riffle sample analysis, groupings which contained no or few samples from sites at which combined samples had also been collected were identified. These groupings were assessed as being distinctive from the remaining riffle data and added to the set of classes already identified in the initial „combined data set‟ analyses. This resulted in an additional six assemblage classes. Thus, a total of 17 macroinvertebrate assemblage classes were identified. The distribution of these macroinvertebrate assemblages is shown in Figure 22.



Figure 22. Location of the 17 distinctive stream macroinvertebrate assemblages identified from AUSRIVAS sample sets identified to species for Ephemeroptera, Plecoptera and Trichoptera (EPT). C and R indicate those assemblages identified from combined habitat samples (riffle and edge) and riffle habitat samples only, respectively. Note absence of samples from Flinders Island, central plateau and far south-west.

Assemblage regionalisation

To develop a map (and GIS data layer) describing the distribution of the main macroinvertebrate assemblages across the state, relationships between environmental variables and the macroinvertebrate assemblage groups derived above were explored in an attempt to model the distribution of assemblages in environmental space.

Discriminant Function Analysis (DFA, in SYSTAT 10.0) was used to explore these relationships, using data on key environmental variables which are unaffected by human impacts. Variables were obtained at regional, catchment and reach scales



from GIS data, and at site scale from field measurements made at the time of macroinvertebrate sampling. A number of discriminatory environmental variables were identified from the DFA. However, the success of re-classification of samples into their site groups using these relationships was low, with a maximum re-classification success rate of only 45%. Neural network analysis was also attempted, training a multi-layer perceptron (with one hidden layer) to classify samples (sites) to site groups using environmental variables. Despite repeated efforts, including use of a variety of data subsets and encoding methods, classification success never exceeded 65%. Numerical methods were therefore abandoned.

A map of macroinvertebrate assemblage regions was therefore derived by hand drawing boundaries between those river sections and sub-catchments with a predominance of distinctive macroinvertebrate assemblage classes. Some areas contained mixtures of two macroinvertebrate assemblage types which could not be separated by regional boundaries. Overall, 13 macroinvertebrate regions were derived from the map of sample site assemblage types. There were two areas of the state for which AUSRIVAS sample sets were inadequate for river characterisation. Examination of other data collected by other methods for these areas supported the formation of two distinct regions: Flinders Island (C7FL) and the Central Plateau (CPL). Macroinvertebrate assemblages of the Flinders Island region are believed to have affinities with those of the far north-east (dominant assemblage type C7) (P. Davies, Freshwater Systems, pers. comm., unpublished data). This region was therefore designated C7FL.

The resulting map, showing the distribution of a total of 15 regional macroinvertebrate assemblage classes is shown in Figure 23. The „C‟ in the class code indicates that the combined data was used and the „R‟ indicates use of the riffle data only.

Finally, current research (J. Gooderham, L. Barmuta & P. Davies, UTas, unpublished data) indicates that sections of drainage in first order or montane (>800 m) streams have macroinvertebrate assemblages which are related to but distinct from the assemblages in the remainder of the catchment. First-order streams often contain naturally depauperate forms of downstream drainage assemblages. Each river section was assigned a macroinvertebrate assemblage class using the rules shown in Appendix 6.3.28, based on the regional boundaries shown in Figure 23, with first order or montane stream sections assigned with a suffix „f‟ or „m‟. The assigned macroinvertebrate assemblage classes are described in Table 20, accompanied by the list of indicator taxa. The prefix „B‟ was added to the class code to distinguish the macroinvertebrate assemblages from other classes when input into the spatial selection algorithm.



Figure 23. Macroinvertebrate assemblage regions.

Data limitations

Absence of macroinvertebrate data collected from Flinders Island, the Central Plateau and the far south-west regions of Tasmania and hand-drawn boundaries.





Table 20. Descriptions of macroinvertebrate assemblages.

Class code Description Species composition

BC1 Assemblage predominantly found in major river sections on King Island. Indicator taxa: Illiesoperla mayi, Notalina spira, Triplectides nigricornis, Symphitoneuria opposita

BC1f Headwater stream assemblage on King Island assemblage; depauperate form of assemblage BC1.

Indicator taxa: Illiesoperla mayi, Notalina spira, Triplectides nigricornis, Symphitoneuria opposita

BC1A Assemblage predominantly found in major streams located in the far north-west, closely related in composition to assemblage BC1.

Indicator taxa: Physidae, Eusiridae, Hydrophilidae, Nousia sp. AV9, Leptoperla beroe, Hirudinea, Acarina, Notalina sp. AV3, Diptera Unid Pupae, Ancylidae, Culicidae, Notonectidae, Atalophlebia australis, Chrysomelidae L.

BC1Af Headwater stream assemblage in far north-west; depauperate form of assemblage BC1A and located in same areas.

Indicator taxa: Physidae, Eusiridae, Hydrophilidae, Nousia sp. AV9, Leptoperla beroe, Hirudinea, Acarina, Notalina sp. AV3, Diptera Unid Pupae, Ancylidae, Culcidae, Notonectidae, Atalophlebia australis, Chrysomelidae L.

BC2C5 Assemblages of streams of the central East Coast in the catchments of the Swan, Wye, Meredith, Cygnet, Buxton and Lisdillon catchments.

Two assemblages potentially present. Indicator taxa: C2: Hellyethira simplex, Apsilochorema gisbum, Notalina fulva, Gomphidae, Atyidae, Gyrinidae L, Ulmerochorema lentum; C5: Koornonga sp. AV1, Taschorema complex, Aeshnidae, Atalophlebia albiterminata, Sialidae, Lectrides varians, Ethochorema nesydrion, Cheumatopsyche sp. AV3, Eusthenia spectabilis, Genus I sp. AV3, Dixidae, Tanypodinae

BC2C5f Headwater first order streams, depauperate form of assemblage BC2C5 and located in same areas.

Two assemblages potentially present. Indicator taxa: C2: Hellyethira simplex, Apsilochorema gisbum, Notalina fulva, Gomphidae, Atyidae, Gyrinidae L, Ulmerochorema lentum; C5: Koornonga sp. AV1, Taschorema complex, Aeshnidae, Atalophlebia albiterminata, Sialidae, Lectrides varians, Ethochorema nesydrion, Cheumatopsyche sp. AV3, Eusthenia spectabilis,

Genus I sp. AV3, Dixidae, Tanypodinae

BC3 Assemblage in major rivers in eastern Tasmania. Indicator taxa: Tasmanocoenis tillyardi, Ceinidae, Anisocentropus latifascia, Coenagrionidae, Veliidae, Baetid Genus 1 MVsp. 5, Triplectides magnus, Hydrobiidae, Planorbidae, Lingora aurata

BC3f Headwater stream assemblages in eastern Tasmania; depauperate form of assemblage BC3 and located in same areas.

Indicator taxa: Tasmanocoenis tillyardi, Ceinidae, Anisocentropus latifascia, Coenagrionidae, Veliidae, Baetid Genus 1 MVsp. 5, Triplectides magnus, Hydrobiidae, Planorbidae, Lingora aurata

BC3BR Major eastern and Central Midland rivers in broadwater/pool/run reaches with either emergent-dominated (Eleocharis sp., Triglochin sp.) macrophyte assemblages, or emergent and submerged macrophyte complexes in broadwater/pool habitats.

Indicator taxa: Atalophlebia australis, Notalina spira, Necterosoma antiporus, Centroptilum sp., Cloeon sp., Leptoperla varia




BC4 Assemblage in major streams on north-west coast. Indicator taxa: Parameletidae, Orthocladiinae

BC4f Headwater stream assemblage from north-west coast; depauperate form of assemblage BC4 and located in same areas.

Indicator taxa: Parameletidae, Orthocladiinae

BC6 Assemblage in major rivers of north coast, west of the Tamar River. Indicator taxa: Bungona sp.

BC6f Headwater stream assemblage in catchments of north coast, west of the Tamar River; depauperate form of assemblage BC6.

Indicator taxa: Bungona sp.

BC7 Assemblage characteristic of streams in the north-east and eastern-central parts of the state, in streams at low elevation (<800 m).

Indicator taxa: Elmidae A, Notalina bifaria, Tasmanophlebia sp. AV1, Taschorema asmanum, Triplectides similis, Nousia sp. AV8, Agapetus sp. AV1

BC7f Headwater first order streams, depauperate form of assemblage BC7 and located in same areas.

Indicator taxa: Elmidae A, Notalina bifaria, Tasmanophlebia sp. AV1, Taschorema asmanum, Triplectides similis, Nousia sp. AV8, Agapetus sp.

AV1

BC7fm Headwater first order alpine streams, depauperate form of assemblage BC7m and located in same areas.


BC7m Assemblage characteristic of streams in the north-east and eastern-central parts of the state, in streams at high elevation (>800 m).


BC8 Assemblage of streams in the central north-east (Plomley‟s Island), and in catchments bordering the Tyler line both north of the Central Plateau (upper Forth and Mersey catchments) and south of the Central Plateau (central Derwent catchment). River sections at elevations <800 m.

Indicator taxa: Baetid Genus 2 MVsp. 3, Notalina sp. AV1, Conoesucus norelus, Asmicridea sp. AV1, Moruya opora, Elmidae L, Dinotoperla serricauda, Tasmanoperla larvalis, Alloecella grisea, Helicopsyche murrumba, Aphilorheithrus sp. AV3, Taschorema ferulum








BC8m Assemblage of upper elevation (>800 m) streams in the central north-east (Plomley‟s Island), and in catchments bordering the Tyler line both north of the Central Plateau (upper Forth and Mersey catchments) and south of the Central Plateau (central Derwent catchment).


BC9 Assemblage of mid to lower elevation (<800 m) streams throughout much of the inland western region of the state, falling along the western edge of the Tyler line/corridor, encompassing part of the WHA, includes upper Franklin and Gordon catchments, middle to lower Huon-Weld-Picton catchments (excluding main-stem Huon and Picton).

Indicator taxa: Trinotoperla zwicki, Austrocercella christinae, Triplectides proximus, Scirtidae, Austrophlebioides sp. AV7, Eusthenia costalis, Tasmanoperla thalia, Blephaceridae, Trinotoperla inopinata, Nousia sp. AV5/6, Simuliidae, Apsilochorema obliquum, Aphilorheithrus sp. AV2 dark





BC9m Assemblage of upper elevation (>800 m) streams throughout much of the inland western region of the state, falling along the western edge of the Tyler line/corridor, encompassing part of the WHA.


BC10 Assemblages of streams on the Tasman Peninsula, south Bruny Island and non-alpine (<800 m) streams of the Esperance, Lune, Catamaran, D‟Entrecasteaux River catchments and coastal environs.

Indicator taxa: Nousia sp. AV7, Caenota plicata, Hydrobiosella sp. AV10, Athericidae, Cardioperla medialobata, Aphilorheithrus sp. AV2 spotty, Moruya sp. AV1, Empididae, Tasmanocerca bifasciata, Conoesucus nepotulus








BC10m Assemblages of alpine (>800 m) streams of the Esperance, Lune, Catamaran, D‟Entrecasteaux River catchments and coastal environs.


BR5R8 Assemblages of coastal drainages on the West coast between Arthur River and Macquarie Harbour.

Two assemblages possibly present. Indicator taxa: R5: Costora luxata, Alloecella pilosa; R8: Cardioperla diversa/edita, Taschorema complex, Cardioperla sp. A, Dytiscidae larvae

BR5R8f Headwater first order streams, depauperate form of assemblage BR5R8 and located in same areas.


BR5R8fm Assemblages of alpine (>800 m) headwater streams of coastal drainages on the West coast between Arthur River and Macquarie Harbour.


BR6R14 Assemblages of rivers and streams of the far south west between Macquarie Harbour and South Cape, including the southern part of the Gordon River catchment, Cracroft and upper Huon (amalgamated due to lack of differentiating data).

Two assemblages possibly present. Indicator taxa: R14: Nousia sp. AV5/6; R6: Taschorema ferulum, Elmidae A.

BR6R14f Headwater first order streams, depauperate form of assemblage BR6R14 and located in same areas.


BR6R14fm Headwater first order alpine streams, depauperate form of assemblage BR6R14m and located in same areas.


BR6R14m Assemblages of alpine (>800 m) streams of the far south west between Macquarie Harbour and South Cape, including the southern part of the Gordon Cracroft and upper Huon River catchments.


BR7 Assemblage of upland drainages west of the Central Plateau - upper Forth, Mersey and Derwent catchments.

Indicator taxa: Cardioperla spinosa, Taschorema sp. AV1, Gordiidae, Alloecella longispina, Nousia sp. AV9 var A, Notalina sp. AV8, Conoesucus brontensis, Genus W sp. AV2, Taschorema sp. AV4

BR7f Headwater first order streams, depauperate form of assemblage BR7 and located in same areas.





BR7fm Headwater first order alpine streams, depauperate form of assemblage BR7m and located in same areas.

Indicator taxa: Cardioperla spinosa, Taschorema sp. AV1, Gordiidae, Alloecella longispina Nousia sp. AV9 var A, Notalina sp. AV8, Conoesucus brontensis, Genus W sp. AV2, Taschorema sp. AV4

BR7m Assemblages of alpine (>800 m) streams of upland drainages west of the Central Plateau - upper Forth, Mersey and Derwent catchments.


BCPL Assemblages characteristic of alpine streams in the Central Plateau above 800 m, including the upper drainage of the Ouse and Nive river systems.

Indicator taxa: Insufficiently known

BCPLfm Headwater first order streams within BCPL assemblage area. Indicator taxa: Insufficiently known

BC7FL Assemblages of streams on Flinders island, related to assemblage BC7 in composition when on similar geology.


BC7Flf Headwater first order streams, depauperate form of assemblage BC7FL and located in same areas.


Appendix 6 – Spatial data layers - Major drainage catchments


6.2.18 Major drainage catchments

Title CFEV Catchments

Custodian WRD, DPIW

Creator John Corbett, GIS Unit, ILS, DPIW

Description Catchment boundaries for Tasmania

Input data

CFEV River Section Catchments (RSCs) spatial data layer (Appendix 6.2.25)

Lineage

The major drainage catchments were created by aggregating RSCs until they resembled the original Land and Water Management Catchments commonly used by water managers and scientists within Tasmania. Some boundaries may vary slightly at a fine scale of viewing.

The RSCs were developed in conjunction with the CFEV river spatial data layer as part of an overall catchment model (see Appendices 6.2.24 and 6.2.25).

Data limitations

In cases where a catchment on mainland Tasmania is located adjacent to a large offshore island (e.g. Montagu and Robbins Island, Derwent and Bruny Island), the two areas may be spatially joined and/or joined by their attributes. For example, the catchment polygon for the Derwent Estuary-Bruny catchment consists of two regions – the area on mainland Tasmania flowing into the Derwent estuary, south of Bridgewater and Bruny Island. Spatially it exists as a multi-part polygon and has all attributes relating the two areas combined (e.g. total area stated is the sum of the two regions).

As per the CFEV RSCs spatial data layer (Appendix 6.2.25)



Other comments

This data layer is part of a nested set of catchments for Tasmania, which also includes the CFEV RSCs (Appendix 6.2.25) and the CFEV Sub-catchments (Appendix 6.2.27). These Catchment layer and the sub-catchment layer make up a nested set of catchments for Tasmania which are useful for reporting, but are not actually used in the CFEV database.

6.2.19 Mining sedimentation

Title Mining sedimentation

Custodian WRD, DPIW

Creator Shivaraj Gurung, WRD, DPIWE

Description Distribution of major river sedimentation due to past mining activities.

Input data

CFEV Acid drainage spatial data layer (Appendix 6.2.1)

Appendix 6 – Spatial data layers - Modified TASVEG


Tasmanian Acid Drainage Reconnaissance, MRT

Lineage

Many rivers downstream of historical mines sites are impacted by mine waste and tailings from past mining activities. The mining sedimentation data layer was developed using data from the Tasmanian Acid Drainage Reconnaissance Survey (Gurung, 2001). The acid drainage spatial data layer produced for the CFEV Project (refer to Appendix 6.2.1) was reviewed, along with historical information on downstream mining sedimentation, by John Pemberton (MRT) and Dr Shivaraj Gurung (DPIWE, formerly of MRT) to produce a historical river mining sedimentation layer. The extent to which significant mining sedimentation continues down a river was observed in the field or estimated depending on the structure of the rock type being discharged from the mining sites (finer materials travel further and more quickly than coarser sediment). The flow regime within the river affected was also taken into consideration.

The mining sedimentation spatial data was assigned to each of the river sections using the rules outlined in Appendix 6.3.32, and resulted in a layer depicting the presence/absence of mining sedimentation.

Data limitations

Except for the Queen and King rivers in western Tasmania, the downstream extent of mining sedimentation of river reaches impacted by historic mining is regarded as approximate. Mine sediments in the Queen and King rivers have been detected along the entire downstream reaches. For other impacted rivers, sediment transport has been estimated from flow regime and sediment types of the river reaches.



References (Gurung, 2001)

6.2.20 Modified TASVEG

Title Modified TASVEG

Custodian WRD, DPIW


Description Distribution of natural and exotic vegetation of Tasmania.

Input data

TASVEG data layer (Version 0.1 May 2004 (interim data set released prior to the release of TASVEG Version 1.0)), DPIW

Lineage

The development of various layers, such as native and exotic riparian vegetation layers, needed data that identified broad areas of natural and exotic vegetation rather than individual vegetation communities. As a result, a layer (termed the „modified TASVEG spatial data layer‟) was created from the TASVEG data layer (Version 0.1 May 2004), which merged all natural TASVEG codes into one group and all exotic TASVEG codes into another. Natural non-vegetation codes, such as water, rocks, etc. were assigned to the natural class whilst other non-vegetation codes such as built up areas were allocated to the exotic (sometimes termed „cultural‟) class. Appendix 12 presents all of the TASVEG vegetation communities and their assigned group (i.e. either natural or exotic).

Appendix 6 – Spatial data layers - Native fish assemblages


The modified TASVEG spatial data layer was used to assess the extent of both exotic and natural vegetation within the riparian (buffer) zone (see Appendix 6.2.2) of rivers, waterbodies and wetlands (Appendix 6.3.39), the lateral extent and width of the backing vegetation of saltmarshes (Appendices 6.3.26 and 6.3.60, respectively), the assessment of vegetation condition within wetlands (Appendix 6.3.59), and the condition of platypus (Appendix 0).

Data limitations

TASVEG is based on the interpretation of aerial photos, and cannot always distinguish between similar vegetation types.



References (DPIW, 2003; Harris and Kitchener, 2003)

6.2.21 Native fish assemblages

Title Native fish assemblages

Custodian WRD, DPIW


Description Distribution of native fish assemblages in Tasmania.

Input data

CFEV DEM (elevation and slope) (Appendix 6.2.6)

Fish distribution records, DPIWE (1997-2004)

Fish distribution records, Freshwater Systems (1997-2004)

Fish distribution records, Inland Fisheries Service (IFS) (1997-2004)

RFA Fish Database (version 1997), DPIW

Lineage

The state fish distribution database, first developed through the RFA process by Davies and Cook (Freshwater Systems, unpublished data) in 1997, was updated by adding more recently collected data sourced from DPIWE, IFS and Freshwater Systems. This revised data set was used by a panel of Tasmanian freshwater fish experts (see Appendix 1) to derive mapping rules to generate pre-European distribution maps for all the non-threatened native species (mapping rules provided below). These maps were combined (intersected) using GIS analysis and a regional native fish assemblage map was produced.

Mapping pre-European native fish species distributions

The native fish assemblage rules were primarily developed for all „common/widespread‟ species not listed under the Threatened Species Protection Act 1995 and included:

Anguilla australis (shortfin eel)

Anguilla reinhardtii (longfin eel)

Galaxias maculatus (common jollytail)

Galaxias truttaceus (mountain galaxias)

Galaxias brevipinnis (climbing galaxias)



Neochanna cleaveri (Tasmanian mudfish, used to be Galaxias cleaveri)

Galaxiella pusilla (dwarf galaxias)

Gadopsis marmoratus (blackfish)

Nannoperca australis (pygmy perch)

Prototroctes maraena (Australian grayling)

Pseudaphritis urvillii (sandy)

Retropinna tasmanica (Tasmanian smelt)

Lampreys (Mordacia mordax and Geotria australis) – separate rules could not be developed due to lack of data

Lovettia sealii (Tasmanian whitebait)

Note some exceptions where some additional assemblages were developed specifically for waterbodies which did include known, historical distributions of threatened species. These included:

Galaxias auratus (golden galaxias) (occurring in lakes Sorell and Crescent)

Galaxias johnstoni (Clarence galaxias) (occurring in Clarence and Wentworth lagoons)

Paragalaxias dissimilis (Shannon paragalaxias) (occurring in Great Lake, Lake Penstock and Shannon Lagoon)

Paragalaxias eleotroides (Great Lake paragalaxias) (occurring in Great Lake, Lake Penstock and Shannon Lagoon)

Paragalaxias julianus (western paragalaxias) (occurring in the Pillans and Julians lakes systems)

Paragalaxias mesotes (Arthurs paragalaxias) (occurring in Arthurs and Woods lakes)

The rules for the common species consisted of logical statements, occasionally combined with a map depicting a distributional line/polygon used in a particular rule, as follows:

Anguilla australis (shortfin eel)

Anguilla australis is distributed in rivers, waterbodies and wetlands, statewide with a high probability of occurrence at elevations <1000 m and a low probability of

occurrence at elevations 1000 m.



Anguilla reinhardtii (longfin eel)

Anguilla reinhardtii is only present at elevations <300 m, east of line 1 (Figure 24). Within this region, it has a high probability of occurrence north of line 2 and a low probability of occurrence south of line 2. For all other areas, (i.e. west of line 1) Anguilla reinhardtii is absent. A. reinhardtii occurs in rivers, waterbodies and wetlands.

Figure 24. Boundaries (lines 1 and 2) indicating variations in the probabilities of Anguilla reinhardtii occurrence.

Galaxias maculatus (common jollytail)

Galaxias maculatus occurs in rivers and wetland with a high probability of occurrence at elevations <100 m, low probability of occurrence at elevations between 100 and <250 m, and absent at elevations >250 m.



Galaxias truttaceus (spotted galaxias)

Galaxias truttaceus found in rivers, waterbodies and wetlands with a high probability of occurrence at elevations <400 m outside line 1 (Figure 25) or inside line 1 (lakes and wetlands only).

Figure 25. Boundary (line 1) indicating variation in the probabilities of Galaxias truttaceus occurrence.



Galaxias brevipinnis (climbing galaxias)

High probability of occurrence of Galaxias brevipinnis west of line 1 (Figure 26), low probability of occurrence east of line 1. G. brevipinnis occurs only in rivers at elevation <1000 m, and only in waterbodies and wetlands at elevations >1000 m.

Figure 26. Boundary (line 1) indicating variation in the probabilities of Galaxias brevipinnis occurrence.

Neochanna cleaveri (Tasmanian mudfish, formerly Galaxias cleaveri)

Neochanna cleaveri occurs in river and wetland with a high probability of occurrence at elevations <100 m. Else absent.



Galaxiella pusilla (dwarf galaxias)

Galaxiella pusilla occurs in wetlands only, with a high probability of occurrence at elevations <50 m and north of line 1 (Figure 27), but does not occur on King Island or other north-west islands except Hunter and Three Hummock. Else absent.

Figure 27. Boundary (line 1) indicating variation in the probabilities of Galaxiella pusilla and Gadopsis marmoratus occurrence.

Gadopsis marmoratus (blackfish)

Gadopsis marmoratus is present in rivers and has a high probability of occurrence north of line 1 (Figure 27) but absent on all Bass Strait islands and elevations <400 m. Else absent.



Nannoperca australis (pygmy perch)

Nannoperca australis occurs in rivers and wetlands with a high probability of occurrence north of line 1 (Figure 28), at elevations <400 m and with a slope <0.2%. Else absent.

Figure 28. Boundary (line 1) indicating variation in the probabilities of Nannoperca australis occurrence.

Prototroctes maraena (Australian grayling)

Prototroctes mareana is distributed only in rivers, statewide, with a high probability of occurrence at elevations <200 m. Else absent.

Pseudaphritis urvillii (sandy)

Pseudaphritis urvillii only occurs in rivers, statewide, with a high probability of occurrence at elevations <200 m. Else absent.

Retropinna tasmanica (Tasmanian smelt)

Retropinna tasmanica occurs only in rivers, statewide, with a high probability of occurrence at elevations <20 m. Else absent.



Lampreys (Mordacia mordax and Geotria australis) – separate rules could not be developed due to lack of data

Mordacia mordax and Geotria australis (as a collective group) is distributed only in rivers, statewide, with a high probability of occurrence at elevations <200 m. Else absent.

Lovettia sealii (Tasmanian whitebait)

Lovettia seallii is present in rivers with a high probability of occurrence west of line 1 and north of line 2 (Figure 29) and at elevations <10 m. Else absent. L. seallii does not occur on the Bass Strait islands.

Figure 29. Boundaries (lines 1 and 2) indicating variations in the probabilities of Lovettia sealii occurrence.



Mapping native fish assemblage distributions

Species distribution maps were combined to develop an overall native fish assemblage map. This process produced over 150 unique combinations of the 14 fish species, which included a large number of assemblages with very small areas. This set was evaluated for redundant or trivial „slivers‟, which were removed by being re-assigned to the adjacent downstream assemblage. This reduced the number of „true‟ assemblages to 55. A number of additional assemblages were identified for specific waterbodies containing species listed under the Tasmanian Threatened Species Protection Act 1995. The final native fish assemblage regional map is shown in Figure 30, illustrating the level of variation across Tasmania, particularly around the coastal regions. Descriptions of each of the assemblage classes are given in Table 21.

Figure 30. Map of native fish assemblage regional distributions for Tasmania (55 classes). Note that this map is provided for illustrative purposes - a legend is not provided due to the large number of assemblages and the small size of the graphic.



The fish assemblage data was assigned to each of the river sections and waterbodies using the rules outlined in Appendix 6.3.33.

Data limitations

Uncertainty in the native fish assemblage map occurred in two areas. A new assemblage was created for the South Esk basin and was manually assigned to the river sections in that catchment. The other uncertainty was the question of whether threatened species should be included in the assemblage map. A decision was made to omit threatened species from river drainages due to limited time and difficulty in adequately estimating habitat within rivers to be modelled. In addition, threatened species have been captured in the Special Values (SVs) assessment within the CFEV framework (see Appendix 12 of the main report and Appendix 0 of this document). Note, the exception of waterbody-dwelling threatened species which were included in assemblages created especially for waterbodies.





Table 21. Description of native fish assemblages used to classify rivers and waterbodies.

Class code

Description Species composition

F0 Fish absent or low probability of occurrence and/or at very low densities (note: headwater streams for rivers).

Fish absent or low probability of occurrence. Assemblage a reduced form of that found immediately downstream.

F1 Assemblage found in coastal streams and waterbodies within the Furneaux group.

Anguilla australis, Galaxias truttaceus, Geotria australis & Mordacia mordax, Prototroctes maraena, Neochanna cleaveri, Pseudaphritis urvillii, Anguilla reinhardtii, Galaxias maculatus, Retropinna tasmanica, Galaxiella pusilla

F2 Assemblage with a very small distribution; only located in a couple of river sections on Three Hummock Island.

Anguilla australis, Galaxias truttaceus, Geotria australis & Mordacia mordax, Prototroctes maraena, Neochanna cleaveri, Pseudaphritis urvillii, Galaxias brevipinnis, Retropinna tasmanica, Galaxiella pusilla

F3 Assemblage found in coastal streams and waterbodies in the north-eastern part of the state.

Anguilla australis, Galaxias truttaceus, Geotria australis & Mordacia mordax, Prototroctes maraena, Neochanna cleaveri, Pseudaphritis urvillii, Gadopsis marmoratus, Anguilla reinhardtii, Galaxias maculatus, Retropinna tasmanica, Galaxiella pusilla

F4 Assemblage found in coastal streams and waterbodies within the Furneaux group.

Anguilla australis, Galaxias truttaceus, Geotria australis & Mordacia mordax, Prototroctes maraena, Neochanna cleaveri, Pseudaphritis urvillii, Anguilla reinhardtii, Nannoperca australis, Galaxias maculatus, Retropinna tasmanica, Galaxiella pusilla

F5 An assemblage with a disjunct distribution including, streams flowing into estuaries on King Island, the Tamar River and many coastal streams and waterbodies in the south east of Tasmania.

Anguilla australis, Galaxias truttaceus, Geotria australis & Mordacia mordax, Prototroctes maraena, Neochanna cleaveri, Pseudaphritis urvillii, Retropinna tasmanica

F6 Assemblage associated with a group of river sections west of Lake Pedder and Lake Gordon, within the WHA.

Anguilla australis, Geotria australis & Mordacia mordax, Prototroctes maraena, Neochanna cleaveri, Galaxias brevipinnis

F7 Assemblage found in lowland stream sections and waterbodies within the Furneaux group and along the east coast, south of St Helens (Georges Bay).

Anguilla australis, Galaxias truttaceus, Geotria australis & Mordacia mordax, Prototroctes maraena, Neochanna cleaveri, Pseudaphritis urvillii, Anguilla reinhardtii, Galaxias maculatus, Retropinna tasmanica

F9 Assemblage associated with coastal streams and waterbodies in the north-east.

Anguilla australis, Galaxias truttaceus, Geotria australis & Mordacia mordax, Prototroctes maraena, Neochanna cleaveri, Pseudaphritis urvillii, Gadopsis marmoratus, Anguilla reinhardtii, Nannoperca australis, Galaxias maculatus, Retropinna tasmanica, Galaxiella pusilla

F10 Assemblage found in river sections and waterbodies within the Furneaux group.

Anguilla australis, Galaxias truttaceus, Geotria australis & Mordacia mordax, Prototroctes maraena, Neochanna cleaveri, Pseudaphritis urvillii, Anguilla reinhardtii, Galaxiella pusilla

F11 Assemblage found in river sections along the north-west coast of Tasmania.

Anguilla australis, Galaxias truttaceus, Geotria australis & Mordacia mordax, Prototroctes maraena, Neochanna cleaveri, Pseudaphritis urvillii, Galaxias brevipinnis, Gadopsis marmoratus, Galaxias maculatus, Retropinna tasmanica, Galaxiella pusilla



Class code


F12 Assemblage found in river sections and waterbodies along the north-east coast of Tasmania.

Anguilla australis, Galaxias truttaceus, Geotria australis & Mordacia mordax, Prototroctes maraena, Neochanna cleaveri, Pseudaphritis urvillii, Gadopsis marmoratus, Anguilla reinhardtii, Galaxias maculatus, Retropinna tasmanica

F13 Assemblage found in river sections and waterbodies along the north-east coast of Tasmania.

Anguilla australis, Galaxias truttaceus, Geotria australis & Mordacia mordax, Prototroctes maraena, Neochanna cleaveri, Pseudaphritis urvillii, Gadopsis marmoratus, Anguilla reinhardtii, Galaxias maculatus, Galaxiella pusilla

F14 Rivers and waterbodies in the Furneaux group and east coast near Scamander.

Anguilla australis, Galaxias truttaceus, Geotria australis & Mordacia mordax, Prototroctes maraena, Neochanna cleaveri, Pseudaphritis urvillii, Anguilla reinhardtii, Nannoperca australis, Galaxias maculatus, Retropinna tasmanica

F15 Assemblage found in north west coast rivers. Anguilla australis, Galaxias truttaceus, Geotria australis & Mordacia mordax, Prototroctes maraena, Neochanna cleaveri, Pseudaphritis urvillii, Galaxias brevipinnis, Gadopsis marmoratus, Nannoperca australis, Galaxias maculatus, Retropinna tasmanica, Galaxiella pusilla

F16 Assemblage distributed within coastal streams and waterbodies that extend along most of the west coast including King Island through to the western edge of the Derwent River.

Anguilla australis, Galaxias truttaceus, Geotria australis & Mordacia mordax, Prototroctes maraena, Neochanna cleaveri, Pseudaphritis urvillii, Galaxias brevipinnis, Galaxias maculatus, Retropinna tasmanica

F17 Assemblage located within inland river sections and waterbodies on Flinders Island and in the northern part of Cape Barren Island.

Anguilla australis, Galaxias truttaceus, Geotria australis & Mordacia mordax, Prototroctes maraena, Neochanna cleaveri, Pseudaphritis urvillii, Anguilla reinhardtii, Nannoperca australis, Galaxias maculatus, Galaxiella pusilla

F18 Assemblage found within river sections and waterbodies on King Island and river sections on Robbins Island.

Anguilla australis, Galaxias truttaceus, Geotria & Mordacia, Prototroctes maraena, Neochanna cleaveri, Pseudaphritis urvillii, Galaxias brevipinnis, Nannoperca australis, Galaxias maculatus, Retropinna tasmanica

F19 Assemblage with limited extent, found on only two river sections on Three Hummock Island.

Anguilla australis, Galaxias truttaceus, Geotria & Mordacia, Prototroctes maraena, Neochanna cleaveri, Pseudaphritis urvillii, Galaxias brevipinnis, Galaxias maculatus, Galaxiella pusilla

F20 Assemblage found within river sections along the north-west coast and down to and including the Arthur River.

Anguilla australis, Galaxias truttaceus, Geotria & Mordacia, Prototroctes maraena, Neochanna cleaveri, Pseudaphritis urvillii, Galaxias brevipinnis, Gadopsis marmoratus, Galaxias maculatus, Retropinna tasmanica

F22 Assemblage found within river sections along the north-east coast. Anguilla australis, Galaxias truttaceus, Geotria & Mordacia, Prototroctes maraena, Neochanna cleaveri, Pseudaphritis urvillii, Gadopsis marmoratus, Anguilla reinhardtii, Nannoperca australis, Galaxias maculatus, Galaxiella pusilla



Class code


F23 Assemblage found in coastal streams and waterbodies within the Furneaux groups and along the east coast extending south from St Helens (Georges Bay) to north of Bicheno.

Anguilla australis, Galaxias truttaceus, Geotria & Mordacia, Prototroctes maraena, Neochanna cleaveri, Pseudaphritis urvillii, Anguilla reinhardtii

F24 Assemblage found in coastal streams, with two separate distributions; around the Tamar River and along the south-east coast, extending from west of the Derwent River up to just north of Bicheno, including stream sections on the Tasman Peninsula and Maria Island.

Anguilla australis, Galaxias truttaceus, Geotria & Mordacia, Prototroctes maraena, Neochanna cleaveri, Pseudaphritis urvillii, Galaxias maculatus, Retropinna tasmanica, Galaxiella pusilla

F25 Assemblage with its distribution in river sections and waterbodies in the far north-west and a scattering of stream sections along the north coast.

Anguilla australis, Galaxias truttaceus, Geotria & Mordacia, Prototroctes maraena, Neochanna cleaveri, Pseudaphritis urvillii, Galaxias brevipinnis, Gadopsis marmoratus, Nannoperca australis, Galaxias maculatus, Galaxiella pusilla

F26 Scattering of streams and waterbodies on Flinders Island, Cape Barren Island and near Scamander.

Anguilla australis, Galaxias truttaceus, Geotria australis & Mordacia mordax, Neochanna cleaveri, Pseudaphritis urvillii, Anguilla reinhardtii, Galaxias maculatus.

F27 Inland river sections and waterbodies near the coast in the north-east region of the state, from east of the Tamar River to Georges Bay.

Anguilla australis, Galaxias truttaceus, Geotria & Mordacia, Prototroctes maraena, Neochanna cleaveri, Pseudaphritis urvillii, Gadopsis marmoratus, Anguilla reinhardtii

F28 Scattering of inland river sections in the north-east region of the state, from east of the Tamar River to Georges Bay.

Anguilla australis, Galaxias truttaceus, Geotria & Mordacia, Prototroctes maraena, Neochanna cleaveri, Pseudaphritis urvillii, Gadopsis marmoratus, Anguilla reinhardtii, Nannoperca australis, Galaxias maculatus

F29 Assemblage distributed within coastal streams and waterbodies that extend along most of the west coast including King Island through to the western edge of the Derwent River.

Anguilla australis, Galaxias truttaceus, Geotria & Mordacia, Prototroctes maraena, Neochanna cleaveri, Pseudaphritis urvillii, Galaxias brevipinnis, Galaxias maculatus.

F30 An assemblage found in a few upper stream sections, near the Tamar River.

Anguilla australis, Galaxias truttaceus, Geotria & Mordacia, Pseudaphritis urvillii, Galaxias maculatus.

F32 Assemblage found within river sections and waterbodies along the north-west coast and down to and including the Arthur River.

Anguilla australis, Galaxias truttaceus, Geotria & Mordacia, Prototroctes maraena, Neochanna cleaveri, Pseudaphritis urvillii, Galaxias brevipinnis, Gadopsis marmoratus

F33 Assemblage associated within inland river sections and waterbodies on King Island.

Anguilla australis, Galaxias truttaceus, Geotria & Mordacia, Prototroctes maraena, Neochanna cleaveri, Pseudaphritis urvillii, Galaxias brevipinnis, Nannoperca australis, Galaxias maculatus

F34 Scattering of inland river sections and waterbodies in the north of the state, west of the Tamar River.

Anguilla australis, Galaxias truttaceus, Geotria & Mordacia, Prototroctes maraena, Neochanna cleaveri, Pseudaphritis urvillii, Galaxias brevipinnis, Gadopsis marmoratus, Nannoperca australis, Galaxias maculatus



Class code


F35 Inland rivers sections in the north-east region of the state, from east of the Tamar River to Georges Bay.

Anguilla australis, Galaxias truttaceus, Geotria & Mordacia, Prototroctes maraena, Gadopsis marmoratus, Anguilla reinhardtii

F36 Assemblage distributed within inland streams and waterbodies extending along most of the west coast including King Island through to the western edge of the Derwent River.

Anguilla australis, Galaxias truttaceus, Geotria & Mordacia, Prototroctes maraena, Galaxias brevipinnis, Galaxias maculatus

F37 Inland stream sections on Flinders Island, Cape Barren Island and near Scamander.

Anguilla australis, Galaxias truttaceus, Geotria & Mordacia, Prototroctes maraena, Anguilla reinhardtii, Galaxias maculatus

F38 Scattering of inland river sections and waterbodies in the north of the state, west of the Tamar River.

Anguilla australis, Galaxias truttaceus, Geotria & Mordacia, Gadopsis marmoratus, Galaxias maculatus

F39 Assemblage found within inland river sections along the north-west coast and down to and including the Arthur River.

Anguilla australis, Galaxias truttaceus, Geotria & Mordacia, Prototroctes maraena, Galaxias brevipinnis, Gadopsis marmoratus, Galaxias maculatus

F40 Assemblage found in inland river sections, with two separate distributions; around the Tamar River and along the south-east coast, extending from west of the Derwent River up to just north of Bicheno, including stream sections on the Tasman Peninsula; also Craigbourne Dam and Lake Trevallyn.

Anguilla australis, Galaxias truttaceus, Geotria & Mordacia, Prototroctes maraena, Galaxias maculatus.

F41 An assemblage with a limited distribution, in a few scattered rivers sections, inland west of the Tamar River.

Anguilla australis, Galaxias truttaceus, Geotria & Mordacia, Prototroctes maraena, Nannoperca australis, Galaxias maculatus

F42 An assemblage with a limited distribution, in a few scattered rivers sections, inland east of the Tamar River.

Anguilla australis, Galaxias truttaceus, Gadopsis marmoratus, Anguilla reinhardtii

F43 Inland rivers sections and waterbodies in the central and north-western region of the state, west of the Tamar River.

Anguilla australis, Galaxias truttaceus, Galaxias brevipinnis, Gadopsis marmoratus

F44 An assemblage with a limited distribution, in a few scattered rivers sections, inland east of the Tamar River.

Anguilla australis, Galaxias truttaceus, Gadopsis marmoratus, Anguilla reinhardtii, Galaxias maculatus

F45 Inland rivers sections and waterbodies in the central and north-western region of the state, west of the Tamar River.

Anguilla australis, Galaxias truttaceus, Galaxias brevipinnis, Gadopsis marmoratus, Galaxias maculatus

F46 An assemblage within rivers sections inland east of the Tamar River.

Anguilla australis, Galaxias truttaceus, Gadopsis marmoratus

F47 An assemblage with a limited distribution, in a few scattered rivers sections, inland east and west of the Tamar River.

Anguilla australis, Galaxias truttaceus, Nannoperca australis



Class code


F48 An assemblage with a limited distribution, in a few scattered rivers sections, inland west of Scamander.

Anguilla australis, Galaxias truttaceus, Anguilla reinhardtii

F49 Extensive assemblage in river sections and waterbodies covering most of the western part of the state (west of Tyler corridor), including the southern part of King Island and also within a few river sections inland in the east.

Anguilla australis, Galaxias brevipinnis

F50 Assemblage mainly found in the western part of the state and through the centre, including the central plateau.

Anguilla australis, Galaxias truttaceus, Galaxias brevipinnis

F51 An assemblage with a limited distribution, in a few scattered rivers sections, inland west of Scamander.

Anguilla australis, Galaxias truttaceus, Anguilla reinhardtii, Galaxias maculatus

F52 An assemblage with a limited distribution, in a few scattered rivers sections, inland east and west of the Tamar River.

Anguilla australis, Galaxias truttaceus, Nannoperca australis, Galaxias maculatus

F53 Assemblage found in inland river sections, which has two separate distributions; around the Tamar River and along the south-east coast, extending from west of the Derwent River up to just north of Bicheno, including stream sections on the Tasman Peninsula.

Anguilla australis, Galaxias truttaceus

F54 Assemblage associated with a group of river sections west of Lake Gordon, within the WHA.

Anguilla australis, Galaxias truttaceus, Galaxias brevipinnis, Galaxias maculatus

F55 Assemblage where only the short-finned eel exists, with three separate distributions; east of Tamar, central plateau (west of Great Lake) and south-east Tasmania, extending across the Midlands area.

Anguilla australis

F56 Assemblage in all river sections within the South Esk basin. Anguilla australis, Gadopsis marmoratus, Nannoperca australis, Galaxias fontanus

F60 Great Lake, Penstock and Shannon Lagoons native fish assemblage.

Anguilla australis, Galaxias truttaceus, Galaxias brevipinnis, Paragalaxias dissimilis, Paragalaxias eleotroides

F61 Arthurs and Woods Lakes native fish assemblage. Anguilla australis, Galaxias truttaceus, Paragalaxias mesotes

F62 Lakes Crescent and Sorell native fish assemblage. Anguilla australis, Galaxias auratus

F63 Clarence and Wentworth Lagoons native fish assemblage. Anguilla australis, Galaxias johnstoni

F65 Pillans and Julians Lakes system native fish assemblage. Anguilla australis, Paragalaxias julianus

Appendix 6 – Spatial data layers - Platypus condition


6.2.22 Platypus condition

Title Platypus condition

Custodian WRD, DPIW


Description Distribution of the fungal disease, Mucormycosis, in platypus.

Input data


LIST 1:100 000 catchment map, DPIW

Mucor amphiborum distribution data (Obendorf et al., 1993; Munday et al., 1998)

Lineage

An index of platypus (Ornithorhynchus anatinus) population condition was developed based on the results of a population viability analysis conducted by Fox et al. (2004). The mapping rules for deriving the condition index were based directly on the outputs of this analysis, combined with considerations of the likely current effects of the fungus, Mucor amphiborum which causes the disease, Mucormycosis (Munday et al., 1998). Expert input and review was also provided by Sarah Munks (Forest Practices Authority, pers. comm.). Overall, platypus population condition is believed to be affected by a combination of land clearance in and adjacent to the riparian zone interacting with the degree of Mucor amphiborum infection.

A Mucormycosis distribution shapefile was generated primarily on results from Munday et al. (1998) and Obendorf (1993). A map showing the known current Mucormycosis disease distribution is presented in Figure 31. The GIS mapping rules for applying this distribution across a broader catchment scale are provided below.

Appendix 6 – Spatial data layers - Platypus condition


Figure 31. Mucormycosis distribution (Sources: Blue – Munday et al. (1998); Red – Obendorf (1993).


A catchment map, showing areas of Mucormycosis infestation, was developed using the following rules:

1. Overlay a simple (1:100 000) catchment map with both the Mucormycosis distribution data layer and the CFEV rivers spatial data layer.

2. All river sections within the 1:100 000 catchment boundaries where Mucormycosis infestation is present should be assigned as Mucormycosis infested (1), otherwise not infested (0).

Note exception to this rule: Arthurs Lake - only assign in-flowing rivers, not the downstream catchments.

A platypus condition score was then assigned to river sections using this Mucormycosis disease distribution map and riparian vegetation data. These rules are outlined in Appendix 0.

Data limitations

Assumes Mucormycosis is distributed across all river sections through entire catchment and not just the main stem as in Figure 31.

Appendix 6 – Spatial data layers - Riparian vegetation condition




References (Obendorf et al., 1993; Munday et al., 1998; Fox et al., 2004)

6.2.23 Riparian vegetation condition

Title Riparian vegetation condition (natural and/or exotic)

Custodian WRD, DPIW


Description Proportion of native and/or exotic vegetation present within the riparian zone of saltmarshes, rivers, waterbodies and wetlands

Input data

CFEV Buffer zone (saltmarshes, rivers, waterbodies and wetlands) spatial layer (Appendix 6.2.2)

CFEV Modified TASVEG spatial data layer (Appendix 6.2.20)

Lineage

A data layer presenting the proportions of natural* and exotic* vegetation within the riparian zone was developed by intersecting the CFEV modified TASVEG spatial data layer with the CFEV buffer zone spatial data layer. For rivers, the buffer zone is a 50 m strip either side of the river section, and for saltmarshes, waterbodies and wetlands it is 100 m around the spatial unit.

The percentage area of natural vegetation present within the buffer area was calculated and assigned to the data as a score ranging between 1 (100% native vegetation present or near-natural to natural condition) and 0 (0% native vegetation present or degraded condition).

The same method was used for identifying the extent of exotic (also termed „cultural‟) vegetation within waterbody riparian zones. The exotic riparian vegetation score also ranges between 1 (0% exotic vegetation present or near-natural to natural condition) and 0 (100% exotic vegetation present or degraded condition).

A riparian vegetation score was then assigned to river sections, waterbodies and wetlands as outlined in Appendix 6.3.39. The saltmarsh riparian vegetation spatial data was visually inspected in combination with aerial photographs (for assessing the lateral extent (Appendix 6.3.26) and width of backing vegetation (Appendix 6.3.60), rather than directly assigned to individual spatial units.

* The natural class included natural non-vegetation TASVEG codes, such as water, rocks, etc. and the exotic (cultural) class included unnatural non-vegetation codes such as built-up areas. Appendix 12 presents all of the TASVEG vegetation communities and their assigned group (i.e. either natural or exotic).

Appendix 6 – Spatial data layers - Rivers (drainage network)


Data limitations

As per the TASVEG data set.




6.2.24 Rivers (drainage network)

Title CFEV Rivers


Creator John Corbett, GIS Unit, ILS, DPIW LIST, ILS, DPIW

Description Drainage network for Tasmania

Input data

Hydro infrastructure and discharge data (location, volume), Hydro Tasmania

Hydro lake level data, Hydro Tasmania

LIST 1:25 000 base topographic data (10 m contours), DPIW

LIST 1:25 000 hydrographic theme (subsets: natural and dammed waterbodies, the mean high water mark (MHWM), natural water courses, artificial water courses, hydrographic connectors, estuary hydrographic closures), DPIW

LIST 25 m DEM (version II), DPIW

Lineage

The river spatial data layer (drainage network) was developed by updating the LIST 1:25 000 drainage network. The resultant data layer is a directed network of drainage, including provision for artificial drainage modifications such as major pipelines and canals.

The drainage network was developed, in several phases, in conjunction with the River Section Catchment spatial data layer (Appendix 6.2.25) as an overall catchment model:

1. Reduction of the base data

Much of the data from the base LIST layers was surplus to the requirements of a drainage model and was therefore discarded. In particular, only the information relating to water catchment and flow was retained.

Using the data from the hydrographic model, individual layers representing lakes, water courses, pipes, the coastline and estuary closures were developed.

The CFEV drainage model distinguishes two different sorts of water conduit:

1. water courses

2. pipes

Water courses represent the surface flow of water, including both natural water courses (rivers and creeks), and artificially altered water courses which collect

Appendix 6 – Spatial data layers - Rivers (drainage network)


drainage from the surrounding catchment area. Pipes on the other hand, do not collect surface drainage from the surrounding catchment area and only allow water to enter and exit from endpoints. Pipes may cross over water courses without interaction and may also divert flow uphill in the case of pumping stations, and sometimes underground as well. The division of water courses into these two types was determined by a combination of LIST hydrographic attributes and infrastructure data supplied by Hydro Tasmania.

The reduction phase also removed many short sections of disconnected drainage, mostly representing areas in which surface drainage was ill-defined or unreliable.

Lakes (waterbodies) were given an elevation attribute for inclusion into the model based on either Hydro Tasmania lake level data, the LIST mapping attributes, or derived by interpolation from the DEM. Lakes smaller than 1 ha in mapped surface area were not included in the drainage model.

2. Automated consistency checking and correction of the base data

Tools were developed to automatically test and flag inconsistencies in the contour, water course, pipe, coastline and estuary closure layers. These tests included the following:

contour closure and intersection tests

adjacent contour elevation tests

contour sink identification

contour, lake, coastline and estuary closure intersection tests

lake elevation tests

water course direction with respect to contour crossing tests

tests for multiple intersections of a water course with the same contour elevation

flagging of downward water course forks and water course sinks

After running these tests, manual alterations were made to the base layers to remove many of the inconsistencies detected and river sections (sections of drainage between confluences) were connected where appropriate to ensure the drainage was flowing in the correct direction. Manual editing of attributes added connectors for underground pipes and artificial drainage.

All sections of differing types were connected and dissolved into one river section. Long river sections were split until they reached a maximum of 3 km as it was considered difficult to adequately assess large sections due to the wide variation in values along a long section. All disconnected drainage length, totalling less than 3 km and not connected to the coast, were removed from the drainage layer.

The river sections were assigned one of three classes:

watercourse

pipes (RS_PIPE)

waterbodies (drainage sections within waterbodies to ensure continuity between river sections flowing in and out of waterbodies) (RS_WBODY)

A unique flow path was determined for the drainage network and associated RSCs from a surface model which was developed to create the catchment data layer. The methodology is described in Step 2 of the rules outlined in Appendix 6.2.25.

Two versions of the drainage network exist in terms of flow connectivity:

Appendix 6 – Spatial data layers - River Section Catchments


Natural (water never flows down a pipe).

Current (the direction of water flow takes the predominant flow regime path and can include artificial pipes).

Both layers are identical to visualise, however have different flow regime attributes (described in Appendix 6.3.40). The spatial units (river sections) within the „current‟ drainage network (i.e. excluding artificial pipes and canals) were the assessment units for rivers, whilst the „natural‟ drainage network was primarily used in the calculation of the Mean Annual Run-off (MAR) (refer to Appendix 0).

Data limitations

Limitations to the drainage network are that although it is linked by attributes, it is not fully linked spatially. Small unconnected sections have been removed and the sink-draining algorithm has taken a „best guess‟ approach when deciding which path water takes.


Scale and coverage 1:25 000; Statewide (excluding Macquarie Island)

Other comments

This data layer is spatially the same as the LIST 1:25 000 drainage network but the attributes are unique to CFEV. The attribute that relates this data set with the LIST drainage network is „UFI‟.

6.2.25 River Section Catchments

Title CFEV River Section Catchments (RSCs)

Custodian WRD, DPIW


Description Catchment boundaries for all individual CFEV river sections, waterbodies and estuaries of Tasmania

Input data



LIST 1:25 000, 10 m contours, DPIW

LIST 1:25 000 coastline (high watermark), DPIW

LIST 1:25 000 hydrographic theme (subsets: natural and dammed waterbodies, the mean high water mark (MHWM), natural water courses, artificial water courses, hydrographic connectors, estuary hydrographic closures), DPIW

Lineage

The RSC spatial data layer was developed in several phases, in conjunction with the rivers spatial data layer (Appendix 6.2.24) as an overall catchment model (see details below). This data layer is a highly detailed sub-catchment subdivision of the state, corresponding to internodal reaches (river sections) of the drainage network, with linkage information appropriate for larger scale aggregation of catchments.



1. Building a triangulated interlaced network (TIN) and catchment polygons

Using specially developed software, Landscape Mapper, all of the information from the contour, water course, pipe, lake, coastline and estuary closure layers were meshed into a single three-dimensional data structure and the coordinates triangulated to form a continuous meshed surface model of the state (a TIN). Coordinate elevations for contour and lake coordinates came from the original layer attributes. The coastline and estuary closure coordinates were given an elevation of 0 m. Water course coordinate elevations were interpolated between contour intersections, ensuring wherever possible that all water courses ran downhill. Since pipes do not necessarily follow the surface of the earth, they were given arbitrary elevations in the model.

Additional ridge and valley breakline coordinates were added to ensure that the implied water flow from the surface model coincided with the flow implied by the rivers spatial data layer and to interpolate elevations in areas with little relief information. This included a process of draining surface sink areas wherever possible.

With a drainage-consistent surface model, catchment divider lines were automatically calculated so that the surface would be partitioned into catchment regions corresponding with catchment seed points. In particular, a catchment polygon was created for each internodal stream reach (river section), for each lake (waterbody) (corresponding exactly to the lake polygon itself), for each surface sink, for each estuary (as defined by the coastline and estuary closures), and for all remaining areas (consisting of areas draining directly to the coast or to a lake edge). The resulting RSC data layer contained 476 857 catchment regions.

2. Accumulating drainage downstream to the ocean

To chart the course of water from its initial raindrop on the model surface to the ocean, a unique flow path was required. In many cases this flow path was ambiguous, due to multiple possible drainage routes. Without a unique flow path for each point on the model surface, the concept of a catchment area is ill-defined. To get around this problem, all but one possible drainage route was ignored during the flow calculation.

To determine which path was to be the „primary flow path‟, a combination of manual and automatic methods was used. In areas regulated by Hydro Tasmania control (i.e. pipes and other Hydro infrastructure), an „ideal flow path‟ was implied on the drainage, representing the most common or dominant flow over the course of a typical year. In all other areas, a flow path was automatically selected according to the following rules:

If the drainage network connects to the ocean then select the shortest possible route to the ocean as the „ideal flow path‟.

If the drainage network is disconnected from the ocean then select the shortest possible route to the drainage sink which is the furthest distance away as the „ideal flow path‟.

The „ideal flow path‟ is represented in the rivers spatial data layer by an attribute linking each river section to the next river section downstream along the „ideal flow path‟ (see Appendix 6.3.40).

Flow paths from the drainage network were transferred to the corresponding catchments in this RSC spatial data layer, allowing a flow network to be created. However, there were still places in which the flow of water to the ocean was not well



defined. These instances were represented as sinks in the catchment flow network and had numerous causes. Some of the possible causes were:

disconnected water courses due to ill-defined drainage or mapping error

unmapped underground flow such as in karst areas or scree

dissipated seepage through to the water table

water evaporation from lakes and wet areas

In such cases there was no clear method for determining the flow of water from catchment to catchment. The solution used was to progressively fill each sink catchment with water until there was overflow into a neighbouring lower catchment area. The outflow from the sink catchment was then redirected to this new catchment, thus ensuring that all water would eventually drain to the ocean. This connectivity was also mapped back to the drainage layer so that drainage sinks would be „virtually connected‟ to an area of a downstream drainage network.

Similar to the drainage network (Appendix 6.2.24), two versions of the catchment layer exists in terms of connectivity (Natural – never flows down pipes and Current – flow takes predominant flow path including pipes).

For many of the condition assessment variables, local catchment and upstream catchment scores were calculated and combined to give an overall condition score. Scores were calculated for each of the RSCs in this data layer. The local RSC is an individual polygon in this layer and the upstream catchment includes all RSCs upstream of it (see Figure 32 for an illustration). Scores for the local and upstream catchments were often combined to give an overall condition score and then assigned to the river sections.

Appendix 6 – Spatial data layers - Saltmarshes


Figure 32. Illustration of catchments showing the local and upstream (consisting of multiple) RSCs. Dotted lines show RSC boundaries.

Data limitations

Limitations of this data layer are closely associated with those of the CFEV rivers spatial data set. The drainage network is linked by attributes but is not fully linked spatially. Small unconnected sections have been removed. The sink-draining algorithm has taken a „best guess‟ approach when deciding which path water takes.


Scale and coverage 1:25 000; Statewide (excluding Macquarie Island)

Other comments

The RSCs were aggregated to develop the CFEV sub-catchment and catchment data layers (refer Appendices 6.2.27 and 6.2.18, respectively). These data layers make up a nested set of catchments for Tasmania.

6.2.26 Saltmarshes

Title CFEV Saltmarshes

Custodian WRD, DPIW


Appendix 6 – Spatial data layers - Sub-catchments


Description Saltmarshes of Tasmania

Input data

TASVEG data layer (Version 0.1 May 2004 (interim data set released prior to the release of TASVEG Version 1.0), DPIW

Lineage

A saltmarsh spatial data layer was developed using the TASVEG data layer. TASVEG codes of Ms (succulent saltmarsh), Mg (graminoid saltmarsh) and Ma (generic saltmarsh) were selected from the vegetation data layer to identify all areas of saltmarsh in Tasmania. Many of the saltmarsh areas were mapped as fragments, therefore, manual clustering was undertaken to group neighbouring polygons. The polygons were subsequently dissolved into multi-part polygons, enabling them to be treated as one polygon. Inland saltmarshes were removed, as these were included as salt lakes within the CFEV waterbodies layer.

