3 Final Report

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Final Report MDFRC Publication 13/2013

Prepared by: W Paul, R Cook, M Shackleton,

P Suter and J Hawking

Investigating the distribution and

tolerances of macroinvertebrate taxa

over 30 years in the River Murray

MD2258

Investigating the distribution and tolerances of macroinvertebrate taxa

over 30 years in the River Murray MD2258

Final Report prepared for the Murray-Darling Basin Authority by The Murray-Darling Freshwater

Research Centre.

Murray-Darling Basin Authority

Level 4, 51 Allara Street | GPO Box 1801

Canberra City ACT 2601

Ph: (02) 6279 0100; Fax: (02) 6248 8053

This report was prepared by The Murray-Darling Freshwater Research Centre (MDFRC). The aim of

the MDFRC is to provide the scientific knowledge necessary for the management and sustained

utilisation of the Murray-Darling Basin water resources. The MDFRC is a joint venture between the

Murray-Darling Basin Authority, La Trobe University and CSIRO (through its Division of Land and

Water). Additional investment is provided through the Australian Government Department of

Sustainability, Environment, Water, Population and Communities.

For further information contact:

Robert Cook

PO Box 991

Wodonga Vic 3689

Ph: (02) 6024 9650; Fax: (02) 6059 7531

Email: [email protected]

Web: www.mdfrc.org.au

Enquiries: [email protected]

Report Citation: Paul W, Cook R, Shackleton M, Suter P and Hawking J (2013) Investigating the

distribution and tolerances of macroinvertebrate taxa over 30 years in the River Murray MD2258 Final Report prepared for the Murray-Darling Basin Authority by The Murray-Darling Freshwater

Research Centre, MDFRC Publication 13/2013, June, 83pp.

Cover Images: Euston 2010

Photographer: P McInerney

http://www.mdfrc.org.au/

mailto:[email protected]

Copyright and Disclaimer:

© Murray-Darling Basin Authority on behalf of the Commonwealth of Australia. 2012

Graphical and textual information in the work (with the exception of the Commonwealth Coat of

Arms, the MDBA logo and all photographs, graphics and trade marks) may be stored, retrieved and

reproduced in whole or in part, provided the information is not sold or used for commercial benefit

and its source is acknowledged. Reproduction for other purposes is prohibited without prior

permission of the Murray-Darling Basin Authority or the copyright holders in the case of

photographs.

To the extent permitted by law, the copyright holders (including its employees and consultants)

exclude all liability to any person for any consequences, including but not limited to all losses,

damages, costs, expenses and any other compensation, arising directly or indirectly from using this

report (in part or in whole) and any information or material contained in it.

The contents of this publication do not purport to represent the position of the Commonwealth of

Australia or the MDBA in any way and are presented for the purpose of informing and stimulating

discussion for improved management of the Basin's natural resources.

Document History and Status

Version Date Issued Reviewed by Approved by Date Approved Revision type

Draft 8 June 2013 M. Kavanagh Rob Cook 12th June 2013 Copy Edit

Draft 13June 2013 N Ning Rob Cook 13 June 2013 Scientific review

Draft June 2013 Tapas

Biswas

Rob Cook 31 March 2014 Client review

Distribution of Copies

Version Quantity Issued to

Final 1 x Word Doc and PDF Brian Lawrence and Tapas Biswas

Filename and path: projects/MDBA/442 Murray macro tolerances/Knowledge Exchange/

Report/ Final Report

Author(s): Paul W, Cook R, Shackleton M, Suter P and Hawking J

Project Manager: Robert Cook

Client: Murray-Darling Basin Authority

Project Title: Investigating the distribution and tolerances of macroinvertebrate taxa

over 30 years in the River Murray

Document Version: Final

Project Number: M/BUS/442

Contract Number: MD2258

Finalised March 2014

Acknowledgements:

Thanks are extended to WATER ECOscience and Australian Water Quality Centre, SA Water for

providing data to the River Murray Biological (Macroinvertebrate) Monitoring Program. We wish to

acknowledge the MDBA for financial support of this project and the MDBA staff who have been

involved with the monitoring program throughout including; Brian Lawrence, Martin Shafron, Mark

Vanner, Richard Moxham, Kris Kleeman and Rob Kingham. We also wish to thank the many

MDFRC staff who contributed to this monitoring program and to Mark Henderson from MDFRC

Mildura for assistance the spatial data.