The vegetation types from TASVEG were assigned to each of the saltmarsh spatial units using the rules outlined in Appendix 6.3.43).

Data limitations

It is considered that the current geographical extent of saltmarshes across Tasmania is far less than existed pre-European settlement. It is therefore likely, that this mapped layer of saltmarshes contains a fragmented representation of the extent of their pre-European settlement distribution.


Date created June 2004



6.2.27 Sub-catchments

Title CFEV Sub-catchments

Custodian WRD, DPIW


Description Sub-catchment boundaries for Tasmania

Input data

CFEV RSCs spatial data layer (Appendix 6.2.25)

Lineage

The CFEV sub-catchment data layer was generated by amalgamating RSCs. This data provides an intermediate scale of catchment boundaries (between RSCs and the major drainage catchments (Appendix 6.2.18)) which was thought to be a more useful scale for management purposes.

Appendix 6 – Spatial data layers - Tidal/wave energy regime


A technique for catchment aggregation was developed to allow different scale catchment regions to be created. Catchments are accumulated downstream until a suitable region is formed, at which stage the process starts again. Methods for terminating catchment regions can be based on:

minimum or maximum size requirements

stream order changes

explicit terminal locations

RSCs were aggregated to create sub-catchments to a maximum size of 40 000 ha with the exception of larger sub-catchments created to be able to include large waterbodies (i.e. Great Lake, Lake Gordon and Lake Pedder).

Data limitations




Other comments

An aggregation of the RSC was also undertaken to develop an even coarser scale catchment layer – the CFEV Catchments (refer Appendix 6.2.18). These two data layers and the sub-catchment layer make up a nested set of catchments for Tasmania which are useful for reporting, but are not actually used in the CFEV database.

6.2.28 Tidal/wave energy regime

Title Tidal/wave energy regime

Custodian WRD, DPIW


Description Zones along the Tasmanian coastline representing variations in tidal range and wave energies.

Input data

Tidal data, Tasmanian Aquaculture and Fisheries Institute (TAFI) (Edgar et al., 1999a)

Wave energy regions, TAFI (derived from Edgar et al. (1999a))

Lineage

A map of tidal ranges consisting of three regions (small, medium and large range) was derived following analysis of tidal range data in Edgar et al. (1999a). Small tidal range regions occurred along both the western and eastern coastlines of Tasmania. These two regions have very different wave energies, and this class was split further to reflect this difference (Figure 33). A description of the four resulting tidal range/wave energy classes is given in Table 22. The tidal/wave energy classes were assigned to the saltmarsh spatial units using the rules outlined in Appendix 6.3.48).

Appendix 6 – Spatial data layers - Tidal/wave energy regime


Figure 33. Tidal/wave energy regions.

Table 22. Description of tidal range/wave energy classes.

Class Location Description

1 North Large tides, moderate wave energy

2 East (including Furneaux Group) Small tides, low wave energy

3 South-east Intermediate tides, low wave energy

4 South and West (including King Island) Small tides, high wave energy

Data limitations

Hand-assigned boundaries (see Lineage).

Date created May 2004


References (Edgar et al., 1999a)

Appendix 6 – Spatial data layers - Tree assemblages


6.2.29 Tree assemblages

Title Tree assemblages

Custodian WRD, DPIW

Creator David Peters, GIS Unit, ILS, DPIW

Description Distribution of tree assemblages in Tasmania.

Input data

GTSpot database, DPIWE

Lineage

The available data for riparian vegetation did not show an adequate statewide distribution, therefore a modelled pre-European tree assemblage data set was used to provide a vegetation context within the landscape. This data layer was first developed as part of the Tasmanian component of the Interim Biogeographic Regionalisation for Australia (IBRA).

Tree assemblages were used because parts of Tasmania have been cleared of vegetation or inundated. The nature of the vegetation in such areas is not known with certainty and as yet there is no comprehensive vegetation reconstruction map for Tasmania. Each of the tree assemblage models represents a series of co-occurring environments which support a particular set of tree species. Thus not all species within an assemblage necessarily co-occur in any particular stand or patch of vegetation.

The methods used to compile the set of tree assemblages are outlined in Peters and Thackway (1998). A subset of the 78 tree species from Kirkpatrick and Backhouse (1997), were used in the analysis. Records of occurrence of each species were gleaned from herbarium sheets, field surveys and published records and compiled into DPIWE‟s GTSpot database (DPIWE, 2003). A model of distribution for each species at a resolution of 1 km x 1 km was constructed using a set of physical environmental factors of climate, topography, geology and soils. The modelling program used was CORTEX (Peters and Thackway, 1998). For each species, a „naive‟ model was constructed initially based on the above environmental parameters. An expert group of ecologists then vetted each of these models to reject, add to or edit them according to personal knowledge of the species ecology and distribution. This step was necessary because many species have distributions that reflect their evolutionary and ecological histories as well as present environmental conditions. The edited models were then used to retrain the CORTEX modelling process to produce the final models (Peters and Thackway, 1998).

The 78 accepted tree species models were then combined in a Principal Components Analysis (PCA). The outputs of the PCA were used to calculate Euclidian distances and to aggregate the models into a set of 50 species groups or tree assemblages (Peters and Thackway, 1998). This scale of resolution was tested subsequently by classifying the tree assemblages using UPGMA (available in McCune (1999)) to produce a dendrogram to assess the degree of similarity among the groups. Inspection of the dendrogram and consideration of the ecological provinces defined by the groups was undertaken to ensure the level of resolution was appropriate for the CFEV objectives.

Descriptions for each of the 50 tree assemblage classes are outlined in Table 23. A single class was assigned to river sections, waterbodies and wetlands using the rules outlined in Appendix 6.3.49).



Data limitations

The tree assemblage data inherits all the data limitations of the derivation processes and input data. Errors associated with the CORTEX modelling are unmeasured.



References (Kirkpatrick and Backhouse, 1997; Peters and Thackway, 1998)



Table 23. Description of tree assemblage classes.

Class code

Tree assemblage description Species present

T1 King Island vegetation. Mostly cleared land, but with coastal and lowland heaths, scrubs and tea tree/paper bark swamps; strips of coastal grassland and saltmarsh and some remnant wet eucalypt forest. Predominantly found on King Island, but with small areas found also on the coast of the far north-western Tasmanian mainland.

Eucalyptus obliqua, Leptospermum scoparium var., Melaleuca ericifolia, Melaleuca squarrosa, Monotoca glauca, Zieria arborescens

T2 Furneaux group native vegetation. A catena of coastal and lowland heaths, scrubs and tea tree/paper bark swamps; strips of coastal grassland and saltmarsh with eucalypt and casuarina dry forests and some areas of cleared land. Confined to the Furneaux group of islands.

Allocasuarina littoralis, Allocasuarina verticillata, Banksia marginata Bursaria spinosa, Casuarina monilifera, Eucalyptus amygdalina, Eucalyptus globulus subsp., Eucalyptus nitida, Eucalyptus ovata, Eucalyptus viminalis, Exocarpos cupressiformis, Leptospermum glaucescens, Leptospermum laevigatum, Leptospermum scoparium var., Melaleuca ericifolia, Melaleuca squarrosa Pomaderris elliptica

T3 Coastal mix of scrub, swamp forest and dry forest on dunes confined to north-western Tasmania.

Acacia melanoxylon, Banksia marginata, Casuarina monilifera, Eucalyptus brookeriana, Eucalyptus nitida, Eucalyptus obliqua, Leptospermum glaucescens, Leptospermum laevigatum, Leptospermum lanigerum, Leptospermum scoparium var., Melaleuca ericifolia, Melaleuca squarrosa, Pomaderris apetala, Zieria arborescens

T4 Coastal dry forests and wet scrub mosaics of eastern and north-eastern Tasmania.

Acacia dealbata, Acacia mearnsii, Acacia melanoxylon, Allocasuarina littoralis, Allocasuarina verticillata, Banksia marginata, Bursaria spinosa, Casuarina monilifera, Dodonaea viscosa, Eucalyptus amygdalina, Eucalyptus globulus subsp., Eucalyptus ovata, Eucalyptus viminalis, Exocarpos cupressiformis, Leptospermum glaucescens, Leptospermum laevigatum, Leptospermum scoparium var., Melaleuca ericifolia, Melaleuca squarrosa, Pomaderris elliptica, Pomaderris pilifera

T5 Dry forests wet sclerophyll and scrub mosaics found on the Furneaux Group of islands, the sand plains of the far north-eastern Tasmanian mainland and along the coastal strip from Forth to Burnie.

Acacia dealbata, Acacia melanoxylon, Allocasuarina littoralis, Allocasuarina verticillata, Banksia marginata, Bursaria spinosa, Casuarina monilifera, Eucalyptus amygdalina, Eucalyptus obliqua, Eucalyptus ovata, Eucalyptus viminalis, Exocarpos cupressiformis, Leptospermum glaucescens, Leptospermum scoparium var., Melaleuca ericifolia, Melaleuca squarrosa, Pomaderris elliptica, Pomaderris pilifera, Zieria arborescens

T6 Wet sclerophyll, implicate rainforest and swamp forest mosaics in north-western Tasmania.

Acacia melanoxylon, Anodopetalum biglandulosum, Atherosperma moschatum, Cenarrhenes nitida, Eucalyptus nitida, Eucalyptus obliqua, Eucryphia lucida, Leptospermum glaucescens, Melaleuca squarrosa, Monotoca glauca, Nothofagus cunninghamii, Phebalium squameum, Phyllocladus aspleniifolius, Pittosporum bicolor, Tasmannia lanceolata, Zieria arborescens



Class code


T7 North-western blackwood, tea-tree and paper bark swamp forests with associated eucalypt forests having rainforest and wet sclerophyll understoreys.

Acacia melanoxylon, Anodopetalum biglandulosum Atherosperma moschatum, Banksia marginata, Cenarrhenes nitida, Eucalyptus brookeriana, Eucalyptus nitida, Eucalyptus obliqua, Eucryphia lucida, Leptospermum glaucescens, Leptospermum lanigerum, Leptospermum scoparium var., Melaleuca ericifolia, Melaleuca squarrosa, Monotoca glauca, Nothofagus cunninghamii, Phebalium squameum, Phyllocladus aspleniifolius, Pittosporum bicolor, Pomaderris apetala, Tasmannia lanceolata, Zieria arborescens

T8 Grid squares containing dry sclerophyll forests, tall wet eucalypt forests and scrubs. This assemblage has two disjunct occurrences, being found in the lowland hinterlands of north-eastern Tasmania and on the drier hill slopes in the Huon valley.

Acacia dealbata, Acacia mearnsii, Acacia melanoxylon, Allocasuarina littoralis, Allocasuarina verticillata, Banksia marginata, Bursaria spinosa, Eucalyptus amygdalina, Eucalyptus obliqua, Eucalyptus ovata, Eucalyptus regnans, Eucalyptus viminalis, Exocarpos cupressiformis, Leptospermum lanigerum, Leptospermum scoparium var., Melaleuca ericifolia, Melaleuca squarrosa, Monotoca glauca, Notelaea ligustrina, Olearia argophylla, Pittosporum bicolor, Pomaderris apetala, Pomaderris elliptica, Pomaderris pilifera, Zieria arborescens

T9 Dry forests and damp sclerophyll forests around Port Sorell and in coastal north-eastern Tasmania.

Acacia dealbata, Acacia mearnsii, Acacia melanoxylon, Allocasuarina littoralis, Allocasuarina verticillata, Banksia marginata, Bursaria spinosa, Casuarina monilifera, Eucalyptus amygdalina, Eucalyptus obliqua, Eucalyptus ovata, Eucalyptus viminalis, Exocarpos cupressiformis, Leptospermum scoparium var., Melaleuca ericifolia, Melaleuca squarrosa, Monotoca glauca, Pomaderris elliptica, Pomaderris pilifera

T10 Rainforests and Eucalyptus delegatensis wet eucalypt forests of north-western Tasmania.

Acacia melanoxylon, Anodopetalum biglandulosum, Atherosperma moschatum, Cenarrhenes nitida, Eucalyptus delegatensis, Eucalyptus nitida, Eucryphia lucida, Monotoca glauca, Nothofagus cunninghamii, Olearia argophylla, Phebalium squameum, Phyllocladus aspleniifolius, Pittosporum bicolor, Tasmannia lanceolata, Zieria arborescens

T11 North-western ash forests mosaics with rainforest and wet sclerophyll understoreys.

Acacia dealbata, Acacia melanoxylon, Anodopetalum biglandulosum, Atherosperma moschatum, Cenarrhenes nitida, Eucalyptus delegatensis, Eucalyptus nitida, Eucalyptus obliqua, Eucalyptus regnans, Eucryphia lucida, Monotoca glauca, Notelaea ligustrina, Nothofagus cunninghamii, Olearia argophylla, Phebalium squameum, Phyllocladus aspleniifolius, Pittosporum bicolor, Pomaderris apetala, Tasmannia lanceolata, Zieria arborescens

T12 Grid cells containing mosaics of wet sclerophyll, damp sclerophyll and dry sclerophyll in northern and north-eastern Tasmania.

Acacia dealbata, Acacia melanoxylon, Allocasuarina littoralis, Banksia marginata, Bursaria spinosa, Eucalyptus amygdalina, Eucalyptus obliqua, Eucalyptus ovata, Eucalyptus regnans, Eucalyptus viminalis, Exocarpos cupressiformis, Leptospermum scoparium var., Melaleuca squarrosa, Notelaea ligustrina, Olearia argophylla, Pittosporum bicolor, Pomaderris apetala, Pomaderris elliptica, Pomaderris pilifera, Zieria arborescens

T13 Similar to tree assemblage T12, but in drier situations with Allocasuarina verticillata and Acacia mearnsii present. Found in East Tamar, Fingal valley and an outlying patch in the upper Derwent valley.

Acacia dealbata, Acacia mearnsii, Acacia melanoxylon, Allocasuarina littoralis, Allocasuarina verticillata, Banksia marginata, Bursaria spinosa, Eucalyptus amygdalina, Eucalyptus obliqua, Eucalyptus ovata, Eucalyptus regnans, Eucalyptus viminalis, Exocarpos cupressiformis, Leptospermum scoparium var., Notelaea ligustrina, Olearia argophylla, Pittosporum bicolor, Pomaderris apetala, Pomaderris elliptica, Pomaderris pilifera, Zieria arborescens



Class code


T14 Dry sclerophyll and damp sclerophyll with tea tree and paperbark scrub mosaics found around Port Sorell, the Tamar valley and extending through the north-east to Rushy Lagoon – Ansons Bay.

Acacia dealbata, Acacia mearnsii, Acacia melanoxylon, Allocasuarina littoralis, Allocasuarina verticillata, Banksia marginata, Bursaria spinosa, Casuarina monilifera, Dodonaea viscosa, Eucalyptus amygdalina, Eucalyptus obliqua, Eucalyptus ovata, Eucalyptus viminalis, Exocarpos cupressiformis, Leptospermum lanigerum, Leptospermum scoparium var., Melaleuca ericifolia, Melaleuca squarrosa, Pomaderris elliptica, Pomaderris pilifera, Zieria arborescens

T15 Wet sclerophyll, mixed forest and rainforest associations in the North-east highlands and southern central Tasmania. Similar to T23 and T26.

Acacia dealbata, Acacia melanoxylon, Atherosperma moschatum, Eucalyptus delegatensis, Eucalyptus obliqua, Eucalyptus regnans, Leptospermum lanigerum, Monotoca glauca, Notelaea ligustrina, Nothofagus cunninghamii, Olearia argophylla, Phebalium squameum, Phyllocladus aspleniifolius, Pittosporum bicolor, Pomaderris apetala, Tasmannia lanceolata, Zieria arborescens

T16 Rainforest, scrub and moorland mosaics on the coast and hinterland north of Macquarie Harbour to the Arthur river, and with outlying patches in the south west around the Ironbound Range.

Acacia melanoxylon, Anodopetalum biglandulosum, Atherosperma moschatum, Cenarrhenes nitida, Eucalyptus nitida, Eucalyptus obliqua, Eucryphia lucida, Leptospermum glaucescens, Leptospermum nitidum, Leptospermum scoparium eximium, Leptospermum scoparium var., Melaleuca squarrosa, Monotoca glauca, Nothofagus cunninghamii, Phebalium squameum, Phyllocladus aspleniifolius, Pittosporum bicolor, Tasmannia lanceolata, Zieria arborescens

T17 Upland mosaics of moorland, scrub and wet eucalypt forests dominated by E. delegatensis on the eastern side

of Lakes Gordon and Pedder and more extensively in inland north-western Tasmania.

Acacia melanoxylon, Anodopetalum biglandulosum, Atherosperma moschatum, Cenarrhenes nitida, Eucalyptus delegatensis, Eucalyptus nitida, Eucryphia lucida, Leptospermum nitidum, Leptospermum scoparium eximium, Monotoca glauca, Nothofagus cunninghamii, Olearia argophylla, Phebalium squameum, Phyllocladus aspleniifolius, Pittosporum bicolor, Tasmannia lanceolata, Zieria arborescens

T18 Damp sclerophyll, wet sclerophyll and mixed forests in two large disjunct patches in north-west Tasmania.

Acacia dealbata, Acacia melanoxylon, Atherosperma moschatum, Banksia marginata Bursaria spinosa, Eucalyptus amygdalina, Eucalyptus obliqua, Eucalyptus ovata, Eucalyptus regnans, Eucalyptus viminalis, Exocarpos cupressiformis, Melaleuca squarros,a Monotoca glauca, Notelaea ligustrina, Nothofagus cunninghamii Olearia argophylla, Pittosporum bicolor, Pomaderris apetala, Pomaderris elliptica, Zieria arborescens

T19 A mosaic of damp sclerophyll, wet eucalypt forest and rainforest extending from coastal north-western Tasmania, through Quamby to the north-eastern highlands in the north in the mid reaches of the Derwent and the lower Huon River in the south. Similar tree composition to assemblage T18.

Acacia dealbata, Acacia melanoxylon, Allocasuarina littoralis, Atherosperma moschatum, Banksia marginata, Bursaria spinosa, Eucalyptus amygdalina, Eucalyptus obliqua, Eucalyptus ovata Eucalyptus regnans, Eucalyptus viminalis, Exocarpos cupressiformis, Leptospermum lanigerum, Leptospermum scoparium var., Melaleuca squarrosa, Monotoca glauca, Notelaea ligustrina, Nothofagus cunninghamii, Olearia argophylla, Phebalium squameum, Phyllocladus aspleniifolius, Pittosporum bicolor, Pomaderris apetala, Pomaderris elliptica, Pomaderris pilifera, Tasmannia lanceolata, Zieria arborescens



Class code


T20 Northern midlands dry sclerophyll vegetation. This assemblage occupies the northern part of the midlands graben south of the Tamar River. It is extensively cleared and is characterised by a relatively low tree diversity, perhaps reflecting the essentially remnant nature of the remaining native vegetation.

Acacia dealbata, Bursaria spinosa, Eucalyptus amygdalina, Eucalyptus ovata, Eucalyptus viminalis, Exocarpos cupressiformis

T21 Western lowland rainforest with tea tree and Huon pine, scrub and moorland mosaic. Forest communities are similar to T38, T43 and T50. Occurs mainly between the Arthur and Pieman Rivers, but with local occurrences at the southern end of the Serpentine impoundment and on the Old River system.

Acacia melanoxylon, Anodopetalum biglandulosum, Atherosperma moschatum, Cenarrhenes nitida, Eucalyptus nitida, Eucryphia lucida, Lagarostrobos franklinii, Leptospermum glaucescens, Leptospermum nitidum, Leptospermum scoparium eximium, Leptospermum scoparium var., Melaleuca squarrosa, Monotoca glauca, Nothofagus cunninghamii, Olearia argophylla, Phebalium squameum, Phyllocladus aspleniifolius, Pittosporum bicolor, Richea pandanifolia, Tasmannia lanceolata, Zieria arborescens

T22 This assemblage consists of a group of heterogeneous sites that are characterised by the presence of only two species. The heterogeneity arises because some sites having no tree species recorded as present are classified into this group. Those in alpine areas or subalpine cliffs and scree slopes should be amalgamated with adjacent alpine vegetation and those in the lowlands with other appropriate tree assemblages.

Nothofagus cunninghamii, Tasmannia lanceolata

T23 Scree slope wet sclerophyll and rainforests. This assemblage is found on the dolerite screes of the Central Plateau, the eastern highlands and the southern forests, including Bruny Island and Tasman Peninsula.

Acacia dealbata, Acacia melanoxylon, Atherosperma moschatum, Beyeria viscosa, Eucalyptus amygdalina, Eucalyptus delegatensis, Eucalyptus obliqua, Eucalyptus regnans, Eucalyptus rubida, Eucalyptus viminalis, Exocarpos cupressiformis, Leptospermum lanigerum, Notelaea ligustrina, Nothofagus cunninghamii, Olearia argophylla, Phebalium squameum, Phyllocladus aspleniifolius, Pittosporum bicolor, Pomaderris apetala, Tasmannia lanceolata, Zieria arborescens

T24 This assemblage contains implicate rainforests, Huon pine forest and Eucalyptus nitida wet sclerophyll. It is found

only in western Tasmania and is essentially the same set of communities as found in tree assemblages 40 and 42, but contains relatively little moorland and scrub.

Anodopetalum biglandulosum, Atherosperma moschatum, Cenarrhenes nitida, Eucalyptus nitida, Eucryphia lucida, Eucryphia milliganii, Lagarostrobos franklinii, Leptospermum nitidum, Monotoca glauca, Nothofagus cunninghamii, Phebalium squameum, Phyllocladus aspleniifolius, Richea pandanifolia, Tasmannia lanceolata

T25 This assemblage is a mosaic of the eastern alpine communities and Eucalyptus coccifera forests found on the Central Plateau and Ben Lomond/Mt Barrow.

Eucalyptus coccifera, Eucalyptus gunnii, Richea scoparia



Class code


T26 Wet sclerophyll, rainforests and upland marshes of the Eastern Tiers, Wayatinah and Wentworth Hills. This assemblage contains many of the same elements as T15 and T23, but is distinguished by the inclusion of extensive marshes and scrub communities.

Acacia dealbata, Atherosperma moschatum, Beyeria viscosa, Eucalyptus amygdalina, Eucalyptus dalrympleana, Eucalyptus delegatensis, Eucalyptus gunnii, Eucalyptus obliqua, Eucalyptus ovata, Eucalyptus pauciflora, Eucalyptus regnans, Eucalyptus rodwayi, Eucalyptus rubida, Eucalyptus viminalis, Exocarpos cupressiformis, Leptospermum lanigerum, Notelaea ligustrina, Nothofagus cunninghamii, Olearia argophylla, Phebalium squameum, Phyllocladus aspleniifolius, Pittosporum bicolor, Pomaderris apetala, Pomaderris pilifera, Zieria arborescens

T27 Western highland rainforests, subalpine eucalypt forests and coniferous forest dominated by Athrotaxis spp. Occurs from Mt Weld and the Snowy Range in the south, through Mt Field and the Cradle Mt-Lake St Clair National Park.

Athrotaxis cupressoides, Athrotaxis selaginoides, Cenarrhenes nitida, Eucalyptus coccifera, Eucalyptus delegatensis, Eucalyptus gunnii, Eucalyptus subcrenulata, Leptospermum lanigerum, Nothofagus cunninghamii, Phyllocladus aspleniifolius, Pittosporum bicolor, Richea pandanifolia, Richea scoparia, Tasmannia lanceolata

T28 Dry sclerophyll and damp sclerophyll forests found on the Permian and Triassic sedimentary rocks of the lower midlands, and Derwent valley, extending to the East Coast west of Bicheno and Long Point.

Acacia dealbata, Acacia mearnsii, Allocasuarina littoralis, Allocasuarina verticillata, Banksia marginata, Beyeria viscosa, Bursaria spinosa, Eucalyptus amygdalina, Eucalyptus globulus subsp., Eucalyptus ovata, Eucalyptus pulchella, Eucalyptus rodwayi, Eucalyptus rubida, Eucalyptus tenuiramis, Eucalyptus viminalis, Exocarpos cupressiformis, Leptospermum scoparium var., Notelaea ligustrina, Olearia argophylla, Pomaderris apetala, Pomaderris elliptica, Pomaderris pilifera

T29 South eastern wet and dry sclerophyll forest and woodland. This assemblage has a disjunct occurrence in the lowland areas of South-East Tasmania. Including Southport Lagoon South Bruny Island, the Wellington Range Maria Island and Freycinet Peninsula and the Eastern flanks of the Western Tiers and Fingal Tier. These areas are characterised by undulating hills giving marked changes in aspect over short distances and by diverse geologies of Jurassic dolerite and Permian and Triassic sediments.

Acacia dealbata, Acacia melanoxylon, Banksia marginata, Bursaria spinosa, Eucalyptus amygdalina, Eucalyptus obliqua, Eucalyptus ovata, Eucalyptus rubida, Eucalyptus viminalis, Exocarpos cupressiformis, Leptospermum scoparium, Notelaea ligustrina, Olearia argophylla, Pomaderris apetala, Pomaderris elliptica, Pomaderris pilifera

T30 Highland King Billy pine rainforest and scrub. Found from Mt Bobs and the Arthur Range in the south through the Frankland Range to the West coast Range and Granite Tor, Mt Algonkian and the western Cradle Mt-Lake St Clair National Park.

Athrotaxis selaginoides, Cenarrhenes nitida, Eucryphia milliganii, Leptospermum nitidum, Nothofagus cunninghamii, Richea pandanifolia

T31 South eastern wet, damp and dry sclerophyll mosaic. A species rich assemblage, reflecting the rapid topographic change, and diverse geologies in south eastern Tasmania. Found around Hobart and south to Bruny Island and the Huon valley, Wielangta and in an arc from Little Swanport River to the Swan river west of Bicheno.

Acacia dealbata, Acacia mearnsii, Acacia melanoxylon, Allocasuarina littoralis, Allocasuarina verticillata, Banksia marginata, Beyeria viscosa, Bursaria spinosa, Callitris rhomboidea, Dodonaea viscosa, Eucalyptus amygdalina, Eucalyptus brookeriana, Eucalyptus globulus subsp., Eucalyptus obliqua, Eucalyptus ovata, Eucalyptus pulchella, Eucalyptus regnans, Eucalyptus rubida, Eucalyptus tenuiramis, Eucalyptus viminalis, Exocarpos cupressiformis, Leptospermum glaucescens, Leptospermum lanigerum, Leptospermum scoparium var., Notelaea ligustrina, Olearia argophylla, Pomaderris apetala, Pomaderris elliptica, Pomaderris pilifera, Zieria arborescens



Class code


T32 Huon pine rainforest, implicate rainforest Eucalyptus nitida

wet sclerophyll tea tree scrub/forest and moorlands in the lowlands between the West Coast range and Frenchmans Cap.

Anodopetalum biglandulosum, Cenarrhenes nitida, Eucalyptus nitida, Eucryphia milliganii, Lagarostrobos franklinii, Leptospermum nitidum, Nothofagus cunninghamii, Phyllocladus aspleniifolius, Richea pandanifolia

T33 Rainforest, King Billy pine forest Eucalyptus nitida wet

sclerophyll, scrub and moorlands on the lower slopes of the western Mountains.

Anodopetalum biglandulosum, Atherosperma moschatum, Athrotaxis selaginoides, Cenarrhenes nitida, Eucalyptus nitida, Eucryphia milliganii, Leptospermum nitidum, Monotoca glauca, Nothofagus cunninghamii, Phyllocladus aspleniifolius, Richea pandanifolia, Tasmannia lanceolata

T34 Lower midlands grasslands, dry sclerophyll woodland and forest, mainly on Triassic sedimentary rocks in upland areas and frost hollows. Many of the communities present are similar to those found in T28, T37 and T46, but 34 is characterised by the presence of Eucalyptus delegatensis, Eucalyptus pauciflora and Eucalyptus rodwayi.

Acacia dealbata, Acacia mearnsii, Allocasuarina littoralis, Allocasuarina verticillata, Banksia marginata, Beyeria viscosa, Bursaria spinosa, Callitris rhomboidea, Eucalyptus amygdalina, Eucalyptus delegatensis, Eucalyptus globulus subsp., Eucalyptus ovata, Eucalyptus pauciflora, Eucalyptus pulchella, Eucalyptus rodwayi, Eucalyptus rubida, Eucalyptus tenuiramis, Eucalyptus viminalis, Exocarpos cupressiformis, Leptospermum grandiflorum, Leptospermum scoparium var., Notelaea ligustrina, Olearia argophylla, Pomaderris apetala, Pomaderris elliptica, Pomaderris pilifera

T35 A mosaic of dry sclerophyll, highland eucalypt, alpine herbfield and highland grasslands on the south eastern Central Plateau from Bradys Lake to Lakes Sorell and Crescent.

Acacia dealbata, Eucalyptus dalrympleana, Eucalyptus delegatensis, Eucalyptus gunnii, Eucalyptus pauciflora, Eucalyptus rodwayi, Eucalyptus rubida, Notelaea ligustrina

T36 Upland rainforest wet eucalypt forest, woodland and sedgy grasslands of the Southern Central Plateau. There is also an outlying occurrence of this community on the south eastern side of the Ben Lomond massif.

Cenarrhenes nitida, Eucalyptus coccifera, Eucalyptus dalrympleana, Eucalyptus delegatensis, Eucalyptus gunnii, Eucalyptus pauciflora, Eucalyptus rodwayi, Eucalyptus subcrenulata, Leptospermum lanigerum, Nothofagus cunninghamii, Phebalium squameum, Phyllocladus aspleniifolius, Pittosporum bicolor, Richea scoparia, Tasmannia lanceolata

T37 East coast lowland dry sclerophyll with Oyster Bay pine. Occurs on the dolerite sandstone and mudstone on the coastal flanks of the Eastern Tiers south of Bicheno.

Acacia dealbata, Acacia mearnsii, Allocasuarina littoralis, Allocasuarina verticillata, Banksia marginata, Beyeria viscosa, Bursaria spinosa, Callitris rhomboidea, Dodonaea viscosa, Eucalyptus amygdalina, Eucalyptus globulus subsp., Eucalyptus ovata, Eucalyptus pulchella, Eucalyptus rubida, Eucalyptus tenuiramis, Eucalyptus viminalis, Exocarpos cupressiformis, Leptospermum grandiflorum, Leptospermum scoparium var., Notelaea ligustrina, Olearia argophylla, Pomaderris apetala, Pomaderris elliptica, Pomaderris pilifera

T38 Western scrub and implicate rainforest mosaic with some moorland and riparian Huon pine rainforest. Similar to T21, T43 and T50.

Acacia melanoxylon, Anodopetalum biglandulosum, Atherosperma moschatum, Cenarrhenes nitida, Eucalyptus nitida, Eucryphia lucida, Lagarostrobos franklinii, Leptospermum glaucescens, Leptospermum lanigerum, Leptospermum nitidum, Leptospermum scoparium eximium, Leptospermum scoparium var., Melaleuca squarrosa, Monotoca glauca, Nothofagus cunninghamii, Phebalium squameum, Phyllocladus aspleniifolius, Pittosporum bicolor, Richea pandanifolia, Tasmannia lanceolata



Class code


T39 Southern Midlands wet sclerophyll, dry sclerophyll and grassy woodlands. Much of this assemblage has been cleared for farming. Mainly on rolling topography with frost hollows and marshes, on sedimentary substrates.

Acacia dealbata, Banksia marginata, Beyeria viscosa, Eucalyptus amygdalina, Eucalyptus dalrympleana, Eucalyptus delegatensis, Eucalyptus ovata, Eucalyptus pauciflora, Eucalyptus pulchella, Eucalyptus rodwayi, Eucalyptus rubida, Eucalyptus viminalis, Exocarpos cupressiformis, Notelaea ligustrina, Olearia argophylla, Pomaderris apetala, Pomaderris elliptica

T40 Western implicate rainforest, Huon pine forest E. nitida wet sclerophyll, and scrub-moorland mosaic. The forest and scrub communities are very similar to those found in T24 and T42, but scrub and moorland vegetation predominates in T40.

Anodopetalum biglandulosum, Atherosperma moschatum, Cenarrhenes nitida, Eucalyptus nitida, Eucryphia lucida, Lagarostrobos franklinii, Leptospermum nitidum, Leptospermum scoparium, Monotoca glauca, Nothofagus cunninghamii, Phebalium squameum, Phyllocladus aspleniifolius, Richea pandanifolia, Tasmannia lanceolata

T41 Dry and wet sclerophyll forest and grassy woodlands of the lower midlands. This assemblage contains many of the same communities found in 39, but also has some heathy understoreys on more siliceous substrates, and the presence of species such as Eucalyptus tenuiramis, Eucalyptus globulus, Leptospermum scoparium, Pomaderris pilifera. Much of this assemblage has been

cleared.

Acacia dealbata, Banksia marginata, Beyeria viscosa, Bursaria spinosa, Eucalyptus amygdalina, Eucalyptus dalrympleana, Eucalyptus delegatensis, Eucalyptus globulus subsp., Eucalyptus ovata, Eucalyptus pauciflora, Eucalyptus pulchella, Eucalyptus rodwayi, Eucalyptus rubida, Eucalyptus tenuiramis, Eucalyptus viminalis, Exocarpos cupressiformis, Leptospermum scoparium var., Notelaea ligustrina, Olearia argophylla, Pomaderris apetala, Pomaderris elliptica, Pomaderris pilifera

T42 Western implicate rainforest, Huon pine forest Eucalyptus nitida wet sclerophyll forest in mosaics with moorland and scrub. The communities are very similar to those in T24 and T40, but forest vegetation predominates over moorland and scrub.

Anodopetalum biglandulosum, Atherosperma moschatum, Cenarrhenes nitida, Eucalyptus nitida, Eucryphia lucida, Lagarostrobos franklinii, Leptospermum nitidum, Monotoca glauca, Nothofagus cunninghamii, Phyllocladus aspleniifolius, Richea pandanifolia, Tasmannia lanceolata

T43 Western lowland riverine rainforest, tea tree forest and wet eucalypt forest. Similar communities to T21, T38 and T50. Found along the Gordon river and its tributaries: the Franklin, Maxwell, Denison and Olga rivers, extending also to the Hardwood River valley.

Anodopetalum biglandulosum, Atherosperma moschatum, Cenarrhenes nitida, Eucalyptus nitida, Eucryphia lucida, Lagarostrobos franklinii, Leptospermum nitidum, Leptospermum scoparium eximium, Leptospermum scoparium var., Melaleuca squarrosa, Monotoca glauca, Nothofagus cunninghamii, Phebalium squameum, Phyllocladus aspleniifolius, Pittosporum bicolor, Tasmannia lanceolata

T44 Lowland to sub alpine catena of Eucalyptus nitida and Eucalyptus delegatensis forest tea tree forest and implicate rainforests. Extends in a discontinuous arc from the vicinity of New Harbour in the South to the east and north of Lakes Pedder and Gordon through the area between the Denison and Prince of Wales Ranges to Mt Ramsay - Mt Cleveland in the north-west.

Anodopetalum biglandulosum, Atherosperma moschatum, Cenarrhenes nitida, Eucalyptus delegatensis, Eucalyptus nitida, Eucryphia lucida, Eucryphia milliganii, Leptospermum lanigerum, Leptospermum nitidum, Leptospermum scoparium eximium, Monotoca glauca, Nothofagus cunninghamii, Phebalium squameum, Phyllocladus aspleniifolius, Pittosporum bicolor, Richea pandanifolia, Tasmannia lanceolata



Class code


T45 A subalpine to alpine catena of Eucalyptus nitida and Eucalyptus delegatensis wet forests, rainforest, King Billy pine forest and alpine communities found from the Southern Ranges through the Prince of Wales, King William, and Cheyne ranges to the Du Caine Range and Cradle Mt.

Anodopetalum biglandulosum, Atherosperma moschatum, Athrotaxis selaginoides, Cenarrhenes nitida, Eucalyptus delegatensis, Eucalyptus nitida, Eucryphia milliganii, Leptospermum nitidum, Monotoca glauca, Nothofagus cunninghamii, Phebalium squameum, Phyllocladus aspleniifolius, Richea pandanifolia, Richea scoparia, Tasmannia lanceolata

T46 Derwent valley lowland dry sclerophyll. Similar tree composition to T37, but lacks elements such as Oyster Bay pine and Leptospermum grandiflorum. Found in the dry

insolated aspects of the Derwent valley and lower midlands, to the Eastern Tiers.

Acacia dealbata, Acacia mearnsii, Allocasuarina littoralis, Allocasuarina verticillata, Banksia marginata, Beyeria viscosa, Bursaria spinosa, Dodonaea viscosa, Eucalyptus amygdalina, Eucalyptus globulus subsp., Eucalyptus ovata, Eucalyptus pulchella, Eucalyptus rubida, Eucalyptus tenuiramis, Eucalyptus viminalis Exocarpos cupressiformis, Leptospermum scoparium var., Notelaea ligustrin, Olearia argophylla Pomaderris apetala, Pomaderris elliptica, Pomaderris pilifera

T47 Upland tall wet eucalypt forest, rainforest and King Billy pine forest of the southern Ranges and the Florentine and upper Gordon River catchments.

Acacia dealbata, Acacia melanoxylon, Anodopetalum biglandulosum, Atherosperma moschatum, Athrotaxis selaginoides, Cenarrhenes nitida, Eucalyptus delegatensis, Eucalyptus nitida, Eucalyptus obliqua, Eucalyptus regnans, Eucryphia lucida, Eucryphia milliganii, Leptospermum lanigerum, Leptospermum nitidum, Monotoca glauca, Notelaea ligustrina, Nothofagus cunninghamii, Olearia argophylla, Phebalium squameum, Phyllocladus aspleniifolius, Pittosporum bicolor, Pomaderris apetala, Richea pandanifolia, Tasmannia lanceolata, Zieria arborescens

T48 South eastern coastal dry sclerophyll and grassy woodlands. Dry coastal woodland and forest of North Bruny Island, Hobart and environs extending through Orford to the surrounds of Moulting Lagoon.

Acacia dealbata, Acacia mearnsii, Allocasuarina littoralis, Allocasuarina verticillata, Banksia marginata, Beyeria viscosa, Bursaria spinosa, Callitris rhomboidea, Casuarina monilifera, Dodonaea viscosa, Eucalyptus amygdalina, Eucalyptus globulus subsp., Eucalyptus ovata, Eucalyptus pulchella, Eucalyptus tenuiramis, Eucalyptus viminalis, Exocarpos cupressiformis, Leptospermum scoparium var., Melaleuca squarrosa, Pomaderris elliptica, Pomaderris pilifera

T49 Tall wet eucalypt forest and rainforests of Southern and Central Tasmania. This assemblage contains some of Tasmania‟s most productive wet forest including the Southern forests of the Huon Picton and Weld, valleys, the Styx and Florentine valleys, Upper Gordon River. And Beech Creek-Counsel Creek.

Acacia dealbata, Acacia melanoxylon, Anodopetalum biglandulosum, Atherosperma moschatum, Cenarrhenes nitida, Eucalyptus delegatensis, Eucalyptus nitida, Eucalyptus obliqua, Eucalyptus regnans, Eucryphia lucida, Leptospermum lanigerum, Leptospermum nitidum, Leptospermum scoparium, Monotoca glauca, Notelaea ligustrina, Nothofagus cunninghamii, Olearia argophylla, Phebalium squameum, Phyllocladus aspleniifolius, Pittosporum bicolor, Pomaderris apetala, Tasmannia lanceolata, Zieria arborescens

T50 Lowland scrub and moorland mosaic with implicate rainforest and Huon pine rainforest. Contains communities similar to T21, T38, T43.

Acacia melanoxylon, Anodopetalum biglandulosum, Atherosperma moschatum, Cenarrhenes nitida, Eucalyptus nitida, Eucryphia lucida, Lagarostrobos franklinii, Leptospermum nitidum, Leptospermum scoparium eximium, Melaleuca squarrosa, Monotoca glauca, Nothofagus cunninghamii, Phebalium squameum, Phyllocladus aspleniifolius, Pittosporum bicolor, Richea pandanifolia, Tasmannia lanceolata

Appendix 6 – Spatial data layers - Tyler corridor


6.2.30 Tyler corridor

Title Tyler corridor

Custodian WRD, DPIW


Description Division of freshwater-dependent ecosystems based on broad scale biogeochemical.

Input data

Lake and river colour data, Professor Peter Tyler, Deakin University

Lineage

The „Tyler corridor‟ (previously referred to as the „Tyler line‟ e.g. (Shiel et al., 1989), P. Tyler pers. comm.) was used to describe a large scale biogeochemical „divide‟ within Tasmania. To the west of the corridor, surface waters – rivers, wetlands and waterbodies – are predominantly dark, humic and with red optical properties, high light attenuation, high (5-20 mg/L) dissolved organic carbon and low pH (4-6). Such waters exist occasionally to the east of the corridor, but generally only in localised wetland swamps and predominantly in coastal dune-wetland systems. The predominant features of waters to the east of the Tyler corridor are higher pH (6-8), low colour with a generally blue-green optical regime, lower light attenuation with occasionally high turbidities, and low dissolved organic carbon. Within the corridor waters are frequently intermediate between the two states, or may be locally differentiated. These features are believed to have fundamental influences on freshwater ecosystem ecology and processes, and reflect differences in the balance of auto and heterotrophy, as well as being broadly correlated with a range of biogeographic distributional differences.

A map of the Tyler corridor (Figure 34) was created using lake and river colour data (unpublished data sets contributed by Professor Peter Tyler). The corridor runs in a south-east/north-west direction with rivers, waterbodies and wetlands identified as being east, west or within the corridor (east = clear water, west = blackwater, inside = transition). A single class was assigned to river sections, waterbodies and wetlands using the rules outlined in Appendix 6.3.51).

Appendix 6 – Spatial data layers - Urbanisation


Figure 34. The Tyler corridor.

Data limitations

Hand-assigned boundaries (see Lineage)


Scale and coverage Unknown (Hand-assigned boundaries); Statewide

References See Appendix 1.

6.2.31 Urbanisation

Title Urbanisation

Custodian WRD, DPIW


Description Extent of urban areas adjacent to rivers

Input data


Appendix 6 – Spatial data layers - Waterbodies


Lineage

An assessment of the extent of urbanisation overlying or adjacent to freshwater-dependent ecosystems in Tasmania was undertaken using the TASVEG data layer (Version 0.1 May 2004). TASVEG codes denoting urban and semi-urban development: Ur (rural miscellaneous), Uc (built-up areas) and Ue (permanent easements), were selected from the TASVEG data layer to create an urbanisation base data layer.

An urbanisation score was assigned to river sections using rules provided in Appendix 6.3.52.

Data limitations





6.2.32 Waterbodies

Title CFEV Waterbodies



Description Lakes and waterbodies for Tasmania

Input data

Hydro infrastructure and discharge data (location), Hydro Tasmania

LIST 1:25 000 hydrographic theme (Subsets: waterbody – natural or dammed freshwater and salt flat), DPIW

Lineage

The waterbodies spatial data layer was derived from the LIST 1:25 000 drainage network. The data layer primarily includes lacustrine ecosystems which are still, open water systems such as lakes. The data layer was developed in the following way.

Waterbodies <1 ha in size were removed from the LIST data set for fear that the CFEV assessment for waterbodies would be swamped by a very large number of very small waterbodies on the Central Plateau. Mapping rules were applied to the remaining polygons (see the „invalid‟ waterbodies rules below) to separate out the natural or „valid‟ waterbodies. Waterbodies were categorised into two classes:

„valid‟ (all natural lakes and large Hydro storages plus some large non-Hydro storages (e.g. Lake Leake and Tooms Lake)

„invalid‟ (includes large farm dams, settling ponds and drinking water reservoirs).

Appendix 6 – Spatial data layers - Waterbodies


„Invalid‟ waterbodies rules

All waterbodies <170 ha in area were considered „invalid‟ between the 700 m elevation contour and a coastal strip. The coastal strip was defined as an area from high tide (including all large estuaries, embayments, etc.) to 1.5 km inland, except along:

coastal strips A and C (Figure 35) where the strip must be 3 km inland from the coast

coastal strip B (Figure 34) where the strip must be 3.5 km inland from the coast.

Figure 35. Coastal areas used in farm dam exclusion rules.

The remaining waterbodies were considered 'valid‟. After applying the „invalid‟ waterbodies rules, a few waterbodies had to be manually re-assigned to the „valid‟ waterbodies data layer. These included Lake Chisholm, coastal lagoons and the meromictic lakes. Four Springs Lake was also added.

The „valid‟ waterbodies were assessed as part of the CFEV Project, while the waterbodies that were excluded using the above rules (the „invalid‟ waterbodies) were retained in a separate „invalid‟ waterbodies data set for input into some of the rivers condition assessment variables such as regulation index, abstraction index and flow variability index.

Appendix 6 – Spatial data layers - Waterbody catchments


Data limitations

Only includes waterbodies 1 ha.



6.2.33 Waterbody catchments

Title Waterbody catchments

Custodian WRD, DPIW


Description Catchment boundaries for waterbodies of Tasmania

Input data

CFEV RSC spatial data layer (Appendix 6.2.25)

Lineage

The waterbody catchments were built into the CFEV RSC spatial data layer (Appendix 6.2.25), corresponding exactly to the lake polygon itself.

For many of the condition assessment variables, local catchment and upstream catchment scores were calculated and combined to give an overall condition score. Scores were calculated using the CFEV RSC spatial data layer and then assigned to the waterbody spatial units. The local waterbody catchment is the waterbody itself and the upstream waterbody catchment includes all RSCs upstream of it (see Figure 36 for an illustration).

Appendix 6 – Spatial data layers - Wetlands


Figure 36. Illustration of waterbody catchments showing the local and upstream catchment (consisting of multiple) RSCs. Dotted lines show RSC boundaries.

Data limitations

As per the CFEV RSCs spatial data layer (Appendix 6.2.25).



6.2.34 Wetlands

Title CFEV Wetlands

Custodian WRD, DPIW


Description Wetlands of Tasmania

Input data

LIST 1: 25 000 hydrographic theme (Subsets: wetland swamp area and wet areas, DPIW


Appendix 6 – Spatial data layers - Wetlands


Lineage

Development of the wetland spatial data layer used the 1: 25 000 LIST Hydrology theme (subsets: wetland swamp area and wet areas) in conjunction with the TASVEG data layer (Version 0.1 May 2004 (interim data set released prior to the release of TASVEG Version 1.0). Wetland vegetation codes (As, CA, ALK, Br, BPB, BF, L, ME, Sm, Pr, Ps, Waf, Was, We, Ws) (described in Appendix 12) were selected from the TASVEG data layer ((DPIW, 2003)). Wetlands from the LIST hydrographic data layer were initially classified as „undifferentiated We‟ and combined with the selected TASVEG polygons using the rules outlined below. Where there was significant overlap, TASVEG polygons were given preference. After the merge of the two data layers, the wetlands were classified as:

1. undifferentiated (LIST „undifferentiated We‟ + TASVEG „We (generic)‟)

2. differentiated (all other polygons)

Wetland polygons that shared boundaries or overlapped were dissolved to form one polygon. A dominant TASVEG type was assigned to the dissolved polygon (specific details given in Appendix 6.3.58).

Rules for amalgamating TASVEG and LIST wetland polygons

The following rules were used to amalgamate the TASVEG and LIST wetland polygons. An illustration for each rule is given in Figure 37.

1. When TASVEG and LIST polygons abut each other, treat as separate polygons.

2. When TASVEG and LIST polygons overlap each other significantly, create the TASVEG polygon (1) first and then create new polygon with remaining LIST polygon (2).

3. When the LIST polygon is inside the TASVEG polygon, create polygon from TASVEG layer (1) and remove LIST polygon.

4. When TASVEG polygon and LIST polygon overlap each other slightly, create polygon from TASVEG layer (1) and remove LIST polygon.

5. When the TASVEG polygon is inside the LIST polygon (almost being the same size as TASVEG polygon), create polygon from TASVEG layer (1) and remove LIST polygon.

6. When the TASVEG polygon is inside the LIST polygon, create TASVEG polygon (1) first and then create LIST polygon with a hole in it where the TASVEG polygon was.

Appendix 6 – Spatial data layers - Willows


Figure 37. Diagrams to accompany rules for amalgamating TASVEG and LIST wetland polygons. Red = TASVEG wetland and Blue = LIST wetland.

Data limitations

Wetlands <0.5 ha that could not be assigned with an appropriate wetland vegetation type (see Appendix 6.3.58) were deleted from the data set.




6.2.35 Willows

Title Willows

Custodian WRD, DPIW

Creator Rod Knight, GIS Services GIS Unit, ILS, DPIW

Description Presence and absence of willows within river riparian zones

Input data

Willow distribution data, Tasmanian Conservation Trust (TCT) (Farrell, 2003)

Before After

Before After

Before After

Before After

Before After

Before After

Appendix 6 – Spatial data layers - Willows


Lineage

The willows data layer was developed from distributional information derived from a National Heritage Trust project carried out through the TCT. Information regarding the distribution of willows within Tasmania was sourced using expert knowledge through a series of workshops (Farrell, 2003). The data collected through this project had been categorised into varying condition classes, with a high level of subjectivity. There was also a lack of consistent information on low to medium density infestations across the state. The data was therefore transformed to indicate only a presence/absence of willows along rivers and was assigned to river sections using the rules described in Appendix 6.3.61.

Data limitations

The willows spatial data layer is believed to indicate the locations of the majority of significant willow infestations as at 2004. It does not comprehensively describe the distribution of willows in Tasmania.



References (Farrell, 2003)

Appendix 6 – Attribute data - Abstraction index


6.3 Attribute data

6.3.1 Abstraction index

Title Abstraction index

Custodian WRD, DPIW


Description The amount of change in volume of long term mean annual flow („yield‟) due to the net effects of all abstractions and diversions of water.

Input data

CFEV MAR attribute data (Appendix 0)


Hydro infrastructure and discharge data (licensed abstractions), Hydro Tasmania

Water Information Management System (WIMS) database, DPIW

Lineage

The flow abstraction index rates all RSCs according to the amount of change in volume of flow due to the net abstraction (removal) and diversion (into and out of the catchment) of water:

MAR Natural

diversionsNet nsabstractioNet Indexn Abstractio

For every RSC, the sum of all abstractions and diversions within the upstream accumulated catchment (including the local RSC) (see Appendix 6.2.25) was divided by the upstream accumulated natural MAR (RSC_AMARNT) for the RSC. The natural MAR (ML/year) was modelled for all RSCs as described in Appendix 0.

Calculation of the abstraction index was undertaken in several steps. Net abstraction and diversion was calculated using data from the WIMS database (including all private and other licensed takes and diversions) and data from Hydro Tasmania using the following rules and subsequently divided by the natural MAR.

1. Calculate the net abstractions for each RSC:

a. Net Hydro Tasmania abstractions

Use the upstream accumulated natural MAR (RSC_AMARNT) and the upstream accumulated current MAR (RSC_AMARNM) (both in ML/year) and calculate as:

Net Hydro Tasmania abstraction = Natural MAR – Current MAR



b. WIMS abstractions

Assign „Period amount‟ data (ML) from the WIMS database (the annual licensed allocation) to the RSCs containing point locations of WIMS licensed takes, noting that:

WIMS „Purpose‟ categories of Town and Water Supply, Fish Farm and Industrial are to be assigned to the RSCs with no adjustment.

All other allocations are to be multiplied by 3.0 (see data limitations below for rationale).

c. Other (non-Hydro and non-WIMS) (i.e. farm dams not in WIMS and other storages (e.g. mine tailings ponds, Lake Leake and Tooms Lake etc.))

Calculated storage volume (ML) using SKM farm dam surface area/volume relationship:

This equation was developed by analysis of relationships between farm dam volume and surface area, using the WIMS data for off-stream and catchment dams (n = 167, after reviewing and screening the data for unreliable and bad records). The relationship compared favourably against a similar relationship developed by Sinclair Knight Merz (SKM, 2003; Lowe et al., 2005).

2. Sum the values calculated in 1b + 1c and assign to each RSC.

3. Accumulate the value (in rule 2) downstream (added values for all upstream RSCs) and then add in 1a to give total accumulated abstractions.

4. Calculate the sum of net abstractions for all upstream RSCs and the local RSC (combined).

5. Divide the sum of upstream net abstractions (value from rule 4) by the natural MAR (RSC_AMARNT). Assign value to each RSC.

The specific GIS rules for assigning river sections, waterbodies, wetlands and karst with an abstraction index are outlined below. The abstraction index has no units and ranges from a large negative number to a large positive number.

The abstraction index was considered to be high with regard to its impact on fluvial geomorphology when >0.50 (50% of MAR abstracted) or <-0.50 (increase in MAR by >50%), with medium impacts occurring between 0.1 and 0.5 (and -0.1 and -0.5), and negligible impact occurring between -0.1 and 0.1.

The abstraction index was initially considered to be high with regard to its impact on stream biota (macroinvertebrates and fish) when >0.75 (75% of MAR abstracted) or <-0.75 (increase in MAR by >50%), with medium impacts occurring between 0.5 and 0.75 (and, -0.5 and -0.75), and negligible impact occurring between -0.5 and 0.5. However, evaluation of the relationship between abstraction index and changes in summer (irrigation season) flows, when most abstractions occur, indicated that abstraction index values of >0.5 represented losses of >100% of summer flows. Hence the following ranges were adopted: high impact = low condition (>0.15 or <-0.15); moderate impact (0.05-0.15, or -0.05 to -0.15): low to no impact = high condition (-0.05 to 0.05).



Data limitations

The abstraction index is limited to mean annual estimates only. No smaller time scale resolution was possible due to data and modelling constraints.

Data quality was a significant issue in calculating this index. Diversion and abstraction data from Hydro Tasmania was taken as being reasonably accurate (certainly within 20%, M. Howland, Hydro Tasmania, pers. comm.). WIMS data on licensed takes and farm dams contained a number of major sources of error. Licensed take and dam locations were frequently erroneous, or not linked to the locations where they occurred. To partially overcome this, WIMS licensed takes were assigned to the RSC in which they fell rather than „snapped‟ to a particular location on a drainage section.

A major source of error was that the WIMS database contains only licensed allocation data, with no confirmation that these amounts are complied with. Recent studies by DPIW (R. Phillips, DPIW, pers. comm.) within a number of Tasmanian catchments indicate that total takes are substantially greater than the sum of licensed takes. This exceedence can range between 2 and 6 times, with an overall average figure of around 3 times. A factor of 3 was applied to the sum of all WIMS licensed take figures (known as „Period amount‟ in the WIMS database) to partially correct for these errors, with the exception of records associated with the following purposes: Town and Water Supply, Fish Farm and Industrial, which are deemed reasonably accurate.



References (DPIWE, 2005)

Column heading Continuous variable: KT_ABSTI, RS_ABSTI, WB_ABSTI, WL_ABSTI

Categorical variable: KT_ABSTI_C, RS_ABSTI_C, WB_ABSTI_C, WL_ABSTI_C

Type of data Continuous but has been converted to categorical format (see Table 24).

Number of classes KT_ABSTI_C = 6, RS_ABSTI_C = 8, WB_ABSTI_C = 6, WL_ABSTI_C = 8


An abstraction index was assigned to river, waterbody, wetland and karst spatial units using the following rules. Note that the abstraction index may be negative (<0) if the result is a net increase in flow from inter-catchment transfers.

Karst (KT_ABSTI)

1. Divide karst RSCs into two subsets: „big river catchments‟ (those RSCs with an accumulated current MAR (RSC_AMARNM)>48.2 GL) and „small river catchments‟ (all other RSCs). Note, more details on the rationale for the big and small catchment split are provided in the MAR section (Appendix 0 - Assigning values to ecosystem spatial units).



2. Calculate the abstraction index for „big river catchments‟ by a MAR weighted average of all downstream-most catchments in the big river catchment group for each karst area as:

RRSC_ACNMMA

RSC_MARRSC_ABSTI...RSC_MARRSC_ABSTIindexn abstractioriver Big 11 nn

Where:

Big river abstraction index = Abstraction index for the karst spatial unit (only taking into account the RSCs of the karst local catchment which have RSC_AMARNM>42.8 GL)

RSC_ABSTI(1…n) = Abstraction index for the RSCs of the karst local catchment which have RSC_AMARNM>42.8 GL

RSC_MAR(1…n) = Current MAR value for the RSCs of the karst local catchment which have RSC_AMARNM>42.8 GL

RSC_ACNMAR = Upstream accumulated current MAR value for the RSCs of the karst local catchment which have RSC_AMARNM>42.8 GL

3. Calculate the abstraction index for „small river catchments‟ by a MAR weighted average of all downstream-most catchments in the small river catchment group for each karst area as:

RRSC_ACNMMA

RSC_MARRSC_ABSTI...RSC_MARRSC_ABSTIindexn abstractioriver Small 11 nn

Where:

Small river abstraction index = Abstraction index for the karst spatial unit (only taking into account the RSCs of the karst local catchment which have RSC_AMARNM≤42.8 GL)

RSC_ABSTI(1…n) = Abstraction index for the RSCs of the karst local catchment which have RSC_AMARNM≤42.8 GL

RSC_MAR(1…n) = Current MAR value for the RSCs of the karst local catchment which have RSC_AMARNM≤42.8 GL

RSC_ACNMAR = Upstream accumulated current MAR value for the RSCs of the karst local catchment which have RSC_AMARNM≤42.8 GL

4. Assign all karst spatial units with an abstraction index as a weighted average of the big and small abstraction values as follows:

Karst spatial units associated with both big and small catchments:

KT_ABSTI = (Big river abstraction index*0.2) + (Small river abstraction index*0.8)

Where:

KT_ABSTI = Abstraction index of the karst spatial unit

Big river abstraction index = Abstraction index for the karst spatial unit (only taking into account the RSCs of the karst local catchment which have RSC_AMARNM>42.8 GL) (calculated in Step 2)

Small river abstraction index = Abstraction index for the karst spatial unit (only taking into account the RSCs of the karst local catchment which have RSC_AMARNM≤42.8 GL) (calculated in Step 3)



River sections (RS_ABSTI)

Assign the abstraction index of the RSC (as calculated above) directly to the river section it is associated with.

Waterbodies (WB_ABSTI)

Assign the abstraction index of the RSC (as calculated above) directly to the waterbody it is associated with.

Wetlands (WL_ABSTI)

1. Add all abstraction indices for RSCs associated with the wetland spatial unit (making up the local wetland catchment).

2. Assign the summed value to wetland spatial unit.

Each of the karst, river, waterbody and wetland spatial data layers had the continuous abstraction index data categorised according to Table 24. The categorical data was used for reporting and mapping purposes.

Table 24. Abstraction index categories for karst, rivers, waterbodies and wetlands.

Category KT_ABSTI_C (Min to max

values)

RS_ABSTI_C (Min to max

values)

WB_ABSTI_C (Min to max

values)

WL_ABSTI_C (Min to max

values)

1 -117.0026368 to <-0.4

-200852040 to <-0.4

-3.5 to <-0.4 -14.7 to <-0.4

2 -0.4 to <-0.1 -0.4 to <-0.2 -0.4 to <-0.1 -0.4 to <-0.2

3 -0.1 to <0 -0.2 to <-0.05 -0.1 to <0 -0.2 to <-0.05

4 0 to <0.1 -0.05 to <0 0 to <0.1 -0.05 to <0

5 0.1 to <0.4 0 to <0.05 0.1 to <0.4 0 to <0.05

6 0.4 to 0.980860665

0.05 to <0.2 0.4 to 1.3 0.05 to <0.2

7 0.2 to <0.4 0.2 to <0.4

8 0.4 to 242327 0.4 to 15.1


Karst>Statewide audit>Condition assessment>Naturalness score (KT_NSCORE)>Hydrology (KY_HYDRO)

Rivers>Statewide audit>Condition assessment>Naturalness score (RS_NSCORE)>Geomorphic condition (RS_GEOM)>Flow change (RS_FLOW)

Rivers>Statewide audit>Condition assessment>Naturalness score (RS_NSCORE)>Biological condition (RS_BIOL)>Macroinvertebrate condition (RS_BUGCO)

Waterbodies>Statewide audit>Condition assessment>Naturalness score (WB_NSCORE)>Hydrology (WB_HYDRO)

Wetlands>Statewide audit>Condition assessment>Naturalness score (WL_NSCORE)>Hydrology (WL_HYDRO)

Appendix 6 – Attribute data - Acid drainage


6.3.2 Acid drainage

Title Acid drainage

Column heading RS_ACID

Input data

CFEV Acid drainage spatial data layer (Appendix 6.2.1)

Type of data Categorical

Number of classes 2


Each river section was assigned a score indicating the presence (1) (low pH, raised metal levels) or absence (0) of acid mine drainage (RS_ACID), using the acid drainage spatial data layer. If any part of the river section intersected with the acid drainage spatial data layer then it was assigned the score of 1 (presence).


Rivers>Condition>Naturalness score (RS_NSCORE)>Sediment input (RS_SEDIN)>Mining sedimentation (RS_MINES)

6.3.3 Area

Title Area

Custodian WRD, DPIW


Description Area (m2) of each spatial unit within each ecosystem spatial data layer.

Input data

CFEV Estuaries spatial data layer (Appendix 6.2.7)

CFEV Karst spatial data layer (Appendix 6.2.14)




Lineage

The area for each of the estuary, karst, saltmarsh, waterbody and wetland spatial units (i.e. a single or multi-part polygon) was calculated using a standard area script in ArcGIS (GIS software).

Data limitations

Unknown



Column heading ES_AREA, KT_AREA, SM_AREA, WB_AREA, WL_AREA

Type of data Continuous

Appendix 6 – Attribute data - Burrowing crayfish



The area (m2) was assigned to each estuary, karst, saltmarsh, waterbody and wetland spatial unit as ES_AREA, KT_AREA, SM_AREA, WB_AREA and WL_AREA, respectively.


Saltmarshes>Statewide audit>Classification>Biophysical classification (SM_BPCLASS)

Saltmarshes>Statewide audit>Condition assessment>Naturalness score (SM_NSCORE)

Waterbodies>Statewide audit>Classification>Physical classification (WB_PCLASS)

Wetlands>Statewide audit>Classification>Physical classification (WL_PCLASS)

6.3.4 Burrowing crayfish

Title Burrowing crayfish

Column heading WL_BCRAYS

Input data

CFEV Burrowing crayfish regions spatial data layer (Appendix 6.2.3)


Number of classes 2


The burrowing crayfish data class (BC1) was assigned to each wetland that predominantly intersected with the burrowing crayfish region data layer. All other wetland spatial units were assigned as having burrowing crayfish absent (BC0).


Wetlands>Statewide audit>Classification

6.3.5 Catchment disturbance

Title Catchment disturbance

Column heading Continuous variable: KT_CATDI, RS_CATDI, WB_CATDI, WL_CATDI

Categorical variable: KT_CATDI_C, RS_CATDI_C, WB_CATDI_C, WL_CATDI_C

Input data

CFEV Catchment disturbance spatial data layer (Appendix 6.2.4)

CFEV Karst spatial data layer (Appendix 6.2.14)



Appendix 6 – Attribute data - Catchment disturbance





Type of data Continuous but has been converted into categorical format (Table 25).

Number of classes KT_CATDI_C = 5, RS_CATDI_C = 5, WB_CATDI_C = 5, WL_CATDI_C = 5


A catchment disturbance score (continuous number between 0 and 1) was assigned to each river, waterbody, wetland and karst spatial unit using the process outlined below. The final score incorporates the extent of catchment disturbance occurring within upstream catchments.

Firstly, using the CFEV catchment disturbance spatial data layer, the percentage area of each catchment disturbance code (0, 0.5 or 1) was calculated for each RSC. A single catchment disturbance score was then derived for each RSC using an area-weighted average. This was known as the catchment disturbance score for the local RSC.

The final catchment disturbance score for the RSC was calculated by accumulating the scores for all the upstream RSCs (including the local RSC) and weighting the values by the RSC‟s current MAR values (refer to Appendix 6.3.30). In this accumulation, the boundaries of the upper catchment stopped where a RSC was directly downstream of a waterbody or a wetland of area >1 ha. This allowed for the influence of a waterbody/wetland acting as a sink for catchment-derived sedimentation. A diagram illustrating this process is presented in Figure 38.

The calculation of upstream accumulated catchment disturbance score for a given RSC is given by the following equation:

RRSC_ACNMMA

RSC_MARRSC_CATDI)MAR_RSCRSC_CATDI...MAR_RSC(RSC_CATDIACATDI_SCR 11 nn

Where:

RSC_ACATDI = Accumulated catchment disturbance score for the RSC

RSC_CATDI(1…n) = Catchment disturbance score of the upstream RSCs

RSC_MAR(1…n) = Current MAR value of the upstream RSCs

RSC_CATDI = Catchment disturbance score of the local RSC

RSC_MAR = Current MAR value of the local RSC

RSC_AMARNM = Accumulated current MAR value for the RSC (includes the MAR of the local RSC)



Figure 38. Illustration of catchments showing the local and upstream RSCs contributing to the calculation of the catchment disturbance scores. Dotted lines show RSC boundaries.

The range of catchment disturbance values was required to be between 0 and 1 for input into the condition expert rule systems, therefore the percent area of catchment disturbance was rescaled accordingly. Poor catchment condition is indicated with a score of 0 which equates to 100% catchment disturbance in the catchment, through to 1 being good/reference condition with 0% catchment disturbance.

Karst (KT_CATDI)

1. Divide karst RSCs into two subsets: „big river catchments‟ (those RSCs with an accumulated current MAR (RSC_AMARNM)>48.2 GL) and „small river catchments‟ (all other RSCs). Note, more details on the rationale for the big and small catchment split are provided in the MAR section (Appendix 0).

2. Calculate the area-weighted catchment disturbance score for „big river catchments‟

3. Calculate the area-weighted catchment disturbance score for „small river catchments‟



4. Assign all karst spatial units with an catchment disturbance score as a weighted average of the big and small abstraction values as follows:


KT_CATDI = (Big river CATDI score*0.2) + (Small river CATDI score *0.8)

Where:

KT_CATDI = Catchment disturbance score of the karst spatial unit

Big river CATDI score = Catchment disturbance score for the karst spatial unit (only taking into account the RSCs of the karst local catchment which have RSC_AMARNM>42.8 GL) (calculated in Step 2)

Small river CATDI score = Catchment disturbance score for the karst spatial unit (only taking into account the RSCs of the karst local catchment which have RSC_AMARNM≤42.8 GL) (calculated in Step 3)

River sections (RS_CATDI)

Assign the catchment disturbance of the RSC (as calculated above) directly to the river section it is associated with.

Waterbodies (WB_CATDI)

Assign the catchment disturbance of the RSC (as calculated above) directly to the waterbody it is associated with.

Wetlands (WL_CATDI)

Calculate the area-weighted catchment disturbance score using each RSC associated with the wetland spatial unit and assign to the wetland spatial unit.

Each of the karst, river, waterbody and wetland spatial data layers had the continuous catchment disturbance data categorised according to Table 25. The categorical data was used for reporting and mapping purposes.

Appendix 6 – Attribute data - Conservation Management Priority – Immediate


Table 25. Catchment disturbance categories for karst, rivers, waterbodies and wetlands.

Category KT_CATDI_C (Min to max

values)

RS_CATDI_C (Min to max

values)

WB_CATDI_C (Min to max

values)

WL_CATDI_C (Min to max

values)

1 0 0 to <0.05 0 0

2 >0 to <0.05 0.05 to <0.6 >0 to <0.05 >0 to <0.05

3 0.05 to <0.95 0.6 to <0.8 0.05 to <0.95 0.05 to <0.95

4 0.95 to <1 0.8 to <0.95 0.95 to <1 0.95 to <1

5 1 0.95 to 1 1 1


Karst>Statewide audit>Condition assessment>Naturalness score (KT_NSCORE)>Sediment flux-surrogate (KT_CATDI)

Karst>Statewide audit>Condition assessment >Naturalness score (KT_NSCORE)>Water chemistry-surrogate (KT_CATDI)

Rivers>Statewide audit>Condition assessment>Naturalness score (RS_NSCORE)>Geomorphic condition (RS_GEOM)>Sediment input (RS_SEDIN)


Waterbodies>Statewide audit>Condition assessment>Naturalness score (WB_NSCORE)>Sediment input (WB_SEDIN)

Wetlands>Statewide audit>Condition assessment>Naturalness score (WL_NSCORE)>Hydrology (WL_HYDRO)

Wetlands>Statewide audit>Condition assessment>Naturalness score (WL_NSCORE)>Sediment input (WL_SEDIN)

Wetlands>Statewide audit>Condition assessment>Naturalness score (WL_NSCORE)>Water quality (WL_WATER)

6.3.6 Conservation Management Priority – Immediate

Title Conservation Management Priority – Immediate (CMPI)

Custodian WRD, DPIW


Description A range of priorities for improving the current management of Tasmania‟s freshwater ecosystem values.

Input data

CFEV Integrated Conservation Value (ICV) attribute data (Appendix 6.3.18)

CFEV LTS attribute data (Appendix 6.3.24)

CFEV Naturalness (see expert rules Appendix 4)

CFEV Representative Conservation Value (RCV) attribute data (Appendix )



Lineage

The CMPI rating highlights those freshwater dependent ecosystems which require immediate implementation of management actions to ensure the protection (and/or restoration) of their significant conservation values.

Two versions of the CMPI output were calculated depending on the input used to describe an ecosystem‟s conservation value (RCV or ICV) (see also Section 3.6 of the main report):

1. CMPI1 (Table 26) uses RCV and as such, does not include the presence of Special Values.

2. CMPI2 (Table 27) uses ICV and does include the presence of Special Values.

Rules for assigning the ecosystem spatial units with a CMPI1 and CMPI2 rating were developed by the CFEV TMG (Appendix 1) and are outlined below.

Data limitations

CMPI is a highly derived index and as such inherits all the data limitations of the input data layers and derivation processes.



Column heading ES_CMPI1, ES_CMPI2, KT_CMPI1, KT_CMPI2, RS_CMPI1, RS_CMPI2, SM_CMPI1, SM_CMPI2, WB_CMPI1, WB_CMPI2, WL_CMPI1, WL_CMPI2


Number of classes 4




The CMPI class (VH, H, M or L), for both CMPI1 and CMPI2, was assigned to estuary, karst, saltmarsh, river, waterbody and wetland spatial units using the rules given in Table 26 and Table 27, respectively. For example, if, for a given spatial unit, LTS is High, Naturalness is High and RCV is A, then CMPI (excluding SV) is Low).

Table 26. CMPI rules, using RCV as an input (CMPI1). For Naturalness and LTS, L = Low, M = Medium, H = High; for RCV, A = first group of units selected, B = second group selected, C = remaining group selected and for CMPI1, L = Lower, M = Moderate, H = High, VH = Very High.

LTS Naturalness RCV CMPI1

H H A L

H H B L

H H C L

H M A M

H M B L

H M C L

H L A H

H L B M

H L C L

M H A H

M H B M

M H C L

M M A H

M M B M

M M C L

M L A H

M L B M

M L C L

L H A VH

L H B M

L H C M

L M A VH

L M B M

L M C L

L L A VH

L L B M

L L C L



Table 27. CMPI rules, using ICV as an input (CMPI2). For Naturalness and LTS, L = Low, M = Medium, H = High and for ICV and CMPI2, L = Lower, M = Moderate, H = High, VH = Very High.

LTS Naturalness ICV CMPI2

H H VH/H L

H H M L

H H L L

H M VH/H M

H M M L

H M L L

H L VH/H H

H L M M

H L L L

M H VH/H H

M H M M

M H L L

M M VH/H H

M M M M

M M L L

M L VH/H H

M L M M

M L L L

L H VH/H VH

L H M M

L H L M

L M VH/H VH

L M M M

L M L L

L L VH/H VH

L L M M

L L L L


Estuaries>Conservation evaluation>Conservation Management Priority

Karst>Conservation evaluation>Conservation Management Priority

Rivers>Conservation evaluation>Conservation Management Priority

Saltmarshes>Conservation evaluation>Conservation Management Priority

Waterbodies>Conservation evaluation>Conservation Management Priority

Wetlands>Conservation evaluation>Conservation Management Priority

Appendix 6 – Attribute data - Conservation Management Priority – Potential


6.3.7 Conservation Management Priority – Potential

Title Conservation Management Priority – Potential (CMPP)

Custodian WRD, DPIW


Description A range of priorities for maintaining and protecting Tasmania‟s freshwater ecosystem values.

Input data

CFEV ICV attribute data (Appendix 6.3.18)

CFEV LTS attribute data (Appendix 6.3.24)

CFEV Naturalness (see expert rules Appendix 4)

CFEV RCV attribute data (Appendix )

Lineage

The CMPP rating highlights those freshwater dependent ecosystems which have a high priority for active conservation management in the situation where future development and/or changes to land, water or vegetation management are proposed within the catchment which may contribute to a change in aquatic ecological condition or status.

Two versions of the CMPP output were calculated depending on the input used to describe an ecosystem‟s conservation value (RCV or ICV) (see also Section 3.6 of the main report):

1. CMPP1 (Table 28) uses RCV and as such, does not include the presence of Special Values.

2. CMPP2 (Table 29) uses ICV and does include the presence of Special Values.

Rules for assigning the ecosystem spatial units with a CMPP1 and CMPP2 rating were developed by the CFEV TMG (Appendix 1) and are outlined below.

Data limitations

CMPP is a highly derived index and as such inherits all the data limitations of the input data layers and derivation processes.



Column heading ES_CMPP1, ES_CMPP2, KT_CMPP1, KT_CMPP2, RS_CMPP1, RS_CMPP2, SM_CMPP1, SM_CMPP2, WB_CMPP1, WB_CMPP2, WL_CMPP1, WL_CMPP2


Number of classes 4


The CMPP class (VH, H, M or L), for both CMPP1 and CMPP2, was assigned to estuary, karst, saltmarsh, river, waterbody and wetland spatial units using the rules given in Table 28 and Table 29, respectively. For example, if, for a given spatial unit, LTS is High, Naturalness is High and RCV is A, then CMPP (excluding SVs) is VH).



Table 28. CMPP rules using RCV as an input (CMPP1). For Naturalness and LTS, L = Low, M = Medium, H = High; for RCV, A = first group of units selected, B = second group selected, C = remaining group selected and for CMPP1, L = Lower, M = Moderate, H = High, VH = Very High.

LTS Naturalness RCV CMPP1

H H A VH

H H B H

H H C M

H M A H

H M B H

H M C M

H L A H

H L B M

H L C M

M H A VH

M H B H

M H C M

M M A H

M M B M

M M C M

M L A H

M L B M

M L C L

L H A VH

L H B H

L H C M

L M A VH

L M B M

L M C M

L L A VH

L L B M

L L C L



Table 29. CMPP rules, using ICV as an input (CMPP2). For Naturalness and LTS, L = Low, M = Medium, H = High and for ICV and CMPP2, L = Lower, M = Moderate, H = High, VH = Very High.

LTS Naturalness ICV CMPP2

H H VH/H VH

H H M H

H H L M

H M VH/H H

H M M H

H M L M

H L VH/H H

H L M M

H L L M

M H VH/H VH

M H M H

M H L M

M M VH/H H

M M M M

M M L M

M L VH/H H

M L M M

M L L L

L H VH/H VH

L H M H

L H L M

L M VH/H VH

L M M M

L M L M

L L VH/H VH

L L M M

L L L L








Appendix 6 – Attribute data - Crayfish regions


6.3.8 Crayfish regions

Title Crayfish regions

Column heading RS_CRAYS, WB_CRAYS

Input data

CFEV Crayfish regions spatial data layer (Appendix 6.2.5)

CFEV Elevation (rivers and waterbodies) attribute data (Appendix 6.3.9)

CFEV Stream order attribute data (Appendix 0)


Number of classes RS_CRAYS = 5, WB_CRAYS = 7


The dominant crayfish class (e.g. C0, C1, C2, etc.) was assigned to river and waterbody spatial units using the following mapping rules. The rules relate to Figure 17 and take into consideration that Astacopsis gouldi is not likely to occur above 400 m in elevation (and may be replaced by A. tricornis in the north-west or A. franklinii in the north east) and that all Astacopsis species have a low probability of occurrence in first order headwater streams.

River sections (RS_CRAYS)

1. Is the river section in region C0?

Yes: Assign class C0 (Astacopsis absent).

No: Go to 2.

2. Does the river section have a stream order (RS_ORDER) = 1?

Yes: Assign class C6 (Astacopsis absent or naturally in low abundance or low probability of occurrence).

No: Go to 3.

3. Is the river section within crayfish region C1?

Yes: Go to 4.

No: Go to 5.

4. Within crayfish region C1, does the river section have an elevation

(RS_ELEVMIN) 400 m?

Yes: Assign class C6 (crayfish absent or naturally in low abundance or low probability of occurrence).

No: Assign class C1 (Astacopsis gouldi present).


Yes: Go to 6.

No: Go to 7.


(RS_ELEVMIN) 400 m?

Yes: Assign class C2 (Astacopsis tricornis present).


Appendix 6 – Attribute data - Crayfish regions



Yes: Go to 8.

No: Go to 9.


(RS_ELEVMIN) 400 m?

Yes: Assign class C3 (Astacopsis franklinii present).




No: Go to 10.



Waterbodies (WB_CRAYS)

1. Is the waterbody in region C0?

Yes: Assign class C0 (Astacopsis absent).

No: Go to 2.

2. Is the waterbody within crayfish region C1?

Yes: Go to 3.

No: Go to 4.

3. Within crayfish region C1, does the waterbody have an elevation

(WB_ELEV) 400 m?

Yes: Assign class C6 (Astacopsis absent or naturally in low abundance or low probability of occurrence).




No: Go to 5.



No: Go to 6.

6. Is the waterbody within crayfish region C4 (i.e. 400 m) ?

Yes: Assign class C4 (Astacopsis gouldi and Astacopsis tricornis present).

No: Go to 7.


Yes: Assign class C5 (Astacopsis gouldi and Astacopsis franklinii present).

Appendix 6 – Attribute data - Elevation



Rivers>Statewide audit>Classification

Waterbodies>Statewide audit>Classification

6.3.9 Elevation

Title Elevation

Custodian WRD, DPIW


Description Elevation (m AHD) of each spatial unit within each ecosystem spatial data layer.

Input data

CFEV DEM (Appendix 6.2.6)




Lineage

The elevation data indicates the average height of an ecosystem spatial unit (m AHD) and was calculated using the DEM developed as part of the CFEV Project (Appendix 6.2.6). The rules for assigning elevation values to the ecosystem spatial units are outlined below.

Data limitations

The elevation data is derived from the DEM and therefore inherits the data limitations associated with that spatial layer.



Column heading RS_ELEVMIN, RS_ELEVMAX, WB_ELEV, WL_ELEV



Rivers (RS_ELEVMAX and RS_ELEVMIN)

The maximum and minimum elevation of a river section was calculated from the grid cell of the DEM which coincided with the most upstream and downstream end of the river section, respectively.

Waterbodies (WB_ELEV) and wetlands (WB_ELEV)

The elevation of the waterbodies and wetlands within the landscape was calculated from the DEM as the average elevation of all grid cells within the polygon.

CFEV assessment framework input

Wetlands>Classification>Physical classification (WL_PCLASS)

Appendix 6 – Attribute data - Estuaries biophysical classification


6.3.10 Estuaries biophysical classification

Title Estuaries biophysical classification

Custodian WRD, DPIW


Description Biophysical classification of Tasmania‟s estuaries.

Input data

Estuaries biological regions, TAFI (Edgar et al., 1999a; Edgar et al., 1999b)

Estuaries physical class, TAFI (Edgar et al., 1999a)

Lineage

A biophysical classification for estuaries was based on a physical classification described by Edgar et al.(1999a), combined with amalgamated bioregions derived from Edgar et al. (1999b) for fish and benthic invertebrates.

The physical classification differentiated estuaries on the basis of seasonal salinities, catchment run-off, tidal range, catchment and estuarine areas and presence of barriers to marine exchange. The biological regions (Bass Strait Islands, East Coast, North Coast and South and West Coasts) depicted broad biogeographic distributions of estuarine fish species and macroinvertebrate assemblages.

A physical (ES_PCLASS) (Table 30) and biological (ES_BCLASS) (Table 31) class was assigned to each of the estuary spatial units based on data from Edgar et al. (1999a).

Table 30. Physical classes for estuaries (from Edgar et al. (1999a)).

Class code Physical class description

1 Barred, low salinity estuary

2 Small open estuaries

3 Marine inlets and bays

4 Hypersaline lagoons

5 Large mesotidal river estuaries

6 Mesotidal drowned river valley

7 Microtidal drowned river valley

8 Large open microtidal river estuary

9 Barred river estuary

Table 31. Biological classes for estuaries (from Edgar et al. (1999a)).

Class code

Biological class description

1 Bass Strait island biogeographic type

2 North coast biogeographic type

3 East coast biogeographic type

4 South and west coast biogeographic type

Appendix 6 – Attribute data - Estuaries biophysical classification


These two classifications were combined in a matrix to give an overall biophysical classification for estuaries, with a total of 19 classes. The biophysical classes were assigned to each of the estuary spatial units based on the rules described in Table 32 (see below).

Data limitations

The biophysical classification inherits all the data limitations of the derivation processes and input data.



Column heading ES_BPCLASS


Number of classes 19


A biophysical class (e.g. Es1, Es2, Es3, etc.) was assigned to estuary spatial units using the rules in Table 32 (e.g. if the estuary is a barred, low salinity estuary and is within the Bass Strait islands, than assign class Es1). Note, only the combinations of physical and biological classes that had estuary membership were included.

Table 32. Summary of biophysical classification rules for estuaries.

Class code Physical class Biological region

Es1 Barred, low salinity estuary Bass Strait Islands

Es2 Barred, low salinity estuary East Coast

Es3 Barred, low salinity estuary South and west coasts

Es4 Small open estuaries Bass Strait Islands

Es5 Small open estuaries North Coast

Es6 Small open estuaries East Coast

Es7 Small open estuaries South and west coasts

Es8 Marine inlets and bays Bass Strait Islands

Es9 Marine inlets and bays North Coast

Es10 Marine inlets and bays East Coast

Es11 Marine inlets and bays South and west coasts

Es12 Hypersaline lagoons Bass Strait Islands

Es13 Hypersaline lagoons East Coast

Es14 Large mesotidal river estuaries North Coast

Es15 Mesotidal drowned river valley (Tamar) North Coast

Es16 Microtidal drowned river valley East Coast

Es17 Microtidal drowned river valley South and west coasts

Es18 Large open microtidal river estuary South and west coasts

Es19 Barred river estuary (Wanderer) South and west coasts


Estuaries>Classification

Appendix 6 – Attribute data - Exotic fish impact


6.3.11 Exotic fish impact

Title Exotic fish impact

Custodian WRD, DPIW


Description An index which rates the relative „nativeness‟ of freshwater fish assemblages, which reflects the presence and relative biomass of exotic fish assemblages.

Input data


CFEV Waterbodies spatial data layer (valid and invalid) (Appendix 6.2.32)

Exotic fish biomass data, Freshwater Systems

Historical trout stocking data, IFS

Hydro infrastructure and discharge data (location), Hydro Tasmania

LIST 1: 250 000 Geology data, DPIW

LIST Waterfall data, DPIW

RFA fish database (updated for the CFEV Project), DPIW

Lineage

The exotic fish data was developed for rivers and waterbodies using two types of information:

the distribution of exotic fish (probabilities of occurrence assigned to river and waterbody spatial units)

the relative biomass of exotic fish (based on the relationships shown in Figure 39).

Exotic fish distribution

Data on exotic freshwater fish distribution was prepared using:

records of known locations with exotic fish prepared during the RFA (P. Davies & L. Cook, Freshwater Systems, unpublished data) and updated for the CFEV Project:

- Salmo trutta (brown trout)

- Oncorhynchus mykiss (rainbow trout)

- Salvelinus fontinalis (brook trout)

- Salmo salar (Atlantic salmon)

- Perca fluviatilis (redfin perch)

- Tinca tinca (tench)

- Carassius auratus (goldfish)

- Gambusia holbrookii (mosquito fish)

- Cyprinus carpio (European carp)

records and input from a number of freshwater fish experts (see Appendix 1).



The following rules for constructing the exotic fish distribution data provide no distinction between the various exotic fish species, noting that brown trout are by far the dominant species in terms of distribution and biomass. The rules were based on characteristics associated with the distribution of salmonid fish and assume that all other exotic fish fall within that statewide distribution. No account is taken of the elimination of fish species by pollutants (e.g. King River). The exotic fish distribution data (present, low probability or absent) was assigned to river sections and waterbodies.

1. Exotic fish are absent in (the following list may have some duplication):

the catchments of Port Davey and Bathurst Harbour

King Island (with the exception of those river sections upstream and downstream of those waterbodies listed as stocked in Table 33)

all river sections (and their associated waterbodies) upstream of the most downstream 100 m reach with a slope of 75% or greater

all river sections (and their associated waterbodies) upstream of all mapped waterfall features (whether they are named or not) (LIST waterfall data)

the river sections/waterbodies listed in Table 34

other unnamed lakes on the Mersey map sheet as per map in (Sloane and French, 1991). Note: All waters upstream of the waters listed in this appendix are also trout free – whether connected by the drainage disconnected from the drainage.

Those waterbodies listed in Table 35.

2. Exotic fish have a low probability of occurrence (and/or low abundance) in:

river sections with fine substrates (sands, silts) or catchments dominated by such reaches (examples include the catchments of Crayfish Creek, Boobyalla River, Tomahawk River, Botanic Gardens Creek at Strahan). This occurs when slope is <10% and geology along the river section and/or in the immediate upstream catchment is predominantly (>50% by total channel length) one of the following types: undifferentiated Quaternary sediments (1:250 000 geology map Rcode 8493 „Q‟), undifferentiated Cainozoic sediments (1:250 000 geology map Rcode 8494 „TQ‟) or sand, gravel and mud of alluvial, lacustrine and littoral origin (1:250 000 geology map Rcode 8499 „Qh‟)?

in all catchments draining to the sea westwards from South-east Cape to Port Davey and from Port Davey to Cape Sorell

3. Exotic fish are believed to be present, i.e. have a high probability of occurrence, in all other river sections where the above rules do not apply, except for selected known locations where they have been stocked upstream of natural barriers. These include:

all lakes in the central highlands to the east of and including Great Lake

Swan River upstream of Hardings Falls

Clarence Lagoon

all Hydro lakes, storages and infrastructure, and river sections upstream for which the above rules do not apply



artificial barriers such as all water storage lakes infrastructure and river sections upstream for which the above rules do not apply (e.g. Rileys Creek dam Pet and Guide, etc.) as identified in Table 33.

Table 33. Waterbodies known to have sustained trout populations established by stocking (IFS trout stocking data).

WB_ID Waterbody name UFI (LIST) for those waterbodies not assessed by CFEV (e.g. farm dams, etc.)

Pet hyd004046581

Guide hyd004046579

Kara hyd004048543

140 Talbots Lagoon

Rileys Creek hyd004916793

Companion Reservoir hyd004046580

Mikany hyd004676549

144 Lake Gairdner

166 Lake Mackenzie

Rushy hyd004129147

131 Curries River Reservoir

Cascade hyd004214444

Frome hyd004214443

Monarch hyd004639583

Near Lake Leake hyd004923553

1200 Craigbourne Dam

1339 Big Lagoon

1258 Lake Skinner

Edgar Levee Pond hyd005060875

Mossy Marsh hyd005004585

Wentworth dam hyd005009957

Dunnys dam hyd005009985

Weasel Plains dam hyd005287901

West Queen Dam 2 hyd004219156



504 Lake Augusta

902 Little Pine Lagoon

638 Lake Selina

1074 Clarence Lagoon

1336 Hartz Lake

1327 Lake Perry

1328 Lake Osborne

1258 Lake Skinner

1230 Lake Belton

1228 Lake Belcher

1222 Lake Seal

1229 Lake Dobson

1224 Lake Fenton

162 Dove Lake

729 Junction Lakes

650 Lake Meston

152 Lake Youl

622 Lake Myrtle



WB_ID Waterbody name UFI (LIST) for those waterbodies not assessed by CFEV (e.g. farm dams, etc.)

557 Lake Bill

148 Lake Lea

995 Lake Beatrice

1096 Lake Bantick

619 Lake Botsford

3 Cask Lake (King Island)

201 Lake Chambers

629 Lake Chipman

527 Lake Dudley

1101 Lake Garcia

197 Lake Johnny

836 Langdon Lagoon (Brook Trout)

1098 Lake Ashwood

1104 Little Bellinger

562 Little Blue Lake

1 Lake Wickham (King Island)

723 Lake Mikany

7 Pennys Lagoon (King Island)

610 Plimsoll (Brook Trout)

617 Rocky Lagoon

628 Second Lagoon

1258 Lake Skinner

554 Tin Hut Lake

1105 Big Jim Lake

1130 Highland Waters

1125 Lake Samuel

1135 Tooms Lake

939 Lake Leake

1215 Lake Webster

1217 Twisted Tarn

1333 Lake Esperance

1219 Lake Hayes

1221 Lake Nicholls

728 Lake Rolleston

Waratah Reservoir hyd005249767

Bischoff Reservoir hyd005249765 hyd005152411 hyd004739827

Waratah Ponds hyd005152405 hyd005152406 hyd005152407



Table 34. Trout-free river sections and waterbodies.

WB_ID Site name Map name Downstream limit of trout-free river section

Easting Northing

Unnamed tributary of Olive Lagoon Olive 446700 5354875

Johnsons Lagoon and headwaters Olive 447650 5350775

Unnamed stream, Skullbone Plains

Ina 449100 5346525

Dyes Marsh and headwaters Bronte Ina 448400 5337500

Tibbs Plain Bronte 440225 5336775

Unnamed lagoon, Wentworth Hills D‟Arcys 443525 5327400

Swan River tributary Henry St John 591400 5367900

Blue Tier Creek Colonels 562125 5337175

Parramores Creek Leake 563800 5342100

Tater Garden Creek – east Colonels 565100 5334050

Tater Garden Creek – west Colonels 565100 5334050

Snaky Creek Colonels 565100 5334050

Tullochgorum Creek Fingal 580500 5383100

St Pauls River St John Fingal 589450 5377500

Dukes River St John Fingal 589450 5377500

Lost Falls Creek Leake 573500 5344800

Cygnet River Snow 573075 5355200

Coghlans Creek Ross Leake 559500 5344100

Green Tier Creek Royalty Tooms 561700 5317500

Brodribb Creek Leake 568350 5342800

Rocka Rivulet Royalty Tooms 563600 5318800

835 Lake Athena

819 Lake Pallas

770 Orion Lakes

631 Chalice Lake

773 Lake Merope

786 Lake Eros

765 Lake Artemis

817 Lake Payanna

652 Cloister Lagoon

763 Ling Roth Lakes

896 Lake Jackie

lake on tributary of Lake Jackie to the north

455 Lake Howe

517 New Years Lake

437 Lake Sidon

408 Lake Thor

371 George Howes Lake

463 Lake Salome

452 Lake Tyre

510 Hunters lake

805 Lake Norman



Table 35. Trout-free waters within the Western Lakes – Central Plateau WHA area. Note: All waters upstream of these waters are also trout free – whether connected by the drainage or free standing.

WB_ID Name

1024 Lake Sappho

993 Rim Lake

892 Lake Riengeena

512 Lake Louisa

520 Lake Adelaide



Relative biomass

Native fish diversity is known to decline with distance from the coast in Tasmanian streams (Davies, 1989) and is associated with a dominance of brown trout in mid and upper catchment reaches. Analysis of quantitative electro fishing survey data (including data derived by Davies (1989), and other unpublished data) from 84 Tasmanian river sites confirmed that the proportion of native fish biomass in riverine fish assemblages decreases with distance from the sea (the tidal limit) (Figure 39).

(a)

(b)

Figure 39. Proportion of fish biomass as native species declines with distance from the sea (data re-analysed from Davies (1989) and P. Davies, Freshwater Systems, unpublished data) (a) scatter plot showing the input data and the relationship between the two variables (line represents a spline fit) (b) box plot showing the median values when data was grouped into four distance classes, based on expert analysis of the data.

0 50 100 150 200

Distance from Sea (km)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Pro

port

ion o

f B

iom

ass

as N

ativ

es

0-20 21-40 41-60 61-150

Distance from Sea (km)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Pro

port

ion o

f B

iom

ass

as N

ativ

es



The exotic fish distribution was combined with biomass proportion information using a final set of rules for assigning an exotic fish index to river sections and waterbodies (given below). The exotic fish condition index spans from 0 (high probability of exotic fish; proportion of fish biomass as native fish = approx. zero) through 0.04, 0.32, 0.65 (high probability of exotic fish, proportion of fish biomass as native fish = 0.04, 0.32 or 0.65, respectively) and 0.8 (low probability of exotic fish) to 1 (exotic fish absent).

The numbers used here are the medians of the proportion of fish assemblage biomass that is made up of native species (PBNAT) as shown in the box plot in Figure 39a.

Data limitations

The exotic fish impact data is highly derived and inherits all the data limitations of the derivation processes and input data.



Column heading RS_EXOTICF, WB_EXOTICF


Number of classes 6


River sections (RS_EXOTICF) and waterbodies (WB_EXOTICF)

An index, based on the proportion of the fish assemblage biomass that consists of native fish species was assigned to river sections and waterbodies using the following rules. Probabilities of the presence of exotic fish described in rules above. The distance the spatial units were from the sea (upstream point of the estuary) was calculated using the drainage network numbering system and cumulative river section lengths.

1. If exotic fish probability = absent, assign score = 1 else

2. If exotic fish probability = low, assign score = 0.8 else

3. If exotic fish probability = present, then:

a. if distance of river section or waterbody is 0-20 km from sea, then assign score = 0.65 else

b. if distance of river section or waterbody is 21-40 km from sea, then assign score = 0.32 else

c. if distance of river section or waterbody is 41-60 km from sea, then assign score = 0.04 else

d. if distance of river section or waterbody is >60 km from sea, then assign score = 0


Rivers>Condition assessment>Naturalness score (RS_NSCORE)>Biological condition (RS_BIOL)

Waterbodies>Condition assessment>Naturalness score (WB_NSCORE)>Native fish condition (WB_FISHCON)

Appendix 6 – Attribute data - Flow variability index


6.3.12 Flow variability index

Title Flow variability index

Custodian WRD, DPIW


Description The degree of change in flow regime variability as a result of human flow manipulation at major regulatory structures.

Input data

CFEV MAR (Appendix 0)



Hydro infrastructure and discharge data (volume, location, operational regime (e.g. Hydro peaking, etc.)), Hydro Tasmania

Lineage

An index of change in flow variability can only be estimated based on the scales of major change in flow regime known to be associated with large storages, especially Hydro dams and power stations (e.g. (Davies et al., 1999)).

The flow variability index was calculated using the CFEV river spatial data layer, current and natural Mean Annual Runoff (MAR), the CFEV waterbodies spatial data layer and the wetlands spatial data layer.

Waterbodies such as Hydro storages, irrigation/town water supplies, etc. (listed in Appendix 14) were rated according to the following categories using expert knowledge (Peter Davies, Freshwater Systems and Mick Howland, Hydro Tasmania):

0 No change to flow variability (i.e. no dam/structure present, unmodified waterbodies)

0.3 Low level of variability at seasonal to annual scales (e.g. irrigation storages, weirs, etc.)

0.6 Moderate to high variability at daily to seasonal scales (e.g. headwater storages, some run of river storages)

1 Very high variability at monthly to hourly scales (e.g. hydro peaking power stations).

These scores (listed for waterbodies in Appendix 14) are designed to reflect the nature of variability in flow releases from the artificial waterbodies (by contrast, the abstraction and regulation indices reflect changes in the quantity and degree of regulation of flow in the drainage). Waterbodies not listed in Appendix 14 were considered natural and rated as 0 for flow variability.

The flow variability rating only describes the influence of major infrastructure on variability, and many river sections will have varying degrees of flow abstraction and regulation, i.e. may have reduced (or enhanced) base/flood flows (this is reflected in the abstraction and regulation indices) without corresponding flow variability scores. For reservoirs used for irrigation/water supply, the rating is 0.3, with some larger storages rated 0.6. Hydro storages have been rated 0.6 or 1, based on knowledge of their release strategies. Different ratings have been assigned for relevant Hydro storages to the river section receiving power station discharge and to that immediately downstream of the storage dam(s).

Appendix 6 – Attribute data - Flow variability index


The calculation of the flow variability index for each river section (RS_FLOVI) was based on the presence and initial rating of upstream storages. The indices were altered (reduced in severity) with each river section downstream. The specific rules for assigning the index to river sections are given below.

Data limitations

The flow variability index inherits all the data limitations of the derivation processes and input data.



Column heading Continuous variable: RS_FLOVI

Categorical variable: RS_FLOVI_C

Type of data Continuous but also exists in a categorical format.

Number of classes RS_FLOVI_C = 4


Each river section was assigned a flow variability index score (RS_FLOVI) according to the following rules:

1. Is the river section immediately downstream of an artificial waterbody (WB_ARTIF = 0) or associated downstream canal/pipe (listed in Appendix 14) (RS_PIPE =1)?

Yes: Assign score to that river section. Note that there may be up to two different downstream paths (natural and normal) as per drainage flow regimes (see Appendix 6.2.24), and hence there can be two flow variability scores associated with each storage. If there is more than one value given, then the maximum value is taken.

Scores were separately applied to river sections immediately downstream of dam structures as well as river sections immediately downstream of any canal/pipe which serves to discharge water from that waterbody (and the canal/pipe‟s associated infrastructure e.g. power station).

No: Assign score of 0.

2. Using the river sections that were assigned values in Step 1 as starting points, dilute the flow variability scores downstream (using a MAR-weighted average) as per the equation below. Typically, as MAR increases downstream, the score decreases until a minimum value close to 0 is reached. Note, that the downstream accumulation stops at any waterbody, and re-starts downstream of that waterbody with the flow variability rating relevant to that waterbody.

RS_ACNMMAR

RS_ACNMMAR(initial) FLOVI)RS_ACNMMAR(initial) FLOVI...RS_ACNMMAR(initial) (FLOVIFLOVI_SR 11 nn

Where:

RS_FLOVI = Accumulated flow variability index for the river section

FLOVI(initial)(1…n) = Initial flow variability rating of the upstream river sections (set as per upstream storage)

Appendix 6 – Attribute data - Fluvial geomorphic mosaics


RS_ACNMMAR(1..n) = Accumulated current MAR value of the upstream river sections

FLOVI(initial) =Initial flow variability for the river section

RS_ACNMMAR = Accumulated current MAR value for the river section

The final value of the flow variability index was assigned to the river sections as the inverse of the above score, i.e. 1 = high condition, with no change in natural flow variability, and 0 = low condition with maximum change in flow variability.

The river spatial data layer had the continuous flow variability data categorised according to Table 36. The categorical data was used for reporting and mapping purposes.

Table 36. Flow variability categories for rivers.

Category RS_FLOVI (Min to max values)

1 0 to <0.1

2 0.1 to <0.5

3 0.5 to <1

4 1


Rivers> Condition assessment>Naturalness score (RS_NSCORE)>Geomorphic condition (RS_GEOM)>Flow change (RS_FLOW)

Rivers> Condition assessment>Naturalness score (RS_NSCORE)>Biological condition (RS_BIOL)>Macroinvertebrate condition (RS_BUGCO)

6.3.13 Fluvial geomorphic mosaics

Title Fluvial geomorphic mosaics

Column heading RS_MOSAIC, WB_MOSAIC, WL_MOSAIC

Input data





The dominant fluvial geomorphic mosaic class (e.g. MO0, MO1, MO2, etc.) was assigned to river, waterbody and wetland spatial units as RS_MOSAIC, WB_MOSAIC and WL_MOSAIC, respectively.


Rivers>Statewide audit>Classification>Fluvial geomorphic river types (RS_TYPE)

Rivers>Statewide audit>Condition assessment>Naturalness score (RS_NSCORE)>Geomorphic condition (RS_GEOM)>Geomorphic responsiveness (RS_GEOMRESP)

Appendix 6 – Attribute data - Fluvial geomorphic river types


Waterbodies>Statewide audit>Classification>Physical classification (WB_PCLASS)

Wetlands>Statewide audit>Classification>Physical classification (WL_PCLASS)>Geomorphic responsiveness (WL_GEOMRESP)

6.3.14 Fluvial geomorphic river types

Title Fluvial geomorphic river types

Column heading RS_TYPE

Input data

CFEV Fluvial geomorphic river types spatial data layer (Appendix 6.2.9)




The dominant geomorphic river type (e.g. G1, G2, G3, etc.) was assigned to each of the river sections as RS_TYPE.



6.3.15 Frog assemblages

Title Frog assemblage regions

Column heading WB_FROGS, WL_FROGS

Input data

CFEV Frog assemblage spatial data layer (Appendix 0)




The dominant frog assemblage class (e.g. F1, F2, F3, etc.) was assigned to each of the waterbody and wetland spatial units as WB_FROGS and WL_FROGS, respectively.




Appendix 6 – Attribute data - Geomorphic responsiveness


6.3.16 Geomorphic responsiveness

Title Geomorphic responsiveness

Column heading RS_GEOMRESP, WL_GEOMRESP

Input data

CFEV Geomorphic responsiveness spatial data layer (Appendix 6.2.11)


Number of classes 3


The dominant fluvial geomorphic responsiveness class (0, 0.5 or 1) was assigned to river and wetland spatial units as RS_GEOMRESP and WL_GEOMRESP, respectively.


Rivers>Statewide audit>Condition assessment>Naturalness score (RS_NSCORE)>Geomorphic condition (RS_GEOM)

Wetlands>Statewide audit>Classification>Physical classification (WL_PCLASS)

6.3.17 Hydrological regions

Title Hydrological regions

Column heading RS_HYDROL

Input data

CFEV Hydrological regions spatial data layer (Appendix 6.2.13)

Number of classes 4



The dominant hydrological region (e.g. H1, H2, H3, etc.) was assigned to the river sections as RS_HYDROL.



6.3.18 Integrated Conservation Value

Title Integrated Conservation Value (ICV)

Custodian WRD, DPIW


Description Ranking of relative conservation value (including Special Values) for Tasmania‟s freshwater-dependent ecosystems

Column heading ES_ICV, KT_ICV, SM_ICV, RS_ICV, WB_ICV, WL_ICV

Input data

Appendix 6 – Attribute data - Integrated Conservation Value



CFEV SV attribute data (Appendix 0)

Lineage

ICV is a comprehensive, relative conservation value of freshwater-dependent ecosystems in Tasmania derived by integrating RCV and SV status. Rules for assigning the ecosystem spatial units with an ICV rating were developed by the CFEV TMG (Appendix 1) and are outlined below.

Data limitations

ICV is a highly derived ranking that inherits all the data limitations of the derivation processes and input data.




Number of classes 4


The ICV class (Very High - VH, High - H, Moderate - M or Lower - L) was assigned to estuary, karst, saltmarsh, river, waterbody and wetland spatial units based on the assigned RCV (e.g. RS_RCV) and modified depending upon the SVs present (e.g. RS_SVDIV, SV_OUTSV, SV_NONSV, SV_UNDIFSV (refer to Appendix 0 for details)). The rules are given in Table 37. For example, if a given spatial unit, RCV is rated as class A and there are multiple outstanding (see Section 12.2 of the main report) SV records present, then ICV is Very High).

Table 37. ICV rule set, (L = Lower, M = Moderate, H = High, VH = Very High).

RCV SVs ICV

A Multiple outstanding SVs present VH

A Single outstanding SV present VH

B Multiple outstanding SVs present VH

C Multiple outstanding SVs present VH

A Multiple non-outstanding SVs or multiple undifferentiated SVs present H

A Single non-outstanding SV or single undifferentiated SV present H

A No known SVs present H

B Single outstanding SV present H

B Multiple non-outstanding SVs or multiple undifferentiated SVs present H

C Single outstanding SV present H

B Single non-outstanding SV or single undifferentiated SV present M

B No known SVs present M

C Multiple non-outstanding SVs or multiple undifferentiated SVs present M

C Single non-outstanding SV or single undifferentiated SV present L

C No known SVs present L

Appendix 6 – Attribute data - Karst catchments



Estuaries>Conservation evaluation

Karst>Conservation evaluation

Rivers>Conservation evaluation

Saltmarshes>Conservation evaluation

Waterbodies>Conservation evaluation

Wetlands>Conservation evaluation

6.3.19 Karst catchments

Title Karst catchments

Custodian WRD, DPIW


Description Catchment boundaries for karst areas of Tasmania

Input data


Lineage

Proximal and distal catchments had been created within the Karst Atlas (Version 3, 2003) for many of the karst polygons, however it was incomplete, particularly in the southern karst areas. Thus, it was decided that new catchments for all the karst areas should be developed based on the CFEV RSC spatial data layer.

Development of the local catchments for the karst areas involved intersecting the karst polygons with the RSC spatial data layer. The percentage area of each RSC that overlapped with the karst polygons was calculated. If the overlap was ≥30%, then the RSC was included as part of the local catchment for the karst area. If there was no RSC that met this criterion, the RSC with the largest overlap was used. Specific rules are provided below. Note, a karst spatial unit can have a local catchment consisting of more than one RSC and it was possible for the same RSC to be used for more than one karst area. Figure 40 illustrates an example of a karst and its local catchment.

The data representing the karst local catchments was generated as a separate attribute table that can be used to link the karst spatial units with the RSCs, rather than as a separate spatial data layer.

The most downstream RSC(s) associated with a karst spatial unit were also identified and used for summing accumulated values across the local catchment area. Rules provided below. More than one RSC can be considered downstream if the karst crosses more than one drainage basin (e.g. two different tributaries) (see Figure 40 for an example).

Appendix 6 – Attribute data - Karst catchments


Figure 40. Illustration of a local karst catchment. Dotted lines show RSC boundaries.

Data limitations





RSCs were assigned to individual karst spatial units as RSC_ID using the following rules. A separate attribute table (CFEVKarstCatchments) was generated to store this data.

1. Intersect the CFEV karst spatial data layer with the CFEV RSCs spatial data layer.

2. Calculate the % area of the RSC that intersects with the karst spatial unit.

3. If % area is greater than 30% then assign the RSC_ID to the karst spatial unit (KT_ID). Otherwise do not include RSC.

4. Also include the RSC with the largest intersected area. if it is greater than the RSC in rule 3.

Appendix 6 – Attribute data - Karst catchment size


A true (1) or false (0) value was assigned to each of the RSC associated with each karst spatial unit as KT_DWNSTRM using the following rule:

1. Of the RSCs assigned to a karst spatial unit, is it the most downstream RSC of a given drainage basin?

Yes: Assign as 1.

No: Assign as 0.

6.3.20 Karst catchment size

Title Karst catchment size

Custodian WRD, DPIW


Description A ratio that compares the area of the karst distal catchment and the karst local catchment.

Input data


Lineage

Karst catchment impacts are generally considered to have a greater effect on proportionally smaller karst areas than larger ones. To reflect this, karst catchment area is used as a dilution factor in calculating the effect of catchment disturbance on N-score (see Appendix 4.5.2).

Data limitations

Karst catchment size inherits all the data limitations of the derivation processes and input data.



Column heading KT_CATCH



A number was assigned to karst spatial units as KT_CATCH. This is expressed as the fraction of the upstream or distal catchment area (KT_DISAREA) divided by the karst spatial unit or local catchment area (KT_AREA).


Karst>Statewide audit>Condition assessment>Naturalness score (KT_NSCORE)

6.3.21 Karst physical classification

Title Karst physical classification

Custodian WRD, DPIW


Description Physical classification of Tasmania‟s karst systems.

Appendix 6 – Attribute data - Karst physical classification


Input data


Lineage

A physical classification was undertaken for karst areas based on lithological (physical character of rock types), topographic and precipitation data in the Karst Atlas (Kiernan, 1995). The precipitation data identifies four Tasmanian rainfall regions:

1. North-east – moderate rainfall, high rainfall intensity and high between year variability.

2. Bass Strait and south-east – generally dry, low rainfall intensity and low between year variability.

3. North-west and central – moderate rainfall, moderate intensity and low between year variability.

4. West and south-west – generally wet, moderate intensity rainfall and low between year variability.

The three sets of data were combined in a matrix to give an overall physical classification for karst areas, with a total of 110 classes. The physical classes were assigned to each of the karst spatial units based on the rules described in Table 38 (see below).

Data limitations

The physical classification inherits all the data limitations of the derivation processes and input data.



Column heading KT_PCLASS




A physical class (e.g. K1, K2, K3, etc.) was assigned to karst spatial units as KT_PCLASS using the rules in Table 38 (e.g. if the karst is part of a Pleistocene Aeolian calcarenite lithological system, has a coastal topography and is within a precipitation system that is typical of the Bass Strait and south-east areas of Tasmania, then assign class K5). Note, only the combinations of physical classes that had karst membership were included.

Table 38. Summary of classification rules for karst systems.

Class code

Lithological system Topographic system Precipitation system

K1 Lithological system undifferentiated hill flank and plain (plain type unspecified)

2

K2 Holocene freshwater limestone (e.g. recent spring mound or tufa deposits)

plain (type unspecified) 3


coastal plain 3



Class code



riverine plain 3

K5 Pleistocene aeolian calcarenite coastal 2



K8 Pleistocene aeolian calcarenite coastal plain 2

K9 Pleistocene aeolian calcarenite coastal plain 3

K10 Pleistocene aeolian calcarenite hill flank 2

K11 Pleistocene aeolian calcarenite hill flank and coastal 2

K12 Pleistocene aeolian calcarenite hill flank and coastal 4

K13 Pleistocene aeolian calcarenite hill flank and plain (plain type unspecified)

2

K14 Pleistocene aeolian calcarenite hill flank and plain (plain type unspecified)

3

K15 Pleistocene aeolian calcarenite hill flank, plain and coastal 2

K16 Pleistocene freshwater limestone (e.g. Pulbeena Limestone)


K17 Tertiary marine limestone undifferentiated coastal 3

K18 Tertiary marine limestone undifferentiated plain (type unspecified) 2

K19 Tertiary marine limestone undifferentiated plain (type unspecified) 3

K20 Tertiary marine limestone undifferentiated coastal plain 2

K21 Tertiary marine limestone undifferentiated coastal plain 3

K22 Tertiary marine limestone undifferentiated riverine plain 3

K23 Tertiary marine limestone undifferentiated hill flank 3

K24 Tertiary marine limestone undifferentiated hill flank and coastal 2

K25 Cainozoic (mostly Tertiary) freshwater limestone (e.g. Geilston Bay deposits)

coastal plain 2


riverine plain 2


hill flank 3

K28 Tertiary limestone over Smithton Dolomite (near Redpa)

riverine plain 3

K29 Permo-Carboniferous limestones undifferentiated

coastal 1


coastal 2


coastal plain 1


coastal plain 3


hill flank 1


hill flank 2



Class code



hill flank 3


hill flank and coastal 2




hill flank and plain (plain type unspecified)

2


riverine plain 1


riverine plain 2

K41 Siluro-Devonian limestones (Eldon Group) undifferentiated

coastal 4


hill flank 4



4


hill flank, plain and coastal 4

K45 Ordovician limestones (Gordon Group) undifferentiated

coastal 3


coastal 4






riverine plain 3


riverine plain 4


hill flank 3


hill flank 4





3



4

K56 Cambrian Ragged Basin Complex dolomites and cherty dolomites

Riverine plain 3


hill flank 3


hill flank 4



Class code


K59 Cambrian carbonate rocks (mainly dolomites) undifferentiated

coastal 2

K60 Precambrian Kanunnah Subgroup/Crimson Creek formation dolomitic and calcareous units.

Hill flank 3


Hill flank 4


Hill flank, plain and coastal 3

K63 Precambrian dolomites undifferentiated coastal 3

K64 Precambrian dolomites undifferentiated plain (type unspecified) 4

K65 Precambrian dolomites undifferentiated coastal plain 4

K66 Precambrian dolomites undifferentiated riverine plain 3

K67 Precambrian dolomites undifferentiated riverine plain 4

K68 Precambrian dolomites undifferentiated hill flank 3

K69 Precambrian dolomites undifferentiated hill flank 4

K70 Precambrian dolomites undifferentiated mountain (alpine karst) 4

K71 Precambrian dolomites undifferentiated hill flank and plain (plain type unspecified)

4

K72 Precambrian Smithton Dolomite plain (type unspecified) 3

K73 Precambrian Smithton Dolomite coastal plain 3

K74 Precambrian Smithton Dolomite riverine plain 3

K75 Precambrian Smithton Dolomite hill flank 3

K76 Precambrian Smithton Dolomite hill flank and plain (plain type unspecified)

3

K77 Precambrian Black River Dolomite, Savage Dolomite, Success Creek Group & correlates

coastal 3


coastal 4






coastal plain 3


coastal plain 4


riverine plain 3



Class code



riverine plain 4


hill flank 3


hill flank 4





3



4

K90 Precambrian Weld River Group dolomite, Jane Dolomite, Hastings Dolomite and correlates

riverine plain 3


riverine plain 4


hill flank 3


hill flank 4


Mountain (alpine karst) 4





K97 Precambrian/Cambrian Arthur Metamorphic Complex sequences (e.g., Keith Schist) not known to contain magnesite units but stratigraphically correlated with dolomitic sequences such as the Oonah Formation.

Hill flank and plain (plain type unspecified)

3

K98 Precambrian Oonah Formation, Burnie Formation and correlated interbedded dolomite/clastic sequences.

Plain (type unspecified) 3


Riverine plain 4


Hill flank 3

Appendix 6 – Attribute data - Karst physical sensitivity


Class code



Hill flank 4

K102 Precambrian Clark Group dolomites riverine plain 4

K103 Precambrian Clark Group dolomites hill flank 3

K104 Precambrian Clark Group dolomites hill flank 4

K105 Precambrian Clark Group dolomites mountain (alpine karst) 4

K106 Precambrian Rocky Cape Group interbedded dolomites (Irby Siltstone – interbedded clastics and dolomites)

hill flank 3





3

K109 Precambrian/Cambrian Magnesite and interbedded Magnesite/Dolomite (Arthur Metamorphic Complex)

hill flank 3

K110 Precambrian/Cambrian Magnesite and interbedded Magnesite/Dolomite (Arthur Metamorphic Complex)

hill flank 4


Karst>Classification

6.3.22 Karst physical sensitivity

Title Karst physical sensitivity

Custodian WRD, DPIW


Description The extent to which karst units are exposed.

Input data


Lineage

Exposed karst areas are deemed to be more susceptible to catchment impacts than covered ones. A karst area that is exposed (or part thereof) is identified where the karst rock crops out at the surface, compared with a karst area that is covered by quaternary sediments. Data was sourced from the Karst Atlas (Kiernan, 1995) and ranged from 0 (exposed) to 1 (covered). The proportion of the total karst area that was covered was calculated for each karst spatial unit.

Data limitations

The Karst Physical Sensitivity data inherits all the data limitations of the input data.


Scale and coverage 1:25 000, Statewide

Appendix 6 – Attribute data - Lake level manipulation


Column heading KT_PHYSSEN



The proportion of the karst area that was covered was calculated for each karst spatial unit and assigned directly to the karst polygon as an attribute.


Karst>Statewide audit>Condition assessment>Naturalness score (KT_NSCORE)

6.3.23 Lake level manipulation

Title Lake level manipulation

Custodian WRD, DPIW


Description The extent of human induced water level variation in Tasmania‟s lakes and waterbodies.

Input data

Hydro infrastructure and discharge data (location, volume), Hydro Tasmania

Lineage

The lake level manipulation score rates all waterbodies according to the intensity of human management of water surface levels using infrastructure (not catchment-wide effects on water balance). Waterbodies were rated, using expert knowledge (Peter Davies, Freshwater Systems and Mick Howland, Hydro Tasmania) in combination with Hydro data, according to the following categories:

0 Extreme human induced variation in waterbody levels (e.g. frequently close to 100% of „average‟ depth at „Full Supply Level (FSL)‟).

0.2 Severe human induced variation in waterbody levels (e.g. through 75-100% of „average‟ depth at „FSL‟).

0.4 Substantial human induced variation in waterbody levels (e.g. through 50-75% of „average‟ depth at „FSL‟).

0.6 Significant human induced variation in waterbody levels (e.g. through 10-50% of „average‟ depth at „FSL‟).

0.8 Minor human induced variation in waterbody levels (e.g. through <10% of „average‟ depth at „FSL‟).

1 No human impact on levels, only natural variation in levels (seasonal, wet/dry).

Note, „average‟ depth means estimated mean depth of entire water body (i.e. not of maximum depth only). „FSL‟ means Full Supply Level, i.e. level when waterbody (natural or man-made) is „full‟. Appendix 13 provides all the lake level manipulation scores for all „valid‟ named waterbodies. All unnamed „valid‟ waterbodies received a score of 1 (no locally human induced variation in level). Specific rules are provided below.

Data limitations

The lake level manipulation data inherits all the data limitations of the input data.


Appendix 6 – Attribute data - Land Tenure Security


Scale and coverage all waterbodies statewide

Column heading WB_LLEVELM


Number of classes 6


A lake level manipulation score (e.g. 0, 0.2, 0.4, etc.) was assigned to the waterbodies as WB_LLEVELM according to the following rules:

1. Assign all named waterbody spatial units with a lake level manipulation score according to Appendix 13.

2. Assign remaining unnamed waterbody spatial units with a lake level manipulation score of 1.



6.3.24 Land Tenure Security

Title Land Tenure Security (LTS)

Column heading ES_LTENSEC, KT_LTENSEC, SM_LTENSEC, RS_LTENSEC, WB_LTENSEC, WL_LTENSEC, ES_LTSMAP, KT_LTSMAP, SM_LTSMAP, RS_LTSMAP, WB_LTSMAP, WL_LTSMAP

Input data

CFEV LTS spatial data layer (Appendix 6.2.15)


Number of classes 3


A LTS category (Low, Medium or High) was assigned to each of the estuary, karst, saltmarsh, river, waterbody and wetland spatial units as **_LTENSEC (where ** is the prefix for each ecosystem theme i.e. ES = estuaries, KT = karst, RS = rivers, SM = saltmarshes, WB = waterbodies and WL = wetlands) using the following rules:

1. Calculate the proportion of the spatial unit‟s local catchment that is made up of each of the LTS categories (i.e. Low, Medium and High).

2. Accumulate the proportional values (weighted by RSC area) for each LTS category (i.e. Low, Medium and High) for all the upstream RSCs (including the local catchment) and assign value to each spatial unit as **_LTS_L, **_LTS_M and **_LTS_H, respectively.

3. Using the accumulated values of L, M and H (e.g. ES_LTS_L, ES_LTS_M and ES_LTS_H, assign a **_LTENSEC category to the spatial unit according to the following thresholds:

a. If the value for Low (**_LTS_L) >0.2, then assign as Low.

b. Else, if the value for Medium (**_LTS_M) >0.2, then assign as Medium.

Appendix 6 – Attribute data - Land use (nutrients)


c. Else, if the value for High (**_LTS_H) >0.8, then assign as High.

d. Otherwise, assign as Medium.

A category was also assigned to each of the spatial units (as **_LTSMAP) to depict if the LTS within the catchment was all of one type (e.g. whole catchment has High LTS) or was of mixed tenure (e.g. part High and part Medium). This was done by applying the following rules:

1. Using the accumulated values of L, M and H (e.g. ES_LTS_L, ES_LTS_M and ES_LTS_H, assign a **_LTSMAP category to the spatial unit:

a. If the value for Low (**_LTS_L) <1, then assign as Mixed.

b. If the value for Medium (**_LTS_M) <1, then assign as Mixed.

c. If the value for High (**_LTS_H) <1, then assign as Mixed.

d. If the value for Low (**_LTS_L) =1, then assign as Low.

e. If the value for Medium (**_LTS_M) =1, then assign as Medium.

f. If the value for High (**_LTS_H) =1, then assign as High.

Certain limitations of this process should be noted.

1. The threshold set is not smooth, consequently there can potentially be a large jump in LTS type with just a small change in catchment coverage.

2. The assigning of LTS data does not take into account the position of the land tenure types within the catchment.

3. The assigning of LTS data does not take into account the variation of potential land use impacts within each category.








6.3.25 Land use (nutrients)

Title Land use (nutrients)

Column heading Continuous variable: WB_NUTRI, WL_NUTRI

Categorical variable: WB_NUTRI_C, WL_NUTRI_C

Input data

CFEV Land use (nutrients) spatial data layer (Appendix 6.2.16)





Appendix 6 – Attribute data - Land use (nutrients)



Number of classes WB_NUTRI_C = 5, WL_NUTRI_C = 5


A land use (nutrients) score (continuous number between 0 and 1) was assigned to each waterbody and wetland spatial unit using the following process.

Firstly, the land use (nutrients) spatial data layer was intersected with the RSC data layer and for each RSC (which sometimes is a waterbody catchment) and wetland catchment, the percentage area of the catchment containing each nutrient score (0, 0.5 or 1) was calculated. Note in the case of wetlands, the catchment may be made up of more than one RSC (see Appendix 6.3.56). A single score for the catchment was then calculated using an area-weighted average. A further step involved taking this value and assigning a final nutrient score for the catchment according to the following rules:

1. If value is ≥0 and ≤0.25, then assign nutrient score as 0 (high impact).

2. If value is >0.25 and ≤0.75, then assign nutrient score as 0.5 (medium impact).

3. If value is >0.75 and ≤1, then assign nutrient score as 1 (low impact).

This value was known as the land use (nutrients) score for the local catchment. For wetlands, this score was assigned directly to the wetland spatial unit as WL_NUTRI.

Nutrient scores for the waterbodies, however, were accumulated for all upstream RSCs, (including the local waterbody catchment) and weighted by the current MAR (described in Appendix 0). In this accumulation, the boundaries of the upper catchment stopped where a RSC was directly downstream of a waterbody or a wetland of area >1 ha. This allowed for the influence of a waterbody/wetland acting as a sink for catchment-derived sedimentation (illustrated in Figure 38).

The calculation of upstream accumulated land use (nutrients) score for a given waterbody catchment is given by the following equation:

RRSC_ACNMMA

RSC_MAR)RSC_NUTRI)MAR_RSCRSC_NUTRI...MAR_RSC(RSC_NUTRIANUTRI_SCR

11 nn

Where:

RSC_ANUTRI = Accumulated land use (nutrients) score for the RSC (waterbody catchment)

RSC_NUTRI(1…n) = Land use (nutrients) score of the upstream RSCs

RSC_MAR(1…n) = Current MAR value of the upstream RSCs

RSC_NUTRI = Land use (nutrients) score of the local RSC (waterbody catchment)

RSC_MAR = Current MAR value of the local RSC

RSC_AMARNM = Accumulated current MAR value for the RSC (includes the MAR of the local RSC)

The upstream accumulated land use (nutrients) score for the waterbody catchment was then assigned directly to the relevant waterbody spatial unit. The overall land use (nutrient) scores range from 0 (poor condition – high nutrient input) to 1 (natural or near-natural condition – low nutrient input).

Appendix 6 – Attribute data - Lateral extent of backing vegetation


Each of the waterbody and wetland spatial data layers had the continuous land use (nutrients) data categorised according to Table 39. The categorical data was used for reporting and mapping purposes.

Table 39. Land use (nutrient) categories for waterbodies and wetlands.

Category WB_NUTRI_C (Min to max values)

WL_NUTRI_C (Min to max values)

1 0 0

2 >0 to <0.05 >0 to <0.05

3 0.05 to <0.95 0.05 to <0.95

4 0.95 to <1 0.95 to <1

5 1 1


Waterbodies>Statewide audit>Condition assessment>Naturalness score (WB_NSCORE)>Sediment quality-surrogate (WB_NUTRI)

Wetlands>Statewide audit>Condition assessment>Naturalness score (WL_NSCORE)>Water quality (WL_WATER)

6.3.26 Lateral extent of backing vegetation

Title Lateral extent of backing vegetation

Description Total length of vegetation (or other natural features) that surrounds a saltmarsh.

Column heading SM_LEVEG

Input data

Aerial photographs

CFEV Saltmarsh riparian vegetation condition spatial data layer (Appendix 6.2.23)




An estimate of the lateral extent (% length of natural features that abut the saltmarsh) of the native vegetation or natural features (e.g. water, rocks, etc.) present adjacent to the saltmarshes was observed from aerial photographs in conjunction with the CFEV Saltmarsh riparian vegetation condition spatial data layer (Figure 41). Data was initially collected on three categories (natural, exotic or other). The „other‟ category was further flagged as being either a natural feature or a non-natural feature (e.g. built-up area). If natural features were present, then the score (either % length or % width) was combined with the score for native vegetation.

A score, representing the proportional length of native vegetation and natural features along the saltmarsh perimeter (0-1, in increments of 0.1), was assigned to each of the saltmarsh spatial units as SM_LEVEG. The proportional length occupied by any natural features (other than vegetation) was added to the native vegetation score.

Appendix 6 – Attribute data - Long axis orientation


Figure 41. Illustration of saltmarshes and the riparian vegetation condition spatial data layer, showing an example of a section measured as the lateral extent of backing vegetation.


Saltmarshes>Statewide audit>Condition assessment>Naturalness score (SM_NSCORE)>Impacts adjacent to saltmarsh (SM_IMADJ)>Adjacent vegetation (SM_VGADJ)>Backing vegetation condition (SM_BKCON)

6.3.27 Long axis orientation

Title Long axis orientation

Custodian WRD, DPIW


Description Orientation of longest open water lake „axis‟ in degrees.

Input data


Lineage

The long-axis orientation of a waterbody is known as the geographic orientation of the dominant axis or longest reach. The long axis orientation was calculated as the angle of the longest point from one edge of the waterbody to another without intersecting with another edge.

The intention was to combine waterbody depth and long-axis orientation information to discriminate waterbodies with varying frequency of mixing and stratification. Long-axis orientation close to a NW to W orientation would increase the probability of deep mixing during pre-frontal to frontal zonal westerly wind events. Without knowing details about the local wind regime, simple rules could not be developed to differentiate lakes with high and low frequencies of wind driven mixing. Therefore, this variable was not used in the CFEV assessment.

The long axis orientation (in degrees) was calculated using an algorithm developed by the GIS Unit, DPIW that systematically calculated the distance between all possible points around the waterbody spatial until the longest length was determined.

Appendix 6 – Attribute data - Macroinvertebrate assemblages


Data limitations

As per the CFEV waterbodies spatial data layer (see Appendix 6.2.32)



Column heading WB_LONGAX



The calculated long axis orientation value was assigned to the waterbody spatial units as WB_LONGAX.


NA

6.3.28 Macroinvertebrate assemblages

Title Macroinvertebrate assemblages

Column heading RS_BUGS

Input data

CFEV Elevation (rivers) attribute data (Appendix 6.3.9)

CFEV Macroinvertebrate assemblages spatial data layer (Appendix 6.2.17)


CFEV Stream order attribute data (Appendix 6.3.47)




The dominant macroinvertebrate assemblage class (e.g. BC1, BC1A) was assigned to the river sections as RS_BUGS, according to the regions shown in Figure 23, with the following exceptions:

Assign all first-order river sections (RS_ORDER = 1) within a given macroinvertebrate region (Figure 23) to a subclass of its class with subscript „f‟ (first-order) (e.g. for region BC9, designate all first-order streams to BC9f).

Assign all river sections within region BC3 which also have a macrophyte assemblage class of Class 4A or Class 6 (see Appendix 6.3.30) to a new subclass BC3BR (broadwater).

Assign all streams with elevations greater than 800 m within a given macroinvertebrate region (Figure 23) to a subclass of its class with subscript „m‟ (montane) (e.g. in region BC9, designate all first-order montane streams to BC9fm, all other montane streams to BC9m). Class CPL can be excluded from this – its region is all >800 m.

Note, the prefix of „B‟ was added to each of the classes (from Figure 23) for input to the spatial selection algorithm.


Rivers>Classification

Appendix 6 – Attribute data - Macroinvertebrate O/E rank abundance


6.3.29 Macroinvertebrate O/E rank abundance

Title Macroinvertebrate Observed/Expected rank abundance(O/Erk)

Custodian WRD, DPIW


Description A measure of departure of macroinvertebrate communities from reference state developed using the Australian River Assessment System (AUSRIVAS) methodology.

Input data

AUSRIVAS Macroinvertebrate data (1994-1998) (site name, site code, O/E value, site location (easting/northing)), DPIWE

AUSRIVAS Macroinvertebrate data (1994-1998) (site name, site code, O/E value, site location (easting/northing)), Freshwater Systems

CFEV Abstraction index attribute data (Appendix 6.3.1)

CFEV Acid drainage attribute data (Appendix )

CFEV Catchment disturbance attribute data (Appendix 6.3.5)


CFEV Flow variability index attribute data (Appendix 0)

CFEV Fluvial geomorphic mosaics attribute data (Appendix 6.3.13)

CFEV Hydrological regions attribute data (Appendix 6.3.17)

CFEV MAR (current) attribute data (Appendix 0)

CFEV Mining sedimentation attribute data (Appendix 6.3.32)

CFEV Regulation index attribute data (Appendix 6.3.37)

CFEV Riparian vegetation condition attribute data (rivers) (Appendix 6.3.39)

CFEV Roading data


CFEV Tree assemblage attribute data (Appendix 6.3.49)

CFEV Tyler corridor attribute data (Appendix 6.3.51)

Lineage

The Observed/Expected (O/E) index is the proportion of expected macroinvertebrate families that are actually found at a river site (usually in either riffle or edge habitats). O/E values range from 0 (no expected families found) to 1 (all expected families found), and are interpreted as an index of impairment of river biotic condition or „health‟. AUSRIVAS sampling has been conducted for a large number of stream sites in Tasmania under the National River Health Program (NRHP) (Schofield and Davies, 1996; Krasnicki et al., 2001). These data allow the derivation of O/E values for several hundred locations within the Tasmanian stream drainage. O/E can be derived using presence/absence data (O/Epa), but can also be derived for Tasmanian stream sites using rank abundance data with rank-abundance based AUSRIVAS models developed by Davies (e.g. (Davies et al., 1999)) for DPIW. A rank abundance based O/E index (O/Erk) has been shown to be more sensitive to disturbance from flow regulation (Davies et al., 1999) and forest clearance (Davies



and Cook, 2003) than O/Epa. O/Erk was therefore used by the CFEV Project as the basis for describing the condition of Tasmanian stream macroinvertebrate assemblages.

Combined-season riffle habitat O/Erk values were derived for 430 Tasmanian stream sites from a wide range of locations within the state drainage, using samples collected during the DPIWE NRHP program, between 1994 and 1998.

A model was required which could be used to assign all CFEV Tasmanian river sections with either an O/Erk value or an impairment band derived from the O/Erk scores. The input data sets (listed above) were used to develop a model to predict a macroinvertebrate O/Erk score or impairment band. A shortlist of these data sets was used in the final regression tree analysis which used the SYSTAT 10.0 package. Results of the regression tree analysis are shown in Figure 42. Variable thresholds were derived for classifying O/Erk scores into the following AUSRIVAS band groups – AB, B, BC, BCD and CD – with these groups listed in order of decreasing median O/Erk (and hence macroinvertebrate condition). GIS mapping rules were developed (see below) using these thresholds and additional rules were added to differentiate true band A sites from sites falling in the AB band group. All sites within the AB group which had catchments with very low and/or zero values for the catchment disturbance index (RS_CATDI >0.99) were differentiated as A. These categories were then translated into a numerical range between 0 (which indicated poor condition) and 1 (representing natural/near natural condition) as follows: A = 1, AB = 0.8, B = 0.6, BC = 0.5, BCD = 0.3 and CD = 0.2.



Figure 42. Regression tree for discriminating benthic macroinvertebrate O/Erk values, showing the environmental variable thresholds and the resulting O/Erk band groups. These groups and these thresholds were used directly in the mapping attribution rule set for developing the river section O/Erk layer. Variables are as follows: RS_NRIPV (proportion of riparian vegetation area that is native), RS_FLOVI and RS_REGI (indices of flow variability and flow regulation), RS_ACRD_C (accumulated proportion of river section length as road crossings), RS_ACRD_U (accumulated proportion of RSC area as unsealed roads), RS_ACID (presence/absence of acid mine drainage impact in river section), RS_ORDER (Strahler stream order).

Data limitations

The macroinvertebrate O/Erk data is highly derived and inherits all the data limitations of the derivation processes and input data.



Column heading RS_BUGSOE


Number of classes 6

ABBC

B

BCBBC

BCD

CD BCD

AB

Split further into

A and AB ( see

mapping rules)

OE

RS_NRIPV <0.321

RS_ACRD_C<0.994

RS_ACRD_U<0.977

RS_ORDER <6.000

RS_ACID <1.000

RS_REGI <0.008

RS_FLOVI <0.411

RS_ACRD_U<0.990

RS_CATDI<0.523

RS_FLOVI<1.000

Split further into

A and AB ( see

mapping rules)




The following rules were used to assign each river section with a macroinvertebrate O/E rank abundance category (RS_BUGSOE):

1. Is acid drainage present (RS_ACID =1) within the river section?

Yes: Assign category BCD.

No: Go to 2.

2. Is the riparian vegetation of the river section <32.1% native (RS_NRIPV<0.321)?

Yes: Go to 3.

No: Go to 6.

3. Is the accumulated percentage of unsealed road crossings within the RSC <99.4% (RS_ACRD_C <0.994)

Yes: Assign category CD.

No: Go to 4.

4. Is the accumulated percentage of unsealed roads within the RSC <97.7% (RS_ACRD_U <0.977)?

Yes: Assign category BCD.

No: Go to 5.

5. Is the stream order of the river section (RS_ORDER) <6?

Yes: Assign category B.

No: Assign category BC.

6. Is the regulation index of the river section (RS_REGI) <0.008?

Yes: Go to 7.

No: Go to 9.

7. Is the flow variability index of the river section (RS_FLOVI) <1?

Yes: Assign category BC.

No: Go to 8.

8. Is catchment disturbance (RS_CATDI) >0.95 AND are the percentages of unsealed roads (RS_ACRD_U), unsealed road crossings (RS_ACRD_C) and sealed roads (RS_ACRD_S) all >99.5% (scores >0.995) AND is native riparian vegetation <0.05% (RS_NRIPV <0.05) AND is the upstream accumulated native riparian vegetation also <0.05 (RS_ACNRIPV <0.05)?

Yes: Assign category A.

No: Assign category AB.

9. Is the flow variability index for the river section (RS_FLOVI) <0.45?

Yes: Assign category BC.

No: Go to 10.

Appendix 6 – Attribute data - Macrophyte assemblages


10. Is the percentage of unsealed roads within the RSC <99% (RS_ACRD_U <0.99)?

Yes: Go to 11

No: Assign category B.

11. Is catchment disturbance (RS_CATDI) >0.95 AND are the percentages of unsealed roads (RS_ACRD_U), unsealed road crossings (RS_ACRD_C) and sealed roads (RS_ACRD_S) all >99.5% (scores >0.995) AND is native riparian vegetation <0.05% (RS_NRIPV <0.05) AND is the upstream accumulated native riparian vegetation also < 0.05 (RS_ACNRIPV <0.05)?

Yes: Assign category A.

No: Assign category AB.


Rivers>Statewide audit>Condition assessment>Naturalness score (RS_NSCORE)>Biological condition (RS_BIOL)>Macroinvertebrate condition (RS_BUGCO)

6.3.30 Macrophyte assemblages

Title Macrophyte assemblages

Custodian WRD, DPIW


Description Distribution of riverine macrophyte assemblages in Tasmania.

Input data


CFEV Fluvial geomorphic mosaics attribute data (Appendix 6.3.13)

CFEV Slope attribute data ?



Expert knowledge of current riverine macrophyte distributions summarised in a workshop (see Appendix 1 for attendants)

LIST 1:250 000 Geology data, DPIW

Macrophyte assemblages originally identified by Hughes (1987b)

Macrophyte percent cover data, MRHI, DPIW

Lineage

Attribution of macrophyte assemblages within rivers was based on information derived from two sources: expert knowledge of current riverine macrophyte distributions summarised in a workshop (see Appendix 1 for attendants) and assemblages originally identified by Hughes (1987b).

Percent riverine macrophyte cover data, collected as part of the National River Health Program‟s Monitoring River Health Initiative (MRHI) from several hundred sites in the period 1994-1998 was also available, and was used to identify



catchments with a high frequency of high densities of river macrophytes. GIS rules were derived (see below) to assign eight classes of macrophyte assemblage to the river sections at a statewide level. These rules considered stream slope, elevation and stream order as local controllers of macrophyte assemblage distributions, within both regional (east v west of „Tyler line‟) and geomorphic contexts. A description of the eight macrophyte assemblages is given in Table 40.

Data on other determinants of macrophyte distribution (nutrients, shading, riparian floristic, etc.) were considered but not available at the scale and coverage required. More detailed descriptions of assemblages were also considered but insufficient distributional information coupled with a lack of knowledge and data on distributional drivers precluded their use.

Table 40. Description of macrophyte assemblages

Class code

Assemblage Probability of occurrence, density

M1 None – occasional individual plants. Low probability, absent/very sparse

M2 High elevation community (see Hughes (1987b)), Lilaeopsis polyantha, Rorippa microphylla, Myriophyllum simulans)

Moderate probability, sparse-locally dense

M3 Macrophytes west of Tyler line (depauperate, see Hughes (1987b) – Group 5) - Triglochin procerum, Myriophyllum amphibium, Potamogeton ochreatus

Moderate probability, sparse/locally patchy, depauperate

M4A Emergent dominated assemblage (Eleocharis, Triglochin)

High probability, often dense/extensive

M4B As for M4A Moderate probability, sparse/locally patchy

M5A Submerged dominated assemblage (Myriophyllum, Potamogeton)


M5B As for M5A Moderate probability, sparse/locally patchy

M6 Emergent and Submerged complex in broadwater/pool habitats (dense, extensive, stable/highly structured). M5A elsewhere.


Data limitations

The macrophyte assemblage data inherits all the data limitations of the input data and errors associated with the modelling process.



References (Hughes, 1987b)

Column heading RS_MPHYTES


Number of classes 8



Assigning values to ecosystem units

The following rules were used to assign each river section with a macrophyte class (RS_MPHYTES):

1. Does the river section have a slope >10% (RS_SLOPE >0.1)?

Yes: Assign class M1.

No: Go to 2

2. Is the river section located at an elevation (RS_ELEVMAX) >800 m?


No: Go to 3.

3. Does the river section have a stream order (RS_ORDER) = 1 or 2?


No: Go to 4.

4. Is the river section located west of Tyler corridor (line A in Figure 43)?


No: Go to 5.

5. Is the geology along the river section and/or in the immediate upstream catchment predominantly (>50% by total channel length) one of the following types: undifferentiated Quaternary sediments (1:250 000 geology map Rcode 8493 „Q‟), or undifferentiated Cainozoic sediments (1:250 000 geology map Rcode 8494 „TQ‟), or sand, gravel and mud of alluvial, lacustrine and littoral origin (1:250 000 geology map Rcode 8499 „Qh‟)?

Yes: Go to 6. High probability emergents.

No: Go to 8. High probability submergents.

6. Is the river section located within polygon B in Figure 43?

Yes: Assign class M4A.

No: Go to 7.

7. Does the river section have a slope <1% (RS_SLOPE <0.01) and is it located within the following mosaics (RS_MOSAIC):

Central East alluvial basins (MO2)

Northern Midlands Tertiary Basin (MO36)

South eastern dolerite dry hills and basins (MO55)

Southern Midlands foothills and drainage divides (MO60)

Southern Midlands Tertiary basin (MO62)


No: Assign class M4B.

8. Is the river section located within polygon B in Figure 43? (Hughes‟ hydrological region 2)?

Yes: Assign class 5A

No: Go to 9.



9. Does the river section have a slope <1% (RS_SLOPE <0.01) and is it located within the following mosaics (RS_MOSAIC):

Central East alluvial basins (MO2)

Northern Midlands Tertiary Basin (MO36)

South eastern dolerite dry hills and basins (MO55)

Southern Midlands foothills and drainage divides (MO60)

Southern Midlands Tertiary basin (MO62)

No: Assign class 5B.

Yes: Assign class 6.

Figure 43. Map supporting macrophyte assemblage rules. A = Tyler Corridor western boundary; B = River Hydrological class H2 (refer to Appendix 6.2.13).



Appendix 6 – Attribute data - Mean annual run-off


6.3.31 Mean annual run-off

Title Mean annual run-off (MAR)

Custodian WRD, DPIW


Description Relative MAR

Input data


Effective precipitation, Bureau of Meteorology

Hydro infrastructure and discharge data, Hydro Tasmania

Mean annual rainfall data (multiple sources)

Lineage

Two versions (Current and Natural) of the MAR data were developed relating to the different flow regimes generated for the drainage network (see Appendix 6.2.24). Development of the MAR data used an effective precipitation layer (where effective precipitation = average annual effective precipitation – evapo-transpiration) sourced from the Bureau of Meteorology (and derived using a statewide data layer for mean annual rainfall and evaporation for the period 1900 to 2000) and the CFEV 1:25 000 RSC spatial data layer.

Climate data consisted of rainfall data from the Bureau of Meteorology and evapo-transpiration data which was derived from a model produced by the BOM and the CRC for Catchment Hydrology (Jerie et al., 2003a). The annual average effective precipitation was calculated by subtracting the average annual evapo-transpiration from the average annual precipitation (Jerie et al., 2003a). The MAR was calculated for each RSC by multiplying the average annual effective precipitation for the RSC by its area. An accumulated MAR was then calculated by adding the values for all the RSCs upstream.

The current MAR was generated by accumulating all values downstream using the current (normal) flow regime and the natural MAR was developed using the natural flow regime and depicts a pre-European MAR. The two MAR versions (Current and Natural) are very similar spatially but differ in their downstream linkages. Differences between the natural MAR and the current MAR relate to the amount of Hydro water releases and transfers. In the calculation of the cumulative value of the current MAR, Hydro data (net flow (ML/year)) was assigned to relevant river sections (e.g. pipes) and this value dictated the amount of runoff being calculated in a particular direction (i.e. if water flowing out of a waterbody has two choices, 1. the natural route or 2. a Hydro pipeline, the natural MAR would have all the runoff accumulated down the natural route, whereas the current MAR would have the runoff split down both routes. The split was based on the Hydro releases and transfer values).

Current MAR is the natural MAR layer but includes all internal and inter-catchment transfers established by the hydro-electric network. It does not contain data on other abstractions or transfers (to account for these, current MAR should be modified using the abstraction index (see Appendix 6.3.1).

The modelled MAR data was validated against long term (generally >50 years) data collected from 30 stream gauging stations across the state (locations shown in Figure 44) and covering catchment areas ranging from 21 to 1829 km2. The results of this analysis showed a strong linear relationship (R2 = 0.9973) between the current MAR and long term flow records as evident in Figure 45. It was considered that the MAR layer accurately reflects the long term yield of the Tasmanian drainage for the



period 1900-2000 for the majority of stream drainages. Accuracy for small first order streams would be significantly less reliable, due to local influences on drainage, hill slope and groundwater processes.

Figure 44. Map showing stream gauging locations (source: Water Assessment and Planning Branch, DPIWE).



Figure 45. Plot of CFEV ‘Current MAR’ v Q (cumec) from 25 gauging stations (see Figure above for locations). Thin line is 1:1.

Data limitations

This data should not be used to provide estimates of yield for dam assessments or other fine-scale water management tasks.

These MAR data were designed to be used in a relative assessment of hydrological change across the Tasmanian drainage network. If estimates of current yield are made from these MAR data, the current MAR values should be adjusted by removing the net proportion of yield abstracted (as indicated by the abstraction index), and then by making any adjustments for the downturn in yield observed since the mid 1970s.

Absolute MAR values should not be used in place of more reliable hydrological models, which are currently being developed for 69 catchments across the state by the Water Assessment Branch, DPIW.

The highly extrapolated cases within this data, i.e. remote areas and upstream river sections should be treated with more caution than those parts of the data that are on or near gauging stations.

References (Jerie et al., 2003a)



y = 0.9592x

R2 = 0.9973

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0

Q gauge

MA

R



Column heading KT_BIGMAR, KT_SMLMAR, RS_ACNMMAR, RS_ACNTMAR, RS_MAR, WB_ACNMMAR, WB_ACNTMAR, WB_MAR



Karst (KT_BIGMAR and KT_SMLMAR)

It was believed that rivers with a large MAR flowing through karst areas had a lot less influence on the condition of the karst than smaller rivers and streams (Earth Science Section, DPIW, pers. comm.). As such, two MAR values were assigned to each of the karst spatial units (one reflecting the upstream accumulated current MAR for the local karst RSCs associated with big rivers (>48.2 GL (Earth Science Section, DPIW, pers. comm.) and one for RSCs associated with small rivers (≤48.2 GL).

For several of the karst condition variables (catchment disturbance, abstraction index and regulation index), a context was set whereby big rivers contributed to only 20% of the catchment impact value calculated using the karst catchments when combined with a small river (which contributed the remaining 80%) (R. Eberhard, Earth Science Section, DPIW, pers. comm). As a result, two methods were employed to accumulate values using upstream catchments.

The MAR associated with big rivers and small rivers were calculated as KT_BIGMAR and KT_SMLMAR, respectively, using the following rules:

1. Divide karst RSCs into two subsets and assign to karst spatial unit as KT_BIG and KT_SMALL:

„Big river catchments‟: Does the karst spatial unit have RSCs in its local catchment that have an accumulated current MAR (RSC_AMARNM) >48.2 GL?

Yes: Assign KT_BIG as 1.

No: Assign KT_BIG as 0.

„Small river catchments‟: Does the karst spatial unit have RSCs in its local catchment that have an accumulated current MAR (RSC_AMARNM) ≤48.2 GL?

Yes: Assign KT_SMALL as 1.

No: Assign KT_SMALL as 0.

2. Calculate all MAR values for the „big river catchment‟ group and „small river catchment‟ group, according to the following rules, and assign to karst spatial units as KT_BIGMAR and KT_SMLMAR, respectively.

When all RSCs associated with karst spatial unit are within the „big river catchment‟ group (KT_BIG = 1), sum the accumulated current MAR (RSC_AMARNM) for all downstream RSCs (i.e. where KT_DWMSTRM = 1 in CFEV Karst Catchments attribute data) in the „big river catchment‟ group and assign as KT_BIGMAR.

When all RSCs associated with karst spatial unit are within the „small river catchment‟ group (KT_SMALL = 1), sum the accumulated current MAR (RSC_AMARNM) for all downstream RSCs (i.e. where KT_DWMSTRM = 1 in CFEV Karst Catchments attribute data) in the „small river catchment‟ group and assign as KT_SMLMAR.

Appendix 6 – Attribute data - Mining sedimentation


When there are RSCs associated with karst spatial unit within both the „big river catchment‟ and „small river catchment‟ groups (KT_BIG = 1 AND KT_SMALL = 1):

- If there are downstream RSCs (i.e. where KT_DWMSTRM = 1 in CFEV Karst Catchments attribute data) in the „big river catchment‟ group, then sum the accumulated current MAR (RSC_AMARNM) for all downstream RSCs only.

- If no downstream RSCs (i.e. where KT_DWMSTRM = 0 in CFEV Karst Catchments attribute data) are present in the „big river catchment‟ group, then sum the accumulated current MAR (RSC_AMARNM) for all RSCs in the group.

- If there are downstream RSCs (i.e. where KT_DWMSTRM = 1 in CFEV Karst Catchments attribute data) in the „small river catchment‟ group, then sum the accumulated current MAR (RSC_AMARNM) for all downstream RSCs (i.e. where KT_DWMSTRM = 1 in CFEV Karst Catchments attribute data) only.

- If no downstream RSCs are present in the „small river catchment‟ group (i.e. where KT_DWMSTRM = 0 in CFEV Karst Catchments attribute data), then sum the accumulated current MAR (RSC_AMARNM) for all RSCs in the group.

Rivers (RS_ACNMMAR, RS_ACNTMAR, RS_MAR)

Assign the local MAR (RS_MAR) and the upstream accumulated MAR (current (RS_ACNMMAR) and natural (RS_ACNTMAR)) of the RSC (as calculated above) directly to the river section it is associated with.

Waterbodies (WB_ACNMMAR, WB_ACNTMAR, WB_MAR)

Assign the local MAR (WB_MAR) and the upstream accumulated MAR (current (WB_ACNMMAR) and natural WB_ACNTMAR)) of the RSC (as calculated above) directly to the waterbody it is associated with.


used extensively in attributes that are accumulated for catchments.

6.3.32 Mining sedimentation

Title Mining sedimentation

Column heading RS_MINES

Input data

CFEV Mining sedimentation spatial data layer (Appendix 6.2.19)


Number of classes 2


Using the mining sedimentation spatial data layer, each river section was assigned a score indicating presence (0) or absence (1) of substantial mining sedimentation

Appendix 6 – Attribute data - Native fish assemblages


(RS_MINES), based on whichever was present along the majority of the river section length.


Rivers>Condition>Naturalness score (RS_NSCORE)>Sediment input (RS_SEDIN)

6.3.33 Native fish assemblages

Title Native fish assemblages

Column heading RS_FISH, WB_FISH

Input data

CFEV DEM (slope) (Appendix 6.2.6)

CFEV Native fish assemblages spatial data layer (Appendix 6.2.21)

CFEV Stream order (Appendix 6.3.47)

LIST 1:25 000 hydrographic theme (subset: waterbody – natural or dammed freshwater and salt flat), DPIW

LIST Waterfall data, DPIW


Number of classes RS_FISH = 54, WB_FISH = 35


Rules which considered native fish assemblage distribution (see Appendix 6.2.21), stream order, stream slope and the presence of natural barriers (waterfalls), were created to assign each river section and waterbody with a native fish assemblage class (e.g. F0, F1, F2, etc.) (RS_FISH and WB_FISH, respectively). A class (F0) was created to assign those features where native fish were deemed to have been absent or to have a low probability of occurrence under pre-European conditions. The attribution rules are outlined below.

River sections (RS_FISH)

1. Does the river section have a stream order (RS_ORDER) = 1 or 2?

Yes: Class F0 = fish absent or at very low density.

No: Go to 2.

2. Does the river section have a slope (available as a gradient in RS_SLOPE) >10%?


No: Go to 3.

3. Is the river section upstream of one or more contiguous (immediately adjoining) river sections which have a slope (RS_SLOPE) >10% and a cumulative length >950 m?


No: Go to 4.

Appendix 6 – Attribute data - Native fish assemblages


4. Is the river section upstream of a waterfall which is located >10 km upstream of the coast or >5 km upstream of a waterbody with fish present?

No: Assign dominant fish assemblage class from the native fish assemblage spatial data layer.

Yes: Go to 5.

5. Is the river section located west of line 1 in Figure 26?

Yes: Class F49 (only Galaxias brevipinnis and Anguilla australis present).

No: Falls east of line 1: Class F0 = fish absent or at very low density.

6. Finally, after waterbodies are assigned (see waterbody native fish attribution rules below).

If the river section is upstream of waterbody classified as class F0 (i.e. WB_FISH = F0), assign it as class F0 also.

Waterbodies (WB_FISH)

1. Is the waterbody identified as being hyd004919825 (from the LIST 1:25 000 hydrographic theme (subset: waterbody – natural or dammed freshwater and salt flat), i.e. Lake Surprise, Frankland Range)?

Yes: Assign dominant fish assemblage class from native fish assemblage spatial data layer.

No: Go to 2.

2. Is the waterbody identified as:

being on Ben Lomond plateau (hyd004214550, hyd004214570, hyd004214668, hyd004214563)? or

being Rim Lake or upstream of it (hyd004986495, hyd004986538, hyd004986537, hyd004986526, hyd004986466)? or

having one or more contiguous (immediately adjoining) river sections downstream of it which have >10% slope and a cumulative length >950 m? or

being upstream of any waterbody with the above characteristics? or

being within a river section assigned to class F0? or

being isolated from the drainage?


No: Go to 3.

3. Is waterbody in a river section assigned to class F49?

Yes: Class F49 (only G. brevipinnis and/or A. australis present)

No: Assign dominant fish assemblage class from native fish assemblage spatial data layer.


Rivers>Classification

Waterbodies>Classification

Appendix 6 – Attribute data - Native fish condition


6.3.34 Native fish condition

Title Native fish condition

Custodian WRD, DPIW


Description Condition of native fish populations based on impacts associated with human activities.

Input data

CFEV Acid drainage attribute data (Appendix )

CFEV Lake level manipulation attribute data (Appendix 6.3.22)

CFEV Mining sedimentation attribute data (Appendix 6.3.32)

CFEV Native fish assemblage attribute data (Appendix 6.3.33)

LIST dams and storages, DPIW

Lineage

The native fish condition layer was created to indicate the effect of human activity on native fish populations using GIS mapping rules. The native fish condition index takes into account the presence of significant man-made barriers to fish, mining sedimentation and acid drainage as significant local influences on Tasmanian native fish distributions.

Only larger dam structures could be included as barriers in deriving this index. Other man-made small barriers such as weirs were not included due to inconsistent mapping and uncertainty over barrier status, i.e. some have fish ladders, leak or are not barriers at all times. A major dam table (Appendix 14) to indicate dams that were significant barriers to fish passage, was created following expert review of all mapped dams and storages. Map layers for acid mine drainage and mining sedimentation were developed through CFEV using data from the Tasmanian Acid Drainage Reconnaissance Survey (Gurung, 2001) (see Appendices 6.2.1 and 6.2.19, respectively).

The presence of exotic fish species within an ecosystem is also an important driver of native fish population condition within Tasmania. It was, however, assessed as a separate index to this native fish condition index (refer to Appendix 6.3.11). These two indices were considered together in developing the expert rule systems for deriving the biological condition index (see Appendix 4.1.5). Any use of these indices which consider status of native fish assemblages should take both indices (and/or their source data) into account. A future revision of the CFEV data should combine both the native fish condition and exotic fish indices into a single index.

The native fish condition score ranges from 1 (pristine/near pristine) to 0 (degraded condition), with a -9 score assigned to areas where native fish are naturally absent or have a low probability of occurrence. Rule for assigning a native fish condition score to river sections and waterbody spatial units are provided below.

Data limitations

The native fish condition inherits all the data limitations of the derivation processes and input data.



Column heading RS_FISHCON, WB_FISHCON

Appendix 6 – Attribute data - Native fish condition



Number of classes RS_FISHCON = 4, WB_FISHCON = 4


River sections (RS_FISHCON)

The following rules were used to assign each river section with a native fish condition score (RS_FISHCON):

1. All river sections that are assigned with a native fish assemblage class (RS_FISH) of „F0‟ to be assigned as -9 (i.e. fish absent).

2. All river sections within the Central Plateau polygon in Figure 46 to be assigned as 1.

3. All river sections upstream of a storage with a significant dam structure to be assigned as 0 (refer to Appendix 14 for list of major dams).

4. All river sections that have a mining sedimentation score (RS_MINES) = 0 to be assigned as 0.5.

5. All river sections that have an acid mine drainage score (RS_ACID) = 1 to be assigned as 0.

6. All remaining river sections to be assigned as 1.

Figure 46. Central plateau polygon for use in native fish condition rules.

Appendix 6 – Attribute data - Naturalness-Representativeness Class


Waterbodies (WB_FISHCON)

The following rules (applied in this order) were used to assign each waterbody with a native fish condition score (WB_FISHCON):

1. All waterbodies that are assigned with a native fish assemblage class (WB_FISH) of „F0‟ to be assigned as -9 (i.e. fish absent).

2. All waterbodies within the polygon in Figure 46 (see above) to be assigned as 1.

3. All waterbodies upstream of or associated with an artificial storage with a significant dam structure to be assigned as 0 (refer to Appendix 14 for list of major dams), with the exception of Lakes Sorell and Crescent, Great Lake, Arthurs Lake, Woods Lake, Lake Pedder, Lake St Clair for which a score of 0.67 applies.

4. All remaining waterbodies with a lake level manipulation score (WB_LLEVELM) of 0.2 or 0.4 to be assigned as 0.33.

5. All remaining waterbodies with a lake level manipulation score (WB_LLEVELM) of 0.6 to be assigned as 0.67.

6. All remaining waterbodies to be assigned as 1.


Rivers>Statewide audit>Condition assessment>Naturalness score (RS_NSCORE)>Biological condition (RS_BIOL)

Waterbodies>Statewide audit>Condition assessment>Naturalness score (WB_NSCORE)>Fish condition (WB_FISHC)

6.3.35 Naturalness-Representativeness Class

Title Naturalness-Representativeness (NR) Class

Custodian WRD, DPIW


Description A set of data that allows the naturalness (using N-score) and representativeness (using RCV) to be visualised together.

Input data

CFEV Naturalness (as generated using an expert rule system – see Appendix 4)


Lineage

The NR class data was developed to allow visualisation of the RCV data whilst still being able to obtain information about the naturalness at a site. When mapped, the RCV data produces a scattered pattern across the landscape with a contrast of areas ranging between Bands A, B, and C compared to the dominant distribution of naturalness which is typically poorer in the agricultural and urban areas and more pristine in the reserved areas, such as the WHA. The latter data set is often the one that most land and water managers are most familiar with. The scattering effect of the RCV data is a result of the spatial selection algorithm emphasising the need for all ecosystem values to have representative examples across their distribution. To illustrate the relationship between representativeness and naturalness, the NR class

Appendix 6 – Attribute data - Naturalness-Representativeness Class


was invented to enable the influence of each factor to be easily mapped and thus, interpreted. The NR class combines the categorised data of N-score (Table 41) with RCV using the rules given below.

Table 41. Naturalness categories for all ecosystem themes.

Category **_NSCOR_C (Min to max values)

Low 0 to 0.6

Medium >0.6 to 0.85

High >0.85 to 1

**the prefix for each ecosystem theme i.e. ES = estuaries, KT = karst, RS = rivers, SM = saltmarshes, WB = waterbodies and WL = wetlands.

Data limitations

The NR class is highly derived and inherits all the data limitations of the derivation processes and input data.



Column heading ES_NRCLASS, KT_NRCLASS, SM_NCLASS, RS_NRCLASS, WB_NRCLASS, WL_NRCLASS


Number of classes ES_NRCLASS = 9, KT_NRCLASS = 9, SM_NCLASS = 9, RS_NRCLASS = 9, WB_NRCLASS = 9, WL_NRCLASS = 9


An NR class (e.g. A1, A2, A3, etc.) was assigned to each of the ecosystem spatial units as **_NRCLASS, using the matrix given in Table 42 (e.g. if an estuary is rated a A-class RCV and has high naturalness, then assign class A1).

Table 42. Classification of representativeness and naturalness (R/N matrix)

R N High (1) Medium (2) Low (3)

A A1 A2 A3

B B1 B2 B3

C C1 C2 C3


NA

Appendix 6 – Attribute data - Platypus condition


6.3.36 Platypus condition

Title Platypus condition

Custodian WRD, DPIW


Column heading RS_PLATYP

Input data

CFEV Platypus condition spatial data layer (Appendix 6.2.22)


Number of classes 8


Rules for assigning each river section with a platypus condition score (RS_PLATYP) (see below) were derived by combining information in (Fox et al., 2004), with information from Sarah Munks (Forest Practices Authority, pers. comm.) and (Munday et al., 1998).

1. Is the riparian vegetation of the river section >75% natural (RS_NRIPV >0.75)?

Yes: Go to 2

No: Go to 3

2. Is the catchment of the river section infected with Mucormycosis (as per the platypus condition spatial data layer)?

Yes: Assign score as 0.5

No: Assign score as 1

3. Does the river section have a stream order (RS_ORDER) = 1?

Yes: Go to 4.

No: (therefore, stream order (RS_ORDER) is 2 or greater) Go to 5.

4. Is the riparian vegetation of river section <25% natural (RS_NRIPV <0.25)?

Yes: a. If river section is within a Mucormycosis infected catchment, assign score as 0 or

b. If river section is not within a Mucormycosis infected catchment, assign score as 0.3.

No: (therefore, riparian vegetation is 25-75% natural (RS_NRIPV = 0.25- 0.75)). a. If the river section is within a Mucormycosis infected catchment, assign score as 0.2 or b. If river section is not within a Mucormycosis infected catchment, assign score as 0.8.

5. Is the riparian vegetation of river section <25% natural (RS_NRIPV <0.25)?

Yes: a. If river section is within a Mucormycosis infected catchment, assign score as 0.4 or

b. If river section is not within a Mucormycosis infected catchment, assign score as 0.6.

Appendix 6 – Attribute data - Regulation index


No: (therefore, riparian vegetation is 25-75% natural (RS_NRIPV = 0.25- 0.75)). a. If river section is within a Mucormycosis infected catchment, assign score as 0.6 or b. If river section is not within a Mucormycosis infected catchment, assign score as 0.8.


Rivers>Statewide audit>Condition assessment>Naturalness score (RS_NCORE)> Biological condition (RS_BIOL)

6.3.37 Regulation index

Title Regulation index

Custodian WRD, DPIW


Description The amount of regulation of the natural flow regime due to the effect of all water storage upstream

Input data

CFEV Natural MAR attribute data (Appendix 0)


CFEV Waterbody artificiality attribute data (Appendix 6.3.53)

CFEV Waterbodies spatial data layer („invalid‟) (Appendix 6.2.32)

Hydro infrastructure and discharge data (volume), Hydro Tasmania

LIST Farm dam layer, DPIW

WIMS database, DPIW

Lineage

The flow regulation index, REGI, rates all river sections according to the amount of regulation of the natural flow regime due to the effect of all water storage upstream. This assumes that one dam volume is captured per year. This is comparable to the active Hydro storage volume. The regulation index is derived by summing all known storage volumes upstream and dividing the sum by the modelled natural MAR for the river section in question:

MAR natural dAccumulate

upstream storage CumulativeIndex Regulation



The sum of all „active‟ storage within the upstream RSCs catchment and the local RSC is divided by the accumulated natural MAR of RSCs (RSC_AMARNT). The accumulated natural MAR was modelled for all river sections and RSCs, as described in Appendix 0. Cumulative storage was calculated by summing all „active‟ storage volumes for all „modified‟ waterbodies upstream in the catchment. These include:

CFEV waterbodies with an artificiality score (WB_ARTIF) = 0 (see Appendix 6.3.53)

all LIST-mapped large and small farm and other storage dams, with volumes estimated from an area-volume relationship (the „invalid‟ waterbodies)

all dams not mapped but included in the WIMS database.

all „active‟ Hydro storage (i.e. useable volumes supplied by Hydro Tasmania).

The cumulative upstream storage was calculated using the following rules:

1. Create the „modified‟ waterbodies data set:

a. Draft data set: Combine the following data sets to develop a single „modified‟ waterbodies data set (note, there may be overlap between the following criteria):

all „invalid‟ waterbodies (see Appendix 6.2.32)

all „valid‟ waterbodies that have been rated as being artificial (WB_ARTIF = 0) (see Appendices 6.2.32 and 6.3.53)

all WIMS dams (catchment dams, off-stream dams, and on-stream dams)

all farm dams on LIST identified as not being included in WIMS (approximately 31% of all farm dams identified from LIST)

b. Final layer: Identify and remove duplicates of WIMS dams:

Remove WIMS dam if a WIMS Point falls within 200 m of another

artificial waterbody of similar volume (within range of 4 times the capacity);

Note:

Arthurs Lake was removed from WIMS data set

Lake Leake storage volume was derived as being = 20% of storage capacity

those WIMS dams with 0 (null) capacity value in the WIMS database were set a nominal capacity = median value of range of all WIMS total capacity records.

2. Calculate storage volume (ML) for all „modified‟ waterbodies (as described above):

For Hydro storages, assign with the useable volume (USE_VOLUME) supplied by Hydro Tasmania

For WIMS dams, assign with the capacity (CAPACITY from WIMS database) (ML)

For all other waterbodies (non Hydro and non WIMS) (i.e. farm dams not in WIMS and other storages (e.g. mine tailings ponds, Lake Leake



and Tooms Lake etc.)), calculate their volumes from their polygon areas using the equation:

Volume (ML)=21.129*(Area(ha)1.1057

)

This equation was developed by analysis of relationships between farm dam volume and surface area, using the WIMS data for off-stream and catchment dams (n = 167, after reviewing and screening the data for unreliable and bad records). The relationship compared favourably against a similar relationship developed by Sinclair Knight Merz (SKM, 2003; Lowe et al., 2005).

3. Assign „modified‟ waterbodies to the RSCs.

4. Calculate the sum of all storage volume per RSC.

5. Calculate the upstream cumulative storage by summing all the storage volumes in all upstream RSCs plus the local RSC.

6. Divide the sum of upstream cumulative storage (value from rule 5) by the natural MAR (RSC_AMARNT). Assign value to each RSC.

The regulation index has no units and ranges from zero, where there are no storages upstream, to a large positive number. Banding ranges for the regulation index were assessed following inspection of index values from a range of river sections and comparing with in-stream impacts observed by Davies et al. (1999) from selected Hydro-impacted streams. Impacts on stream biotic condition and geomorphology were considered to be high (low condition) when the regulation index is >0.15, medium when between 0.05 and 0.15, and low to absent when <0.05.

The specific rules for calculating the regulation index for karst, rivers, waterbodies and wetlands are outlined below.

Data limitations

The regulation index inherits all the data limitations of the derivation processes and input data.



Column heading KT_REGI, RS_REGI, WB_REGI

Type of data Continuous but also exists in a categorical format

Number of classes KT_REGI_C = 3, RS_REGI_C = 3, WB_REGI_C = 3




A regulation index was assigned to karst, river and waterbody spatial units using the following rules.

Karst (KT_REGI)

1. Divide RSCs assigned to each karst unit into two subsets: „big river catchments‟ (those RSCs with an accumulated current MAR (RSC_AMARNM)>48.2 GL) and „small river catchments‟ (all other RSCs). Note, more details on the rational for the big and small catchment split are provided in the MAR section (Appendix 0).

2. Calculate the regulation index for „big river catchments‟ by a MAR weighted average of all downstream-most catchments in the big river catchment group for each karst area as:

RRSC_ACNMMA

RSC_MARRSC_REGI...RSC_MARRSC_REGIindex regulationriver Big 11 nn

Where:

Big river regulation index = Regulation index for the karst spatial unit (only taking into account the RSCs of the karst local catchment which have RSC_AMARNM>42.8 GL)

RSC_REGI(1…n) = Regulation index for the RSCs of the karst local catchment which have RSC_AMARNM>42.8 GL

RSC_MAR(1…n) = Current MAR value for the RSCs of the karst local catchment which have RSC_AMARNM>42.8 GL

RSC_ACNMAR = Upstream accumulated current MAR value for the RSCs of the karst local catchment which have RSC_AMARNM>42.8 GL

3. Calculate the regulation index for „small river catchments‟ by a MAR weighted average of all downstream-most catchments in the small river catchment group for each karst area as:

RRSC_ACNMMA

RSC_MARRSC_REGI...RSC_MARRSC_REGIindex regulationriver Small 11 nn

Where:

Small river regulation index = Regulation index for the karst spatial unit (only taking into account the RSCs of the karst local catchment which have RSC_AMARNM≤42.8 GL)

RSC_REGI(1…n) = Regulation index for the RSCs of the karst local catchment which have RSC_AMARNM≤42.8 GL

RSC_MAR(1…n) = Current MAR value for the RSCs of the karst local catchment which have RSC_AMARNM≤42.8 GL

RSC_ACNMAR = Upstream accumulated current MAR value for the RSCs of the karst local catchment which have RSC_AMARNM≤42.8 GL



4. Assign all karst spatial units with an regulation index as a weighted average of the big and small regulation values as follows:


KT_REGI = (Big river regulation index * 0.2) + (Small river regulation index * 0.8)

Where:

KT_REGI = Regulation index of the karst spatial unit

Big river regulation index = Regulation index for the karst spatial unit (only taking into account the RSCs of the karst local catchment which have RSC_AMARNM>42.8 GL) (calculated in Step 2)

Small river regulation index = Regulation index for the karst spatial unit (only taking into account the RSCs of the karst local catchment which have RSC_AMARNM≤42.8 GL) (calculated in Step 3)

River sections (RS_REGI)

Assign the regulation index of the RSC (as calculated above) directly to the river section it is associated with.

Waterbodies (WB_REGI)

Assign the regulation index of the RSC (as calculated above) directly to the waterbody it is associated with.

Each of the karst, river and waterbody spatial data layers has the continuous regulation index data categorised according to Table 43. The categorical data was used for reporting and mapping purposes.

Table 43. Regulation index categories for karst, rivers and waterbodies.

Category KT_REGI_C (Min to max values)

RS_REGI_C (Min to max

values)

WB_REGI_C (Min to max values)

1 0 to <0.05 0 to <0.05 0.15 to 153.4145

2 0.05 to <0.15 0.05 to <0.15 0.05 to <0.15

3 0.15 to 0.778101616 0.15 to 1581154893

0 to <0.05


Karst>Statewide audit>Condition assessment>Naturalness score (KT_NSCORE)>Hydrology (KY_HYDRO)

Rivers>Statewide audit>Condition assessment>Naturalness score (RS_NSCORE)>Geomorphic condition (RS_GEOM)>Flow change (RS_FLOW)

Rivers>Statewide audit>Condition assessment>Naturalness score (RS_NSCORE)>Geomorphic condition (RS_GEOM)>Sediment capture-surrogate (RS_SEDCA)


Appendix 6 – Attribute data - Representative Conservation Value


6.3.38 Representative Conservation Value

Title Representative Conservation Value (RCV)

Custodian WRD, DPIW


Description Ranking of relative conservation value for Tasmania‟s freshwater-dependent ecosystems

Input data

Output from the CFEV Project‟s spatial selection algorithm

Lineage

RCV is a ranking or relative conservation expressed as the relative importance of a representative biophysical class, with a priority on spatial units of high naturalness. The rules (outlined below) for assigning the ecosystem spatial units with an RCV rating were developed by the CFEV TMG (Appendix 1) using the raw ranking data output from the spatial selection algorithm (Appendix 5). This output is included in the CFEV database for each ecosystem theme as ES_SORTIT, KT_SORTIT, etc.).



Column heading ES_RCV, KT_RCV, SM_RCV, RS_RCV, WB_RCV, WL_RCV


Number of classes ES_RCV = 3, KT_RCV = 3, SM_RCV = 3, RS_RCV = 3, WB_RCV =3, WL_RCV = 3


An RCV ranking (A, B or C) was assigned to estuary, karst, saltmarsh, river, waterbody and wetland spatial units based on the Project‟s conservation objectives. The conservation objectives include conserving at least two examples of every ecosystem biophysical class (e.g. (Brown et al., 1983)), conserving a minimum of approximately 15% of total extent of each ecosystem theme, (e.g. JANIS biodiversity criteria, sensu (Commonwealth of Australia, 1997))) and giving priority to spatial units in better condition. These conservation principles are translated to banding rules in the following sections for each ecosystem theme.

Estuaries (ES_RCV)

The banding of RCV for estuaries was based on the number, not area, of estuaries, as estuary values were dependent on the entire estuary as a unit, and not consistently on estuary size. Two examples of most biophysical classes were selected in the first 30% of estuaries selected by the spatial selection algorithm, including the best example of each biophysical class. At this point, all biophysical classes which occur in only one estuary were also selected within band A. There were no estuaries selected within the C band due to the low number of estuaries assessed. There were also a relatively low number of total biophysical classes, several of which occurred in only one or two estuaries.

The RCV banding rules for estuaries were as follows:

Assign at least the first two estuaries selected by the spatial selection algorithm for each biophysical class as A.



Note: Some biophysical classes may occur in a single estuary, in which case assign as A.

Assign estuaries selected by the spatial selection algorithm within the first 30% of total estuary number as A.

Assign all remaining estuaries as B.

No C band was assigned to this ecosystem theme.

Karst (KT_RCV)

The banding of RCV for karst was based on the number of karst systems, not area, due to the integrated nature of individual karst systems. By the time 30% of the total number of karst units had been selected by the spatial selection algorithm, an example of every class had been included in band A. At this point, 36 of the rarest karst classes had 100% of the karst systems in which they occurred selected within band A. The inclusion of at least two examples for each karst class in band A would require capturing a large proportion of the total number of karst systems. However, inclusion of the first rule (below) ensures that two examples of every class were selected within band A. C band was not populated for karst systems, due to the low number of mapped karst units and the unique biological diversity and high levels of local endemism thought to be present within individual karst systems. While a karst biological classification was not undertaken as part of the CFEV Project, the banding rules (absence of band C) recognise the biological significance of karst systems.

The RCV banding rules for karst units were as follows:

Assign at least the first two karst selected by the spatial selection algorithm for each biophysical class as A.

Assign karst selected by the spatial selection algorithm within the first 30% of total karst number as A.

Assign all remaining karst as B.

No C band was assigned to this ecosystem theme.

Rivers (RS_RCV)

The banding of RCV for rivers was based on river section length. Upon selection of 15% of the total river section length by the spatial selection algorithm (in band A), all of the biophysical classes were selected within at least two river clusters, except for one biophysical class which was totally selected within one river cluster. At this point, five of the rarest biophysical classes had 100% of the river sections in which they occurred selected, and another two biophysical classes had greater than 80% selected in band A. A large proportion of the common biophysical classes were also selected within the 15% threshold, hence some rules were set to reduce the amount of those selected within band A. An expert panel determined the rules which resulted in appropriate proportions of river sections being allocated within each RCV band.



The RCV banding rules for rivers were as follows:

Assign at least the first two river clusters selected by the spatial selection algorithm for each biophysical class as A.

Assign river sections selected by the spatial selection algorithm within the first 15% of total river section length as A, with the following exceptions:

If the biophysical classes driving the selection of the river section has a total extent >50 000 km, only assign those river sections that are selected by the spatial selection algorithm within the first 5% of total river section length as A, otherwise assign as B.

If the biophysical class driving the selection of the river section has a total extent of 10 000-50 000 km, only assign those that are selected by the spatial selection algorithm within the first 10% of total river section length as A, otherwise assign as B.

If the biophysical class driving the selection of the river section has a total extent <10 000 km, only assign those that are selected by the spatial selection algorithm within 15-50% of total river section length as B.

If the biophysical class driving the selection of the river section has a total extent of 10 000-50 000 km, only assign those that are selected by the spatial selection algorithm within 10-40% of total river section length as B.

If the biophysical class driving the selection of the river section has a total extent of >50 000 km, only assign those that are selected by the spatial selection algorithm within 5-30% of total river section length as B.

If the biophysical class driving the selection of the river section has a total extent <10 000 km, assign those that are selected by the spatial selection algorithm within 50-100% of total river section length as C.

If the biophysical class driving the selection of the river section has a total extent of 10 000-50 000 km, assign those that are selected by the spatial selection algorithm within 40-100% of total river section length as C.

If the biophysical class driving the selection of the river section has a total extent of >50 000 km, assign those that are selected by the spatial selection algorithm within 30-100% of total river section length as C.

Saltmarshes (SM_RCV)

The banding of RCV for saltmarshes was based on saltmarsh area, since saltmarshes with larger area are seen as having greater bioconservation significance (e.g. diversity and area of meso-habitats, etc.) than smaller ones. By the time of 50% of the total number of saltmarshes had been selected by the spatial selection algorithm, two examples of the majority of biophysical classes were selected within band A and these include the best examples. To ensure that an adequate proportion of the rare biophysical classes were included in band A, it was then decided that any biophysical class with an extent <100 ha should have the saltmarshes in which it occurs grouped within band A. C band was not populated as it was believed that



current saltmarshes were mere fragments of their former extent, hence conservation of these remaining saltmarsh ecosystems was considered highly important.

The RCV banding rules for saltmarshes were as follows:

Assign at least the first two saltmarshes selected by the spatial selection algorithm for each biophysical class as A.

Assign saltmarshes containing any biophysical class with a total extent <100 ha as A.

Assign saltmarshes selected by the spatial selection algorithm within the first 50% of total saltmarsh area as A.

Assign all remaining saltmarshes as B.

No ‘C’ band was assigned to this ecosystem theme.

Waterbodies (WB_RCV)

The banding of RCV for waterbodies was based on the number of spatial units rather than area, as waterbody values were dependent on the entire waterbody as a unit, and not consistently on waterbody size. In most cases, two examples of every biophysical class were selected within band A upon selection of 30% of the total number of waterbodies. Where only one example was selected within the first 30%, it was usually because this biophysical class only occurred in one waterbody. At this point, approximately 40% of all waterbody biophysical classes were totally selected. Some of the more common biophysical classes have a high proportion of their occurrences selected in the first 30% of the spatial selection algorithm iterations . It was decided that a maximum threshold of 50 waterbodies would apply to the representation of each biophysical class in band A, with the remaining waterbodies selected in the top 30% assigned to band B.

The RCV banding rules for waterbodies were as follows:

Assign at least the first two waterbodies selected by the spatial selection algorithm for each biophysical class as A.

Assign the first 30% of all waterbodies selected by the spatial selection algorithm as A, with the following exception:

- If the biophysical class driving the selection of the waterbody has a total number >50 of occurrences within the first 30% of waterbodies, only assign the first 50 waterbodies selected by the spatial selection algorithm with that biophysical class as A, otherwise assign as B.

Group remaining waterbodies by biophysical classes (i.e. waterbody will occur in more than one group).

Sort by Naturalness score and then selection order (i.e. conservation value ranking, the order of output from the spatial selection algorithm).

Select the top half of waterbodies within each group and assign as B; assign the bottom half within each group as C.

If a waterbody is in the top half of at least one biophysical class group (i.e. any of its biophysical classes has been assigned as „B‟ at least once), that waterbody is assigned as B; otherwise assign as C.



Wetlands (WL_RCV)

The banding of RCV for wetlands was based on wetland area, since wetlands with larger area are seen as having more bioconservation significance (e.g. diversity and area of meso-habitats, etc.) than smaller ones. Upon selection of 20% of the total area of wetlands by the spatial selection algorithm, all of the best examples for each biophysical class were selected within band A, however, not always two examples. Upon selection of 30% of the total area of wetlands, all but one biophysical class either had two examples selected or 100% of the wetlands in which they occurred were selected in band A. Approximately four of the rarer biophysical classes had 100% of the wetlands in which they occurred selected in band A. To ensure an adequate selection of the rare biophysical classes within band A, it was decided to include 100% of wetland spatial units containing biophysical classes (i.e. the class driving the selection of the wetland polygon) with a total extent <500 ha.

When 30% of the total area of wetlands had been selected, some of the extensive classes were selected within a high proportion of the selected wetlands. The TMG decided it was not appropriate to select all of these extensive classes within band A. Hence for any biophysical class that had a total extent >5000 ha, only those selected after 12.5% of the total wetland area were allocated to band A.

The RCV banding rules for wetlands were as follows:

Assign at least the first two wetlands selected by the spatial selection algorithm for each biophysical class as A.

Assign wetlands containing any biophysical class with a total extent <500 ha as A.

Assign wetlands selected by the spatial selection algorithm within the first 30% of total wetland area as A, with the following exceptions:

- If the biophysical class driving the selection of the waterbody was a total extent >5000 ha, only assign those wetlands that were selected by the spatial selection algorithm within the first 12.5% of total wetland area as A; the remaining wetlands with biophysical classes >5000 ha (selected by the spatial selection algorithm within 17.5 – 30% of total wetland area) should be assigned as B.

Group remaining wetlands by biophysical classes (i.e. wetland will occur in more than one group).

Sort by Naturalness score and then selection order (i.e. conservation value ranking, the order of output from the spatial selection algorithm).

Select the top half of wetlands within each group and assign as B; assign the bottom half within each group as C.

If a wetland was in the top half of at least one biophysical class group (i.e. was assigned as „B‟ at least once), that wetland was assigned as B; otherwise assign as C.


Estuaries>Conservation evaluation Karst>Conservation evaluation Rivers>Conservation evaluation Saltmarshes>Conservation evaluation Waterbodies>Conservation evaluation Wetlands>Conservation evaluation

Appendix 6 – Attribute data - Riparian vegetation condition


6.3.39 Riparian vegetation condition

Title Riparian vegetation condition (Native and/or exotic)

Column heading RS_NRIPV (Native riparian vegetation), WB_CRIVE (Exotic riparian vegetation), WB_NRIVE (Native riparian vegetation), WL_NRIVE (Native riparian vegetation)

Input data

CFEV Buffer zone (rivers, waterbodies and wetlands) spatial data layer (Appendix 6.2.2)

CFEV Modified TASVEG spatial data layer (Appendix 6.2.20)


Number of classes RS_NRIPV = 4, WB_CRIVE = 4, WB_NRIVE = 4, WL_NRIVE = 4


Rivers section native riparian vegetation (RS_NRIPV)

1. Intersect CFEV modified TASVEG spatial data layer with the rivers buffer zone spatial data layer.

2. Calculate % area within buffer zone (50 m buffer either side of river section) that is Natural.

3. Assign proportional value (i.e. 100% = 1 to 0% = 0) to the river section.

Waterbodies (WB_NRIVE) and wetlands (WL_NRIVE) native riparian vegetation

1. Intersect CFEV modified TASVEG spatial data layer with the waterbody and wetlands buffer zone spatial data layers.

2. Calculate % area within buffer zone (100 m buffer around waterbody and wetland polygons) that is Natural.

3. Assign proportional value (i.e. 100% = 1 to 0% = 0) to the waterbody and wetland spatial units.

Waterbodies exotic riparian vegetation (WB_CRIVE)

1. Intersect CFEV modified TASVEG spatial data layer with the waterbody buffer zone spatial data layer.

2. Calculate % area within buffer zone (100 m buffer around) that is Exotic.

3. Assign proportional value (i.e. 100% = 1 to 0% = 0) to the waterbody spatial units.

Each of the river, waterbody and wetland spatial data layers had the continuous riparian vegetation condition data categorised according to Table 44. The categorical data was used for reporting and mapping purposes.

Appendix 6 – Attribute data - River section numbering


Table 44. Riparian vegetation condition categories for rivers, waterbodies and wetlands.

Category RS_NRIPV_C (Min to max

values)

WB_CRIVE_C (Min to max

values)

WB_NRIVE_C (Min to max

values)

WL_NRIVE_C (Min to max

values)

1 0 0 to <0.2 0 0

2 >0 to 0.2 0.2 to <0.8 >0 to 0.2 >0 to 0.2

3 >0.2 to 0.8 0.8 to <1 >0.2 to 0.8 >0.2 to 0.8

4 >0.8 to 1 1 >0.8 to 1 >0.8 to 1


Rivers>Statewide audit>Condition assessment>Naturalness score (RS_NSCORE)>Biological condition (RS_BIOL) – Native riparian vegetation

Waterbodies>Statewide audit>Condition assessment>Naturalness score (WB_NSCORE) – Native riparian vegetation

Waterbodies>Statewide audit>Condition assessment>Naturalness score (WB_NSCORE)>Sediment input (WB_SEDIN) – Exotic riparian vegetation

Wetlands>Statewide audit>Condition assessment>Naturalness score (WL_NSCORE)>Native vegetation (WL_NATVE) – Native riparian vegetation

6.3.40 River section numbering

Title River section numbering

Column heading RS_DESTNM, RS_NUMNM, RS_UNUMNM, RS_VDESTNM, RS_VNUMNM, RS_VUNUMNM, RS_DESTNA, RS_NUMNA, RS_UNUMNA, RS_VDESTNA, RS_VNUMNA, RS_VUNUMNA

Input data




Each of the river sections was assigned with an upstream/downstream numbering system. A breakdown of the column headings identify the different options for the stream numbering as follows:

DEST – Refers to the most downstream segment (i.e. if you keep going downstream from the segment, you will eventually arrive at the terminal segment).

NUM – One of the two attributes used in the numbering scheme to identify the order of river section linkages (indicated as NUMBER in the rules below).

UNUM – One of the two attributes used in the numbering scheme to identify the order of river section linkages (indicated as UP_NUMBER in the rules below).

NM – Refers to the Current (or Normal) flow regime whereby the direction of water flow takes the predominant flow regime path and can include artificial pipes.

Appendix 6 – Attribute data - River section numbering


NA – Refers to the Natural flow regime whereby water takes the most natural path and never flows down a pipe.

V – Refers to „virtual‟ numbers that have had all the sinks removed (i.e. the network as described by the „virtual‟ links ensures that all drainage lines run to the ocean. The non-virtual numbers have lots of places where the flow stops before it gets to the coast.

Numbering scheme

The numbering scheme uses two attributes, NUMBER and UP_NUMBER to encode relationships.

Numbering starts from a stream sink and progresses upwards through the drainage network. For example, the first stream sink may be numbered 1 (i.e. NUMBER = 1).

The next segment to be numbered will be the leftmost of the stream segments that flow into stream 1. This will be numbered 2. The next leftmost upstream segment will be numbered 3 and so on up the drainage network.

At some stage a headwater river section is reached. When this happens, numbering returns to the next downstream segment and the next incoming stream to the right is chosen as the next numbered segment.

Numbering again continues upstream until the next headwater river section is encountered, and after each headwater river section, numbering continues at the next unnumbered downstream segment.

This process continues until all river sections upstream of the original sink are numbered. From there, the next sink is chosen and numbering continues with the next available number.

Eventually, all river sections are assigned a NUMBER value. At this stage, the UP_NUMBER attributes are filled in. The UP_NUMBER attribute value is defined as being the maximum NUMBER values of all upstream features (headwater river sections will have UP_NUMBER = NUMBER).

The stream numbering scheme used was a new scheme and allows simple attribute selections such as:

1. Select all features downstream of, or equal to feature A.

2. Select all features upstream of feature A.

3. Select all features downstream of feature A and upstream of feature B.

4. Is feature A upstream of feature B?

The four example selections above can then be specified as follows:

1. Select X where A.NUMBER ≥ X.NUMBER and A.NUMBER ≤ X_UP_NUMBER.

2. Select X where X.NUMBER > A.NUMBER and X.NUMBER ≤ A.UP_NUMBER.

3. Select X where (A.NUMBER > X.NUMBER and A.NUMBER ≤ X.UP_NUMBER) and (X.NUMBER > B.NUMBER and X.NUMBER ≤ B.UP_NUMBER).

4. (A.NUMBER > B.NUMBER and A.NUMBER ≤ B.UP_NUMBER)?

This numbering scheme was the basis for assigning a Strahler stream order to each of the river sections in the rivers spatial data layer (see Appendix 0 for details).

Appendix 6 – Attribute data - Saltmarsh biophysical classification


6.3.41 Saltmarsh biophysical classification

Title Saltmarsh biophysical classification

Custodian WRD, DPIW


Description Biophysical classification of Tasmania‟s saltmarshes.

Input data

CFEV Area (saltmarshes) attribute data (Appendix 6.3.3)

CFEV Saltmarsh location attribute data (Appendix 6.3.42)

CFEV Saltmarsh vegetation attribute data (Appendix 6.3.43)

CFEV Tidal/wave energy zone attribute data(Appendix 6.3.48)

Lineage

A biophysical classification for saltmarshes was undertaken using data on its location, dominant vegetation, tidal/wave energy and size as descriptors. For the classification, the saltmarsh area data was grouped into three size classes (1 & 2 = <1 ha, 3 & 4 = ≥1 to <100 ha and 5 = ≥100 ha) before being combined with the other input components.

These four data sets were combined in a matrix to give an overall biophysical classification for saltmarshes, resulting in a total of 23 classes. The biophysical classes were assigned to each of the saltmarsh spatial units based on the rules described in Table 45 (see below).

Data limitations

The biophysical classification inherits all the data limitations of the derivation processes and input data.



Column heading SM_BPCLASS




A biophysical class (e.g. Sm1, Sm2, Sm3, etc.) was assigned to saltmarsh spatial units as SM_BPCLASS using the rules in Table 45 (e.g. if the saltmarsh is located within the Furneaux group of islands, is dominated by generic saltmarsh vegetation (TASVEG code Ma), is within tidal/wave energy zone 1 or 2 and is small (area category 1 & 2), then assign Sm1). Note, only the combinations of biophysical classes that had saltmarsh membership were included.

Table 45. Summary of classification rules for saltmarshes.

Class code Location Dominant vegetation Tidal/Wave energy zone Area (ha)

Sm1 Furneaux group Ma 1 or 2 1 & 2

Sm2 Furneaux group Ma 1 or 2 3 & 4

Sm3 Furneaux group Mg 1 or 2 3 & 4

Sm4 Furneaux group Mg 1 or 2 5

Appendix 6 – Attribute data - Saltmarsh location


Class code Location Dominant vegetation Tidal/Wave energy zone Area (ha)

Sm5 Furneaux group Ms 1 or 2 1 & 2

Sm6 Furneaux group Ms 1 or 2 3 & 4

Sm7 King Island Ma 4 3 & 4

Sm8 Mainland Ma 1 1 & 2




Sm12 Mainland Ma 2 or 3 1 & 2

Sm13 Mainland Ma 2 or 3 3 & 4

Sm14 Mainland Ma 2 or 3 5

Sm15 Mainland Mg 1 1 & 2

Sm16 Mainland Mg 1 3 & 4

Sm17 Mainland Mg 1 5

Sm18 Mainland Mg 2 or 3 1 & 2

Sm19 Mainland Mg 2 or 3 3 & 4

Sm20 Mainland Ms 1 3 & 4

Sm21 Mainland Ms 2 or 3 1 & 2

Sm22 Mainland Ms 2 or 3 3 & 4

Sm23 Mainland Ms 2 or 3 5


Saltmarshes>Classification

6.3.42 Saltmarsh location

Title Saltmarsh location

Custodian WRD, DPIW


Description General location of saltmarshes within Tasmania.

Input data

Location map of Tasmania

Lineage

The location of the saltmarshes within a state context was identified as being either from King Island, within the Furneaux group or on the main part of Tasmania. These areas are thought to be biologically distinct, particularly in terms of invertebrates and migratory birds.

The saltmarsh location was assigned to each of the saltmarsh spatial units based on the rules described in Table 45 (see Appendix 6.3.41 above).


Appendix 6 – Attribute data - Saltmarsh vegetation



Column heading SM_LOCAT


Number of classes 3


A saltmarsh location category (Furneaux group, King Island or Mainland) was assigned each of the saltmarsh spatial units according to their geographic location within the state.



6.3.43 Saltmarsh vegetation

Title Saltmarsh vegetation

Description Dominant vegetation type within saltmarshes

Column headings SM_MG, SM_MS, SM_MA, SM_DOM

Input data



Type of data Continuous (%): SM_MG, SM_MS, SM_MA

Categorical: SM_DOM

Number of classes 3 (SM_DOM)


The proportion of each of the saltmarsh vegetation types (Ms – succulent saltmarsh, Ma – generic saltmarsh, Mg – graminoid saltmarsh) within each saltmarsh spatial unit was determined by calculating the % area of each TASVEG code within each saltmarsh cluster. The proportion (0-1) of each vegetation type was assigned to each saltmarsh spatial unit as SM_MS, SM_MA and SM_MG, respectively.

The dominant vegetation type (Ms, Ma or Mg) within each cluster was then assigned to each saltmarsh spatial unit as SM_DOM.



6.3.44 Shoreline complexity

Title Shoreline complexity

Custodian WRD, DPIW


Description Assessment of the variability of each waterbody‟s edge.

Appendix 6 – Attribute data - Shoreline complexity


Input data

CFEV Area (waterbodies) attribute data (Appendix )


Lineage

Shoreline complexity, also known as shoreline development, was calculated for all waterbodies as it was thought to correlate with habitat heterogeneity. It is a measure of the variability of a waterbody‟s edge. Shoreline development is the ratio of the length of the shoreline to the length of the circumference of a circle whose area is equal to that of the waterbody. The shoreline development formula used in this analysis is given below (Hakanson and Jansson, 1983):

SA

SLDL

2

Where: DL = Shoreline complexity

SL = Shoreline length (m) (excluding island shores)

SA = Lake surface area (m²) (excluding island areas)

Shoreline lengths and waterbody surface areas were derived from waterbody polygon perimeters and areas obtained from the CFEV waterbodies spatial data layer (WB_SHORPER and WB_AREA, respectively). These variables were calculated using standard perimeter and area scripts in ArcGIS (GIS software).

The shoreline complexity value was calculated and assigned to each of the waterbody spatial units based as per the rules described below.

Data limitations

As per the CFEV waterbodies spatial data layer (Appendix 6.2.32)



Column heading WB_SHOREDE



The calculated shoreline complexity value (DL) was assigned to the waterbody spatial units as WB_SHOREDE.

References (Hakanson and Jansson, 1983)


Waterbodies>Classification>Physical class (WB_PCLASS)

Appendix 6 – Attribute data - Spartina anglica (rice grass)


6.3.45 Spartina anglica (rice grass)

Title Spartina anglica

Custodian WRD, DPIW


Description Presence or absence of Spartina anglica (rice grass) adjacent to saltmarshes

Input data

Distribution of rice grass, Marine Environment Section, DPIWE

Lineage

Rice grass (Spartina anglica) is a vigorous exotic grass, which is commonly found along the edges of estuaries and within saltmarshes. The grass is known to spread quickly, destroying fish habitat and smothering native plants in the process.

A series of data layers showing the distribution of rice grass was provided by the Marine Environment Section (DPIWE). During 1999-2003, the Marine Environment section of DPIWE received funding through the Natural Heritage Trust (NHT) and Fisheries Action Program to implement the Rice Grass Management Program. An output of this project was mapped layers showing the distribution of rice grass following this eradication program. The Spartina data layers consisted of point data representing small patches of rice grass of either 3 m2 or 5 m2 in size and polygon data representing „meadows‟ of large continuous areas of rice grass, all surveyed in late 2003 (C. Shepherd, DPIW, pers. comm.).

The data layers described above were used in conjunction with aerial photographs to determine the presence or absence of Spartina anglica adjacent to saltmarshes around Tasmania (see rules below).

Data limitations

The Spartina angelica data inherits all the data limitations of the input data.



Column heading SM_SPADJ


Number of classes 2


If Spartina anglica was found adjacent to a saltmarsh, it was assigned a score of 0 (present). If absent, the spatial unit was assigned a score of 1 (absent).


Saltmarshes>Statewide audit>Condition assessment>Naturalness score (SM_NSCORE)>Impacts adjacent to saltmarsh (SM_IMADJ)>Adjacent vegetation (SM_VGADJ)

Appendix 6 – Attribute data - Special values


6.3.46 Special values

Title CFEV Special Values (SVs)

Custodian WRD, DPIW


Description Freshwater special value records

Input data

GTSpot database, DPIWE

Native fish database, IFS

Scientific literature (see individual sections for each SV type)

Tasmanian Geoconservation Database, DPIWE


Threatened species records from the Threatened Species Unit, DPIWE (other than those in the GTSpot database)

Lineage

Special Values are distinctive values associated with freshwater-dependent ecosystems and consist of the following types (the page references relate to sections below which provide information on each of the SV types):

Threatened flora species (Page 290)

Threatened fauna species (Page 296)

Threatened flora communities (Page 302)

Priority flora communities (Page 302)

Priority fauna communities (Page 303)

Priority flora species (Page 304)

Priority fauna species (Page 304)

Priority geomorphic features (Page 310)

Priority limnological features (Page 314)

Fauna species richness (Page 315)

Fauna species of phylogenetic distinctiveness (Page 316)

Palaeolimnological sites (Page 320)

Palaeobotanical sites (Page 320)

Important bird sites (Page 321)

Individual SVs were selected from priority listings, using expert knowledge or using criteria as outlined in Section 12 of the main report. Only SVs relating to the six freshwater-dependent ecosystem themes were considered. Data records for each of the SVs were collated from a range of different sources (e.g. GTSpot, TASVEG, University of Tasmania, Queen Victoria Museum and Art Gallery, Tasmanian Geoconservation Database). Each SV can have more than one data record relating to its distribution and only data records reliable within 500 m were used. The SVs were broadly ranked, by experts, according to their overall importance (outstanding, non-outstanding and undifferentiated – explained in detail in Section 12 of the main report) and were only assigned to spatial units of relevant ecosystem themes (e.g.



the threatened Port Davey Skate (Dipturus sp.) is only know to occur in estuaries, so its data records are only assigned to the nearest estuary spatial units).

The following sections detail the different types of SV, including a list of the SVs, their rankings, the ecosystem themes they are known to be associated with, and various reference information including their nomination as a SV and data sources.

Initially, the SV data was collated as separate point (e.g. threatened species records) and polygon (distribution of vegetation communities) data and were then assigned to ecosystem spatial units using a set of rules outlined below (after the descriptions of each SV type). In instances where an entire ecosystem spatial unit was the SV (e.g. Lake Chisholm is a Priority limnological feature), the SV was assigned directly to the spatial unit. The product of assigning SVs to ecosystem spatial units was an attribute look-up table of SV data for each of the ecosystem themes (e.g. named WL_specialvalues for wetlands), which could then be linked to the ecosystem spatial data layer. After assigning the SV data to the spatial units, it was included in the CFEV database as attribute data rather than spatial data. This was due to the sensitivity of some of the SV data (e.g. specific locations of threatened species).

For every ecosystem spatial unit, a count of SVs (including a break-down of how many outstanding, non-outstanding and undifferentiated SVs were present) was undertaken. This data was assigned directly to individual spatial units and is included in their respective spatial data layers (see below). While multiple data records exist in the CFEV database for reference, if more than one data record for the same SV was associated with a single spatial unit, it was only counted once. Nevertheless, if a SV was listed under more than one SV type, it would be counted multiple times (e.g. Allanaspides hickmani (Hickman‟s Pigmy Mountain Shrimp) is listed as a Threatened aquatic fauna species and as a Fauna species of phylogenetic distinctiveness, so its data record would be counted twice).

Threatened aquatic flora species

Rare and threatened flora species associated with freshwater-dependent ecosystems such as riparian and wetland vegetation, were selected from the Tasmanian Threatened Species List (October 2003, DPIWE). Only obligate riparian species were included in the final list.

Species on the Tasmanian Threatened Species List are either listed in the schedules of the Tasmanian Threatened Species Protection Act 1995 or in the schedules of the Commonwealth Environment Protection and Biodiversity Conservation Act 1999. Table 46 provides a list of the threatened aquatic flora species used in the CFEV SV assessment, detailing their status as ranked by the CFEV Project, the ecosystems they are known to be associated with, and their state and national listing status.

Location records were primarily sourced from the GTSpot database ((DPIWE, 2003)) with some additional records also sourced from the Threatened Species Unit, DPIWE.

Appendix 6 – Attribute data - Special values - Table 46. Threatened flora species.


Table 46. Threatened flora species.

* listed in the schedules of the Threatened Species Protection Act 1995 (Tas) (x = extinct, e = endangered, v = vulnerable, r = rare)

** listed in the schedules of the Environment Protection and Biodiversity Conservation Act 1999 (Cwlth) (EX = extinct in the wild, CR = critically endangered, EN = endangered, VU = vulnerable)

Species name Common name Ecosystem CFEV status TSPA* EPBCA**

Acacia axillaris Midlands wattle Rivers Outstanding v VU

Acacia siculiformis Dagger wattle Rivers, Wetlands Outstanding r

Agrostis aff. australiensis Flat-leaf southern bent Rivers, Waterbodies, Wetlands Outstanding r

Agrostis propinqua Alpine winter bent Rivers Outstanding r

Alternanthera denticulata Lesser joyweed Rivers, Wetlands Outstanding e

Amphibromus macrorhinus Long-nosed swamp wallaby grass Rivers, Waterbodies, Wetlands Outstanding e

Amphibromus neesii Swamp wallaby grass Rivers, Waterbodies, Wetlands Outstanding r

Anzybas fordhamii Banded helmet orchid Wetlands Outstanding e

Aphelia gracilis Slender aphelia Wetlands Outstanding r

Asperula subsimplex Water woodruff Rivers Outstanding r

Asplenium hookerianum Hooker‟s spleenwort Rivers Outstanding v VU

Atriplex suberecta Sprawling saltbush Saltmarshes Undifferentiated v

Austrocynoglossum latifolium Forest hound‟s tongue Rivers Outstanding r

Ballantinia antipoda Southern ballantine Rivers Outstanding x EN

Barbarea australis Native wintercress Rivers Outstanding e CR

Baumea articulata Jointed twig rush Rivers, Wetlands Outstanding r

Baumea gunnii Slender twig rush Rivers, Wetlands Outstanding r

Bertya tasmanica ssp. tasmanica Tasmanian bertya Rivers Outstanding v

Blechnum cartilagineum Gristle fern Rivers Non-oustanding v

Bolboschoenus caldwellii Sea club-rush Waterbodies, Estuaries Outstanding r

Bolboschoenus medianus Marsh club-rush Waterbodies, Wetlands Outstanding r

Brachyscome aff. radicans Snow daisy Rivers Outstanding r

Callitriche sonderi Matted water starwort Rivers, Wetlands Outstanding r

Callitriche umbonata Water starwort Rivers, Wetlands Outstanding r

Callitris oblonga ssp. oblonga South Esk pine Rivers Outstanding v EN




Calystegia sepium Great bindweed Rivers, Wetlands Outstanding r

Carex capillacea Yellow-leaf sedge Wetlands Outstanding r

Carex gunniana Mountain sedge Rivers, Wetlands Outstanding r

Carex hypandra Dark fen sedge Wetlands Outstanding r

Carex longebrachiata Drooping sedge Rivers, Wetlands Outstanding r

Carex tasmanica Curly sedge Wetlands Outstanding VU

Centipeda cunninghamii Common sneezeweed Wetlands Outstanding r

Centrolepis monogyna ssp. paludicola Centrolepis Wetlands Outstanding VU

Centrolepis pedderensis Pedder centrolepis Waterbodies Outstanding e VU

Chorizandra enodis Black bristle-rush Wetlands Undifferentiated v

Cotula vulgaris var. australasica Slender cotula Wetlands, Saltmarshes Outstanding r

Cryptandra amara Bitter cryptandra Rivers Outstanding e

Cuscuta tasmanica Golden dodder Wetlands, Saltmarshes Outstanding r

Cyathea cunninghamii Slender treefern Rivers Outstanding e

Cyathea X marcescens Skirted treefern Rivers Outstanding v

Damasonium minus Star fruit Wetlands Outstanding r

Deyeuxia densa Heath bent grass Wetlands Outstanding r

Discaria pubescens Hairy anchor plant Rivers Outstanding e

Doodia caudata Small rasp fern Rivers Outstanding v

Elaeocarpus reticulatus Blueberry ash Rivers Outstanding r

Epacris acuminata Clasping-leaf heath Rivers Outstanding r EN

Epacris aff. exserta „Union Bridge‟ Union Bridge heath Rivers Outstanding v

Epacris apsleyensis Apsley heath Rivers Outstanding e EN

Epilobium pallidiflorum Showy willowherb Rivers, Wetlands Outstanding r

Eucalyptus radiata ssp. robertsonii Forth River peppermint Rivers Outstanding r

Euphrasia collina ssp. deflexifolia Eastern eyebright Rivers Outstanding r

Euphrasia gibbsiae ssp. psilantherea Swamp eyebright Wetlands Outstanding e CR

Euphrasia gibbsiae ssp. pulvinestris Cushion plant eyebright Rivers, Wetlands Outstanding r




Euphrasia scabra Yellow eyebright Rivers, Wetlands Outstanding e

Glossostigma elatinoides Small mudmat Rivers, Wetlands Outstanding r

Grevillea australis var. linearifolia Narrow-leaf southern grevillea Rivers Outstanding r

Grevillea australis var. planifolia Flat-leaf southern grevillea Rivers Outstanding r

Gynatrix pulchella Common hemp bush Rivers Outstanding r

Haloragis aspera Rough raspwort Rivers, Wetlands Outstanding v

Hovea corrickiae Glossy hovea Rivers Outstanding r

Hovea tasmanica Hill Hovea Rivers Outstanding r

Hydrorchis orbicularis Swamp onion orchid Wetlands Outstanding r

Hypolepis distans Scrambling ground fern Wetlands Outstanding v EN

Hypolepis muelleri Harsh ground fern Rivers Outstanding r

Hypoxis vaginata Sheathing yellow-star Wetlands Outstanding r

Isoetes drummondii ssp. drummondii Plain quillwort Waterbodies, Wetlands Outstanding r

Isoetes elatior Tall quillwort Rivers Outstanding r

Isoetes humilior Veiled quillwort Rivers, Waterbodies Outstanding r

Isoetes sp. nova "Maxwell River" Maxwell River quillwort Waterbodies, Wetlands Outstanding r

Isolepis habra Alpine club rush Rivers Outstanding r

Isolepis stellata Star club rush Wetlands, Saltmarshes Outstanding r

Juncus amabilis Gentle rush Wetlands Outstanding r

Juncus fockei Slender joint-leaf rush Wetlands Outstanding r

Juncus prismatocarpus Branching rush Rivers, Wetlands Outstanding r

Juncus vaginatus Clustered rush Rivers, Wetlands Outstanding r

Lepidosperma forsythii Stout rapier sedge Wetlands Outstanding r

Lepilaena australis Austral water mat Waterbodies, Wetlands, Estuaries Outstanding x

Lepilaena marina Sea water mat Estuaries, Saltmarshes Outstanding r

Lepilaena patentifolia Spreading water mat Rivers, Estuaries, Saltmarshes Outstanding r

Lepilaena preissii Slender water mat Waterbodies, Wetlands, Estuaries Outstanding r

Limonium australe Sea lavender Saltmarshes Outstanding r




Limonium baudinii Baudin‟s sea lavender Saltmarshes Outstanding

Lobelia pratioides Poison lobelia Rivers, Wetlands Undifferentiated v

Lycopus australis Native gipsywort Wetlands Outstanding e

Lythrum salicaria Purple loosestrife Rivers, Wetlands Outstanding v

Melaleuca pustulata Cranbrook paperbark Rivers Outstanding r

Mentha australis River mint Rivers Outstanding x

Milligania johnstonii Johnston‟s milligania Wetlands Outstanding r

Milligania longifolia Pendant milligania Rivers Outstanding r

Myriophyllum glomeratum Clustered water milfoil Waterbodies, Wetlands Outstanding x

Myriophyllum integrifolium Tiny water milfoil Wetlands Outstanding v

Myriophyllum muelleri Hooded water milfoil Waterbodies, Wetlands Outstanding r

Parmotrema crinitum Lichen Rivers Outstanding r

Persicaria decipiens Slender knotweed Rivers Non-outstanding v

Persicaria subsessilis Bristly knotweed Rivers Outstanding e

Phebalium daviesii Davies‟ wax flower Rivers Outstanding e CR

Pilularia novae-hollandiae Austral pillwort Rivers, Waterbodies, Wetlands Outstanding r

Pimelea axiflora ssp. axiflora Bootlace bush Rivers Outstanding e

Pneumatopteris pennigera Lime fern Rivers Outstanding v

Pomaderris phylicifolia ssp. phylicifolia Narrow leaf pomaderris Rivers Outstanding r

Potamogeton pectinatus Fennel pondweed Wetlands, Estuaries, Saltmarshes Outstanding r

Prasophyllum pulchellum Pretty leek orchid Rivers, Wetlands Outstanding e CR

Prasophyllum tadgellianum Tadgell‟s leek orchid Rivers, Wetlands Outstanding r

Prostanthera rotundifolia Roundleaf mint bush Rivers Outstanding v

Pterostylis falcata Sickle greenhood Rivers, Wetlands Outstanding r

Puccinellia stricta var. perlaxa Spreading saltmarsh grass Waterbodies, Saltmarshes Outstanding r

Ranunculus acaulis Dune buttercup Waterbodies Outstanding r

Ranunculus collicola Lake Augusta buttercup Waterbodies Outstanding r

Ranunculus jugosus Twinned buttercup Rivers, Wetlands Outstanding r




Ranunculus prasinus Tunbridge buttercup Wetlands Outstanding e EN

Ranunculus pumilio var. pumilio Ferny buttercup Rivers, Wetlands Outstanding r

Rumex bidens Mud dock Rivers, Wetlands Outstanding r

Ruppia megacarpa Large-fruit tassel Estuaries, Saltmarshes Outstanding r

Ruppia tuberosa Tuberous tassel Estuaries, Saltmarshes Outstanding r

Schoenoplectus validus River club sedge Waterbodies, Wetlands Outstanding r

Schoenus brevifolius Zig zag bog sedge Waterbodies, Wetlands Outstanding r

Schoenus latelaminatus Medusa bog sedge Wetlands Outstanding e

Siloxerus multiflorus Small wrinklewort Rivers Outstanding r

Sporobolus virginicus Salt couch Saltmarshes Outstanding r

Spyridium lawrencei Small leaf spyridium Rivers Outstanding v EN

Spyridium parvifolium var. molle Soft furneaux spyridium Rivers Outstanding r

Stylidium despectum Small trigger plant Wetlands, Saltmarshes Outstanding r

Thelymitra holmesii Holmes‟ sun orchid Wetlands Outstanding r

Thelymitra mucida Plum orchid Wetlands Outstanding r

Triglochin minutissimum Tiny arrow grass Wetlands, Saltmarshes Outstanding r

Trithuria submersa Trithuria Wetlands Outstanding r

Uncinia elegans Handsome hook sedge Rivers Outstanding r

Utricularia australis Yellow bladderwort River Outstanding r

Utricularia violaceae Violet bladderwort Wetlands Outstanding v

Vallisneria americana Ribbon weed Rivers, Waterbodies Outstanding r

Villarsia exaltata Erect marsh flower Rivers, Waterbodies, Wetlands Outstanding r

Viola caleyana Swamp violet Rivers Outstanding r

Wilsonia humilis Silky wilsonia Saltmarshes Outstanding r

Wilsonia rotundifolia Roundleaf wilsonia Saltmarshes Outstanding r

Appendix 6 – Data layer development - Special values


Threatened fauna species

Rare and threatened fauna species associated with freshwater-dependent ecosystems were selected from the Tasmanian Threatened Species List (October 2003, DPIWE). Species on this list are either listed in the schedules of the Tasmanian Threatened Species Protection Act 1995 or in the schedules of the Commonwealth Environment Protection and Biodiversity Conservation Act 1999. A full list of aquatic fauna species selected for the CFEV SV assessment is provided in Table 47. The table also outlines details of their status as ranked by the CFEV Project, the ecosystems they are known to be associated with, and their state and national listing status.

Location records were primarily sourced from the GTSpot database (2003, DPIWE) with some additional records also sourced from the various studies conducted throughout Tasmania.

Appendix 6 – Attribute data - Special values - Table 47. Threatened fauna species.


Table 47. Threatened fauna species.

* listed in the schedules of the Threatened Species Protection Act 1995 (Tas) (x = extinct, e = endangered, v = vulnerable, r = rare)

** listed in the schedules of the Environment Protection and Biodiversity Conservation Act 1999 (Cwlth) (EX = extinct in the wild, CR = critically endangered, EN = endangered, VU = vulnerable)


Alcedo azureas Azure kingfisher Rivers Outstanding e

Allanaspides hickmani Hickman‟s pigmy mountain shrimp Waterbodies, Wetlands Outstanding r

Amelora acontistica Chevron looper moth Saltmarshes Undifferentiated v

Astacopsis gouldi Giant freshwater crayfish Rivers, Waterbodies Undifferentiated v VU

Beddomeia angulata Hydrobiid snail (Rapid River) Rivers Outstanding r

Beddomeia averni Hydrobiid snail (West Gawler) Rivers Outstanding r

Beddomeia bellii Hydrobiid snail (Heazlewood River) Rivers Outstanding r

Beddomeia bowryensis Hydrobiid snail (Bowry Creek) Rivers Outstanding r

Beddomeia briansmithi Hydrobiid snail (Fern Creek) Rivers Outstanding r

Beddomeia camensis Hydrobiid snail (Cam River) Rivers Outstanding r

Beddomeia capensis Hydrobiid snail (Table Cape) Rivers Outstanding r

Beddomeia fallax Hydrobiid snail (Heathcote Creek) Rivers Outstanding r

Beddomeia forthensis Hydrobiid snail (Wilmot River) Rivers Outstanding r

Beddomeia franklandensis Hydrobiid snail (Frankland River) Rivers Outstanding r

Beddomeia fromensis Hydrobiid snail (Frome River) Rivers Outstanding r

Beddomeia fultoni Hydrobiid snail (Farnhams Creek) Rivers Outstanding r

Beddomeia gibba Hydrobiid snail (Salmon River Road) Rivers Outstanding r

Beddomeia hallae Hydrobiid snail (Buttons Rivulet) Rivers Outstanding r

Beddomeia hermansi Hydrobiid snail (Viking Creek) Rivers Outstanding r

Beddomeia hullii Hydrobiid snail (Heazlewood River) Rivers Outstanding r

Beddomeia inflata Hydrobiid snail (Heathcote Creek) Rivers Outstanding r

Beddomeia kershawi Hydrobiid snail (Macquarie River) Rivers Outstanding r




Beddomeia kessneri Hydrobiid snail (Dip Falls) Rivers Outstanding r

Beddomeia krybetes Hydrobiid snail (St. Pauls River) Rivers Undifferentiated v

Beddomeia launcestonensis Hydrobiid snail (Cataract Gorge) Rivers Outstanding r

Beddomeia lodderae Hydrobiid snail (Upper Castra Rivulet) Rivers Outstanding r

Beddomeia mesibovi Hydrobiid snail (Arthur River) Rivers Outstanding r

Beddomeia minima Hydrobiid snail (Scottsdale) Rivers Outstanding r

Beddomeia petterdi Hydrobiid snail (Blyth River) Rivers Outstanding r

Beddomeia phasianella Hydrobiid snail (Keddies Creek) Rivers Outstanding r

Beddomeia protuberata Hydrobiid snail (Emu River) Rivers Outstanding r

Beddomeia ronaldi Hydrobiid snail (St. Patricks River) Rivers Outstanding r

Beddomeia salmonis Hydrobiid snail (Salmon River) Rivers Outstanding r

Beddomeia tasmanica Hydrobiid snail (Terrys Creek) Rivers Outstanding r

Beddomeia topsiae Hydrobiid snail (Williamson Creek) Rivers Outstanding r

Beddomeia trochiformis Hydrobiid snail (Bowry Creek) Rivers Outstanding r

Beddomeia tumida Hydrobiid snail (Great Lake) Rivers, Waterbodies Undifferentiated v

Beddomeia turnerae Hydrobiid snail (Minnow River) Rivers Outstanding r

Beddomeia waterhouseae Hydrobiid snail (Clayton‟s Rivulet) Rivers Outstanding r

Beddomeia wilmotensis Hydrobiid snail (Wilmot river) Rivers Outstanding r

Beddomeia wiseae Hydrobiid snail (Blizzards Creek) Rivers Outstanding r

Beddomeia zeehanensis Hydrobiid snail (Little Henty River) Rivers Outstanding r

Benthodorbis pawpela Hydrobiid snail (Great Lake) Rivers, Waterbodies Outstanding r

Brachionichthys hirsutus Spotted handfish Estuaries Outstanding e EN

Chrysolarentia decisaria Tunbridge looper moth Waterbodies, Saltmarshes Outstanding e

Costora iena Caddis fly (Great Lake) Waterbodies Undifferentiated x

Dasybela achroa Saltmarsh looper moth Saltmarshes Undifferentiated v

Diplectrona castanea Caddis fly (Mt. Field) Rivers, Waterbodies, Wetlands Undifferentiated x

Diporochaeta pedderensis Lake pedder earthworm Waterbodies Outstanding e




Dipturus sp. Port Davey skate Estuaries Outstanding e EN

Ecnomina vega Caddis fly (Macquarie River) Rivers, Waterbodies, Wetlands Outstanding r

Engaeus granulatus Central north burrowing crayfish Rivers, Wetlands Outstanding e

Engaeus martigener Furneaux burrowing crayfish Rivers Undifferentiated v EN

Engaeus orramakunna Mt. Arthur burrowing crayfish Rivers Undifferentiated v VU

Engaeus spinicaudatus Scottsdale burrowing crayfish Rivers Outstanding e EN

Engaeus yabbimunna Burrowing crayfish (Burnie) Rivers Undifferentiated v VU

Galaxias auratus Golden galaxias Waterbodies, Wetlands Outstanding r

Galaxias fontanus Swan galaxias Rivers Outstanding e EN

Galaxias johnstoni Clarence galaxias Rivers, Waterbodies, Wetlands Outstanding e EN

Galaxias parvus Swamp galaxias Rivers, Wetlands Outstanding r

Galaxias pedderensis Pedder galaxias Waterbodies Outstanding e EN

Galaxias tanycephalus Saddled galaxias Waterbodies Outstanding v VU

Galaxiella pusilla Dwarf galaxias Rivers, Wetlands Outstanding r VU

Goedetrechus mendumae Cave beetle (Ida Bay) Karst systems Outstanding r

Goedetrechus parallelus Cave beetle (Junee-Florentine) Karst systems Undifferentiated v

Haliaeetus leucogaster White bellied sea eagle Rivers, Waterbodies, Estuaries Non-outstanding v

Haloniscus searlei Salt lake slater Waterbodies, Saltmarshes Outstanding r

Hickmanoxyomma cavaticum Cave harvestman Karst systems Outstanding r

Hickmanoxyomma gibbergunyar Cave harvestman Karst systems Outstanding r

Hydrobiosella armata Caddis fly (Mt. Wellington) Rivers, Waterbodies, Wetlands Outstanding r

Hydrobiosella sagitta Caddis fly (St. Columba Falls) Rivers, Waterbodies, Wetlands Outstanding r

Hydroptila scamandra Caddis fly (Upper Scamander River) Rivers, Waterbodies, Wetlands Outstanding r

Idacarabus cordicollis Cave beetle (Hastings Cave) Karst systems Outstanding r

Idacarabus troglodytes Cave beetle (Precipitous Bluff) Karst systems Outstanding r

Leptocerus souta Caddis fly (Macquarie River) Rivers, Waterbodies, Wetlands Outstanding r

Limnodynastes peroni Striped marsh rrog Wetlands Outstanding r




Litoria raniformis Green and gold frog Wetlands Undifferentiated v VU

Mesacanthotelson setosus Isopod (Great Lake) Waterbodies Outstanding r

Mesacanthotelson tasmaniae Isopod (Great Lake) Waterbodies Outstanding r

Micropathus kiernani Cave cricket Karst systems Outstanding r

Neophema chrysogaster Orange-bellied parrot Saltmarshes Outstanding e EN

Numenius madagascariensis Eastern curlew Wetlands, Estuaries, Saltmarshes Outstanding e

Oecetis gilva Caddis fly (South Esk River) Rivers, Waterbodies, Wetlands Outstanding r

Olgania excavata Cave spider (Bubs Hill Cave) Karst systems Outstanding r

Onchotelson brevicaudatus Isopod (Great Lake & Shannon Lagoon) Waterbodies Outstanding r

Onchotelson spatulatus Isopod (Great Lake) Waterbodies Outstanding r

Orphninotrichia maculata Caddis fly (Wedge River) Rivers, Waterbodies, Wetlands Outstanding r

Orthotrichia adornata Caddis fly (Derwent River) Rivers, Waterbodies, Wetlands Outstanding r

Oxyethira mienica Caddis fly (Ouse River) Rivers, Waterbodies, Wetlands Outstanding r

Paragalaxias dissimilis Shannon paragalaxias Waterbodies Undifferentiated v

Paragalaxias eleotroides Great Lake paragalaxias Waterbodies Undifferentiated v

Paragalaxias julianus Western paragalaxias Waterbodies Outstanding r

Paragalaxias mesotes Arthurs paragalaxias Waterbodies Outstanding e

Parvotettix rangaensis Cave cricket Karst systems Outstanding r

Parvotettix whinrayi Whinray‟s cave cricket Karst systems Outstanding r

Phrantela annamurrayae Hydrobiid snail (Heazlewood River) Rivers Outstanding r

Phrantela conica Hydrobiid snail (Little Henty River) Rivers Outstanding r

Phrantela marginata Hydrobiid snail (Heazlewood River) Rivers Outstanding r

Phrantela pupiformis Hydrobiid snail (Tyenna River) Rivers Outstanding r

Podiceps cristatus Great crested grebe Rivers, Waterbodies, Wetlands, Estuaries Outstanding r

Prototroctes maraena Australian grayling Rivers, Estuaries Undifferentiated v VU

Pseudotyrannochthonius typhlus Cave pseudoscorpion (Mole Creek) Karst systems Outstanding r

Ramirheithrus kocinus Caddis fly (Corinna) Rivers, Waterbodies, Wetlands Outstanding r




Smilasterias tasmaniae Seastar Estuaries Outstanding r

Stenopsychodes lineata Caddis fly (Bluff Hill Creek) Rivers, Waterbodies, Wetlands Outstanding r

Sterna albifrons sinensis Little tern Estuaries, Saltmarshes Outstanding e

Sterna nereis nereis Fairy tern Estuaries, Saltmarshes Outstanding r

Tasimia drepana Caddis fly (Huon & Picton Rivers) Rivers, Waterbodies, Wetlands Outstanding r

Taskiria mccubbini Caddis fly (Lake Pedder) Rivers, Waterbodies, Wetlands Outstanding e

Taskiropsyche lacustris Caddis fly (Lake Pedder) Rivers, Waterbodies, Wetlands Outstanding e

Tasmanotrechus cockerilli Cave beetle (Mole Creek) Karst systems Outstanding r

Tasniphargus tyleri Amphipod (Great Lake) Waterbodies Outstanding r

Uramphisopus pearsoni Isopod (Great Lake) Waterbodies Outstanding r



Threatened flora communities

Rare and threatened forest communities, associated with freshwater-dependent ecosystems (wetland or riparian vegetation communities), were selected from the RFA list of threatened communities (Tasmanian Public Land Use Commission, 1997a) (Table 48). Distribution data for these vegetation communities was sourced from the TASVEG data layer (Version 0.1 May 2004) (Harris and Kitchener, 2003).

Table 48. Threatened aquatic flora communities.

* listed in the RFA (Tasmanian Public Land Use Commission, 1997a) (R2 = Rare, E2 = Endangered)

Community type Ecosystem CFEV status RFA*

Melaleuca ericifolia coastal

swamp forest Rivers, Wetlands Outstanding R2, E2

Shrubby Eucalyptus ovata forest

Rivers, Wetlands Outstanding E2

Eucalyptus rodwayi forest Rivers, Wetlands Outstanding Rare and depleted old growth community

Priority flora communities

Rare and threatened non-forest vegetation communities that are predominantly associated with freshwater-dependent ecosystems, such as riparian and wetland vegetation, were selected from the Comprehensive, Adequate, Representative Scientific Advisory Group (CARSAG) non-forest vegetation draft priority list (CARSAG, 2003). Vegetation communities that were listed as endangered (E), vulnerable (V) or rare (R) were considered to be priority aquatic flora communities for the CFEV SV assessment (Table 49).

Distribution data for these vegetation communities was sourced from the TASVEG data layer (Version 0.1 May 2004) (Harris and Kitchener, 2003).

Table 49. Priority flora communities.

* Draft recommendations from CARSAG (E = endangered, R = rare, V = vulnerable).

Community Type Ecosystem CFEV status *Statewide status

Alkaline pans Wetlands Outstanding R

Coniferous heath Waterbodies, Wetlands Non-outstanding R

Cushion moorland Waterbodies, Wetlands Non-outstanding R

Highland grassy sedgeland Rivers, Waterbodies, Wetlands

Non-outstanding R

Highland Poa grassland Rivers, Waterbodies, Wetlands

Non-outstanding R, E

Lowland Poa grassland Rivers, Wetlands Non-outstanding E

Marginal herbfield/grassland Rivers, Waterbodies, Wetlands

Outstanding R

Restionaceae flatland Waterbodies, Wetlands Non-outstanding R

Riparian Rivers Non-outstanding V

Sedge/rush wetland Waterbodies, Wetlands Outstanding R

Sedgy fern bog Waterbodies, Wetlands Outstanding R

Short paperbark swamp Wetlands Non-outstanding V

Sphagnum Wetlands Outstanding R

Western graminoid moorland/herbfield

Waterbodies, Wetlands Non-outstanding R



Priority fauna communities

Fauna communities that are associated with freshwater-dependent ecosystems were nominated by specialists and were supported by particular studies. Important freshwater faunal communities included assemblages of benthic estuarine macroinvertebrates (G. Edgar, TAFI, pers. comm.), riverine macroinvertebrates (P. Davies, Freshwater Systems and H. Dunn, Landmark Consulting, pers. comm.) and karst fauna (A. Richardson, UTas, pers. comm.) (Table 50). Location data for each community types was provided by the respective experts.

Table 50. Priority fauna communities.

Community name Ecosystem CFEV status Source/reference

Estuarine invertebrates: Bathurst Channel benthic Macroinvertebrate Community

Estuaries Outstanding Expert nomination: Dr Graham Edgar, TAFI, UTas

Freshwater invertebrates: Ansons River Upper

Rivers Outstanding Expert nomination: Dr Peter Davies, Freshwater Systems

Freshwater invertebrates: Crayfish Creek (1 & 2)


Freshwater invertebrates: D‟Entrecasteaux River at South Cape Road

Rivers Undifferentiated Expert nomination: Dr Peter Davies, Freshwater Systems

Freshwater invertebrates: Hellyer River


Freshwater invertebrates: Iris River at Cradle Road


Freshwater invertebrates: James River


Freshwater invertebrates: Junee River Junee Road


Freshwater invertebrates: Lake Ayr outflow


Freshwater invertebrates: Meander/Falls Road


Freshwater invertebrates: Mountain River above Trestle Creek


Freshwater invertebrates: Newitts Creek

Rivers Non-outstanding Expert nomination: Dr Helen Dunn, Landmark Consulting

Freshwater invertebrates: Coldstream River


Freshwater invertebrates: Netherby Creek


Freshwater invertebrates: Biscuit Creek


Freshwater invertebrates: Great Lake

Waterbodies

Outstanding Expert nomination: Dr Peter Davies, Freshwater Systems

Cave fauna assemblages: Mole Creek Complex

Karst systems

Outstanding Expert nomination: Assoc. Prof. Alastair Richardson, UTas

Cave fauna assemblages: Mystery Creek

Karst systems


Cave fauna assemblages: Exit Cave

Karst systems


Cave fauna assemblages: Mt Cripps Caves

Karst systems


Cave fauna assemblages: Precipitous Bluff Caves

Karst systems




Priority flora species

A list of priority flora species was formulated from the priority species list within the Tasmania RFA (Commonwealth of Australia and State of Tasmania, 1997) and from nominations by an expert (see Table 51). Priority flora species are those plant species that are considered important and have a limited distribution but have not been formally listed on the Tasmanian Threatened Species List. Only flora species known to associated with freshwater-dependent species were included on the list (Table 51). The table also outlines details of their status as ranked by the CFEV Project and the ecosystems they are known to be associated with.

Location records for the priority flora species were primarily sourced from the GTSpot database (2003, DPIWE) with some additional records provided by scientific experts.

Table 51. Priority flora species.

Species name Common name Ecosystem CFEV status Source/reference

Carex bichenoviana Sedge Wetlands Outstanding (Commonwealth of Australia and State of Tasmania, 1997)

Grevillea australis var. tenuifolia

Southern grevillea Rivers Outstanding (Commonwealth of Australia and State of Tasmania, 1997)

Isachne globosa Wetlands Outstanding Expert nomination: Dr Michael Askey-Doran, DPIW

Priority aquatic fauna species

A list of priority aquatic fauna species was formulated from the priority species list within the Tasmanian RFA (Commonwealth of Australia and State of Tasmania, 1997), the „Red‟ and „Grey‟ lists of rare and threatened vertebrates and invertebrates (Invertebrate Advisory Committee, 1994; Vertebrate Advisory Committee, 1994), and from nominations by experts (see Table 52).

Priority fauna species are those animal species that are considered important and have a limited distribution but have not been formally listed on the Tasmanian Threatened Species List. Only fauna species known to associated with freshwater-dependent species were included on the list (Table 52). The table also outlines details of their status as ranked by the CFEV Project and the ecosystems they are known to be associated with.

Location records for the priority aquatic fauna species were primarily sourced from the GTSpot database (2003, DPIWE) and the IFS fish database, with some additional records provided by scientific experts and other scientific studies.

Appendix 6 – Attribute data - Special values - Table 52. Priority fauna species.


Table 52. Priority fauna species.


Amarinus paralacustris Freshwater crab Saltmarshes Undifferentiated Expert nomination: Assoc. Prof. Alastair Richardson

Aphilorheithrus luteolus Caddis fly Rivers Outstanding (Invertebrate Advisory Committee, 1994)

Archipetalia auriculata Alpine dragonfly Wetlands Outstanding (Invertebrate Advisory Committee, 1994)

Austropyrgus elongatus Freshwater snail Rivers Outstanding (Clark et al., 2003)

Austropyrgus mersus Freshwater snail Rivers Outstanding (Clark et al., 2003)

Austropyrgus nanus Freshwater snail GDEs Outstanding (Clark et al., 2003)

Austropyrgus pagodoides Freshwater snail Waterbodies Outstanding (Clark et al., 2003)

Austropyrgus parvus Freshwater snail Rivers Outstanding (Clark et al., 2003)

Austropyrgus pisinnus Freshwater snail Rivers Outstanding (Clark et al., 2003)

Austropyrgus praecipitis Freshwater snail Rivers Outstanding (Clark et al., 2003)

Austropyrgus privus Freshwater snail Rivers Outstanding (Clark et al., 2003)

Austropyrgus solitarius Freshwater snail Rivers Outstanding (Clark et al., 2003)

Austropyrgus tateiformis Freshwater snail Rivers Outstanding (Clark et al., 2003)

Beachflea "4-d Granville Harbour" Beach flea Saltmarshes Outstanding Expert nomination: Assoc. Prof. Alastair Richardson

Beachflea "4-d Old Beach" Beach flea Saltmarshes Outstanding Expert nomination: Assoc. Prof. Alastair Richardson

Beddomeia sp. nov. Hydrobiid snail (Dip River) Rivers Outstanding Expert nomination: Dr Peter Davies, Freshwater Systems

Benthodorbis fultoni Freshwater snail Rivers Outstanding (Ponder and Avern, 2000)

Calidris canutus Red knot Estuaries Outstanding (Vertebrate Advisory Committee, 1994)

Cavernotettix flindersensis Cave cricket Karst systems Undifferentiated (Vertebrate Advisory Committee, 1994)

Conoesucus notialis Caddis fly Rivers Outstanding (Invertebrate Advisory Committee, 1994)

Daternomia jacksonae Caddis fly Rivers, Waterbodies, Wetlands Outstanding (Invertebrate Advisory Committee, 1994)

Diplectrona n. sp. undescribed Caddis fly Rivers Outstanding (Invertebrate Advisory Committee, 1994)

Engaeus sp. nov. Burrowing crayfish (NE) Rivers Outstanding (Doran and Richards, 1996)




Eorchestia n. sp. 1 Marsh hopper Saltmarshes Outstanding Expert nomination: Assoc. Prof. Alastair Richardson



Ethochorema ithyphallicum Caddis fly Rivers Outstanding (Invertebrate Advisory Committee, 1994)

Eusthenia reticulata Stone fly Rivers Outstanding (Invertebrate Advisory Committee, 1994)

Gallirallus tenebrosa Dusky moorhen Rivers, Waterbodies, Wetlands Non-outstanding (Vertebrate Advisory Committee, 1994)

Glacidorbis circulus Freshwater snail Rivers Outstanding (Ponder and Avern, 2000)

Glacidorbis costatus Freshwater snail Wetlands Outstanding (Ponder and Avern, 2000)

Haliastur sphenurus Whistling kite Rivers, Waterbodies, Wetlands, Estuaries

Outstanding (Vertebrate Advisory Committee, 1994)

Hemiphlebia mirabilis Damselfly Wetlands Outstanding (Trueman et al., 1992; Endersby, 1993)

Hesperilla chrysotricha Chrysotricha skipper Wetlands Undifferentiated Expert nomination: Dr Peter McQuillan, UTas

Hickmanoxyomma clarkei Cave harvestman Karst systems Outstanding (Invertebrate Advisory Committee, 1994)

Hickmanoxyomma eberhardi Cave harvestman Karst systems Outstanding (Invertebrate Advisory Committee, 1994)

Hickmanoxyomma goedei Cave harvestman Karst systems Outstanding (Invertebrate Advisory Committee, 1994)

Hydrobiosella orba Caddis fly Rivers Outstanding (Invertebrate Advisory Committee, 1994)

Hyridella narracanensis Freshwater mussel Rivers Undifferentiated Expert nomination: Dr Brian Smith, QVMAG

Idacarabus longicollis Cave beetle Karst systems Outstanding (Invertebrate Advisory Committee, 1994)

Kimminsoperla biloba Stonefly Rivers Outstanding (Invertebrate Advisory Committee, 1994)

Limosa lapponica Bar-tailed godwit Estuaries Outstanding (Invertebrate Advisory Committee, 1994)

Lomanella troglodytes Cave harvestman Karst systems Outstanding (Invertebrate Advisory Committee, 1994)

Lovettia sealii sp. nov. A Whitebait (northern stock) Estuaries Outstanding (Fulton and Pavuk, 1988)

Lovettia sealii sp. nov. B Whitebait (western stock) Estuaries Outstanding (Fulton and Pavuk, 1988)

Lovettia sealii sp. nov. C Whitebait (Derwent stock) Estuaries Outstanding (Fulton and Pavuk, 1988)




Lovettia sealii sp. nov. D Whitebait (Huon stock) Estuaries Outstanding (Fulton and Pavuk, 1988)

Lovettia sealii sp. nov. E Whitebait (Tasman stock) Estuaries Outstanding (Fulton and Pavuk, 1988)

Macquaria colonorum Estuary perch Estuaries Undifferentiated Expert nomination: Dr Jean Jackson, Inland Fisheries Service (IFS)

Mesacanthotelson decipiens Phreatoicid Waterbodies Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Mesacanthotelson fallax Phreatoicid Waterbodies Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Mesacanthotelson setosus Phreatoicid Waterbodies Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Mesacanthotelson sp. Phreatoicid Waterbodies Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Mesacanthotelson spinosus Phreatoicid Waterbodies Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Mesacanthotelson tasmaniae Phreatoicid Waterbodies Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Metaphreatoicus affinis Phreatoicid Wetlands Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Metaphreatoicus magistri Phreatoicid Rivers Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Metaphreatoicus sp. Phreatoicid Rivers Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Nannoperca sp. nov. Southern pygmy perch Rivers Undifferentiated (Hammer, 2001)

Nanocochlea monticola Freshwater snail Rivers Outstanding (Invertebrate Advisory Committee, 1994)

Nanocochlea parva Freshwater snail Rivers Outstanding (Invertebrate Advisory Committee, 1994)

Nanocochlea pupoidea Freshwater snail Rivers Outstanding (Invertebrate Advisory Committee, 1994)

Nanoplectrus truchanasi Caddis fly Rivers Undifferentiated (Invertebrate Advisory Committee, 1994)

Notoniscus n. sp. undescribed Slater Karst systems Undifferentiated (Invertebrate Advisory Committee, 1994)

Nousia sp. nov. AV 18 Mayfly Rivers Outstanding Expert nomination: Dr Peter Davies, Freshwater Systems




Nousia sp. nov. AV 19 Mayfly Rivers Outstanding Expert nomination: Dr Peter Davies, Freshwater Systems

Nycticorax caledonicus Rufous night heron Rivers, Waterbodies, Estuaries Outstanding (Vertebrate Advisory Committee, 1994)

Oecetis umbra Caddis fly Rivers Outstanding (Invertebrate Advisory Committee, 1994)

Ombrastacoides denisoni Freshwater crayfish Rivers, Wetlands Outstanding Expert nomination: Assoc. Prof. Alastair Richardson

Ombrastacoides ingressus Freshwater crayfish Rivers, Wetlands Outstanding Expert nomination: Assoc. Prof. Alastair Richardson

Ombrastacoides parvicaudatus Freshwater crayfish Rivers, Wetlands Outstanding Expert nomination: Assoc. Prof. Alastair Richardson

Oriesplanus munionga larana Marrawah skipper Wetlands Undifferentiated (Neyland, 1994)

Paraphreatoicus relictus Phreatoicid Rivers Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Philypnodon grandiceps Big-headed gudgeon Rivers Undifferentiated Expert nomination: IFS

Phrantela kutikina Freshwater snail Rivers Outstanding (Invertebrate Advisory Committee, 1994)

Phrantela richardsoni Hydrobiid snail Rivers Outstanding (Invertebrate Advisory Committee, 1994)

Phrantela umbilicata Hydrobiid snail Rivers Outstanding (Invertebrate Advisory Committee, 1994)

Phreatoicoides sp. Phreatoicid Rivers Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Pluvialis dominica Lesser golden plover Estuaries, Saltmarshes Outstanding (Vertebrate Advisory Committee, 1994)

Poecilochorema circumvoltum Caddis fly Rivers Outstanding (Invertebrate Advisory Committee, 1994)

Poecilochorema evansi Caddis fly Rivers Outstanding (Invertebrate Advisory Committee, 1994)

Pseudophryne semimarmorata Southern toadlet Wetlands Outstanding Expert nomination: Dr Karyl Michaels, World Wildlife Fund (WWF)

Pseudotricula eberhardi Cave snail Karst systems Outstanding (Invertebrate Advisory Committee, 1994)

Smicrophylax simplex Caddis fly Rivers Outstanding (Invertebrate Advisory Committee, 1994)

Sterna caspia Caspian tern Waterbodies, Estuaries, Saltmarshes

Outstanding (Vertebrate Advisory Committee, 1994)

Styloniscus n. sp. undescribed Cave invertebrate Karst systems Undifferentiated (Invertebrate Advisory Committee, 1994)




Synthemiopsis gomphomacromioides Dragonfly Wetlands Outstanding Expert nomination: Assoc. Prof. Alastair Richardson

Synthemis macrostigma orientalis Swamp dragonfly Wetlands Outstanding (Invertebrate Advisory Committee, 1994)

Taschorema dispatens Caddis fly Rivers Outstanding (Invertebrate Advisory Committee, 1994)

Tasmanoplegas sp. Caddis fly Rivers Undifferentiated (Invertebrate Advisory Committee, 1994)

Theclinesthes serpentata subsp. lavara Saltmarsh butterfly Estuaries, Saltmarshes Undifferentiated Expert nomination: Dr Peter McQuillan, UTas

Tringa brevipes Grey-tailed tattler Estuaries Outstanding (Vertebrate Advisory Committee, 1994)

Tupua cavernicola Web spinning cave spider Karst systems Outstanding (Invertebrate Advisory Committee, 1994)

Unnamed n. sp. (Phreatoicidae) Phreatoicid Karst systems Outstanding (Invertebrate Advisory Committee, 1994)

Uramphisopus n. sp. undescribed Phreatoicid Waterbodies Outstanding (Invertebrate Advisory Committee, 1994)

Velesunio moretonicus Freshwater mussels Rivers Outstanding (Davies and Humphries, 1995; Davies and Cook, 2001) Expert nomination: Dr George (Buz) Wilson, Australian Museum

Westriplectes pedderensis Caddis fly Rivers Outstanding (Invertebrate Advisory Committee, 1994)

Yulia yuli Freshwater amphipod Waterbodies Outstanding (Invertebrate Advisory Committee, 1994)

Appendix 6 – Data layer development - Special values


Priority geomorphic features

Geomorphic attributes were assessed with reference to the following criteria adapted from the RFA geomorphic assessment (Tasmanian Public Land Use Commission, 1997a) and applicable to the Tasmanian Geoconservation Database (DPIWE, 2004):

1. rare or uncommon geomorphic or hydrological features or processes at large and small scale

2. features or systems which are unusual in degree of complexity, scale or display

3. features which provide outstanding example or geomorphic history.

An initial list of sites were selected from the Tasmanian Geoconservation Database (DPIWE, 2004) and a study to assess the condition and status of Tasmania‟s wetlands and riparian vegetation (the „Audit‟) (Dunn, 2002) (see Table 53), according to the criteria. This list was then reviewed by expert panel of geomorphologists (Appendix 1). The final list of important geomorphic features relating to freshwater-dependent ecosystems is provided in Table 53, which also outlines the criteria it met, details of their status as ranked by the CFEV Project and the ecosystems they are known to be associated with.

Location records for the features were sourced from the Tasmanian Geoconservation database (DPIWE, 2004) and the Audit (Dunn, 2002).

Appendix 6 – Attribute data - Special values - Table 53. Priority geomorphic features.


Table 53. Priority geomorphic features.

Feature Criteria Ecosystem CFEV status Source/reference

"Fernhill" broadwater sequence (South Esk River) 1 Rivers Non-outstanding (Dunn, 2002)

"The Broadwater", Barton (Macquarie River) 1 Rivers Non-outstanding (Dunn, 2002)

Algonkian Rivulet Karst 1, 2 Karst systems Undifferentiated (DPIWE, 2004)

Apsley Gorge Rivers Non-outstanding (DPIWE, 2004)

Apsley River (Apsley Meadows Hole broadwaters) 1 Rivers Non-outstanding (Dunn, 2002)

Avenue River 1 Rivers Undifferentiated (Dunn, 2002)

Big Reedy Lagoon Lunette 1, 3 Waterbodies Outstanding (DPIWE, 2004)

Blythe River 1 Rivers Undifferentiated (Dunn, 2002)

Boggy Creek Tufa Terraces 1, 2 Rivers, Karst systems Outstanding (Dunn, 2002; DPIWE, 2004)

Broad River 1, 3 Rivers Non-outstanding (Dunn, 2002)

Bubs Hill Karst system Karst systems Undifferentiated (Dunn, 2002)

Colliers Swamp - Seal Bay 2, 3 Wetlands Non-outstanding (DPIWE, 2004)

Cracroft karst system Karst systems Undifferentiated (Dunn, 2002)

Dante Rivulet karst system 2, 3 Karst systems Undifferentiated (Dunn, 2002)

Dead Island Area Marsh and String Bogs 2 Wetlands Outstanding (DPIWE, 2004)

Deep Glen coastal karst system Karst systems Undifferentiated (Dunn, 2002)

Dip Falls Basalt Flows 1 Rivers Outstanding (DPIWE, 2004)

Dismal Swamp Polje 1, 2, 3 Wetlands Non-outstanding (DPIWE, 2004)

Douglas River Gorge 1, 2 Rivers Outstanding (DPIWE, 2004)

Duck River karst system Karst systems Undifferentiated (Dunn, 2002)

Dukes Marshes and broadwater Rivers, Wetlands Non-outstanding (Dunn, 2002)

Durham Creek Meander Cave with Constructional Karst 1, 2, 3 Karst systems Non-outstanding (DPIWE, 2004)

Emu River Incised Meanders 1, 2 Rivers Non-outstanding (DPIWE, 2004)

Ettrick coastal karst system Karst systems Undifferentiated (Dunn, 2002)

Exit Cave - D‟Entrecasteaux Valley Karst Area 1, 2 Karst systems Non-outstanding (DPIWE, 2004)

First Gorge Structure 2, 3 Rivers Outstanding (DPIWE, 2004)

Grim-Trefoil coastal karst system Karst systems Undifferentiated (Dunn, 2002)

Gordon River Gorge above Cataract Creek 1 Rivers Non-outstanding (DPIWE, 2004)

Gunns Plains karst system 1, 2 Karst systems Outstanding (Dunn, 2002)

Hastings - Upper Creekton Rivulet Karst 1, 2 Karst systems Outstanding (DPIWE, 2004)

Hogan Island Springs and Peat 1 Rivers, Wetlands Undifferentiated (DPIWE, 2004)




Ile de Golfe Karst systems Non-outstanding (Dunn, 2002)

Irishtown - Sedgey Creek karst system 1, 2 Karst systems Undifferentiated (Dunn, 2002)

Junee - Florentine Karst Systems 1, 2, 3 Karst systems Outstanding (DPIWE, 2004)

Keith River magnesite karst system 1, 2 Karst systems Undifferentiated (Dunn, 2002)

Knyvet Falls Basalt Flow 1, 3 Rivers Outstanding (DPIWE, 2004)

Lagoon of Islands 1 Waterbodies Non-outstanding (Dunn, 2002)

Lake Adelaide Glacial Rock Basin Lake 2, 3 Waterbodies Outstanding (DPIWE, 2004)

Lake Fidler 1, 2 Waterbodies Outstanding (DPIWE, 2004)

Lake Flannigan 1 Waterbodies Non-outstanding (DPIWE, 2004)

Lake Flora hematite 2 Waterbodies Non-outstanding (DPIWE, 2004)

Lake Lea and Vale of Belvoir wetlands Waterbodies, Wetlands, Karst systems

Non-outstanding (Dunn, 2002)

Lake Morrison 1, 3 Waterbodies Outstanding (DPIWE, 2004)

Lake Sydney Glaciokarstic Lake 1, 2 Waterbodies, Karst systems Non-outstanding (DPIWE, 2004)

Lawrence Rivulet Polje 1, 3 Rivers, Karst systems Non-outstanding (DPIWE, 2004)

Long Marsh "den sequence" 1 Rivers Non-outstanding (Dunn, 2002)

Loongana karst system 2 Karst systems Undifferentiated (Dunn, 2002)

Louisa Plains Blanket Bog and Peat Mounds 1, 2, 3 Wetlands Outstanding (DPIWE, 2004)

Lower Franklin Valley Karst 1, 3 Karst systems Non-outstanding (DPIWE, 2004)

Lyons River magnesite karst system 1, 2 Karst systems Undifferentiated (Dunn, 2002)

Main Creek - Bowry creek magnesite karst system 1, 2 Karst systems Undifferentiated (Dunn, 2002)

Marble Hill - D‟Entrecasteaux karst system Karst systems Undifferentiated (Dunn, 2002)

Middle Franklin River Cross-strike Drainage 1 Rivers Non-outstanding (DPIWE, 2004)

Middle Gordon River Cross-strike Drainage 1, 3 Rivers Non-outstanding (DPIWE, 2004)

Mole Creek Karst 1, 2, 3 Karst systems Outstanding (DPIWE, 2004)

Montagu River karst 1, 3 Karst systems Outstanding (DPIWE, 2004)

Mt Anne - Upper Weld karst 1, 3 Karst systems Undifferentiated (Dunn, 2002)

Mt Cripps Karst 2, 3 Karst systems Outstanding (DPIWE, 2004)

Mt Ronald Cross karst system Karst systems Undifferentiated (Dunn, 2002)

Mt Weld Karst 1, 2, 3 Karst systems Non-outstanding (DPIWE, 2004)

Nelson Lagoon Lunette 2, 3 Waterbodies Outstanding (DPIWE, 2004)

Nelson River Karst Karst systems Outstanding (DPIWE, 2004)

Newdegate Pass String Bog 1, 2, 3 Wetlands Non-outstanding (DPIWE, 2004)




North Lune and Lune Plains Karst 1, 2, 3 Karst systems Outstanding (DPIWE, 2004)

Old River mouth channelised meadows Rivers, Wetlands Undifferentiated (Dunn, 2002)

Pelverata Falls 1 Rivers Non-outstanding (DPIWE, 2004)

Precipitous Bluff karst system Karst systems Undifferentiated (Dunn, 2002)

Redpa karst system Karst systems Undifferentiated (Dunn, 2002)

Regents Plain "den sequence" Rivers Non-outstanding (Dunn, 2002)

Rocky Sprent Falls 1 Rivers Non-outstanding (DPIWE, 2004)

Seal Rocks coastal karst Karst systems Undifferentiated (Dunn, 2002)

South Rapid River Large Knickpoint-retreat Waterfall 1, 2 Rivers Undifferentiated (DPIWE, 2004)

St Patricks River "den sequence" Rivers Non-outstanding (Dunn, 2002)

Sulphide Pool 1, 2 Waterbodies Outstanding (DPIWE, 2004)

The Splits Rivers Undifferentiated (Dunn, 2002)

Three Mile Sand coastal karst system Karst systems Undifferentiated (Dunn, 2002)

Thunder and Lightning Bay Tufa and Springs 1, 2, 3 Rivers, Karst systems Non-outstanding (DPIWE, 2004)

Trowutta - Sumac Karst Systems 1, 2 Karst systems Non-outstanding (DPIWE, 2004)

Trowutta Arch 1, 2, 3 Waterbodies, Karst systems Outstanding (DPIWE, 2004)

Upper St Pauls River wetland complex 1 Wetlands Undifferentiated (Dunn, 2002)

Vanishing Falls 1, 3 Rivers, Karst systems Non-outstanding (DPIWE, 2004)

Wargata Mina (Judds Cavern) Karst System 1, 2, 3 Karst systems Outstanding (DPIWE, 2004)

Warners Landing - Perched Lake Sediments and Karst 1, 2, 3 Karst systems Outstanding (DPIWE, 2004)

Wingaroo Lagoonal Peats 2, 3 Wetlands Outstanding (DPIWE, 2004)

Wombat Plain sequence (St Patricks River) 1 Rivers Non-outstanding (Dunn, 2002)

Yarra Creek Gorge 1 Rivers Non-outstanding (DPIWE, 2004)

Zion Vale Bog 2 Wetlands Non-outstanding (DPIWE, 2004)



Priority limnological features

Priority limnological features were suggested by Dr Peter Davies and their nomination was supported by various studies as indicated in Table 54. The table also outlines details of their status as ranked by the CFEV Project and the ecosystems they are known to be associated with. The data source for this SV type was the CFEV waterbodies spatial data layer.

Table 54. Priority limnological features.

Feature name Ecosystem CFEV status Source/reference

Bains Lagoon Waterbodies Outstanding (Walsh et al., 1995; Walsh, 1996)

Big Lagoon Waterbodies Outstanding (Walsh et al., 1995; Walsh, 1996)

Black Bog Wetlands Outstanding (Rolfe et al., 2001)

Cape Barren 1 Waterbodies Outstanding (Rolfe et al., 2001)




D‟Arcys Lagoon Wetlands Outstanding (Walsh et al., 1995; Walsh, 1996)

Gibbs Lagoon Waterbodies Outstanding (Walsh et al., 1995; Walsh, 1996)

Granite Lagoon Wetlands Outstanding (Rolfe et al., 2001; Walsh et al., 2001)

Hibbs Lagoon Waterbodies Outstanding (Walsh et al., 2004)

Jocks Lagoon Waterbodies Outstanding (Walsh et al., 2004)

Lagoon of Islands Waterbodies Outstanding (Tyler, 1976)

Lake Bantick Waterbodies Outstanding (Walsh et al., 2004)

Lake Chisholm Waterbodies Outstanding (Bowling and Tyler, 1988)

Lake Fidler Waterbodies Outstanding (Bowling and Tyler, 1984; Hodgson and Tyler, 1996)

Lake Garcia Waterbodies Outstanding (Walsh et al., 2004)

Lake Koonya Waterbodies Outstanding (Walsh et al., 2004)

Lake Martha Lavinia Waterbodies Outstanding (Rolfe et al., 2001; Walsh et al., 2001)

Lake Morrison Waterbodies Outstanding (Hodgson and Tyler, 1996)}(Bowling and Tyler, 1984)

Lake Strahan Waterbodies Outstanding (Walsh et al., 2004)

Little Lagoon Waterbodies Outstanding (Walsh et al., 1995; Walsh, 1996)

Meatsafe Lagoon Waterbodies Outstanding (Rolfe et al., 2001; Walsh et al., 2001)

Moriarty Lagoon Wetlands Outstanding (Walsh et al., 2004)

Pennys Lagoon Waterbodies Outstanding (Rolfe et al., 2001; Walsh et al., 2001)

Perched Lake Waterbodies Outstanding (King and Tyler, 1981)

Reedy Lagoon Waterbodies, Wetlands

Outstanding (Rolfe et al., 2001; Walsh et al., 2001)

Sea Elephant Road cluster

Wetlands Outstanding (Rolfe et al., 2001; Walsh et al., 2001)

Seal Rocks Lagoons Wetlands Outstanding (Rolfe et al., 2001)

Sulphide Pool Waterbodies Outstanding (Hodgson and Tyler, 1996)}(Bowling and Tyler, 1984)

Two Swans Lagoon Wetlands Outstanding (Rolfe et al., 2001)

Walters Lagoon Wetlands Outstanding (Rolfe et al., 2001; Walsh et al., 2001)

Windmill Lagoon Wetlands Outstanding (Walsh et al., 2004)



Fauna species richness

Places of high fauna species richness associated with freshwater-dependent ecosystems were nominated by specialists and were supported by particular studies (see Table 55). Sites important for the preservation of high species diversity of freshwater fauna were based on various biological groups including benthic estuarine macroinvertebrates (Edgar et al., 1999a), microcrustacea (Walsh, 1996), riverine macroinvertebrates (P. Davies, Freshwater Systems and H. Dunn, Landmark Consulting, pers. comm.) and karst fauna (A. Richardson, UTas, pers. comm.). Table 55 outlines each site‟s status as ranked by the CFEV Project and the ecosystems they are known to be associated with. Location data for each the sites was sourced from the respective scientific studies or relevant experts.

Table 55. Sites of fauna species richness.

Fauna type Site name Ecosystem CFEV status Source/reference

Estuarine benthic macroinvertebrates

Low Head, Tamar River

Estuaries Outstanding (Edgar et al., 1999b)

Estuarine benthic macroinvertebrates

North East River Mouth

Estuaries Outstanding (Edgar et al., 1999b)

Microcrustacea Lake Ashwood Waterbodies Outstanding (Walsh, 1996)

Microcrustacea Lake Bellinger Waterbodies Outstanding (Walsh, 1996)

Microcrustacea Little Bellinger Waterbodies Outstanding (Walsh, 1996)

Microcrustacea Lake Garcia Waterbodies Outstanding (Walsh, 1996)

Microcrustacea Lake Bantick Waterbodies Outstanding (Walsh, 1996)

Microcrustacea Big Waterhouse Lake Waterbodies Outstanding (Walsh, 1996)

Microcrustacea Little Waterhouse Lake Waterbodies Outstanding (Walsh, 1996)

Microcrustacea Blackmans Lagoon Waterbodies Outstanding (Walsh, 1996)

Microcrustacea Lake Fidler Waterbodies Outstanding (Walsh, 1996)

Microcrustacea Sulphide Pool Waterbodies Outstanding (Walsh, 1996)

Microcrustacea Lake Morrison Waterbodies Outstanding (Walsh, 1996)

Microcrustacea Bains Lagoon Waterbodies Outstanding (Walsh, 1996)

Microcrustacea D‟Arcys Lagoon Wetlands Outstanding (Walsh, 1996)

Microcrustacea Little Lagoon Waterbodies Outstanding (Walsh, 1996)

Microcrustacea Big Lagoon Waterbodies Outstanding (Walsh, 1996)

Microcrustacea Gibbs Lagoon Waterbodies Outstanding (Walsh, 1996)

Microcrustacea Moriarty Lagoon Wetlands Outstanding (Walsh, 1996)

Microcrustacea Windmill Lagoon Wetlands Outstanding (Walsh, 1996)

Microcrustacea Jocks Lagoon Waterbodies Outstanding (Walsh, 1996)

Microcrustacea Cape Naturaliste Lagoon 2

Waterbodies Outstanding (Walsh, 1996)

Microcrustacea Cape Naturaliste Lagoon 4a


Microcrustacea Cape Naturaliste Lagoon 4b


Microcrustacea Cape Naturaliste Lagoon 4c


Microcrustacea Cape Naturaliste Lagoon 5a


Microcrustacea Cape Naturaliste Lagoon 5b


Microcrustacea Cape Naturaliste Lagoon 5c


Microcrustacea Bowlers Lagoon Waterbodies Outstanding (Walsh, 1996)

Microcrustacea Tregaron Lagoons No. 1.






Fauna type Site name Ecosystem CFEV status Source/reference





Microcrustacea Portland Lagoons No. 2.










River benthic macroinvertebrates

Little Swanport River upstream Eastern Marshes Rivulet

Rivers Outstanding Expert nomination: Dr Peter Davies


Great Musselroe River upper at New England Road



Macquarie/ Morningside Brook



Pine River upstream of Pine Tier Lagoon



Pipers/Venns Road Rivers Outstanding Expert nomination: Dr Peter Davies


Styx River at Cataract Road



Florentine River at Florentine Road



Great Musselroe River mid at Tebrakunna Road



Dew River above Clyde River



Macquarie River at Wilderness Track



Musselroe Creek at Tebrakunna Road



Plenty River upstream of Feilton



St Pauls/Upstream of Royal George



Julius River Rivers Non-outstanding

Expert nomination: Dr Helen Dunn

Cave faunal communities

Mole Creek complex Karst systems Outstanding Expert nomination: Assoc. Prof. Alastair Richardson


Mystery Creek Karst systems Outstanding Expert nomination: Assoc. Prof. Alastair Richardson


Exit Caves Karst systems Outstanding Expert nomination: Assoc. Prof. Alastair Richardson


Mt Cripps Caves Karst systems Outstanding Expert nomination: Assoc. Prof. Alastair Richardson


Precipitous Bluff caves Karst systems Outstanding Expert nomination: Assoc. Prof. Alastair Richardson

Fauna species of phylogenetic distinctiveness



A list of fauna species that are phylogenetically distinct was formulated using expert knowledge. Specialists nominated fauna species (see Table 56) associated with freshwater-dependent ecosystems that were considered important for taxonomic reasons. Table 56 lists the fauna species of phylogenetic distinctiveness, their status as ranked by the CFEV Project and the ecosystems they are known to be associated with.

Distribution records for the listed species were sourced from the GTSpot database (2003, DPIWE) and various experts (refer Table 56).

Appendix 6 – Attribute data - Special values - Table 56. Fauna species of phylogenetic distinctiveness.


Table 56. Fauna species of phylogenetic distinctiveness.


Allanaspides hickmani Hickman‟s pigmy mountain shrimp

Waterbodies, Wetlands Outstanding Expert nomination: Michael Dreissen, DPIW

Anaspides spinulae Mountain shrimp Rivers, Waterbodies Outstanding (O'Brien, 1990)

Anaspides tasmaniae Mountain shrimp Rivers, Waterbodies, Karst Outstanding (O'Brien, 1990)

Archipetalia auriculata Alpine dragonfly Wetlands Outstanding Expert nomination: Assoc. Prof. Alastair Richardson

Brachionichthys hirsutus Spotted handfish Estuaries Outstanding Dr Graham Edgar, TAFI, UTas

Colubotelson dubius Phreatoicid Waterbodies Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Colubotelson evansi Phreatoicid Rivers Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Colubotelson flynni Phreatoicid Rivers Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Colubotelson fontinalis Phreatoicid Rivers Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Colubotelson gesmithi Phreatoicid Rivers Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Colubotelson huonensis Phreatoicid Rivers Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Colubotelson intermedius Phreatoicid Waterbodies Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Colubotelson minor Phreatoicid Waterbodies Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Colubotelson saycei Phreatoicid Waterbodies Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Colubotelson setiferus Phreatoicid Rivers Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Colubotelson thomsoni Phreatoicid Rivers, Waterbodies Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Dipturus sp. Port Davey skate Estuaries Outstanding Expert nomination: Dr Peter Last, Commonwealth Scientific and Industrial Research Organisation (CSIRO)

Eucrenonaspides sp. Mountain shrimp Karst Undifferentiated (Knott and Lake, 1980; Eberhard et al., 1991)

Appendix 6 – Attribute data - Special values - Table 56. Fauna species of phylogenetic distinctiveness.



Hemiphlebia mirabilis Damselfly Wetlands Outstanding (Trueman et al., 1992)

Hypsimetopus intrusor Phreatoicid Rivers Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Koonunga sp. Mountain shrimp Karst Outstanding (Eberhard et al., 1991)

Mesacanthotelson decipiens Phreatoicid Waterbodies Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Mesacanthotelson fallax Phreatoicid Waterbodies Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Mesacanthotelson spinosus Phreatoicid Waterbodies Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Mesacanthotelson setosus Isopod (Great Lake) Waterbodies Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Mesacanthotelson tasmaniae Isopod (Great Lake) Waterbodies Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Metaphreatoicus affinis Phreatoicid Wetlands Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Metaphreatoicus magistri Phreatoicid Rivers Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Micraspides calmani Mountain shrimp Karst Outstanding (Eberhard et al., 1991)

Onchotelson brevicaudatus Isopod (Great Lake & Shannon Lagoon)

Waterbodies Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Onchotelson spatulatus Isopod (Great Lake) Waterbodies Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum

Ornithorhynchus anatinus Platypus Rivers, Waterbodies, Wetlands Non-outstanding Expert nomination: Dr Sarah Munk, FPA

Ornithorhynchus anatinus subsp. Platypus (King Island) Rivers, Waterbodies, Wetlands Non-outstanding Expert nomination: Dr Sarah Munk, FPA

Paranaspides lacustris Mountain shrimp Waterbodies Outstanding (Fulton, 1982)

Paraphreatoicus relictus Phreatoicid Rivers Outstanding (Fulton, 1982)

Synthemiopsis gomphomacromioides Dragonfly Wetlands Outstanding Expert nomination: Assoc. Prof. Alastair Richardson

Uramphisopus pearsoni Isopod (Great Lake) Waterbodies Outstanding Expert nomination: Dr George (Buz) Wilson, Australian Museum



Palaeolimnological sites

Sites of palaeolimnological significance (waterbodies containing aquatic life or sediment from previous geological time periods were nominated by Dr Peter Davies based on various sources of scientific literature (Table 57). Site location data was sourced from the selected scientific papers. Table 57 also outlines details each site‟s status, as ranked by the CFEV Project, and the ecosystems they are known to be associated with.

Table 57. Palaeolimnological sites.

Site name Ecosystem CFEV status Source/reference

Ooze Lake Waterbodies Outstanding (Macphail and Colhoun, 1985)

Lake Vera Waterbodies Outstanding (Macphail, 1979)

Lake Fidler Waterbodies Outstanding (Hodgson et al., 1996; Hodgson et al., 1998)

Lake Dobson Waterbodies Outstanding (Kirkpatrick and Fowler, 1998)

Owens Tarn Waterbodies Outstanding (Hodgson et al., 2000)

Lake Johnston Waterbodies Outstanding (Anker et al., 2001)

Lake Chisholm Waterbodies Outstanding (Bowling and Tyler, 1988)

Lake Nicholls Waterbodies Outstanding (Cameron et al., 1993)

Palaeobotanical sites

Sites of palaeobotanical importance to be included in the CFEV database were selected from known geological sites within Tasmanian that contain fossils of leaves, flowers, fruit or wood of Cenozoic age (i.e. the last 65 million years) (Jordan and Hill, 1998). Only those sites listed by Jordan and Hill (1998) that are associated with freshwater-dependent ecosystems were included (Table 58). The table also details each site‟s status, as ranked by the CFEV Project, and the ecosystems they are known to be associated with. Site location data was supplied by Dr Greg Jordan from UTas.

Table 58. Palaeobotanical sites.

Site name Ecosystem CFEV status Source/reference

Lea River Rivers Outstanding (Jordan and Hill, 1998)

Linda (Idaho Formation) Rivers Outstanding (Jordan and Hill, 1998)

Wilsons Creek Rivers Outstanding (Jordan and Hill, 1998)

Coal Head Estuaries Outstanding (Jordan and Hill, 1998)

Leven River Rivers Undifferentiated (Jordan and Hill, 1998)

Mersey/Cathedral Rivers Outstanding (Jordan and Hill, 1998)

Brown‟s Bluff Wetlands Outstanding (Jordan and Hill, 1998)

Glenora Bridge Rivers Undifferentiated (Jordan and Hill, 1998)

Corra Linn Rivers Undifferentiated (Jordan and Hill, 1998)

Cornelian Bay Estuaries Undifferentiated (Jordan and Hill, 1998)

Taroona Estuaries Undifferentiated (Jordan and Hill, 1998)

Shark Point Estuaries Undifferentiated (Jordan and Hill, 1998)



Important bird sites

Information about important places for birds was determined by an expert panel including members of Birds Australia. The expert group suggested key sites where important bird species (i.e. those listed under legislation and those with numerous records of sighting over a number of decades) were known to breed or sustained permanent populations. Only birds considered dependent upon freshwater-dependent ecosystems were included except for a very few cases of critical refugia. Criteria for selecting priority bird sites were:

1. rare, threatened or uncommon species

2. key breeding or feeding areas for coastal, wetland and waterbirds

3. sites important for migratory wader species

4. refugial sites (rivers, waterbodies) important in landscape for birds more generally, including Tasmanian endemic bird species.

The list of important bird sites relating to freshwater-dependent ecosystems is provided in Table 59, which also outlines the criteria each addresses, details of their status as ranked by the CFEV Project and the ecosystems they are known to be associated with. Site location data for the important bird sites were provided by the expert panel.

Appendix 6 – Attribute data - Special values - Table 59. Important bird sites.


Table 59. Important bird sites.

Site name Values Criteria Ecosystem CFEV status

Ansons Bay Waders, Terns 2, 3 Estuaries Outstanding

Apsley Marshes Bittern, Waterfowl 2 Wetlands Non-outstanding

Bar Lagoon (Ellinthorpe Plains Lagoon Complex) Waterbodies Undifferentiated

Bell Lagoon (Ellinthorpe Plains Lagoon Complex) Waterfowl 1, 4 Waterbodies Outstanding

Bernacchis Dam, Maria Island Forty-spotted Pardalote 4 Rivers Outstanding

Big Lagoon, Bruny Island Waterbodies Non-outstanding

Big Lake, King Island Waterbodies Undifferentiated

Big Sandy Lagoon, Flinders Island Waders, Terns 1, 2, 3 Waterbodies Outstanding

Big Waterhouse Lake Waterbodies Outstanding

Birch‟s Inlet Azure Kingfisher 1 Rivers, Estuaries Outstanding

Blackman Bay Waders, Great Crested Grebe 2, 3 Estuaries Outstanding

Blackmans Lagoon Waterbodies Non-outstanding

Bream Creek (Marchwiel Marsh) Waterfowl 4 Wetlands, Estuaries Undifferentiated

Bridport Estuary (Hurst Creek) Coastal Birds - Terns Habitat Estuaries Outstanding

Brushy Lagoon Waterbodies Undifferentiated

Buxton River Terns 1 Estuaries Non-outstanding

Calverts Lagoon Waders, Waterfowl, Black-winged Stilt 1, 2, 3 Waterbodies Outstanding

Camerons Inlet, Flinders Island Waders, Waterfowl 1, 2, 3 Estuaries Outstanding

Cape Portland Area Lagoons (Tregaron Lagoon North) Migratory waders and waterfowl 1, 2, 3 Waterbodies Outstanding

Cape Portland Area Lagoons (Tregaron Lagoon South) Migratory waders and waterfowl 1, 2, 3 Waterbodies Outstanding

Cape Portland Area Lagoons (Vinegar Hill Lagoon) Migratory waders and waterfowl 1, 2, 3, 4 Waterbodies Outstanding

Cape Portland Lagoon 4 Migratory waders and waterfowl 1, 2, 3 Waterbodies Outstanding

Carters Lakes Latham‟s Snipe and Golden Plover 1 Waterbodies Undifferentiated

Chain of Lagoons, Flinders Island Waders 3 Waterbodies Undifferentiated

Chain of Lagoons, Flinders Island Waders 3 Waterbodies Undifferentiated

Clarks Lagoon (Ellinthorpe Plains Lagoon Complex) Wetlands Undifferentiated

Clear Lagoon Waders 1 Wetlands Outstanding

Coffee Creek Forty-spotted Pardalote 1, 4 Rivers Outstanding




Derwent River Estuaries Outstanding

Derwent River Great Crested Grebe Habitat, Waterfowl 1, 2, 4 Estuaries Outstanding

Derwent River Estuaries Outstanding

Diana Basin Waterfowl, Terns 2 Estuaries Outstanding

Duck Bay Coastal Birds - Terns Habitat 2 Estuaries, Saltmarshes Outstanding

Earlham Lagoon Waterbodies, Saltmarshes Outstanding

East Coast Cape Barren Island Wetlands Outstanding

East Coast Cape Barren Island Waterbodies Outstanding



East Coast Cape Barren Island Estuaries Outstanding

East Coast Cape Barren Island Waterbodies Outstanding

East Inlet, Stanley Coastal Birds - Terns Habitat 2, 3 Saltmarshes Outstanding

Falmouth Estuary (Henderson Lagoon) Terns 1, 2 Estuaries Outstanding

Folly Lagoon (Ellinthorpe Plains Lagoon Complex) Waterbodies Undifferentiated

Foochow Inlet, Flinders Island Coastal Birds 1 Estuaries Outstanding

Forest Lagoon (Ellinthorpe Plains Lagoon Complex) Waterbodies Undifferentiated

Forth River Wading birds 2, 3 Estuaries Undifferentiated

Four Springs Creek Waterfowl, Blue-billed duck, Latham‟s Snipe 1, 2, 4 Waterbodies Outstanding

Georges Bay Waders, Waterfowl, Terns 1, 2, 3 Estuaries Outstanding

Goulds Lagoon 1 Rails, waterfowl 1, 2 Wetlands Outstanding

Goulds Lagoon 2 Rails, waterfowl 1, 2 Wetlands Outstanding

Great Forester River Estuary Coastal Birds - Terns Habitat Estuaries Outstanding

Henty River Sanderling 3 Estuaries Outstanding

Intermediate Lagoon Waders, Waterfowl 2, 3 Waterbodies Undifferentiated

Kent Bay (Rices River), Cape Barren Island Curlew Habitat, Waders 1, 2, 3 Estuaries Outstanding

Lades Beach, Little Forester River Estuary Coastal Birds - Terns Habitat, Waders 2, 3 Estuaries Outstanding

Lagoon of Islands Great Crested Grebe Habitat 1, 2 Waterbodies Non-outstanding

Lake Augusta Latham‟s Snipe and Golden Plover 1 Waterbodies Outstanding

Lake Chisholm Australasian Grebe Breeding 2 Waterbodies Undifferentiated




Lake Dulverton Waterfowl, Great Crested Grebe Habitat. Rail and Black-winged Stilt breeding area

1, 2 Waterbodies Outstanding

Lake Flannigan, King Island Waterbodies Outstanding

Lake Tiberias Australasian Bittern, rails 1, 2 Waterbodies Outstanding

Lauderdale Waders 1, 2, 3 Estuaries Outstanding

Lisdillon Lagoon Waterfowl 4 Estuaries Outstanding

Little Lagoon (Ellinthorpe Plains Lagoon Complex) Waterbodies Undifferentiated

Little Mussleroe Bay Wader Habitat 2, 3 Estuaries Outstanding

Little Waterhouse Lake Waterbodies Outstanding

Logan Lagoon, Flinders Island Waterfowl, Terns, Waders 1, 2, 3 Waterbodies Outstanding

Long Point, Flinders Island Waders, Eastern Curlew 1, 3 Saltmarshes Outstanding

Lower Pittwater Great Crested Grebe Habitat, Waders 1, 2, 3 Estuaries Outstanding

Meredith River Common Greenshank 1,3 Estuaries Outstanding

Mersey River Great Egrets, Royal Spoonbills 2 Estuaries Undifferentiated

Montagu River Coastal Birds - Terns Habitat around the mouth 2, 3 Estuaries Outstanding

Mosquito Inlet, Robbins Island Coastal Birds - Waders including Eastern Curlew and Terns Habitat

1, 2, 3 Estuaries Outstanding

Moulting Lagoon Waterfowl, Great Crested Grebe Habitat. 2 Estuaries Non-outstanding

Moulting Lagoon (King Bay, mouth of Swan River) Waterfowl, Great Egret, Eastern Curlew 1, 2, 3 Estuaries Outstanding

Mrs Teddys Lagoon 1 (Ellinthorpe Plains Lagoon Complex)

Wetlands Undifferentiated

Mrs Teddys Lagoon 2 (Ellinthorpe Plains Lagoon Complex)

Wetlands Undifferentiated

Mussleroe Bay Waders, Coastal Birds - Terns Habitat 2, 3 Estuaries Outstanding

Nant Lagoon Waterfowl, Pink eared Duck Habitat 1 Wetlands Outstanding

Narawntapu Lagoon Waterfowl 2, 4 Wetlands Outstanding

North Beach West Waders 2, 3 Wetlands Undifferentiated

North East Inlet, Flinders Island Waders including Eastern Curlew 1, 2, 3 Estuaries Outstanding

Orielton Lagoon Waders including Eastern Curlew, Great Crested Grebe Habitat

1, 2 Estuaries, Saltmarshes Outstanding

Patriarch Inlet, Flinders Island Waders, Terns 1, 2 Estuaries Outstanding




Pear Shape Lagoon 1, King Island Waterbodies Undifferentiated

Pear Shape Lagoon 2, King Island Waterbodies Undifferentiated

Piper‟s River Estuaries Outstanding

Port Sorell Waders 2, 3 Estuaries Non-outstanding

Pump Lagoon 1 (Ellinthorpe Plains Lagoon Complex) Wetlands Undifferentiated

Pump Lagoon 2 (Ellinthorpe Plains Lagoon Complex) Wetlands Undifferentiated

Queechy Ponds - North Esk River Little Pied Cormorant, Dusky Moorhen, Cattle Egret, Waterfowl

1, 2 Waterbodies Non-outstanding

Reedy Lagoon (Ellinthorpe Plains Lagoon Complex) Waterbodies Undifferentiated

Ringarooma River Coastal Birds - Terns Habitat, Migratory Waders 2, 3 Estuaries Outstanding

Rostrevor Lagoon Great Crested Grebe and Bittern Habitat 1, 2 Waterbodies Outstanding

Rushy Lagoon Bittern when high water level was maintained, Waterfowl 1 Wetlands Non-outstanding

Scamander River Terns 1, 2 Estuaries Outstanding

Sea Elephant River Waders/Orange Bellied Parrot 1, 2, 4 Estuaries Outstanding

Secret Lagoon, Cape Portland Waders 2, 3 Waterbodies Outstanding

Sellars Lagoon, Flinders Island Waders, Bittern 1, 2, 3 Waterbodies Outstanding

Silo Lagoon (Ellinthorpe Plains Lagoon Complex) Wetlands Undifferentiated

Southern end of Ralphs Bay Saltmarshes Outstanding

Swale Lagoon Waders (including Hooded Plover) 1, 2, 3 Waterbodies Undifferentiated

Tamar River Coastal Birds - Terns Habitat 2 Estuaries Outstanding

Tamar River (George Town mudflats) Waders, Eastern Curlew, Whimbrel, Crested Terns 1, 2, 3 Estuaries Outstanding

Tamar Wetlands Waterfowl, Crested and Caspian Terns, Australasian Bittern, Latham‟s Snipe, Crakes

1, 2, 4 Wetlands Outstanding

The Narrows Ducks Wetlands Outstanding

The Salties Waders 1, 3 Waterbodies Outstanding

Upper Pittwater Great Crested Grebe Habitat, Eastern Curlew 1, 3 Estuaries, Saltmarshes Outstanding

Weedy Lagoon (Ellinthorpe Plains Lagoon Complex) Wetlands Undifferentiated

Welcome River Coastal Birds - Terns Habitat Estuaries Outstanding

West Inlet, Stanley Coastal Birds - Terns Habitat, Eastern Curlew 1, 2, 3 Saltmarshes Outstanding

Wrinklers Lagoon Little Terns 2 Estuaries Outstanding



Data limitations

The SV data is based on real observations, however, in the context of the CFEV assessment it is subjective in the sense that it only includes records of important values from sites where specific studies have been carried out. As such, areas which may include SVs but have never been studied, will not have been assigned any SVs. An assessment of accuracy of individual records (i.e. the observation is believed to be reliable within x metres) has been provided in the SV data set as SV_ACCURAC.


Scale and coverage Variable

Column heading ES_SVDIV, KT_SVDIV, SM_SVDIV, RS_SVDIV, WB_SVDIV, WL_SVDIV, ES_OUTSV, KT_OUTSV, SM_OUTSV, RS_OUTSV, WB_OUTSV, WL_OUTSV, ES_NONSV, KT_NONSV, SM_NONSV, RS_NONSV, WB_NONSV, WL_NONSV, ES_UNDIFSV, KT_UNDIFSV, SM_UNDIFSV, RS_UNDIFSV, WB_UNDIFSV, WL_UNDIFSV

Type of data various - points and polygons


Assigning the relevant ecosystem themes to the SV data

Each SV was assessed as whether it was known to be relevant for each ecosystem them. This data (0 – not relevant; 1 – relevant) was assigned to the SV data as SV_ESTUARY for estuaries, SV_GDE for Groundwater Dependent Ecosystems (GDEs), SV_KARST for karst, SV_SMARSH for saltmarshes, SV_RIVER for rivers, SV_WBODY for waterbodies and SV_WETLAND for wetlands.

Assigning SV data to the spatial units

Special Values (as point and polygon data) were assigned to individual spatial units as SV_ID using the following rules. Any given spatial unit may have more than one SV and/or an SV can be assigned to more than one spatial unit, so a separate attribute table was generated for each ecosystem theme (**_specialvalues, where ** is the prefix for each ecosystem theme i.e. ES = estuaries, KT = karst, RS = rivers, SM = saltmarshes, WB = waterbodies and WL = wetlands) to store this data.

1. Any SV point that falls within a RSC and is a riverine special value (i.e. SV_RIVER = 1), to be assigned to the river section.

2. Any SV point that falls within a waterbody spatial unit or waterbody catchment and is a waterbody special value (i.e. SV_WBODY = 1) to be assigned to the waterbody spatial unit.

3. All SV point data with an accuracy within 100 m or less (SV_ACCURAC ≤100) to be assigned to all ecosystem spatial units (ES = estuaries, KT = karst, SM = saltmarshes and WL = wetlands) within that distance range.

4. All SV point data with an accuracy of >100-500 m (SV_ACCURAC >100 and ≤500) that is known to be associated with only one ecosystem to be assigned to nearest spatial unit within its range of accuracy (e.g. if the point has an accuracy of 200 m (SV_ACCURAC = 200) and is only associated with waterbodies (SV_WBODY = 1), find the waterbody spatial unit closest to that point within a 200 m range).

5. All SV point data with an accuracy of >100-500 m (SV_ACCURAC >100 and ≤500) that is known to be associated with more than one ecosystem, and

Appendix 6 – Attribute data - Stream order (position in drainage)


more than one spatial unit occurs within the accuracy range of a point, then flag for manual inspection. These points to be inspected by experts to assign the point to the most appropriate ecosystem spatial unit.

6. All SV polygon data to be given a 100 m buffer and intersected with the relevant ecosystem spatial unit. Assign the SV to relevant spatial units that intersect its distribution. The exception to this rule is for SVs whose distribution was determining using the RSCs (i.e. Whitebait (Lovettia spp.) and platypus (Ornithorhynchus anatinus)). In this case, the SV was assigned to the relevant ecosystem that was directly associated with the RSC.

Assigning the number of SV records to the spatial units

The number of SVs for each ecosystem spatial unit was assigned to each of the spatial units using the following rules:

1. Count the total number of unique SV records (SV_ID in **_specialvalues attribute data set) and assign as **_SVDIV.

2. Count the total number of outstanding SV records (SV_STATUS = Outstanding in CFEV Special Values attribute data set) and assign as **_OUTSV.

3. Count the total number of non-outstanding SV records (SV_STATUS = Non-outstanding in CFEV Special Values attribute data set) and assign as **_NONSV.

4. Count the total number of undifferentiated SV records (SV_STATUS = Undifferentiated in CFEV Special Values attribute data set) and assign as **_UNDIFSV.


Estuaries>Conservation evaluation>Integrated Conservation Value

Karst>Conservation evaluation>Integrated Conservation Value

Rivers>Conservation evaluation>Integrated Conservation Value

Saltmarshes>Conservation evaluation>Integrated Conservation Value

Waterbodies>Conservation evaluation>Integrated Conservation Value

Wetlands>Conservation evaluation>Integrated Conservation Value

6.3.47 Stream order (position in drainage)

Title Strahler stream order

Column heading RS_ORDER

Input data



Number of classes 9


Strahler stream ordering is an internationally recognised stream ordering classification for stream networks which rates each stream segment according to the orders of the incoming upstream segments.

Appendix 6 – Attribute data - Tidal/wave energy regime


The Strahler stream order (Strahler, 1957) was derived from the rivers spatial data layer, where headwater reaches were assigned a stream order of 1 through to reaches entering estuaries, which could attain a maximum stream order of 9 (in Tasmania).

Each time two or more stream segments with order „n‟ merge, the downstream order increases to „n+1‟. See Figure 47 for a diagrammatic explanation of Strahler stream order.

Figure 47. Diagram of the stream order procedure used by the CFEV Project (after (Strahler, 1957)).


Rivers>Classification>Crayfish regions (RS_CRAYS)

6.3.48 Tidal/wave energy regime

Title Tidal/wave energy regime

Column heading SM_TIDAL

Input data

CFEV Tidal/wave energy spatial data layer (Appendix 6.2.28)


Number of classes 4


The dominant tidal/wave energy class was assigned to the each of the saltmarshes as SM_TIDAL.



1

1

1

1

1

1 1

1 1 1

1 1

1

1

1 1 1

1

1

1

1

1

2

2

2

2

2

2

2

2

3

3

4

Appendix 6 – Attribute data - Tree assemblages


6.3.49 Tree assemblages

Title Tree assemblages

Column heading RS_TREES, WB_TREES, WL_TREES

Input data

CFEV Tree assemblage spatial data layer (Appendix 0)


Number of classes RS_TREES = 50, WB_TREES = 44 (subset),

WL_TREES = 50


The dominant tree assemblage class (e.g. T1, T2, T3, etc.) was assigned to the river sections, waterbodies and wetlands as RS_TREES, WB_TREES and WL_TREES, respectively.





6.3.50 ‘Tyler’ biogeochemical classification

Title Tyler classification

Custodian WRD, DPIW


Description Biogeochemical classification of waterbodies and wetlands based on studies undertaken by Professor Peter Tyler.

Input data




LIST 1:250 000 Geology data, DPIW

Published work by Professor Peter Tyler, Deakin University (Appendix 8)

Lineage

A broad biogeochemical classification of waterbodies was developed using information from published works by Professor Peter Tyler and associates ((Timms, 1978; Shiel et al., 1989)), geology data and the Tyler corridor.

GIS rules were derived (see below) to assign nine Tyler classes to waterbodies and/or wetland spatial units. A description of the nine Tyler classes is given in Table 60.

Appendix 6 – Attribute data - ‘Tyler’ biogeochemical classification


Table 60. Description of Tyler classes for biochemical classification of waterbodies and wetlands.

Class code

Name Description

TY1 „Clearwater‟ wetlands and waterbodies

Waterbody most likely to have blue-green optical environment („gilvin dominated‟); East of Tyler corridor

TY2 „Blackwater‟ wetlands and waterbodies

Waterbody with red-window optical environment („fulvic dominated‟); West of Tyler corridor

TY3 „Transition‟ wetlands and waterbodies

Waterbody with either red-window optical environment („fulvic dominated‟) or blue-green optical environment („gilvin dominated‟) or intermediate between these; Inside Tyler corridor

TY4 Mesotrophic upland lakes

Shallow, productive lakes often with dense macrophytes and/or fringing wetlands, e.g. Lakes Sorell and Crescent

TY5 Lowland salt lakes and pans

Lakes with permanent or periodic natural saline water phase, and/or occasional dry saline phase; Midland lowlands, east and north-west of Tunbridge

TY6 Coastal lagoons, lakes, wetlands – Timms Types i, ii, iv.

Statewide, adjacent to or within dune-scapes. Often with red-window optical environment („fulvic dominated‟), occasionally brackish to saline.

TY7 Furneaux coastal lagoons.

Flinders and Cape Barren Islands, adjacent to or within modern and ancient dune-scapes. Red-window optical environment („fulvic dominated‟) or blue-green optical environment („gilvin dominated‟) or intermediate between these.

TY8 King Island coastal lagoons.

King Island, adjacent to or within modern and ancient dune-scapes. Red-window optical environment („fulvic dominated‟) or blue-green optical environment („gilvin dominated‟) or intermediate between these.

TY9 Meromictic lakes (Lower Gordon)

Lakes with natural meromictic state (only in Lower Gordon). Red-window optical environment („fulvic dominated‟). Marked stratification, with fresh epilimnion and saline to brackish hypolimnion.

Data limitations

The Tyler classification inherits all the data limitations of the derivation processes and input data.



Column heading WB_TYLERC, WL_TYLERC


Number of classes WB_TYLERC = 9, WL_TYLERC = 7

Appendix 6 – Attribute data - ‘Tyler’ biogeochemical classification



A biogeochemical „Tyler‟ class (e.g. TY1, TY2, Ty3, etc.) was assigned to waterbody and wetland spatial units as WB_TYLERC and WL_TYLERC, respectively, using the rules outlined below. Note, that for the wetlands, classes TY4 and TY9 were not relevant.

1. Is the waterbody named Lake Sorell or Lake Crescent?

Yes: Assign as TY4.

No: Go to 2.

2. Is the waterbody or wetland a salt lake (i.e. listed in Table 61)?

Table 61. Salt lakes and pans.

Waterbody/Wetland CFEV spatial data layer Easting Northing

Folly Lagoon Waterbody 530691 5345264

Bar Lagoon Waterbody 528071 5345515

Township Lagoon Waterbody 535358 5333520

Brents Lagoon Waterbody 537943 5332262

Clarks Lagoon Wetland 529750 5345276

Reedy Lagoon Wetland 530560 5343847

Bells Lagoon Waterbody 528216 5343774

Tunbridge No. 2 Waterbody 536332 5334326

Tunbridge No. 2a Waterbody 538653 5334236

Glen Morey saltpan Wetland 539700 5333750

Mona Vale saltpan Wetland 539900 5335600

Yes: Assign as TY5.

No: Go to 3.

3. Is the waterbody or wetland on mainland Tasmania and is the geology described as Quaternary-coastal sands and gravels (1:250 000 geology map Rcode 8496 „Qps‟)?

Yes: Assign as TY6.

No: Go to 4.

4. Is the waterbody or wetland on Flinders, Cape Barren or Clarke Islands and is the geology described as Quaternary-coastal sands and gravels (1:250 000 geology map Rcode 8496 „Qps‟)?

Yes: Assign as TY7.

No: Go to 5.

5. Is the waterbody or wetland on King Island and is the geology described as Quaternary-coastal sands and gravels (1:250 000 geology map Rcode 8496 „Qps‟)?

Yes: Assign as TY8.

No: Go to 6.

Appendix 6 – Attribute data - Tyler corridor


6. Are the waterbodies meromictic lakes (i.e. listed in Table 62)?

Table 62. Meromictic lakes.

Waterbody Easting Northing

Lake Fidler 387229 5300633

Lake Morrison 391305 5292876

Sulphide Lagoon 390927 5291215

Perched Lake 392159 5286681

Yes: Assign as TY9.

No: Go to 7.

7. Is the waterbody or wetland east of the Tyler corridor (WB_TYLER = east or WL_TYLER = east)?

Yes: Assign as TY1.

No: Go to 8.

8. Is the waterbody or wetland west of the Tyler corridor (WB_TYLER = west or WL_TYLER = west)?

Yes: Assign as TY2.

No: Assign as TY3 - The waterbody or wetland is within the Tyler corridor (WB_TYLER = within or WL_TYLER = within)?




6.3.51 Tyler corridor

Title Tyler corridor

Column heading WB_TYLER, WL_TYLER

Input data

CFEV Tyler corridor spatial data layer (Appendix 6.2.30)


Number of classes 3


The dominant Tyler corridor class (i.e east, west or within) was assigned to the river sections, waterbodies and wetlands as RS_TYLER, WB_TYLER and WL_TYLER, respectively.


Waterbodies>Classification>Tyler classification (WB_TYLERC)

Wetlands>Classification>Physical classification (WL_PCLASS)

Wetlands>Classification>Tyler classification (WL_TYLERC)

Appendix 6 – Attribute data - Urbanisation


6.3.52 Urbanisation

Title Urbanisation

Column heading RS_URBAN

Input data

CFEV Urbanisation spatial data layer (Appendix 6.2.31)


Number of classes 2


Using the urbanisation spatial data layer, each river section was assigned a score (RS_URBAN) indicating presence (0) or absence (1), based on whichever was present along the majority of the river section length.


Rivers>Condition>Naturalness score (RS_NSCORE)>Geomorphic condition (RS_GEOM)>Sediment input (RS_SEDIN)

6.3.53 Waterbody artificiality

Title Waterbody artificiality

Custodian WRD, DPIW


Description The extent to which a waterbody has been modified

Input data

Hydro infrastructure and discharge data, Hydro Tasmania

Lineage

The artificiality and true artificiality score rates all waterbodies according to their ability to contain natural habitat features. Waterbodies were rated, using expert knowledge (Peter Davies, Freshwater Systems) in combination with Hydro data, according to the following categories:

0 completely artificial waterbody (i.e. a river that has been dammed to make a waterbody (e.g. Lake Rowallan)

0.5 partly artificial waterbody (i.e. lake that has been raised or modified by infrastructure (e.g. Lake Cumberland, Great Lake)

1 natural waterbody (i.e. formed by natural processes and undammed)

One out of these three categories was assigned to waterbodies as the true artificiality score. The artificiality scores were determined by merging the 0.5 and 1 categories into a single category of 1, resulting in two categories (0 and 1). Appendix 13 provides all the artificiality and true artificiality scores for all „valid‟ named waterbodies. All unnamed „valid‟ waterbodies received a score of 1 (natural waterbody). Specific rules are provided below.

Data limitations

The artificiality data inherits all the data limitations of the input data.


Appendix 6 – Attribute data - Waterbody depth



Column heading WB_ARTIF, WB_TRARTI

Number of classes WB_ARTIF = 2, WB_TRARTI = 3



An artificiality score (0 or 1) and a true artificiality score (0, 0.5, 1) were assigned to the waterbodies as WB_ARTIF and WB_TRARTI, respectively, according to the following rules:

1. Assign all named waterbody spatial units with an artificiality and true artificiality score as listed in Appendix 13.

2. Assign remaining unnamed waterbody spatial units with an artificiality and true artificiality score of 1. This assumes they are natural.


Waterbodies>Condition assessment>Naturalness score (WB_NSCORE)

6.3.54 Waterbody depth

Title Waterbody depth

Custodian WRD, DPIW


Description Depth classification of Tasmania‟s waterbodies.

Input data

Published work by Professor Peter Tyler, Deakin University (Appendix 8)

Other scientific literature (e.g. (Peterson and Missen, 1979))

Lineage

Maximum depth for each of the waterbodies was classified as shallow (S), deep (D) or very deep (VD), with shallow being <30 m maximum depth, deep being 30-50 m maximum depth and very deep being >50 m maximum depth. These thresholds were derived by an expert, Dr Peter Davies, following review of a wide range of publications of Professor Peter Tyler and associates (see Appendix 8) in relation to the potential for thermal and/or chemical stratification, and in relation to the mixing characteristics („mixis‟) of known Tasmanian lakes and storages (e.g. shallow lakes tend to be will mixed whilst deeper lakes may be permanently stratified; others may change their status with the seasons or temporarily).

Lake bathymetric and depth information was reviewed from Peterson and Missen, (1979), the publications of Peter Tyler and information from unpublished lake and other waterbody surveys (P. Davies, Freshwater Systems, unpublished data and A. Uytendaal, IFS, unpublished data). All known deep and very deep waterbodies were identified and the list reviewed by an expert panel (see Appendix 1). Appendix 13 provides all the depth categories for all „valid‟ named waterbodies. All unnamed „valid‟ waterbodies received a depth category of S (shallow). Specific rules are provided below.

Appendix 6 – Attribute data - Waterbodies physical classification


Data limitations

The waterbody depth data inherits all the data limitations of the derivation processes and input data.



Column heading WB_DEPTH

Number of classes 3



A depth class (i.e. S, D, VD) was assigned to the waterbodies as WB_DEPTH according to the following rules:

1. Assign all named waterbody spatial units with a depth category according to Appendix 13.

2. Assign remaining unnamed waterbody spatial units with a depth category of S.



6.3.55 Waterbodies physical classification

Title Waterbodies physical classification

Custodian WRD, DPIW


Description Physical classification of Tasmania‟s waterbodies.

Input data

CFEV Area (waterbodies) attribute data (Appendix )

CFEV Fluvial geomorphic mosaic spatial data layer (Appendix 6.2.8)

CFEV Shoreline complexity attribute data(Appendix 6.3.44)

CFEV Waterbody depth attribute data (Appendix 6.3.54)

Lineage

A physical classification was undertaken for waterbodies based on size, depth, shoreline complexity and fluvial geomorphic context. A number of other physico-chemical variables believed to be of importance were also considered but not included in this assessment due to lack of data with sufficient coverage, detail or quality.

Prior to the classification, a number of the variables were grouped into categories. The waterbody area data was grouped into three size classes (Large = >400 ha, Moderate-small = 3-400 ha and Small = <3 ha). After inspection of a range of waterbody shapes and shoreline complexity scores (DL values), waterbodies were grouped as having simple (S) (WB_SHOREDE <3) or complex (L) shoreline complexity (WB_SHOREDE >3). Some of the fluvial geomorphic mosaics were considered similar and were grouped as per Table 63, while others were used directly from the mosaic list (see Appendix 6.2.8).



Table 63. Groups used to bundle fluvial geomorphic mosaics prior to classification.

All Glacial Karst Coastal sediments and dunefields (W, NW, E)

Midlands All other, non karst

Glacially dissected dolerite and Parmeener plateau

North-west moderate relief karst

Eastern granite hills and coastal sediments

Southern Midlands foothills and valleys

All of the less common mosaics

Glacially dissected quartzite plateau

South-eastern glacio-karst

North Eastern coastal dunefields

Southern Midlands Tertiary Basin

Glaciated dolerite and Parmeener peaks

South-western karst basins, rolling

Western coastal sediments, terraces, and remnant surfaces

Central Plateau glacial till and outwash plains

Northern karst basin

South-east rolling hills and coastal sediments

Glaciated quartzite peaks

Eastern dolerite rolling hills

Glaciated quartzite valleys

Central East alluvial basins

Glaciated dolerite valleys

Strongly glaciated plateau

The physical classes were assigned to each of the waterbody spatial units based on the rules described in Table 64 (see below).

Data limitations

The physical classification inherits all the data limitations of the derivation processes and input data. „Invalid‟ waterbodies were not been assigned a physical class.



Column heading WB_PCLASS




A physical class (e.g. Wb1, Wb2, Wb3, etc.) was assigned to waterbody spatial units as WB_PCLASS using the rules in Table 64 (e.g. if the waterbody is of any size, is shallow in depth, has a simple shoreline and is within the “All glacial” group of fluvial geomorphic mosaics, then assign class Wb1).



Table 64. Summary of physical classification rules for waterbodies.

Class code

Waterbody area

Depth Shoreline complexity

Geomorphic mosaic group

Examples

Wb1 Any S S All glacial (see Table 63) Lake Augusta, Lake Cygnus

Wb2 Any S L All glacial (see Table 63) Pillans Lake

Wb3 Mod-Small S S High alpine dolerite plateau

Gunns, Lake Fergus

Wb4 Large S S High alpine dolerite plateau

Great Lake

Wb5 Large S S Central & eastern dolerite plateau

Arthurs Lake, Lake Sorell, Lake Crescent, Woods Lake

Wb6 Mod-Small S S Central & eastern dolerite plateau

Wihareja Lagoon, Allwrights Lagoons

Wb7 Mod-Small D or VD S All glacial (see Table 63) Lake Oberon

Wb8 Large D or VD S All glacial (see Table 63) Lake St Clair

Wb9 Any S S Karst (see Table 63) Lake Tim, Lake Chisholm, Lake Lea

Wb10 Any S S Southern Midlands foothills and drainage divides

Only Lake Tiberias, Lake Dulverton

Wb11 Any S Any Midlands (see Table 63) Tunbridge salt lakes

Wb12 Mod-Small S Any Coastal sediments and dunefields (see Table 63)

Coastal lagoons

Wb13 Large S Any Coastal sediments and dunefields (see Table 63)

Sellars and Logan Lagoons

Wb14 Large D or VD L All other, non karst (see Table 63)

Big river valley storages which can stratify: Gordon Dam, Lake Barrington, Meadowbank Dam, Craigbourne Dam, etc

Wb15 Any Any S All other, non karst (see Table 63)

Larger upland storages which do not significantly stratify: Lake Echo, Lake Leake, Lake Rowallan, Dee Lagoon, Lake King William

Wb16 Mod-Small S Any All other, non karst (see Table 63)

Small to medium storages which don‟t stratify e.g. Talbots Lagoon, Lake Cumberland, Bradys Lake

Wb17 All waterbodies remaining unclassified; generally small artificial waterbodies

Large farm dams, odd small dams, etc.

Wb18 Small S S NA Meromictic lakes of the lower Gordon (Lake Fidler, Lake Morrison, Sulphide Pool)

Wb19 Small S S NA Lowland, warm monomictic lake in SW Tasmania (Perched Lake only)



Appendix 6 – Attribute data - Wetland catchments


6.3.56 Wetland catchments

Title Wetland catchments

Custodian WRD, DPIW


Description Catchment boundaries for wetlands of Tasmania

Input data



Lineage

Development of the local catchments for wetlands involved intersecting the wetland polygons with the RSC spatial data layer. The percentage area of each RSC that overlapped with the wetland polygons was calculated. If the overlap was ≥30%, then the RSC was included as part of the local catchment for the wetland. If there was no RSC that met this criterion, the RSC with the largest overlap was used. Specific rules are provided below. Note, a wetland spatial unit can have a local catchment consisting of more than one RSC and it was possible for the same RSC to be used for more than one wetland. Figure 48 illustrates an example of a wetland and its local catchment.

The data representing the wetland local catchments was generated as a separate attribute table that can be used to link the wetland spatial units with the RSCs, rather than as a separate spatial data layer.

Figure 48. Illustration of a local wetland catchment.

Appendix 6 – Attribute data - Wetlands physical classification


Data limitations





RSCs were assigned to individual wetland spatial units as RSC_ID using the following rules. A separate attribute table (CFEVWetlandCatchments) was generated to store this data.

1. Intersect the CFEV wetlands spatial data layer with the CFEV RSCs spatial data layer.

2. Calculate the % area of the RSC that intersects with the wetland spatial unit.

3. If % area is greater than 30% then assign the RSC_ID to the wetland spatial unit (WL_ID).

4. If no RSCs have an intersected area of greater than 30%, then choose the RSC with the largest intersected area.

6.3.57 Wetlands physical classification

Title Wetlands physical classification

Custodian WRD, DPIW


Description Physical classification of Tasmania‟s wetlands.

Input data

CFEV Area (wetlands) attribute data (Appendix )

CFEV Elevation (wetlands) attribute data (Appendix 6.3.9)

CFEV Fluvial geomorphic responsiveness attribute data (Appendix 0)


Lineage

A physical classification was conducted for wetlands using data on the position relative to the Tyler corridor, geomorphic responsiveness, size and elevation.

Prior to classification input, two of the variables were grouped into categories. The wetland area data was grouped into five size classes (0-1 ha, >1-10 ha, >10-100 ha, >100-1000 ha and >1000 ha) and the elevation values were grouped into four classes (0-20 m, 20-700 m, 100-800 m and >800 m).

The four sets of data were combined in a matrix to give an overall physical classification for wetlands, with a total of 71 classes. The physical classes were assigned to each of the wetland spatial units based on the rules described in Table 65 (see below).

Data limitations

The physical classification inherits all the data limitations of the derivation processes and input data.





Column heading WL_PCLASS




A physical class (e.g. WLP1, WLP2, WLP3, etc.) was assigned to wetland spatial units as WL_PCLASS using the rules in Table 65 (e.g. if the wetland is positioned east of the Tyler corridor, has a highly responsive geomorphology, is small (0-1 ha) and has a low elevation (0-20 m), then assign class WLP1). Note, only the combinations of physical classes that had wetland membership were included.

Table 65. Summary of physical classification rules for wetlands.

Class code

Tyler corridor Geomorphic responsiveness Area (ha) Elevation (m)

WLP1 east 1 0-1 0-20

WLP2 east 1 0-1 20-100

WLP3 east 1 0-1 100-800

WLP4 east 1 0-1 >800

WLP5 east 1 1-10 0-20

WLP6 east 1 1-10 20-100

WLP7 east 1 1-10 100-800

WLP8 east 1 1-10 >800

WLP9 east 1 10-100 0-20

WLP10 east 1 10-100 20-100

WLP11 east 1 10-100 100-800

WLP12 east 1 10-100 >800

WLP13 east 1 100-1000 0-20

WLP14 east 1 100-1000 20-100

WLP15 east 1 100-1000 100-800

WLP16 east 1 100-1000 >800

WLP17 east 1 >1000 0-20

WLP18 east 1 >1000 20-100

WLP19 east 0.5 or 0 0-1 0-20

WLP20 east 0.5 or 0 0-1 20-100

WLP21 east 0.5 or 0 0-1 100-800

WLP22 east 0.5 or 0 0-1 >800

WLP23 east 0.5 or 0 1-10 0-20

WLP24 east 0.5 or 0 1-10 20-100

WLP25 east 0.5 or 0 1-10 100-800

WLP26 east 0.5 or 0 1-10 >800



Class code


WLP27 east 0.5 or 0 10-100 0-20

WLP28 east 0.5 or 0 10-100 20-100

WLP29 east 0.5 or 0 10-100 100-800

WLP30 east 0.5 or 0 10-100 >800

WLP31 east 0.5 or 0 100-1000 0-20

WLP32 east 0.5 or 0 100-1000 100-800

WLP33 east 0.5 or 0 100-1000 >800

WLP34 east 0.5 or 0 >1000 >800

WLP35 west or corridor 1 0-1 0-20



WLP38 west or corridor 1 0-1 >800













WLP51 west or corridor 1 >1000 0-20



WLP54 west or corridor 0.5 or 0 0-1 0-20



WLP57 west or corridor 0.5 or 0 0-1 >800






Appendix 6 – Attribute data - Wetland vegetation


Class code









WLP70 west or corridor 0.5 or 0 >1000 20-100

WLP71 west or corridor 0.5 or 0 >1000 100-800


Wetlands>Classification

6.3.58 Wetland vegetation

Title Wetland (internal) vegetation

Custodian WRD, DPIW


Description Dominant vegetation type within wetlands

Input data



Lineage

A representation of what vegetation was present inside each wetland spatial unit was given as a dominant vegetation type using the TASVEG data layer (Version 0.1 May 2004).

Wetland TASVEG codes (AGC, ALK, As, BF, Br, BRO, BPB, CA, Hg, Hw, L, ME, Pr, Ps, Sm, St, Sw, Waf, Was, Wh, Ws, Gl) (see Table 66 or Appendix 12 for descriptions) were identified by experts, selected from the TASVEG data layer and assigned to the wetland polygons, using the rules provided below.

Table 66. Description of wetland vegetation classes.

Class code TASVEG Code Community Type

Dv-AGC AGC Graminoid moorland/herbfield

Dv-ALK ALK Alkaline pan

Dv-As As Eastern alpine sedgefields/fernfields

Dv-BF BF Acacia melanoxylon on flats

Dv-Br Br Restionaceae flatland, not alpine

Dv-BRO BRO Western moorland

Dv-BPB BPB Pure buttongrass

Appendix 6 – Attribute data - Wetland vegetation


Class code TASVEG Code Community Type

Dv-CA CA Cushion moorland

Dv-Hg Hg Lowland and coastal sedgy heath

Dv-Hw Hw Wet health

Dv-L L Leptospermum lanigerum/Melaleuca squarrosa swamp forest

Dv-ME ME Melaleuca ericifolia forest

Dv-Pr Pr Sphagnum peatland with emergent trees

Dv-Ps Ps Treeless Sphagnum peatland

Dv-Sm Sm Short paperbark swamp

Dv-St St Leptospermum spp. scrub

Dv-Sw Sw Fine-leaf wet scrubs

Dv-Waf Waf Freshwater aquatic plants

Dv-Was Was Saline aquatic plants

Dv-Wh Wh Herbfield and grassland marginal to wetland

Dv-Ws Ws Sedge rush wetland

Dv-Gl Gl Lowland Poa

Dv-Other-Exot

Other – Exotic Improved pasture and crop-land; Exotic invasions

Dv-Other-Gene

Other – Generic Generic wetland

Dv-Other-Euca

Other – Eucalyptus

Eucalyptus coccifera woodland occasional forest; Eucalyptus pauciflora forest on Jurassic dolerite

Dv-Other Other All remaining wetlands

Data limitations




Column heading WL_DVEG




The following rules were used to assign each wetland spatial unit with a macrophyte class (WL_DVEG). Note, that the prefix „Dv-‟(dominant vegetation) was added to each of the TASVEG codes for input to the spatial selection algorithm.

1. Assign the TASVEG code which has the greatest area within the wetland spatial unit.

Appendix 6 – Attribute data - Wetland vegetation condition


2. Adjust vegetation codes according to the following rules, to ensure that wetland-related vegetation types (Table 66) are primarily assigned to the spatial units:

All very small wetlands (<0.5 ha) that were not tagged with one of the TASVEG codes in Table 66 were not considered further in the CFEV assessments, with the exception of those spatial units assigned as We (generic wetland).

All the remaining wetlands that were assigned with either Fi (improved pasture and crop land), Fw (exotic invasions) or We (generic wetland) were assigned with a wetland-related TASVEG code (as per Table 66) based on the dominant vegetation type of the closest neighbouring wetlands (within a 5 km vicinity).

Any Fi and Fw wetlands that did not get re-assigned a wetland-related vegetation code using the rule above (i.e. were more than 5 km from the closest neighbour) were finally assigned as Dv-Other-Exot.

Any We wetlands that did not get re-assigned from this process were assigned as Dv-Other-Gene.

Any wetlands with TASVEG code of C (Eucalyptus coccifera woodland occasional forest) or PS (Eucalyptus pauciflora forest on non-Jurassic dolerite) were re-assigned as Dv-Other-Euca.

All remaining wetlands without a wetland-related vegetation code from Table 66 were assigned as Dv-Other.


Wetlands>Classification

6.3.59 Wetland vegetation condition

Title Wetland vegetation condition

Custodian WRD, DPIW


Description The proportion of native vegetation within wetlands.

Input data

CFEV modified TASVEG vegetation layer (Appendix 6.2.20)


Lineage

An assessment of the naturalness of the vegetation inside wetlands was conducted using the CFEV TASVEG modified vegetation layer (described in Appendix 6.2.19). The percentage area of natural* vegetation (from the TASVEG modified vegetation layer) within the wetland spatial unit was assigned to each wetland (specific rules outlined below). The wetland vegetation condition score ranged between 1 (100% native vegetation present or near-natural to natural condition) and 0 (0% native vegetation present or degraded condition).

* The natural class included natural non-vegetation TASVEG codes, such as water, rocks, etc. and the exotic (cultural) class included unnatural non-vegetation codes

Appendix 6 – Attribute data - Width of backing vegetation


such as built-up areas. Appendix 12 presents all of the TASVEG vegetation communities and their assigned group (i.e. either natural or exotic).

Data limitations




Column heading WL_NVEG


Number of classes WL_NVEG_C = 4


The following rules were applied to calculate the wetland vegetation condition score (WL_NVEG) for each wetland spatial unit:

1. Overlay the CFEV TASVEG modified vegetation layer (refer Appendix 6.2.19) with the CFEV wetland spatial data layer and calculate % area of the wetland polygon which is Natural.

2. Assign calculated value as a proportion (0-1) to the wetland spatial unit.

The wetland spatial data layer had the continuous wetland vegetation condition data categorised according to Table 67. The categorical data was used for reporting and mapping purposes.

Table 67. Wetland vegetation condition categories for wetlands.

Category WL_NVEG_C (Max to min values)

1 0

2 >0 to 0.2

3 >0.2 to 0.8

4 >0.8 to 1


Wetlands>Statewide audit>Condition assessment>Naturalness score (WL_NSCORE)>Native vegetation condition (WL_NATVE)

6.3.60 Width of backing vegetation

Title Width of backing vegetation

Description Average width of vegetation buffer surrounding saltmarshes.

Column heading SM_WIDVEG

Input data

Aerial photographs

CFEV Saltmarsh riparian vegetation condition spatial data layer (Appendix 6.2.23)


Appendix 6 – Attribute data - Willows




An estimate of the width (how far into the buffer zone, the natural features extend) of the native vegetation or natural features (e.g. water, rocks, etc.) present adjacent to the saltmarshes was observed from aerial photographs in conjunction with the CFEV Saltmarsh riparian vegetation condition spatial data layer (Figure 49). Data was initially collected from three categories (natural, exotic or other). The „other‟ category was further flagged as being either a natural feature or a non-natural feature (e.g. built-up area). If natural features were present, then the score (% width) was combined with the score for native vegetation.

A visual estimate of the proportional width of the backing vegetation within a 100 m buffer around the saltmarsh (0-1, in increments of 0.25), was assigned to each of the saltmarsh spatial units as SM_WIDVEG. The width of buffer occupied by any natural features (other than vegetation) was averaged with the native vegetation score.

Figure 49. Illustration of saltmarshes and the riparian vegetation condition spatial data layer, showing an example of the sections measured as the average width of backing vegetation.


Saltmarshes>Statewide audit>Condition assessment>Naturalness score (SM_NSCORE)>Impacts adjacent to saltmarsh (SM_IMADJ)>Adjacent vegetation (SM_VGADJ)>Backing vegetation condition (SM_BKCON)

6.3.61 Willows

Title Willows

Column heading RS_WILLOW

Input data

CFEV Willows spatial data layer (Appendix 6.2.35)


Number of classes 2

Appendix 6 – Attribute data - Willows



Using the derived willow infestation distribution data layer, each river section was assigned a score indicating a dominant presence (0) or absence (1) of significant willow infestations (RS_WILLOWS) for the river section length.


Rivers>Statewide audit>Condition>Naturalness score (RS_NSCORE)>Biological condition (RS_BIOL)>Native riparian vegetation (RS_NRIPV)

Appendix 7 – Data validation layers


7 Data validation layers

When possible during development, the data layers used in the Conservation of Freshwater Ecosystem Values (CFEV) assessment were cross-checked against any alternative datasets that were available. These comparisons have CFEV against them in the comments column and a brief comment on how well they compared. Two ground truthing projects have conducted field and desktop based validation of CFEV data layers (see Section 13.1.2 of the main report). These projects are funded by the National Water Initiative (NWI) and Natural Resource Management (NRM) and focus on different components of the CFEV assessment from slightly different geographic areas. The NWI project concentrates on the South Esk, Macquarie, Meander, Welcome and Montagu rivers. The NRM version of the project is confined to the parts of the state within the NRM North and NRM South regions. In many cases the externally sourced datasets used in the CFEV Project were validated by their creators, and these “internal validations” are also noted in Table 68.

Table 68. Data validation sources.

CFEV data set Validation data set Comments*

Geomorphic mosaics Jerie et al. (2003) internal validation

state Rivercare plans (15 plans Statewide)

Field characterisation of approx. 60 river sections

NRM

NWI

Hydrological regions Water Assessment Branch flow regionalisation

CFEV - Partial comparisons were a good match

Macroinvertebrate assemblages

Independent Australian River Assessment System (AUSRIVAS) data

RAP samples from field assessment

CFEV

NRM/NWI

Native Fish assemblages

Regional Forest Agreement (RFA) Fish Database – independent fish data

Field assessment

CFEV

NRM

Macrophytes Field characterisation of approx. 60 river sections

NWI

Tree assemblages Tasmanian Vegetation Map (TASVEG) for reference regions

NRM

Native fish condition RFA and independent fish data NWI

Exotic fish condition RFA and independent fish data NWI

Macroinvertebrates Observed/Expected (O/E)

MRHI/AUSRIVAS – independent data

RAP samples from field assessment

CFEV

NWI/NRM

Native riparian vegetation

TASVEG internal validation

Field assessment

NWI/NRM

Mean Annual Run-off (MAR)

Stream gauging data (WAP)

Modified SKM tool (S. Gurung 2007)

Department of Primary Industries and Water (DPIW) hydrological models

CFEV -Good match at the coarse scale

Ongoing observations - Not so good a match at finer scales – possibly also not so good a match due to the use of different span hydrological records (30 v 100 yrs)

Appendix 7 – Data validation layers


CFEV data set Validation data set Comments*

Frog assemblages Field assessment NWI/NRM in part

Land use (nutrients) 1 - land use internal validation/ 2 - nutrient load data

CFEV

Burrowing crayfish Field assessment NWI/NRM in part

Wetland vegetation (dominant type)

TASVEG internal validation

Independent data ((Kirkpatrick and Harwood, 1981; Smith, 2002; Heffer, 2003))

Field assessment

CFEV

NWI/NWI

NWI/NRM

Wetland vegetation (native v cultural)

TASVEG internal validation – independent data

Field assessment

CFEV

NWI/NRM

Vegetation Independent data and TASVEG internal validation

Field assessment

CFEV

NWI/NRM

Backing vegetation – lateral extent

Independent data and TASVEG internal validation

Field assessment

CFEV

NRM

Backing vegetation – width


Field assessment

CFEV

NRM

Backing vegetation – natural vegetation condition


Field assessment

CFEV

NRM

Landfill within saltmarsh Field assessment NRM

Roads/tracks within saltmarsh

Field assessment NRM

Roads/tracks adjacent saltmarsh


Urban development adjacent saltmarsh


Landfill adjacent to saltmarsh


Spartina sp. adjacent to saltmarsh


Grazing within saltmarsh


Drainage disturbance within saltmarsh


Grazing adjacent to saltmarsh


Drainage disturbance adjacent to saltmarsh


Spartina Field assessment NRM

Estuarine Macroinvertebrates

Edgar et al. (1999b) Internal validation

Estuarine Fish Edgar et al. (1999b) Internal validation

* NWI/NRM refers to components addressed by the NWI/NRM validation projects (see Section 13.1.2 of main report).

Appendix 8 – Tyler References


8 Limnological publications by Professor Peter Tyler and associates

Baker, A. L., Baker, K. K. & Tyler, P. A. (1985). Close interval sampling of migrating Chaoborus larvae across the chemocline of meromictic Lake Fidler, Tasmania. Archiv fur Hydrobiologie 103: 51-59.

Baker, A. L., Baker, K. K. & Tyler, P. A. (1985). Fine-layer depth relationships of lake water chemistry, planktonic algae and photosynthetic bacteria in meromictic Lake Fidler, Tasmania. Freshwater Biology 15: 735-747.

Bayly, I. A. E., Lake, P. S., Swain, R. & Tyler, P. A. (1972). Lake Pedder, its importance to biological science. In: ‘Pedder Papers - Anatomy of a Decision’. (Australian Conservation Foundation: Victoria).

Bayly, I. A. E., Peterson, J. A., Tyler, P. A. & Williams, W. D. (1966). Preliminary limnological survey of Lake Pedder, Tasmania. Australian Society of Limnology Newsletter 5: 30-41.

Bowling, L. C. & Tyler, P. A. (1984). Endangered lakes of scientific and cultural value in the World Heritage area of south-west Tasmania. Biological Conservation 30: 201-209.

Bowling, L. C. & Tyler, P. A. (1984). Physico-chemical differences between lagoons of King and Flinders Islands, Bass Strait. Australian Journal of Marine and Freshwater Research 35: 655-662.

Bowling L. C. & Tyler P. A. (1986). The demise of meromixis in riverine lakes of the World Heritage Wilderness of south-west Tasmania. Archiv fur Hydrobiologie 107: 53-73.

Bowling, L. C. & Tyler, P. A. (1986). The underwater light-field of lakes with marked physicochemical and biotic diversity in the water column. Journal of Plankton Research 8: 69-77.

Bowling, L. C. & Tyler, P. A. (1988). Lake Chisholm, a polyhumic forest lake in Tasmania. Hydrobiologia 161: 55-67.

Bowling, L. C. & Tyler, P. A. (1990). Chemical stratification and partial meromixis in reservoirs in Tasmania. Hydrobiologia 194: 67-83.

Bowling, L. C., Banks, M. R., Croome, R. L. & Tyler, P. A. (1993) Reconnaissance Limnology of Tasmania. II. Limnological features of Tasmanian freshwater coastal lagoons. Archiv fur Hydrobiologie 126: 385-403.

Bowling, L. C., Steane, M. S. & Tyler, P. A. (1986). The spectral distribution and attenuation of underwater irradiance in Tasmanian inland waters. Freshwater Biology 16: 313-335.

Buckney, R. T. & Tyler, P. A. (1973). Chemistry of some sedgeland waters, Lake Pedder, south-west Tasmania. Australian Journal of Marine and Freshwater Research 24: 267- 273.

Buckney, R. T. & Tyler, P. A. (1973). Chemistry of Tasmanian inland waters. Internationale Revue der gesamten Hydrobiologie 58: 61-78.

Buckney, R. T. & Tyler, P. A. (1976). Chemistry of salt lakes and other waters of the sub-humid regions of Tasmania. Australian Journal of Marine and Freshwater Research 27: 359- 366.



Cameron, N. G., Tyler, P. A., Rose, N. L. & Appleby, P. G. (1993). The recent palaeoecology of Lake Nicholls, Mount Field National Park, Tasmania. Hydrobiologia 269/270: 361-370.

Cheng, D. M. H. & Tyler, P. A. (1973). Lakes Sorell and Crescent - a Tasmanian paradox. Internationale Revue der gesamten Hydrobiologie 58: 307-343.

Cheng, D. M. H. & Tyler, P. A. (1973). The effect of diatom populations on silica concentrations of Lakes Sorell and Crescent, Tasmania, and the utilisation of tripton as a source of silica. British Phycological Journal 8: 249-256.

Cheng, D. M. H. & Tyler, P. A. (1976). Nutrient economies and trophic status of Lakes Sorell and Crescent, Tasmania. Australian Journal of Marine and Freshwater Research 27: 151- 163.

Cheng, D. M. H. & Tyler, P. A. (1976). Primary productivity and trophic status of Lakes Sorell and Crescent, Tasmania. Hydrobiologia 48: 59-64.

Croome, R. L. & Tyler, P. A. (1972). Physical and chemical limnology of Lake Leake and Tooms Lake, Tasmania. Archiv fur Hydrobiologie 70: 341-354.

Croome, R. L. & Tyler, P. A. (1973). Plankton populations of Lake Leake and Tooms Lake - oligotrophic Tasmanian lakes. British Phycological Journal 8: 239-247.

Croome, R. L. & Tyler, P. A. (1975). Phytoplankton biomass and primary productivity of Lake Leake and Tooms Lake, Tasmania. Hydrobiologia 46: 435-443.

Croome, R. L. & Tyler, P. A. (1984). Microbial microstratification and crepuscular photosynthesis in meromictic Tasmanian lakes. Verhandlungen Internationale Vereinigung Limnologie 22: 1216-1223.

Croome, R. L. & Tyler, P. A. (1986) Taxonomy and ecology of the phytoplankton of Lake Fidler and Sulphide Pool, meromictic Tasmanian lakes. Hydrobiologia 140: 135-141.

Croome, R. L. & Tyler, P. A. (1988). Mirobial microcosms and devolving meromixis in Tasmania. Verhandlungen Internationale Vereinigung Limnologie 23: 594-597.

Croome, R. L. & Tyler, P. A. (1988). Phytoflagellates and their ecology in Tasmanian polyhumic lakes. Hydrobiololgia 161: 245- 253.

Croome, R. L., Kristiansen, J. & Tyler, P. A. (1998). A description of Mallomonas marsupialis (Synurophyceae), a new species from Australia, with comments on the endemicity of Australian freshwater algae. Nordic Journal of Botany. 18: 633-639.

Finlay, B. J., Esteban, G. F., Olmo, J.L. & Tyler, P. A. (1999). Global distribution of free-living microbial species. Ecography 22: 138-144.

Fulton, W. and Tyler, P.A. (1993). Fauna and flora of the lakes and tarns. In: „Tasmanian Wilderness - World Heritage Values‟. (Eds. S. J. Smith & M. R. Banks). Royal Society of Tasmania, pp.109-113.

Goodman, A. & Peterson, J. A. (1985). Morphometric classification and estimates of lake bottom dynamics for some major Tasmanian water storages. Australian Society of Liminology Bulletin 10: 3-14.

Hakanson, L. & Jansson, M. (1983). Principles of lake sedimentology. Springer-Verlag, New York. 316 pp.

Haworth, E.Y. & Tyler, P. A. (1993). Morphology and taxonomy of Cyclotella tasmanica spec. nov., a newly described diatom from Tasmanian Lakes. Hydrobiologia 269/270: 49-56.



Hodgson, D. A., Vyverman, W. & Tyler, P. A. (1997). Diatoms of meromictic lakes adjacent to the Gordon River, and of the Gordon River estuary in south-west Tasmania. (Bibliotheca Diatomologica, Band 35) E. Schweizerbart‟sche Verlagsbuchhandlung, Science Publishers, Stuttgart. 172 pp.

Hodgson, D. H., Vyverman, W. G., Chepstow-Lusty, A. & Tyler, P. A. (2000). From rainforest to wasteland in 100 years: the limnological legacy of the Queenstown mines, western Tasmania. Archiv für Hydrobiologie 149: 153-176.

Hodgson, D. A. & Tyler, P. A. (1996). The impact of a hydro-electric dam on the stability of meromictic lakes in south west Tasmania, Australia. Archiv fur Hydrobiologie 137: 301 - 323.

Hodgson, D. A., Tyler, P. A. & Vyverman, W. (1996). The palaeolimnology of Lake Fidler, a meromictic lake in south-west Tasmania and the significance of recent human impact. Journal of Paleolimnology. 18: 313-333.

Hodgson, D. A., Wright, S. W., Tyler, P. A. & Davies, N. (1997). Analysis of fossil pigments from algae and bacteria in meromictic Lake Fidler, Tasmania, and its application to lake management. Journal of Paleolimnology 19: 1-22.

King, R. D. & Tyler, P. A. (1981). Limnology of Perched Lake, south-west Tasmania. Australian Journal of Marine and Freshwater Research 32: 501-515.

King, R. D. & Tyler, P. A. (1981). Meromictic lakes of south- west Tasmania. Australian Journal of Marine and Freshwater Research 32: 741-756.

King, R. D. & Tyler, P. A. (1982). Downstream effects of the Gordon River Power Development, south-west Tasmania. Australian Journal of Marine and Freshwater Research 33: 431- 442.

King, R. D. & Tyler, P. A. (1982). Lake Fidler, a meromictic lake in Tasmania. Archiv fur Hydrobiologie 93: 393-422.

King, R. D. & Tyler, P. A. (1983). Sulphide Pool and Lake Morrison, meromictic lakes of south-west Tasmania. Archiv fur Hydrobiologie 96: 139-163.

Kirk, J. T. O. & Tyler, P. A. (1986). The spectral absorption and scattering properties of dissolved and particulate components in relation to the underwater light field of freshwaters of tropical Australia. Freshwater Biology 16: 573-584.

Kirkpatrick, J. B. & Tyler, P. A. (1987). Tasmanian wetlands and their conservation. In: ‘The Conservation of Australian Wetlands’. (Eds. A. McComb & P. S. Lake). Surrey, Beatty and Co., Sydney. pp 1-16.

Ling, H. U., Croome, R. L. & Tyler, P. A. (1989). Freshwater dinoflagellates of Tasmania, a taxonomic and ecological survey. British Phycological Journal 24: 111-129.

Ling, H. U & Tyler, P. A. (2000). Australian Freshwater Algae (exclusive of diatoms). (Bibliotheca Phycologia, Band 105) J. Cramer, Berlin and Stuttgart. 643 pp.

Peterson, J.A. & Missen, J. E. (1979). Morphometric analysis of Tasmanian freshwater bodies. Australian Society of Liminology Special Publication No. 4 (December 1979). 116 pp.

Rolfe, S. F., Kew, P. L. & Tyler, P. A. (2001). Reconnaissance limnology of Tasmania.VI. Physicochemical features of coastal lagoons of the Bass Strait islands. Archiv fur Hydrobiologie 150: 693-704.

Steane, M. S. & Tyler, P. A. (1982). Anomalous stratification behaviour of Lake Gordon, headwater reservoir of the lower Gordon River, Tasmania. Australian Journal of Marine and Freshwater Research 33: 739-760.



Tyler, P. A. (1971). Freshwater productivity in Tasmania. The Australian Science Teachers Journal 17: 41-47.

Tyler, P. A. (1974). Limnological studies. In: ‘Biogeography and Ecology in Tasmania’. (Ed. W.D. Williams) Monographia Biologicae 25: 29-61

Tyler, P. A. (1976). Lagoon of Islands, Tasmania - death knell for a unique ecosystem? Biological Conservation 9: 1-11.

Tyler, P. A. (1976). Lagoon of Islands. Tasmanian Year Book No.10. pp.64-73.

Tyler, P. A. (1980). Limnological problems in the management of Tasmanian water resources. In: ‘An Ecological Basis for Water Resource Management’ (Ed. W.D. Williams). A.N.U. Press, Canberra. pp. 43-46.

Tyler, P. A. (1981). Stratification cycles. In: ‘Destratification of Lakes and Reservoirs to Improve Water Quality’. (Eds. F. L. Burns & I. J. Powling). Australian Government Publishing Service, Canberra. pp. 21-33.

Tyler, P. A. (1986). Anthropological limnology in the Land of Moinee. In: ‘Limnology in Australia’. (Eds. P. De Deckker & W. D. Williams). CSIRO, Melbourne and W. Junk, Dordrecht. pp. 523-537.

Tyler, P. A. (1992). A Lakeland from the Dreamtime: The Second Founder‟s Lecture. British Phycological Journal 27: 353-368.

Tyler, P. A. (1993). Reconnaissance Limnology of Tasmania. I. The Picton Massif. Archiv fur Hydrobiologie 27: 257-272.

Tyler, P. (1996). Endemism in Freshwater Algae with Specified Reference to the Australian Region. In: Biogeography of Freshwater Algae). (Ed. J. Kristiansen). Hydrobiologia 336:127-135

Tyler, P. A. (1996). The significance of north-east Tasmania for the biogeography of endemic Australian freshwater algae. Records of the Queen Victoria Museum, Launceston 103: 133-135.

Tyler, P. A. (1998). Endemicity in Australian diatoms. Proceedings of the 1st Australian Diatom Workshop. pp. 22-23.

Tyler, P. A. (2001). Lake Pedder - a limnologist‟s lifetime view. In: ‘Lake Pedder: Values and Restoration’. (Ed. C. Sharples). Proceedings of a Symposium, Hobart, April 1995. Occasional Paper No. 27, Centre for Environmental Studies, University of Tasmania, pp.51-60.

Tyler, P. A. & Bowling, L. C. (1990). The wax and wane of meromixis in estuarine lakes in Tasmania. Verhandlungen Internationale Vereinigung Limnologie 24: 117-121.

Tyler, P. A. & Buckney, R. T. (1973). Pollution of a Tasmanian river by mine effluents. I. Chemical evidence. Internationale Revue der gesamten Hydrobiologie 58: 873-883.

Tyler, P. A. & Buckney, R. T. (1974). Stratification and biogenic meromixis in Tasmanian reservoirs. Australian Journal of Marine and Freshwater Research 25: 299-313.

Tyler, P. A., Sherwood, J. E., Magilton, C. J. & Hodgson, D. A. (1996). Limnological and geomorphological considerations underlying Pedder 2000 - the campaign to restore Lake Pedder. Archiv fur Hydrobiologie 136: 343 - 361.

Tyler, P. A., Terry, C. & Howland, M. (2001). Appendix 11: Gordon River meromictic lake assessment. In: ‘Basslink Integrated Impact Assessment Statement. Potential



Effects of Changes to Hydro Power Generation’. (Ed. Anon.). Hydro Tasmania, Hobart. 43 pp.

Tyler, P.A. & Williams, W.D. (2003). Lakes and Rivers. In: ‘Ecology, an Australian Perspective’ (Eds. Attiwill, P. & Wilson, B.). Oxford University Press, Sydney. 664 pp.

Vyverman, W., Vyverman, R., Hodgson, D & Tyler, P. A. (1995). Diatoms from Tasmanian mountain lakes: a reference data set (TASDIAT) for environmental reconstruction and a systematic and autecological study. (Bibliotheca Diatomologica, Band 33) E. Schweizerbart‟sche Verlagsbuchhandlung, Science Publishers, Stuttgart. 192 pp.

Vyverman, W., Vyverman, R., Rajendram, V. S. & Tyler, P. A. (1996). The distribution of benthic diatom assemblages in Tasmanian highland lakes and their possible use as indicators of climate-related environmental changes. Canadian Journal of Fisheries and Aquatic Science 53: 493-508.

Walsh, R. G. J. & Tyler, P. A. (2000). Reconnaisance limnology of Tasmania.V.Anthropogenic factors in the distribution of freshwater calanoid copepods (Crustacea: Centropagidae). Archiv für Hydrobiologie 148: 625-638.

Walsh, R. G. J., Shiel, R. J. & Tyler, P. A. (2004). Reconnaissance limnology of Tasmania. VIII. Tasmanian coastal lagoons - epicentres of endemism in the Australian aquatic microbiota. Papers and Proceedings of the Royal Society of Tasmania 138: 67-79.

Walsh, R. G. J., Shiel, R. J. & Tyler, P. A. (2001). Reconnaissance limnology of Tasmania. VII. Coastal lagoons of Bass Strait islands, with reference to endemic microflora and microfauna. Archiv fur Hydrobiologie 152: 489-510.

Walsh, R. G. J. & Tyler, P. A. (1998). Reconnaissance limnology of Tasmania. IV. The distribution and ecological preferences of Tasmanian species of freshwater calanoid copepods (Crustacea: Centropagidae). Archiv für Hydrobiologie 141: 403-420.

Walsh, R. G.J., Vyverman, W. G. & Tyler, P. A. (1995). Reconnaissance limnology of Tasmania. III. Coastal lagoons of Bruny Island. Archiv fur Hydrobiologie 136: 247-260.

Walsh, R. G. J. (1996). Limnology of Tasmanian freshwater coastal dune lakes with particular reference to the microcrustacea. PhD Thesis. University of Tasmania, Hobart. pp. 120.

Appendix 9 –DIWA and Ramsar sites


9 Ramsar and Directory of Important Wetlands in Australia (DIWA) Wetlands

Wetlands in shaded rows with bold type are also Ramsar wetlands.

DIWA Code

Wetland name Conservation of Freshwater Ecosystem Values (CFEV) spatial

unit identifier

CFEV Integrated Conservation

Value (ICV)

TAS001 Blackmans Lagoon WB_ID=124 VH

TAS002 Jocks Lagoon WB_ID=138 VH

TAS003 Little Waterhouse Lake WB_ID=116 VH

TAS004 Surveyors Creek No match

TAS005 The Chimneys (Lower Ringarooma River floodplain)

WL_ID=19976 VH

TAS006 Tregaron Lagoons 1 WB_ID=99 H

TAS007 Tregaron Lagoons 2 WB_ID=101 VH

TAS008 Unnamed wetland (near Great Mussleroe) No match

TAS009 Unnamed wetland (near Cape Portland) WB_ID=93 WL_ID=20055

VH L

TAS010 Unnamed wetland (near Cape Portland) WL_ID=20062 VH

TAS011 Unnamed wetland (near Cape Portland) No match

TAS012 Unnamed wetland (near Little Mussleroe) No match

TAS013 Unnamed wetland (near Waterhouse Point) WL_ID=19922 VH

TAS014 Unnamed wetland (near Waterhouse Point) No match

TAS015 Unnamed wetland (near Pipers Estuary) No match

TAS016 Allwrights Lagoons WB_ID=1080 WB_ID=1081 WB_ID=1083

H H H

TAS017 Clarence Lagoon WB_ID=1074 VH

TAS018 Dublin Bog No match

TAS019 Eagle Tarn Sphagnum No match

TAS020 Great Lake WB_ID=264 VH

TAS021 Interlaken Lakeside Reserve (Lake Crescent)

WL_ID=16167 VH

TAS022 Kemps Marsh (Lake Sorell) WL_ID=16200 VH

TAS023 Lake Kay WB_ID=673 VH

TAS024 Lake Lea WB_ID=148 VH

TAS025 Maggs Mountain Sphagnum WL_ID=9975 VH

TAS026 Mt Rufus Sphagnum No match

TAS027 Shadow Lake Sphagnum WL_ID=2218 VH

TAS028 D‟Arcys Lagoon WL_ID=15343 VH

TAS029 Oyster Cove No match

TAS030 South East Cape Lakes WL_ID=14718 WL_ID=14722 WL_ID=14722

M M M

TAS031 Apsley Marshes WL_ID=16939 VH

TAS032 Douglas River RS_ID=331260 H

TAS033 Earlham Lagoon ES_ID=65 WL_ID=15668 WL_ID=15669 WL_ID=15670 WL_ID=15695

SM_ID=119

VH M M M L

VH

TAS034 Freshwater Lagoon ES_ID=55 M

TAS035 Hardings Falls Forest Reserve RS_ID=328300 VH

TAS036 Maria Island Marine Reserve No match

Appendix 9 – DIWA and Ramsar sites


DIWA Code


unit identifier


Value (ICV)

TAS037 Moulting Lagoon ES_ID=57 VH

TAS038 Unnamed wetland (near Butlers Point) WB_ID=846 M

TAS039 Fergusons Lagoon WL_ID=22664 VH

TAS040 Flyover Lagoon 1 WL_ID=23157 M

TAS041 Flyover Lagoon 2 WL_ID=23152 WB_ID=60

M VH

TAS042 Hogans Lagoon WL_ID=22662 H

TAS043 Little Thirsty Lagoon No match

TAS044 Logan Lagoon ES_ID=44 WB_ID=52

M H

TAS045 Sellars Lagoon ES_ID=42 WB_ID=13

WL_ID=23440

H VH VH

TAS046 Stans Lagoon WL_ID=23303 M

TAS047 Syndicate Lagoon WB_ID=54 H

TAS048 Thompsons Lagoon No match

TAS049 Unnamed wetland (near Sellars Lagoon) No match

TAS050 Unnamed wetland (near Sellars Lagoon) No match

TAS051 Unnamed wetland (near Cape Barren) WL_ID=22956 M

TAS052 Unnamed wetland (near Cone Point) No match

TAS053 Bells Lagoon WL_ID=1075 H

TAS054 Blackman River 1 RS_ID=258793 RS_ID=258794 RS_ID=258800 RS_ID=258801 RS_ID=258802

H H H H H

TAS055 Calverts Lagoon WB_ID=1281 WL_ID=15396

VH H

TAS056 Cataract Gorge No match

TAS057 Elizabeth River Gorge RS_ID=316738 VH

TAS058 Glen Morey Saltpan WL_ID=16458 H

TAS059 Glen Morriston Rivulet 1 RS_ID=258975 RS_ID=258977 RS_ID=259035

M H M

TAS060 Goulds Lagoon WL_ID=15428 WL_ID=15429

VH H

TAS061 Lake Dulverton WB_ID=1169 WL_ID=15816

VH H

TAS062 Lake Tiberias WB_ID=1186 VH

TAS063 Macquarie River 2 No match

TAS064 Macquarie River 4 No match

TAS065 Mona Vale Saltpan WL_ID=16467 H

TAS066 Near Lagoon WL_ID=16541 VH

TAS068 River Derwent No match

TAS067 Pitt Water and Orielton Lagoon ES_ID=68 VH

TAS069 South Esk River 1 RS_ID=292245 RS_ID=292252

VH VH

TAS070 Tin Dish Rivulet 1 RS_ID=257309 M

TAS071 Township Lagoon WB_ID=1114 WL_ID=16450

VH M

TAS072 White Lagoon WL_ID=16502 VH

TAS073 Bungaree Lagoon WB_ID=11 WL_ID=6808

H L

TAS074 Lake Flannigan WB_ID=4 VH

Appendix 9 –DIWA and Ramsar sites


DIWA Code


unit identifier


Value (ICV)

WL_ID=7223 H

TAS075 Lavinia Nature Reserve (Lake Martha Lavinia)

WL_ID=9184 VH

TAS076 Pearshape Lagoon 1 WB_ID=36 WL_ID=5837

H L

TAS077 Pearshape Lagoon 2 WB_ID=37 WL_ID=5840

VH M

TAS078 Pearshape Lagoon 3 No match

TAS079 Pearshape Lagoon 4 No match

TAS080 Rocky Cape Marine Area No match

TAS081 Unnamed wetland (near Bluff Hill Point) No match

TAS082 Hatfield Sphagnum No match

TAS083 Lake Ashwood WB_ID=1098 WL_ID=777

H M

TAS084 Lake Bantick WB_ID=1096 VH

TAS085 Lake Chisholm WB_ID=132 VH

TAS086 Lake Garcia WB_ID=1101 VH

TAS087 Lake Surprise WB_ID=1274 H

TAS088 Lake Sydney WB_ID=1353 VH

TAS089 Boulanger Bay - Robbins Passage No match

East Coast Cape Barren Island Lagoons No match

Appendix 10 – Edgar et al. (1999) estuaries


10 Conservation assessment of estuaries by Edgar et al. (1999a)

Estuary name Conservation of Freshwater Ecosystem Values (CFEV) estuary

identifier (ES_ID)

CFEV Integrated

Conservation Value (ICV)

Edgar et al. (1999a) conservation significance

Yellow Rock 1 M Moderate (C)

Sea Elephant 2 H High (B)

Ettrick 3 M Moderate (C)

Yarra 4 M Degraded - Low (D)

Seal 5 M Moderate (C)

Mosquito Inlet 6 VH High (B)

Welcome 7 H Moderate (C)

Duck Bay 8 H Degraded - Low (D)

West Inlet 9 M Moderate (C)

East Inlet 10 M Moderate (C)

Black/Dip 11 H Critical (A)

Crayfish 12 M Degraded - Low (D)

Detention 13 H Moderate (C)

Inglis 14 H Severely degraded - Low (E)

Cam 15 H Severely degraded - Low (E)

Emu 16 M Severely degraded - Low (E)

Blythe 17 H Degraded - Low (D)

Leven 18 H Severely degraded - Low (E)

Forth 19 H Degraded - Low (D)

Don 20 M Severely degraded - Low (E)

Mersey 21 H Severely degraded - Low (E)

Port Sorell 22 H Degraded - Low (D)

Tamar 23 VH Critical (A)

Curries 24 H Degraded - Low (D)

Piper 25 VH Moderate (C)

Little Forester 26 H Moderate (C)

Tomahawk 27 M Moderate (C)

Boobyalla Inlet 28 VH High (B)

Little Musselroe 29 VH Moderate (C)

Great Musselroe 30 VH Moderate (C)

Rices 31 H High (B)

Rocky Head 32 M High (B)

Modder 33 H High (B)

Lee 34 M High (B)

Dover 35 M High (B)

Shag Rock 36 M High (B)

Thirsty Lagoon 37 VH Critical (A)

Pats 38 M Moderate (C)

North East Inlet 39 VH Critical (A)

Foochow Inlet 40 H High (B)

Patriarch 41 H High (B)

Sellars Lagoon 42 H High (B)

Cameron Inlet 43 H High (B)

Logan Lagoon 44 M High (B)

Ansons Bay 45 VH Moderate (C)

Big Lagoon 46 H High (B)

Sloop Lagoon 47 M High (B)




identifier (ES_ID)

CFEV Integrated



Grants Lagoon 48 M Moderate (C)

Georges Bay 49 VH Degraded - Low (D)

Scamander 50 VH Degraded - Low (D)

Hendersons Lagoon 51 H Moderate (C)

Templestowe 52 H Moderate (C)

Denison 53 M Degraded - Low (D)

Saltwater Lagoon 54 H Moderate (C)

Freshwater Lagoon 55 M High (B)

Bryans Lagoon 56 H Critical (A)

Moulting Lagoon 57 VH High (B)

Meredith 58 M Degraded - Low (D)

Stoney 59 M Moderate (C)

Buxton 60 M Moderate (C)

Lisdillon 61 H Moderate (C)

Grindstone 62 M Moderate (C)

Spring Bay 63 H Degraded - Low (D)

Prosser 64 H Degraded - Low (D)

Earlham Lagoon 65 VH Moderate (C)

Blackman Bay 66 VH Moderate (C)

Carlton 67 H Degraded - Low (D)

Pittwater 68 VH Degraded - Low (D)

Pipeclay Lagoon 69 H Degraded - Low (D)

Browns 70 M Severely degraded - Low (E)

Derwent 71 VH Moderate (C)

Garden Island 72 M Degraded - Low (D)

North West Bay 73 M Severely degraded - Low (E)

Crooks 74 M Degraded - Low (D)

Port Cygnet 75 M Degraded - Low (D)

Esperance 76 M Moderate (C)

Cloudy Bay 77 H High (B)

Cockle Ck. 78 M Moderate (C)

Southport 79 M Moderate (C)

Southport Lagoon 80 H Critical (A)

D`Entrecasteaux 81 H High (B)

Catamaran 82 H High (B)

South Cape Rivulet 83 M High (B)

New River Lagoon 84 VH Critical (A)

Louisa River 85 M High (B)

Louisa Creek 86 M High (B)

Freney 87 H High (B)

Bathurst Harbour 88 VH Critical (A)

Mulcahy 89 M High (B)

Giblin 90 H High (B)

Lewis 91 H High (B)

Mainwaring 92 M High (B)

Wanderer 93 H Critical (A)

Spero 94 M High (B)

Hibbs Lagoon 95 M High (B)

Macquarie Harbour 96 VH Degraded - Low (D)

Henty 97 H High (B)

Little Henty 98 M Moderate (C)




identifier (ES_ID)

CFEV Integrated



Pieman 99 H Moderate (C)

Lagoon 100 H High (B)

Pedder 101 M High (B)

Nelson Bay 102 M High (B)

Arthur 103 H High (B)

Mines 104 M High (B)

Middle Inlet 105 H High (B)

Douglas 106 M Moderate (C)

Montagu 109 M Moderate (C)

Brid 110 VH Degraded - Low (D)

Little Swanport 111 VH Moderate (C)

Huon 112 H Moderate (C)

Payne Bay 113 VH Critical (A)

Appendix 11 – Karst Atlas priorities


11 Karst areas priortised by the Karst Atlas

Karst name Conservation of Freshwater Ecosystem Values (CFEV)

karst identifier (KT_ID)

CFEV Integrated Conservation Value

(ICV)

Karst Atlas ranking

(karstification)

Acheron River 1 M A

Adamsfield 2 M A

Alarm - Detention 3 H D

Albina Bay 4 H B

Alma River (Plain of the Mists) 5 M B

Andromeda Galaxy 6 M D

Arthur - Huon 7 H B

Badger River 8 M B

Bald Tier 9 M B

Balfour (1) 10 M B

Balfour (2) 11 M B

Beaconsfield 12 M B

Bell Creek 13 M C

Ben Lomond Rivulet 14 M C

Birthday Bay - Nielson Creek 15 H B

Blackwater Creek 16 M A

Blowhole 17 H B

Bluff Point 18 H C

Boat Harbour Beach 19 M B

Boomers Bottom 20 M D

Boyd River 21 H B

Brittons Swamp 22 M B

Brookside 23 M B

Brookstead 24 M C

Brougham River (Whitehawk Creek)

25 M B

Brushy Hill Creek 26 M C

Bubs Hill 27 H A

Burnt Gully Creek 28 M C

Butler Rivulet 29 H A

Calverts Hill 30 M C

Cann Creek 31 H C

Cape Paul Lamanon 32 H C

Cape Portland 33 H C

Carbonate Creek 34 M B

Cardigan River 35 M A

Cascades 36 M C

Castle Carey Rivulet 37 M D

Catamaran River 38 M B

Charter - Southwell 39 M B

City of Melbourne Bay 40 H C

Claude Creek 41 M C

Cook Creek (Abrotonella) 42 M A

Corinna 43 M B

Cracroft 44 VH A

Dalmayne 45 M C

Dante Rivulet (Lake Spicer, Lake Dora)

46 M A

Deep Glen 47 H C

Devils Gully 48 M C






(ICV)

Karst Atlas ranking

(karstification)

Dismal Swamp 49 M B

Doctors Rocks 50 M B

Dromedary 51 M C

Dubbil Barril 52 M B

Duckhole Valley - Adamson 53 VH B

Dundas Rivulet Valley (Misery Hill)

54 M B

Dunkley (Parting Ck./Station Ck.)

55 M B

Eagle Creek 56 M A

East Inkerman 57 M B

East Maxwell - Algonkian 58 H A

East Tower - Byatts Razorback 59 M C

Eastons Creek 60 H B

Emita - Memana (1) 61 H B

Emita - Memana (2) 62 H B

Erebus - Denison 63 M A

Ettrick 64 M B

Everlasting Hills 65 M A

Ewart Creek 66 M B

Fairview (Redpa) 67 H B

Falls Creek 68 M D

Farnhams Creek (Christmas Hills)

69 H C

Fen Creek 70 M B

Fingal 71 M C

Flowerdale 72 H D

Flowery Gully 73 M A

Folly Hill 74 H D

Forest Hills 75 H A

Fourteen Mile Creek 76 M B

Franklin Hills 77 M D

Franklin River 78 H A

Fraser River 79 H B

Frenchmans 80 H C

Friendly Beaches (Mt Peter) 81 M C

Geilston Bay 82 H B

Gell River 83 H D

German Town - Dublin Town 84 M C

Giblin River 85 VH B

Glenlusk (Collinsvale/Berriedale)

86 M C

Glenorchy 87 M C

Glovers Bluff 88 M B

Golden Valley 89 M D

Goodwins Creek 90 M A

Gordon - Sprent 91 VH A

Governor - Fincham 92 H D

Granton - Claremont 93 M C

Granville Harbour 94 M C

Grassy 95 M C

Gray - Mt Elephant 96 M B






(ICV)

Karst Atlas ranking

(karstification)

Grim - Trefoil 97 H B

Gunns Plains 98 M A

Guy Fawkes Creek 99 M C

Hardwood-Davey 100 VH A

Harts Hill 101 M C

Hastings 102 VH A

Hazell Creek (Brisbane Braddon)

103 M B

Healy Creek 104 M B

Heazlewood River - Whaleback Ridge

105 M D

Henty River 106 M B

High Rocky Point 107 H C

Hockeys Creek 108 M C

Huon Plains 109 M B

Huskisson River 110 M B

Ile du Golfe 111 H A

Irishtown - Sedgy Creek 112 H B

Iron Creek - Varna Bay 113 H B

Iron Creek Bay 114 H D

Jane Goldfields 115 M B

Jims Plain 116 H B

Jubilee-Styx (Jubilee Ridge) (1) 117 H B

Jubilee-Styx (Jubilee Ridge) (2) 118 H C

Jukes - Darwin (Kelly Basin) 119 H B

Junction Peak 120 M A

Junee - Florentine 121 VH A

Kara (Hampshire) 122 H C

Keith River 123 H C

Killiecrankie 124 M B

Killymoon - Kooringa 125 H C

Kimberley 126 M D

Lachlan 127 M C

Lackrana 128 H B

Lagoon Creek 129 H B

Lake Sydney 130 M A

Leslie Creek 131 M B

Liberty Creek 132 H B

Liffey 133 M D

Lightning Plains 134 M A

Lime Kiln Spur 135 M D

Linda 136 M B

Little Castray River 137 M B

Little Denison River 138 M C

Little Donaldson - Donaldson 139 H B

Little Eldons 140 H D

Little Savage River 141 H B

Longback 142 M B

Loongana 143 M A

Lorinna 144 H B

Lower Andrew River 145 VH A

Lower Black River - South 146 H B






(ICV)

Karst Atlas ranking

(karstification)

Forest

Lower Cracroft 147 H B

Lower Gordon 148 M A

Lower Lea River 149 M D

Lower Maxwell 150 M A

Lower Olga 151 M A

Lower Quamby Brook (Golden Valley)

152 M B

Lune Plains (North Lune, Mesa-Gleichenia)

153 VH B

Lymestone Creek - Barra Barra 154 H D

Lyons Bend 155 H B

Lyons River 156 H B

Main Creek - Bowry Creek 157 H A

Manuka Creek - Blakes Opening 158 H B

Marble Hill - D‟Entrecasteaux 159 VH A

Maria North 160 H C

Maria South 161 M C

Martha Lavinia 162 M B

McPartlan 163 M D

McLean Creek (Mt Zeehan) 164 M B

McRae Hills 165 H B

Meander - Western Creek 166 VH D

Melrose-Paloona (Eugenana) 167 H C

Merton - Sweeney Creek 168 H C

Micks Creek 169 H D

Middle Arm 170 H D

Middle Olga 171 M A

Mikes Creek 172 M C

Misery 173 H D

Mitford Hills 174 M D

Modder River 175 H B

Moina 176 M C

Monument 177 M B

Mount Ronald Cross 178 H A

Mt Anne - Upper Weld (1) 179 VH A

Mt Anne - Upper Weld (2) 180 H C

Mt Anne - Upper Weld (3) 181 H D

Mt Barrow 182 M C

Mt Bell 183 M C

Mt Bischoff 184 M D

Mt Cameron West (1) 185 M B

Mt Cripps (Mt Mayday, Mackintosh)

186 VH A

Mt Elizabeth 187 M C

Mt Gell (Cheyne Range) 188 M B

Mt Lindsay 189 H C

Mt Mary - Flat Bluff 190 H D

Mt Stanley Creek 191 H B

Mt Weld (Fairyland) 192 H A

Mt Youngbuck 193 M D

Myalla 194 M D






(ICV)

Karst Atlas ranking

(karstification)

Nabageena 195 M B

Needles - Mueller 196 H B

Nelson River 197 M A

Nicholls Range 198 VH A

Palana - Limestone Bay 199 M B

Palmers 200 H D

Pawleena 201 M D

Pearce 202 M C

Phoques 203 H B

Picton River 204 M B

Pine Hill 205 H B

Point Hibbs - Hibbs Bay 206 H B

Pokana 207 H D

Port Hills 208 H B

Port Sorell 209 H C

Precipitous Bluff 210 VH A

Prion Beach (Point Cecil) 211 M A

Professor - Amber 212 M B

Puncheon 213 M B

Quamby Brook 214 M D

Queen River Valley (2) 215 M B

Railton 216 M C

Ramsay River 217 M D

Ranga 218 H B

Rapid River 219 H B

Rebecca Creek 220 H C

Redwater Creek 221 H B

Renison Bell 222 H D

Risbys Basin (Pillinger Creek) 223 M A

Riveaux 224 M A

Robbins Island (1) 225 H B

Robbins Island (2) 226 M B

Roberts River (South Boomerang)

227 H A

Rocky Boat - Point Vivian 228 H B

Roger River Hills 229 H D

Roger River Hills (2) 230 H D

Rough Hills 231 M C

Royal George 232 M C

Salmon - Blackwater 233 H B

Savage - Delville 234 H B

Savage River Mine 235 M B

Sawards Creek 236 H B

Scotchfire Creek 237 M B

Scotts Peak 238 H A

Seal Rocks 239 H B

Silkstone - Durham 240 M C

Silver Hill 241 M C

Sisters Hills 242 H D

Sophia River 243 M A

Sorell Rivulet 244 H C

South Balaclava 245 H D






(ICV)

Karst Atlas ranking

(karstification)

South Cape Rivulet 246 M B

South Eldon 247 M B

South Loddon 248 M A

South Pindars 249 M A

Spear Hill 250 M D

Spence River 251 M A

St Dizier 252 H D

St Patricks Head 253 M C

St Pauls Dome 254 H C

Staff Hill 255 M B

Stewarts Road 256 M B

Stingray 257 H B

Stockers Plain (Muddy Ck, Golden Vly)

258 M B

Stokes - Red Hut 259 H B

Sugarloaf Spur 260 M D

Surprise Bay 261 H A

Surveyor - Jane 262 M B

Temma 263 M C

Thomas Currie-Newall 264 M D

Three Mile Sand (Marrawah Beach)

265 H B

Thunder and Lightning 266 H B

Tim Shea 267 M B

Timbertop Creek 268 M B

Tin Spur Creek 269 M C

Trousers Point (Fotheringate Bay)

270 H B

Tunnack 271 M C

Upper Andrew River 272 M A

Upper Erebus Rivulet 273 M B

Upper Harcus River Plain 274 M B

Upper Leven 275 M C

Upper Loddon 276 M A

Upper Ridge Creek (Upper Peak Creek)

277 M B

Vale of Belvoir 278 M A

Vale of Rasselas 279 VH A

Vanishing Falls 280 M A

Variety Bay 281 H D

Vinegar Hill 282 H B

Watsons Hill 283 M D

Webbs Creek 284 M B

Wedge Creek 285 H B

West Arm Creek 286 H D

West End 287 H B

West Hobart 288 H D

West Maxwell - Algonkian 289 H A

Westerway - Farrell 290 M B

Wet Cave Point - Rocky Cape 291 H D

Whalers Cove (Reidle Bay) 292 H B

Whistler-Porky 293 H B






(ICV)

Karst Atlas ranking

(karstification)

White Kangaroo Rivulet 294 M D

Wilmot River 295 M C

Wilson River 296 M B

Wingaroo 297 H B

Wombat Glen 298 M D

Wright River 299 M A

Wynyard 300 H C

Zeehan 301 M B

Welcome River 1 302 H B

Welcome River 6 303 M B







Montagu River 1 310 M B

Montagu River 2 311 H B

Montagu River 3 312 H B

Duck River 1 313 H B

Duck River 3 314 H B

Duck River 2 315 M B

Roger River - Ekberg 1 316 M B

Roger River - Ekberg 2 317 M B

Trowutta-Sumac 3 318 H B

Trowutta-Sumac 4 319 H B

Trowutta-Sumac 5 320 M B




Hellyer - Heazlewood 1 324 M D

Hellyer - Heazlewood 2 325 H D

Hellyer - Heazlewood 3 326 H D

Mole Creek 1 327 VH A


Mole Creek 4 329 VH B


Lilydale - Patersonia 4 331 M C

Lilydale - Patersonia 1 332 H C

Lilydale - Patersonia 2 333 M C

Lilydale - Patersonia 3 334 H C

Appendix 12 – TASVEG codes


12 Vegetation communities (Tasmanian Vegetation Map (TASVEG))

Version 0.1 (May 2004)

Vegetation code

Vegetation group Community name Natural (N) or Exotic (E)

Fi Agricultural productive Improved pasture and crop-land E

Fj Agricultural productive Regenerated cleared land E

Fk Agricultural productive Bracken E

Fw Agricultural productive Exotic invasions E

PL Agricultural productive Plantation E

Uc Agricultural productive Built-up areas E

Ur Agricultural productive Rural miscellaneous E

ACS Alpine Alpine coniferous heathland/Diselma shrubbery N

Ae Alpine Eastern alpine vegetation N

AGC Alpine Graminoid moorland/herbfield N

Ah Alpine Eastern alpine heathland N

As Alpine Eastern alpine sedgefields/fernfields N

Aw Alpine Western alpine shrubbery heathland N

CA Alpine Cushion moorland N

ALK Buttongrass and moorland Alkaline pan N

Bb Buttongrass and moorland Buttongrass moorland N

BEA Buttongrass and moorland Eastern buttongrass moorland N

BG Buttongrass and moorland Sparse buttongrass on slopes N

Bm Buttongrass and moorland Melaleuca squamea with/without buttongrass on slopes N

BML Buttongrass and moorland Melaleuca squamea/Leptospermum nitidum open scrub with buttongrass N

BPB Buttongrass and moorland Pure buttongrass N

Br Buttongrass and moorland Restionaceae flatland, not alpine N

BRO Buttongrass and moorland Western moorland N

BSW Buttongrass and moorland South-west buttongrass moorland N

AC Eucalypt forest Coastal Eucalyptus amygdalina forest N

AD Eucalypt forest Eucalyptus amygdalina forest dolerite N

AI Eucalypt forest Inland Eucalyptus amygdalina forest N

AIC Eucalypt forest Inland Eucalyptus amygdalina - E. viminalis - E. pauciflora forest on Cainozoic deposits N

AM Eucalypt forest Eucalyptus amygdalina forest on mudstone N



Vegetation code


AS Eucalypt forest Eucalyptus amygdalina forest sandstone N

BA Eucalypt forest Eucalyptus brookeriana wet forest N

C Eucalypt forest Eucalyptus coccifera woodland occasional forest N

CO Eucalypt forest Eucalyptus cordata forest N

D Eucalypt forest Eucalyptus delegatensis dry forest N

DA Eucalypt forest Eucalyptus dalrympleana forest N

DP Eucalypt forest Eucalyptus dalrympleana/Eucalyptus pauciflora forest N

DR Eucalypt forest Eucalyptus delegatensis over rainforest N

DSC Eucalypt forest Eucalyptus viminalis/Eucalyptus ovata/Eucalyptus amygdalina/Eucalyptus obliqua damp sclerophyll forest N

DT Eucalypt forest Eucalyptus delegatensis wet forest N

DW Eucalypt forest Eucalyptus delegatensis forest over broadleaf of sclerophyll N

ENL Eucalypt forest Eucalyptus nitida over tall tea-tree N

EOB Eucalypt forest Eucalyptus obliqua broadleaf wet forest N

EOL Eucalypt forest Eucalyptus obliqua tea-tree wet forest N

EOR Eucalypt forest Eucalyptus obliqua mixed forest N

G Eucalypt forest Eucalyptus viminalis/Eucalyptus globulus coastal shrubby forest N

GG Eucalypt forest Grassy/shrubby Eucalyptus globulus forest N

KG Eucalypt forest King Island Eucalyptus globulus forest N

MO Eucalypt forest Eucalyptus morrisby forest N

N Eucalypt forest Eucalyptus nitida dry forest N

NF Eucalypt forest Furneaux Eucalyptus nitida forest N

NR Eucalypt forest Eucalyptus nitida over rainforest N

NT Eucalypt forest Eucalyptus nitida wet forest N

O Eucalypt forest Eucalyptus obliqua dry forest N

OT Eucalypt forest Eucalyptus obliqua wet forest N

OV Eucalypt forest Shrubby Eucalyptus ovata/Eucalyptus viminalis forest N

P Eucalypt forest Eucalyptus pulchella/Eucalyptus globulus/Eucalyptus viminalis grassy/shrubby dry forest complex N

PJ Eucalypt forest Eucalyptus pauciflora forest on Jurassic dolerite N

PS Eucalypt forest Eucalyptus pauciflora forest on non-Jurassic dolerite N

R Eucalypt forest Eucalyptus regnans forest N

RI Eucalypt forest Eucalyptus risdonii forest N

RO Eucalypt forest Eucalyptus rodwayi forest N

SG Eucalypt forest Eucalyptus sieberi forest granite N



Vegetation code


SO Eucalypt forest Eucalyptus sieberi forest on non-granite substrates N

T Eucalypt forest Eucalyptus tenuiramis forest granite N

TD Eucalypt forest Eucalyptus tenuiramis forest dolerite N

TI Eucalypt forest Inland Eucalyptus tenuiramis forest N

V Eucalypt forest Eucalyptus viminalis grassy forest N

VF Eucalypt forest Furneaux Eucalyptus viminalis woodland-forest N

VW Eucalypt forest Eucalyptus viminalis wet forest on basalt N

Ea Eucalypt woodland Generic Eucalyptus amygdalina woodland N

Eac Eucalypt woodland Coastal Eucalyptus amygdalina woodland N

Ead Eucalypt woodland Eucalyptus amygdalina woodland on dolerite N

Eai Eucalypt woodland Inland Eucalyptus amygdalina woodland N

Eas Eucalypt woodland Eucalyptus amygdalina woodland on sandstone N

Eb Eucalypt woodland Eucalyptus brookeriana woodland N

Ed Eucalypt woodland Eucalyptus delegatensis dry woodland N

Eda Eucalypt woodland Eucalyptus delegatensis sub-alpine woodland N

Edt Eucalypt woodland Eucalyptus delegatensis woodland N

Ee Eucalypt woodland Eucalyptus barberi woodland N

Eg Eucalypt woodland Grassy Eucalyptus globulus woodland N

Eh Eucalypt woodland Eucalyptus ovata heathy woodland N

Ei Eucalypt woodland Eucalyptus gunnii woodland N

Ekg Eucalypt woodland King Island Eucalyptus globulus woodland N

El Eucalypt woodland Eucalyptus obliqua dry woodland N

Em Eucalypt woodland Eucalyptus pulchella/Eucalyptus globulus/Eucalyptus viminalis grassy dry woodland complex N

Eo Eucalypt woodland Shrubby Eucalyptus ovata woodland N

Eop Eucalypt woodland Midlands woodland complex N

Epj Eucalypt woodland Eucalyptus pauciflora woodland on Jurassic dolerite N

Eps Eucalypt woodland Eucalyptus pauciflora woodland on sediments N

Eq Eucalypt woodland Eucalyptus perriana woodland N

Er Eucalypt woodland Eucalyptus rodwayi woodland N

Esg Eucalypt woodland Eucalyptus sieberi woodland on granite N

Eso Eucalypt woodland Eucalyptus sieberi woodland on other substrates N

Et Eucalypt woodland Generic Eucalyptus tenuiramis woodland N

Etd Eucalypt woodland Eucalyptus tenuiramis woodland on dolerite N



Vegetation code


Etg Eucalypt woodland Eucalyptus tenuiramis woodland on granite N

Eti Eucalypt woodland Inland Eucalyptus tenuiramis woodland N

Eu Eucalypt woodland Eucalyptus dalrympleana woodland N

Evg Eucalypt woodland Eucalyptus viminalis and/or Eucalyptus globulus heathy shrubby woodland N

Ew Eucalypt woodland Eucalyptus viminalis grassy woodland N

Ex Eucalypt woodland Eucalyptus regnans woodland N

Ey Eucalypt woodland Eucalyptus dalrympleana Eucalyptus pauciflora woodland N

Gc Grassland Coastal grass and herbfield N

Gi Grassland Induced highland grassland on basalt N

Gl Grassland Lowland Poa N

Gn Grassland Danthonia/Austrostipa/sparse Themeda grassland N

Grp Grassland Rock plate grassland N

Gs Grassland Highland Poa N

Gsh Grassland Highland grassy sedgeland and sedgy grassland N

Gsl Grassland Lowland grassy sedgeland and sedgy grassland N

Gt Grassland Themeda native grassland N

Gw Grassland Exotic coastal grasslands E

Ha Heath Sub-alpine heath scree flora N

Hc Heath Shrubby coastal heath N

Hg Heath Lowland and coastal sedgy heath N

Hh Heath Lowland and intermediate heath N

Hl Heath Heath on calcarenite N

Hr Heath Heath on granite N

Hs Heath Graminoid sub-alpine heath N

Ht Heath Seabird rookery N

Hw Heath Wet heath N

Hck Heath/scrub complex King Island coastal complex N

HSc Heath/scrub complex Tall or wind-pruned coastal scrub or shrubby coastal heath N

HSf Heath/scrub complex Flinders heath scrub mosaic N

HSk Heath/scrub complex King Island sedge heath mosaic N

Hsw Heath/scrub complex Wingaroo Complex N

AV Non-Eucalypt trees Allocasuarina verticillata woodland-forest N

BF Non-Eucalypt trees Acacia melanoxylon on flats N



Vegetation code


BR Non-Eucalypt trees Acacia melanoxylon on rises N

BS Non-Eucalypt trees Banksia serrata woodland N

CR Non-Eucalypt trees Callitris rhomboidea forest N

L Non-Eucalypt trees Leptospermum lanigerum/Melaleuca squarrosa swamp forest N

LA Non-Eucalypt trees Leptospermum lanigerum/Acacia mucronata short forest N

LM Non-Eucalypt trees Sub-alpine tea-tree woodland N

LN Non-Eucalypt trees Sub-alpine Leptospermum nitidum dwarf forest and woodland N

LT Non-Eucalypt trees Tea-tree forest N

ME Non-Eucalypt trees Melaleuca ericifolia forest N

NP Non-Eucalypt trees Notelaea/Pomaderris forest N

SI Non-Eucalypt trees Acacia dealbata forest N

Ta Non-Eucalypt trees Allocasuarina littoralis closed forest-woodland N

Tw Non-Eucalypt trees Allocasuarina dealbata woodland N

Tz Non-Eucalypt trees Scrubby Bursaria/Acacia/Dodonaea on slope N

Ro Other Talus, boulder fields, rock plates N

Rs Other Sand, mud N

Ue Other Permanent easements E

W Other Water, sea N

ADF Rainforest Deciduous beech shrubbery N

Ar Rainforest Montane dwarf rainforest N

CRF Rainforest Littoral rainforest N

F Rainforest King Billy pine with deciduous beech N

FEN Rainforest Ferns other than bracken N

H Rainforest Huon pine N

KBr Rainforest King Billy pine alpine forest and woodland N

LR Rainforest Leptospemum/Nothofagus short forest N

M- Rainforest Short rainforest N

M+ Rainforest Tall rainforest N

PD Rainforest Pencil pine deciduous beech N

PP Rainforest Pencil pine N

SPH Rainforest Pencil pine open woodland N

X Rainforest King Billy pine N

Ri Riparian Riparian vegetation N



Vegetation code


Ma Saltmarsh Generic saltmarsh N

Mg Saltmarsh Graminoid saltmarsh N

Mr Saltmarsh Spartina E

Ms Saltmarsh Succulent saltmarsh N

BAS Scrub Banksia wet scrub N

Hws Scrub Western sub-alpine scrub N

RS Scrub Highland rainforest scrub with King Billy pine N

Sa Scrub Scrub on alkaline sands N

Sb Scrub Broad-leaf shrubbery N

Sc Scrub Tall or wind-pruned scrub N

Sd Scrub Sand dune vegetation N

Sf Scrub Flinders Island scrub N

Sl Scrub Dry scrub N

Slp Scrub Melaleuca pustulata scrub N

Sm Scrub Short paperbark swamp N

Sn Scrub Western wet scrub with Eucalyptus nitida N

Sq Scrub Queenstown regrowth N

Sr Scrub Rainforest scrub N

St Scrub Leptospermum spp. scrub N

Sw Scrub Fine-leaf wet scrubs N

Pr Sphagnum Sphagnum peat-land with emergent trees N

Ps Sphagnum Treeless Sphagnum peat-land N

Waf Wetland Freshwater aquatic plants N

Was Wetland Saline aquatic plants N

We Wetland Generic wetland N

Wh Wetland Herbfield and grassland marginal to wetland N

Ws Wetland Sedge rush wetland N

Appendix 13 – In-lake impact scores


13 In-lake impact scores

This Appendix includes all named waterbodies in the CFEV waterbodies spatial data layer. All remaining unnamed waterbodies were rated as „S‟ for depth, „1‟ for lake level manipulation, „1‟ for true artificiality and „1‟ for artificiality.

Conservation of Freshwater

Ecosystem Values (CFEV) waterbody

identifier (WB_ID)

Waterbody name Depth Lake level manip-ulation score

True artificiality

score

Artificiality score

599 Ah Chees Lake S 0.8 1 1

1080, 1081, 1083 Allwrights Lagoons S 1 1 1

1142 Arrow Tarn S 0.8 1 1

705 Arthurs Lake S 0.4 0.5 1

1106 Australia Tarn S 0.8 1 1

1223 Backhouse Tarn S 0.8 1 1

1334 Bains Lagoons S 0.8 1 1

1173 Banana Lake S 0.8 1 1

1052 Bar Lagoon S 0.8 1 1

917 Basin Lake S 0.8 1 1

1075 Bells Lagoon S 0.8 1 1

1105 Big Jim Lake S 0.8 0 0

1339 Big Lagoon S 0.8 1 1

48 Big Lake S 0.8 1 1

123 Big Waterhouse Lake S 0.8 1 1

124 Blackmans Lagoon S 0.4 1 1

1265 Bluff Tarn S 0.8 1 1

8 Bob Lagoon S 0.8 1 1

114 Bowlers Lagoon S 0.8 1 1

1136 Bradys Lake S 0.4 0 0

1123 Brents Lagoon S 0.8 1 1

56 Brodies Lagoon S 0.8 1 1

1129 Bronte Lagoon S 0.4 0 0

11 Bungaree Lagoon S 0.8 1 1

900 Burrow Lagoon S 0.8 1 1

1281 Calverts Lagoon S 0.8 1 1

827 Camerons Lagoon S 0.2 1 1

565, 575, 581 Carter Lakes S 0.8 1 1

567 Carters Lagoon S 0.8 1 1

3 Cask Lake S 0.8 1 1

631 Chalice Lake S 0.8 1 1

46 Chapmans Lagoon S 1 1 1

580 Chapter Lake S 0.8 1 1





identifier (WB_ID)


True artificiality

score

Artificiality score

1062, 1063, 1067 Cherry Tree Lagoons S 0.8 1 1

904 Chigah Lake S 0.8 1 1

136 Chimneys Lagoon S 0.8 1 1

585 Christys Lagoon S 0.8 1 1

730 Chummy Lake S 0.8 1 1

1074 Clarence Lagoon S 0.8 1 1

28 Clears Lagoon S 0.8 1 1

25 Clewers Lagoon S 0.8 1 1

652 Cloister Lagoon S 0.8 1 1

1195 Cluny Lagoon D 0.4 0 1

2 Corduroy Lake S 0.8 1 1

1200 Craigbourne Dam D 0.2 0 0

163 Crater Lake S 0.8 1 1

1271 Croaking Lake S 0.8 1 1

1206 Crooked Lake S 0.8 1 1

77 Crystal Lagoon S 0.8 1 1

703 Cumberland Lake S 0.8 0.5 1

967 Cuppa Lake S 0.8 1 1

131 Curries River Reservoir D 1 0 0

365 Daisy Lakes S 0.8 1 1

229 Dead End Lake S 0.8 1 1

16 Dead Sea S 0.8 1 1

1139 Dee Lagoon S 1 0 0

23 Deep Lagoons S 0.8 1 1

272 Deep Lake S 0.8 1 1

53 Denbys Lagoon S 0.8 1 1

632 Double Lagoon S 0.8 1 1

162 Dove Lake D 1 1 1

21 Drain Lagoon S 0.8 1 1

1252 Dungeon Tarn S 0.8 1 1

799 Eagle Lake S 0.8 1 1

1337 Earl Lake S 0.8 1 1

651 East Rocky Lagoon S 0.8 1 1

654, 657 Emma Tarns S 0.8 1 1

637 First Lagoon S 0.8 1 1

175 Flynns Tarn S 0.8 1 1

60 Flyover Lagoon S 1 1 1





identifier (WB_ID)


True artificiality

score

Artificiality score

1043 Folly Lagoon S 0.8 1 1

1035 Forest Lagoon S 1 1 1

1089 Forgotten Lake S 0.8 1 1

1303 Fortescue Lagoon S 0.8 1 1

633 Frances Lagoon S 0.8 1 1

1073 Franklin Tarns S 0.8 1 1

188 Frozen Lagoon S 0.8 1 1

371 George Howes Lake S 0.8 1 1

1316 Glassworm Tarn S 0.8 1 1

1143 Godwin Tarn S 0.8 1 1

213 Grassy Lake S 0.8 1 1

800 Grassy Lake S 0.8 1 1

264 Great Lake S 0.2 0.5 1

126 Green Point Lagoon S 1 1 1

285 Grub Lake S 0.8 1 1

1049 Gum Tree Hole S 0.8 1 1

155 Gun Lagoon S 0.8 1 1

668 Gunns Lake S 0.8 1 1

26 Halfmoon Lagoon S 0.8 1 1

57 Halfway Lagoon S 0.8 1 1

49 Hammonds Lagoon S 0.8 1 1

1202 Hanging Lake S 0.8 1 1

1351 Hanging Lake S 0.8 1 1

1336 Hartz Lake D 0.8 1 1

1319 Haven Lake S 0.8 1 1

1138 Heron Pond S 0.8 1 1

1211 Hibbs Lagoon S 0.8 1 1

172 Hidden Lake S 0.8 1 1

1130 Highland Waters S 0.8 0 1

679 Hood Lagoon S 0.8 1 1

510 Hunters Lake S 0.8 1 1

1030 Ina Lagoon S 0.8 1 1

623 Isabella Lagoon S 0.8 1 1

1239 Islet Lake S 1 1 1

183 Jacks Lagoon S 0.8 1 1

1152 Jetty Lake S 0.8 1 1

138 Jocks Lagoon S 0.8 1 1





identifier (WB_ID)


True artificiality

score

Artificiality score

890 Johnsons Lagoon S 0.8 1 1

1225 Johnston Tarn S 0.8 1 1

349 Julian Lakes S 0.8 1 1

729 Junction Lake S 0.8 1 1

1054 Kenneth Lagoon S 0.8 1 1

772 Kita Lake S 0.8 1 1

1077 Lagoon of Islands S 0.4 0.5 1

591 Lake Ada S 0.8 1 1

991 Lake Adam S 0.8 1 1

1145 Lake Adela S 0.8 1 1

520 Lake Adelaide D 0.8 1 1

669 Lake Agnes S 0.8 1 1

262 Lake Agnew S 0.8 1 1

222 Lake Andrews S 0.8 1 1

1164 Lake Anne S 0.8 1 1

660 Lake Antimony S 0.8 1 1

907 Lake Apollos S 0.8 1 1

1311 Lake Ariel S 0.8 1 1

1148 Lake Arlette S 0.8 1 1

765 Lake Artemis S 0.8 1 1

1098 Lake Ashwood S 0.8 1 1

835 Lake Athena S 0.8 1 1

504 Lake Augusta S 0.2 0.5 1

479 Lake Ayr S 0.8 1 1

665 Lake Baillie S 0.8 1 1

149 Lake Baker S 1 1 1

532 Lake Ball S 0.8 1 1

170 Lake Balmoral S 0.8 1 1

1096 Lake Bantick S 0.8 1 1

976 Lake Barnabas S 0.8 1 1

137 Lake Barrington VD 0.6 0 0

995 Lake Beatrice VD 0.8 1 1

1022 Lake Beatrix S 0.8 1 1

1228 Lake Belcher D 0.8 1 1

1230 Lake Belton S 0.8 1 1

1346 Lake Bewsher S 1 1 1

555 Lake Bigfoot S 0.8 1 1





identifier (WB_ID)


True artificiality

score

Artificiality score

557 Lake Bill S 0.8 1 1

1150 Lake Binney S 0.4 0 1

677 Lake Booth S 0.8 1 1

619 Lake Botsford S 0.8 1 1

1017 Lake Burbury D 0.4 0 0

1329 Lake Burgess S 0.8 1 1

444 Lake Butters S 0.8 1 1

158 Lake Carruthers S 0.8 1 1

1187 Lake Catagunya D 0.4 0 0

1016 Lake Catherine S 0.8 1 1

1162 Lake Cecily S 0.8 1 1

1302 Lake Ceres S 0.8 1 1

145 Lake Cethana VD 0.4 0 0

201 Lake Chambers S 0.8 1 1

598 Lake Charles S 0.8 1 1

629 Lake Chipman S 0.8 1 1

132 Lake Chisholm S 1 1 1

1343 Lake Cracroft S 0.8 1 1

1109 Lake Crescent S 0.4 0.5 1

1196 Lake Curly S 0.8 1 1

340 Lake Curran S 0.8 1 1

1299 Lake Cygnus S 0.8 1 1

1191 Lake Daphne S 0.8 1 1

1260 Lake Denison S 0.8 1 1

735 Lake Denton S 0.8 1 1

1310 Lake Dione S 0.8 1 1

1229 Lake Dobson S 0.8 1 1

810 Lake Dora S 0.8 1 1

871 Lake Dorothy S 0.8 1 1

205 Lake Douglas S 0.8 1 1

527 Lake Dudley S 0.8 1 1

1169 Lake Dulverton S 0.8 1 1

1094 Lake Echo D 0.2 0 0

1167 Lake Eleanor S 0.8 1 1

240 Lake Ellen S 0.8 1 1

788 Lake Elysia S 0.8 1 1

1213 Lake Emmett S 0.8 1 1





identifier (WB_ID)


True artificiality

score

Artificiality score

786 Lake Eros S 0.8 1 1

1333 Lake Esperance D 0.8 1 1

801 Lake Eurynome S 0.8 1 1

1146 Lake Eva S 0.8 1 1

236 Lake Evans S 0.8 1 1

879 Lake Ewart S 0.8 1 1

191 Lake Explorer S 0.8 1 1

535 Lake Fanny S 0.8 1 1

1224 Lake Fenton S 1 1 1

831 Lake Fergus S 0.8 1 1

5001 Lake Fidler S 0 1 1

278 Lake Field D 0.8 1 1

4 Lake Flannigan D 0.8 1 1

720 Lake Flora S 0.8 1 1

1298 Lake Fortuna S 0.8 1 1

206 Lake Fox S 0.8 1 1

255 Lake Furmage S 0.8 1 1

144 Lake Gairdner D 0.4 0 0

676 Lake Galaxias S 0.8 1 1

1317 Lake Ganymede S 0.8 1 1

1101 Lake Garcia D 0.8 1 1

1348 Lake Gaston S 0.8 1 1

507 Lake Gaye S 0.8 0 1

1350 Lake Geeves S 0.8 1 1

1362 Lake Geeves S 0.8 1 1

1141 Lake George D 0.8 1 1

1163 Lake Gertrude S 0.8 1 1

1212 Lake Gordon VD 0.2 0 0

1155 Lake Gwendolen S 0.8 1 1

212 Lake Halkyard S 0.8 1 1

165 Lake Hanson S 0.8 1 1

1219 Lake Hayes S 0.8 1 1

1040 Lake Helen S 0.8 1 1

724 Lake Helios S 0.8 1 1

769 Lake Hermes S 0.8 1 1

1087 Lake Hermione S 0.8 1 1

235 Lake Holmes S 0.8 1 1





identifier (WB_ID)


True artificiality

score

Artificiality score

256 Lake How S 0.8 1 1

455 Lake Howe S 0.8 1 1

791 Lake Huntley VD 0.8 1 1

1027 Lake Ina S 0.8 1 1

848 Lake Ingrid S 0.8 1 1

195 Lake Ironstone S 0.8 1 1

896 Lake Jackie S 0.8 1 1

294 Lake James S 0.8 1 1

197 Lake Johnny S 0.8 1 1

571 Lake Johnston S 0.8 0 1

1266 Lake Judd D 0.8 1 1

662 Lake Julia S 0.8 1 1

1313 Lake Juno S 0.8 1 1

1323 Lake Jupiter S 0.8 1 1

959 Lake Kaljee S 0.8 1 1

673 Lake Kay S 0.8 1 1

872 Lake Kellatie S 0.8 1 1

1116 Lake King William D 0 0 0

1134 Lake Knight S 0.8 1 1

1180 Lake Lancaster S 0.8 1 1

998 Lake Laura D 0.8 1 1

1192 Lake Laurel S 0.8 1 1

148 Lake Lea S 0.8 1 1

939 Lake Leake S 0.4 0 0

861 Lake Lenone S 0.8 1 1

1332 Lake Leo S 0.8 1 1

476 Lake Leonis S 0.8 1 1

466 Lake Lepera S 0.8 1 1

1172 Lake Liapootah S 0.6 1 0

161 Lake Lilla S 0.8 1 1

905 Lake Linnhe S 0.8 1 1

416 Lake Loane S 0.8 1 1

816 Lake Loretta S 0.8 1 1

512 Lake Louisa D 0.8 1 1

856 Lake Louise D 0.8 1 1

180 Lake Lucy Long S 0.8 1 1

870 Lake Lula S 0.8 1 1





identifier (WB_ID)


True artificiality

score

Artificiality score

166 Lake MacKenzie S 0.2 0 0

154 Lake MacKintosh VD 0.4 0 0

1292 Lake Maconochie S 1 1 1

857 Lake Magdala S 0.8 1 1

1168 Lake Magdalen S 0.8 1 1

1201 Lake Malana S 0.8 1 1

790 Lake Malbena D 0.8 1 1

837 Lake Malcolm S 0.8 1 1

926 Lake Margaret D 0.2 1 0

1165 Lake Marilyn S 0.8 1 1

901 Lake Marion D 0.8 1 1

849 Lake Mark S 0.8 1 1

1320 Lake Mars S 0.8 1 1

873 Lake Martha S 0.8 1 1

6 Lake Martha Lavinia S 0.8 1 1

883 Lake Mary S 0.8 1 1

834 Lake Matthew S 0.8 1 1

485 Lake McCoy S 0.8 1 1

640 Lake McFarlane S 0.8 1 1

251 Lake McRae S 0.8 1 1

1325 Lake Mercury S 0.8 1 1

773 Lake Merope S 0.8 1 1

650 Lake Meston D 0.8 1 1

723 Lake Mikany S 0.8 1 1

1171 Lake Millicent S 0.8 1 1

1315 Lake Mimas S 0.8 1 1

1057 Lake Mingundie S 0.8 1 1

1318 Lake Miranda S 0.8 1 1

867 Lake Monica S 0.8 1 1

5000 Lake Morrison S 0 1 1

384 Lake Murchison VD 0 0 1

1203 Lake Murray D 0.8 1 1

622 Lake Myrtle D 0.8 1 1

194 Lake Nameless S 0.8 1 1

1154 Lake Nancy S 0.8 1 1

752 Lake Naomi S 0.8 1 1

686 Lake Nearana S 0.8 1 1





identifier (WB_ID)


True artificiality

score

Artificiality score

1296 Lake Neptune S 0.8 1 1

1218 Lake Newdegate S 0.8 1 1

1221 Lake Nicholls S 0.8 1 1

951 Lake Nive S 0.8 1 1

372 Lake Nixon S 0.8 1 1

805 Lake Norman S 0.8 1 1

566 Lake Nugara S 0.8 1 1

762 Lake Nugetena S 0.8 1 1

434 Lake Nutting S 0.8 1 1

1304 Lake Oberon D 0.8 1 1

1048 Lake Oenone S 0.8 1 1

753 Lake Olive S 0.8 1 1

802 Lake Ophion S 0.8 1 1

1328 Lake Osborne S 0.8 1 1

1326 Lake Ovoid S 0.8 1 1

819 Lake Pallas S 0.8 1 1

135 Lake Paloona D 0.4 0 0

156 Lake Parangana D 0.6 0 0

431 Lake Paterson S 0.8 1 1

817 Lake Payanna S 0.8 1 1

1347 Lake Payens S 0.8 1 1

1233 Lake Pedder D 0.6 0 0

1236 Lake Pedder D 0.6 1 0

1327 Lake Perry D 0.8 1 1

927 Lake Peter S 0.8 1 1

1050 Lake Petrarch S 0.8 1 1

920 Lake Philip S 0.8 1 1

1305 Lake Picton D 0.8 1 1

189 Lake Pieman VD 0.4 0 0

193 Lake Pitt S 0.8 1 1

1294 Lake Pluto S 0.8 1 1

609 Lake Poa S 0.8 1 1

702 Lake Pogana S 0.8 1 1

946 Lake Polycarp S 0.8 1 1

436 Lake Price S 0.8 1 1

1193 Lake Repulse D 0.4 0 0

1208 Lake Rhona D 0.8 1 1





identifier (WB_ID)


True artificiality

score

Artificiality score

1174 Lake Richmond D 0.8 1 1

892 Lake Riengeena D 0.8 1 1

1309 Lake Riveaux D 0.8 1 1

177 Lake Rodway S 0.8 1 1

728 Lake Rolleston D 0.8 1 1

355 Lake Rosa S 0.8 1 1

190 Lake Rosebery VD 0.4 0 0

659 Lake Rotuli S 0.8 1 1

207 Lake Rowallan D 0.2 0 0

1166 Lake Rufus D 0.8 1 1

601 Lake Sally S 0.8 1 1

1153 Lake Sally Jane S 0.8 1 1

463 Lake Salome S 0.8 1 1

1125 Lake Samuel S 0.8 0 1

515 Lake Sandra S 0.8 0 1

1024 Lake Sappho S 0.8 1 1

1307 Lake Saturn S 0.8 1 1

842 Lake Scott S 0.8 1 1

1222 Lake Seal S 1 1 1

758 Lake Selene S 0.8 1 1

638 Lake Selina S 0.8 1 1

1335 Lake Shaw S 0.8 1 1

437 Lake Sidon S 0.8 1 1

1258 Lake Skinner D 0.8 1 1

1085 Lake Solitude S 0.8 1 1

582 Lake Sonja S 0.8 1 1

1161 Lake Sophie S 0.8 1 1

1064 Lake Sorell S 0.8 0.5 1

893 Lake Spicer S 0.8 1 1

1013 Lake St Clair VD 0.6 0.5 1

1181 Lake Stuart VD 0.8 1 1

1274 Lake Surprise D 0.8 1 1

1353 Lake Sydney S 0.8 1 1

1158 Lake Tahune S 0.8 1 1

779 Lake Tartarus S 0.8 1 1

408 Lake Thor S 0.8 1 1

1186 Lake Tiberias S 1 1 1





identifier (WB_ID)


True artificiality

score

Artificiality score

887 Lake Tiddler S 0.8 1 1

1257 Lake Timk S 0.8 1 1

1308 Lake Titania S 1 1 1

596 Lake Toorah S 0.8 1 1

141 Lake Trevallyn S 0.4 0 0

1297 Lake Triton S 0.8 1 1

1177 Lake Tudor S 0.8 1 1

813 Lake Tyndall S 0.8 1 1

452 Lake Tyre S 0.8 1 1

1306 Lake Uranus S 0.8 1 1

1322 Lake Venus S 0.8 1 1

1159 Lake Vera S 0.8 1 1

1312 Lake Vesta S 0.8 1 1

1149 Lake Vincent S 0.8 1 1

1178 Lake Warwick S 0.8 1 1

1215 Lake Webster S 0.8 1 1

645 Lake Westwood S 0.8 1 1

1170 Lake Whitham S 0.8 1 1

1 Lake Wickham S 0.8 1 1

169 Lake Wilks S 0.8 1 1

215 Lake Will S 0.8 1 1

369 Lake Willson S 0.8 1 1

1243 Lake Wilmot S 1 1 1

300 Lake Windermere S 0.8 1 1

1204 Lake Wurawina D 0.8 1 1

1179 Lake York S 0.8 1 1

714 Lake Youd S 0.8 1 1

152 Lake Youl S 0.8 1 1

185 Last Lagoon S 0.8 1 1

1124 Laughing Jack Lagoon S 0 0 0

649 Lina Tarn S 0.8 1 1

763 Ling Roth Lakes S 0.8 1 1



1104 Little Bellinger S 0.8 1 1

562 Little Blue Lagoon S 0.8 1 1

1037 Little Lagoon S 0.8 1 1





identifier (WB_ID)


True artificiality

score

Artificiality score

1340 Little Lagoon S 0.8 1 1

678 Little Lake S 0.8 1 1

30 Little Nelsons Lagoon S 0.8 1 1

902 Little Pine Lagoon S 0.6 0 0

214 Little Throne Lake S 0.8 1 1

116 Little Waterhouse Lake S 1 0.5 1

1284 Lobster Lake S 0.8 1 1

52 Logan Lagoon S 0.8 1 1

844 Long Lake S 0.8 1 1

352, 383, 398 Long Tarns S 0.8 1 1

1133 Lower Lake Jukes S 0.8 1 1

533 Lunka Lake S 0.8 1 1

1227 MacKenzie Tarn S 0.8 1 1

798 Mary Tarn S 0.8 1 1

839 Maxfield Tarn S 0.8 1 1

1197 Meadowbank Lake D 0.6 0 0

5 Meatsafe Lagoon S 0.8 1 1

829 Michael Tarn S 0.8 1 1

203 Middle Lake S 0.8 1 1

1278 Moraine Tarn S 0.8 1 1

31 Nelsons Lagoon S 0.8 1 1

517 New Years Lake S 0.8 1 1

263 Nips Lake S 0.8 1 1

33 No Duck Lagoon S 0.8 1 1

45 North Chain Lagoon S 1 1 1

698 O‟Dells Lake S 0.8 1 1

1144 Odo Tarn S 0.8 1 1

523 Old Mines Lagoon S 0.8 1 1

803 Olive Lagoon S 0.8 1 1

1361 Ooze Lake S 0.8 1 1

1248 Orb Lake S 0.8 1 1

770 Orion Lakes S 0.8 1 1



1371 Oval Lake S 0.8 1 1

143 Paddys Lake S 0.8 1 1

718 Pats Tarn S 0.8 1 1





identifier (WB_ID)


True artificiality

score

Artificiality score

36, 37 Pearshape Lagoon S 0.8 1 1

641 Penah Lake S 0.8 1 1

344 Pencil Pine Tarn S 0.8 1 1

7 Pennys Lagoon S 0.8 1 1

1079 Penstock Lagoon S 0.4 0 0

5003 Perched Lake S 0 1 1

1262 Picone Lake S 0.8 1 1

402 Pillans Lake S 0.8 1 1

220 Pine Lake S 0.8 1 1

1349 Pine Lake S 0.8 1 1

1072 Pine Tier Lagoon D 0.4 1 0

1226 Platypus Tarn S 1 1 1

12 Porky Lagoon S 0.8 1 1

35 Possums Lagoon S 0.8 1 1

977 Profile Lake S 0.8 1 1

1314 Promontory Lake S 0.8 1 1

133 Rebecca Lagoon S 0.4 1 1

1078 Reedy Duckhole S 0.8 1 1

1046 Reedy Lagoon S 0.8 1 1

494 Reedy Lake S 0.8 1 1

1358, 1359 Reservoir Lakes S 0.8 1 1

993 Rim Lake S 0.8 1 1

617 Rocky Lagoon S 0.8 1 1

1156 Rouen Tarn S 0.8 1 1

771 Saddle Lake S 0.8 1 1

250 Sales Lake S 0.8 1 1

1253 Sanctuary Lake S 0.8 1 1

34 Sandy Lagoon S 0.8 1 1

84 Sandy Lagoon S 0.8 1 1

583 Sandy Lake S 0.8 1 1

1354 Satellite Lake S 0.8 1 1

1247 Sceptre Lake S 1 1 1

1147 Scoparia Lake S 0.8 1 1

351 Second Bar Lake S 0.8 1 1

628 Second Lagoon S 0.8 1 1

43 Second Saltpan S 0.8 1 1

13 Sellars Lagoon S 0.8 1 1





identifier (WB_ID)


True artificiality

score

Artificiality score

490 Shaded Lake S 0.8 0 1

1088 Shadow Lake S 0.8 1 1

895 Shannon Lagoon S 0.4 1 1

225 Shark Hole S 0.8 1 1

51 Shearing Shed Lagoon S 0.8 1 1

1086 Shepherds Hut Lagoon S 0.8 1 1

888 Shrouded Lake S 0.8 1 1

78 Silver Lake S 0.8 1 1

655 Silver Lake S 0.8 1 1

368 Slab Lagoon S 0.8 1 1

1261 Sloping Lagoon S 0.8 1 1

1282 Smiths Tarn S 0.8 1 1

196 Snake Lake S 0.8 1 1

427, 430, 438 Solomons Jewels S 0.8 1 1

570 Solveig Lake S 0.8 1 1

50 South Chain Lagoon S 0.8 1 1

22 Spoonbill Lagoon S 0.8 1 1

1301 Square Lake S 1 1 1

1092 St Clair Lagoon S 0.8 0.5 1

14 Stony Lagoon S 0.8 1 1

456 Stretcher Lake S 0.8 1 1

528 Stumps Lake S 0.8 1 1

5002 Sulphide Pool 0 1

1157 Surprise Tarn S 0.8 1 1

171 Suttons Tarn S 0.8 1 1

1363 Swallows Nest Lakes D 0.8 1 1

1365 Swallows Nest Lakes D 0.8 1 1

15 Swan Lagoon S 0.8 1 1

1242 Swan Lagoon S 0.8 1 1

811 Symphony Lake S 0.8 1 1

54 Syndicate Lagoon S 1 1 1

140 Talbots Lagoon S 0.8 0 0

642 Talinah Lagoon S 0.8 1 1

561, 576, 584 Talleh Lagoons S 0.8 1 1

853 Tam O‟Shanter Lake S 0.8 1 1

441 Tarn of Islands S 0.8 1 1

92 Teal Lagoon S 0.8 1 1





identifier (WB_ID)


True artificiality

score

Artificiality score

643 Tent Tarn S 0.8 1 1

616 Terry Tarn S 0.8 1 1

1263 The Moat S 1 1 1

630 Theresa Lagoon S 0.8 1 1

605 Third Lagoon S 0.8 1 1

621 Three Arm Lake S 0.8 1 1

353 Tiger Lake S 0.8 1 1

554 Tin Hut Lake S 0.8 1 1

29 Toms Lagoon S 1 1

1135 Tooms Lake S 0.4 0 1

1240 Top Lagoon S 0.8 1 1

1114 Township Lagoon S 0.8 0.5 1

1069 Travellers Rest Lagoon D 1 1 1

1028 Travellers Rest Lake S 0.8 1 1

99, 101 Tregaron Lagoon S 1 1 1

727 Triangle Lake D 0.8 1 1

1279 Trout Lake S 0.8 1 1

1216 Twilight Tarn S 0.8 1 1

1189, 1190 Twin Lakes S 0.8 1 1

1217 Twisted Tarn S 0.8 1 1

1131 Upper Lake Jukes S 0.8 1 1

491 Wadleys Lake S 0.8 1 1

1185 Wayatinah Lagoon S 0.6 0 0

44 Westgate Lagoon S 0.8 1 1

187 Westons Lake S 0.8 1 1

71 White Lagoon S 0.8 0.5 1

27 Whitewash Lagoon S 0.8 1 1

1056 Wihareja Lagoon S 0.8 0.5 1

1199 Windy Lake S 0.8 1 1

1061 Woods Lake S 0.4 0.5 1

1245 Woolleys Tarn S 0.8 1 1

159 Yeates Lagoon S 0.8 1 1

150 Youls Tarn S 0.8 1 1

559 Zig Zag Lakes S 0.8 1 1

Appendix 14 – Major dams and flow variability rating


14 Major dams and waterbodies flow variability ratings

This Appendix includes waterbodies that are considered to be a major impediment to fish passage or modify the flow regime of downstream river sections.

Flow variability rating


Ecosystem Values (CFEV) spatial unit

identifier (WB_ID or WL_ID)

Waterbody or wetland name

Major dam - impedes

fish passage?

Natural river sections

downstream (Natural flow

regime)

Canal/Pipe downstream (Current flow

regime)

Invalid Appleby Creek Reservoir Y 0.3

Invalid Argent Dam Y 0.3

Invalid Arthur Dam Y 0.3

705 Arthurs Lake Y 0.3 0.6

Invalid Beaconsfield Reservoir Y 0.3

Invalid Bischoff Reservoir Y 0.3

Invalid Bradys Creek Reservoir Y 0.3

1136 Bradys Lake Y 0.3 0.3

Invalid Briseis Hole Y 0.3

1129 Bronte Lagoon Y 0.3 0.6

Invalid Bruins Pond N 0.3

Invalid Brushy Lagoon Y 0

Invalid Cascade Basin Y 0.3

Invalid Cascade Dam Y 0.3

Invalid Chester Dam Y 0.3

Invalid Chimney Saddle Reservoir

Y 0.3

Invalid Clarence Dam Y 0.3

1195 Cluny Lagoon Y 0.6

Invalid Colebrook Reservoir Y 0.3

Invalid Companion Reservoir Y 0.3

1200 Craigbourne Dam N 0.6

703 Cumberland Lake Y 0

131 Curries River Reservoir Y 0.3

1139 Dee Lagoon N 0.3 0.6

Invalid Echo Dam Y 0.6 0.6

Invalid Echo Forebay Y 0.6 0.6

Invalid Edgar Pond Y 0

Invalid Ellesmere Dam Y 0.3

Invalid Flagstaff Gully Reservoir Y 0.3

Invalid Frome Dam Y 0.3

264 Great Lake Y 0.3 0.6

Invalid Guide Reservoir Y 0.3

Invalid Havenview Reservoir Y 0.3

Invalid Hearps Reservoir Y 0.3








Major dam - impedes

fish passage?



regime)


regime)

1130 Highland Waters Y 0

Invalid Howell Reservoir Y 0.3

Invalid Huntingfield Pond Y 0.3

Invalid Illa Brook Reservoir Y 0.3

Invalid Kelly Dam Y 0.3

Invalid Knights Creek Reservoir Y 0.3

1077 Lagoon of Islands Y 0.3

504 Lake Augusta Y 0.6

137 Lake Barrington Y 0.6

1150 Lake Binney N 0.3 0.3

1017 Lake Burbury Y 0.3 1

Invalid Lake Catagunya N 0.6

145 Lake Cethana Y 0.6

1109 Lake Crescent N 0.6

1094 Lake Echo Y 0.3 0.6

Invalid Lake Eugenana Y 0

144 Lake Gairdner Y 0.3 0.6

1212 Lake Gordon Y 0.3 1

Invalid Lake Henty Y 0.3 0.6

Invalid Lake Isandula Y 0

Invalid Lake Kara Y 0

1116 Lake King William Y 1 1

936 Lake Leake Y 0.3

1172 Lake Liapootah Y 0.6

Invalid Lake Llewellyn Y 0.3

166 Lake MacKenzie Y 0.6

154 Lake MacKintosh Y 0.6

926 Lake Margaret Y 0.3 0.6

384 Lake Murchison Y 0.6

Invalid Lake Newton Y 0.3

135 Lake Paloona Y 0.6

156 Lake Parangana Y 0.6

1233, 1236 Lake Pedder Y 0.3 0.3

189 Lake Pieman Y 1

Invalid Lake Plimsoll N 0.3 0.6

1193 Lake Repulse Y 0.6

190 Lake Rosebery Y 0.6

207 Lake Rowallan Y 0.6

1064 Lake Sorell N 0.3








Major dam - impedes

fish passage?



regime)


regime)

1013 Lake St Clair Y 0.3

141 Lake Trevallyn Y 0.6 0.6

645 Lake Westwood Y 0

1124 Laughing Jack Lagoon Y 0.6 0.6

Invalid Lauriston Reservoir Y 0.3

Invalid Limekiln Gully Reservoir Y 0.3

Invalid Little Pine Lagoon Y 0.3 0.6

Invalid Lower Glenorchy Reservoir

Y 0.3

Invalid Magnet Dam Y 0

1197 Meadowbank Lake Y 1

Invalid Monarch Dam Y 0

Invalid Mont Albert Reservoir Y 0.3

Invalid Mornington Reservoir Y 0.3

Invalid Mossy Marsh Pond Y 0.3 0.3

Invalid Native Lass Dam N 0.3

Invalid Old Chum Dam Y 0

Invalid Parting Creek Lake N 0.3

Invalid Pawleena Reservoir Y 0.3

Invalid Penrhyn Pond Y 0

1079 Penstock Lagoon Y 0.3

Invalid Pet Reservoir Y 0.3

1072 Pine Tier Lagoon Y 0.3 0.3

Invalid Pioneer Dam Y 0.3 0.3

Invalid Pioneer Lake Y 0

Invalid Pump Pond Y 0.3 0.6

Invalid Ridgeway Reservoir Y 0.3

Invalid Rileys Creek Reservoir Y 0.3

Invalid Risdon Brook Reservoir Y 0.3

Invalid Romaine Reservoir Y 0.3

Invalid Rosedale Reservoir Y 0.3

Invalid Rostrevor Reservoir Y 0.3

1092 St Clair Lagoon Y 0.3

140 Talbots Lagoon Y 0.3

1135 Tooms Lake Y 0.3

Invalid Tungatinah Lagoon Y 0.3 0.6

Invalid Valley Pond N 0.3

Invalid Vaucluse Reservoir Y 0.3

Invalid Von Bibras Reservoir Y 0.3








Major dam - impedes

fish passage?



regime)


regime)

Invalid Waratah Reservoir Y 0

1185 Wayatinah Lagoon Y 0.6 0.6

Invalid White Spur Lake Y 0.3 0.3

Invalid Williams Reservoir Y 0.3

Invalid Woods Dam Y 0.3

1061 Woods Lake Y 0.3

Invalid Wynyard Reservoir Y 0.3

Invalid Yolla Reservoir Y 0.3

Appendix 15 – References


15 References

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Technical Report: Appendices Report... · 2014-06-11 · This report has been divided into two parts: 1. Main report and 2. Appendices Citation: DPIW. (2008). Conservation of Freshwater