Contents Executive summary ................................................................................................................................. 1

Recommendations .................................................................................................................................. 2

Introduction ............................................................................................................................................ 3

Causal modelling ................................................................................................................................. 4

Aims ........................................................................................................................................................ 6

Methods .................................................................................................................................................. 7

Study sites ........................................................................................................................................... 7

Site 800 ............................................................................................................................................ 7

Site 801 ............................................................................................................................................ 7

Site 804 ............................................................................................................................................ 9

Site 808 ............................................................................................................................................ 9

Site 810 ............................................................................................................................................ 9

Site 811 ............................................................................................................................................ 9

Site 812 .......................................................................................................................................... 10

Site 814 .......................................................................................................................................... 10

Monitoring Methods ......................................................................................................................... 10

Report methods................................................................................................................................. 11

Structural causal modelling ............................................................................................................... 11

Data analysis.................................................................................................................................. 12

Transition from drought .................................................................................................................... 13

Results ................................................................................................................................................... 14

Causal modelling ............................................................................................................................... 14

Causal diagram .............................................................................................................................. 14

Exploratory analyses for the community data ............................................................................... 17

Modelling community data as a function of space and time ........................................................ 21

Models for environmental variables .............................................................................................. 25

Transition from drought to flooding ................................................................................................. 28

Water quality and discharge ......................................................................................................... 28

Macroinvertebrates – total community composition .................................................................... 35

Macroinvertebrates – artificial substrate sample ......................................................................... 37

Discussion.............................................................................................................................................. 64

Causal modelling ............................................................................................................................... 64

Transition from drought to flood ...................................................................................................... 67

Progress towards recommendations of Cook et al. (2011) .................................................................. 70

Conclusion ............................................................................................................................................. 72

Appendix I Sorted species scores for PCO1–6 ...................................................................................... 73

References ............................................................................................................................................ 79

List of figures

Figure 1. The process of structural causal modelling ............................................................................. 6

Figure 2. Map of the River Murray Biological Monitoring Program monitoring sites ........................... 8

Figure 3. Causal diagram for the River Murray Biological (Macroinvertebrate) Monitoring Program. 15

Figure 4. Scree plots for the PCO analysis............................................................................................. 18

Figure 5. Space-time plots for PCO axes 1-6. ........................................................................................ 19

Figure 6. Space-time plots for PCO axes 7-12. ...................................................................................... 20

Figure 7. Space-time plots for PCO axes 1–6 with predictions (lines) from the dbRDA model. ........... 22

Figure 8. Space–time plots for PCO axes 1–6 with predictions (lines) from the dbRDA model. .......... 24

Figure 9. Space–time plots for alkalinity, pH, conductivity, turbidity, nitrate + nitrite and FRP .......... 26

Figure 10. Space–time plots for (air) temperature, water temperature, rainfall, and discharge ........ 27

Figure 11. Principal components analysis (PCA) summarising environmental variables for site 801 .. 28







Figure 18. Total number of taxa collected from each site from 1980–2012 ........................................ 35

Figure 19. Total number of taxa collected from each site in each decade between 1980–2012 ........ 36

Figure 20. Relative contribution of each major group to the total taxa richness at each site ............. 37

Figure 21. Artificial substrate sampler taxa richness (mean ± 1SE) sites 800, 801, 804 and 808. ...... 38

Figure 22. Artificial substrate sampler taxa richness (mean ± 1SE) sites 810, 811, 812 and 814. ........ 39

Figure 23. Artificial substrate sampler abundance (mean ± 1SE) sites 800, 801, 804 and 808. ......... 41

Figure 24. Artificial substrate sampler abundance (mean ± 1SE) sites 810, 811, 812 and 814. .......... 42

Figure 25. Artificial substrate sampler EPT richness (mean ± 1SE) sites 800, 801, 804 and 808. ......... 43

Figure 26. Artificial substrate sampler EPT richness (mean ± 1SE) sites 810, 811, 812 and 814. ......... 44

Figure 27. Artificial substrate sampler EPT abundance (mean ± 1SE) sites 800, 801, 804 and 808. ... 45

Figure 28. Artificial substrate sampler EPT abundance (mean ± 1SE) sites 810, 811, 812 and 814. .... 46

Figure 29. Proportional contribution of taxa richness in each major group to the taxa richness in

each decade sites 800, 801, 804 and 808. ............................................................................................ 48

Figure 30. Proportional contribution of taxa richness in each major group to the total taxa richness

sites 810, 811, 812 and 814. ................................................................................................................. 49

Figure 31. Proportional contribution of the abundance in each major group to the total abundance

sites 800, 801, 804 and 808. ................................................................................................................. 51

Figure 32. Proportional contribution of the abundance in each major group to the total abundance

sites 810, 811, 812 and 814. ................................................................................................................. 52

Figure 33. Functional feeding group (FFG) relative abundance sites 800, 801, 804 and 808. ............. 54

Figure 34. Functional feeding group (FFG) relative abundance sites 810, 811, 812 and 814. ............. 55

Figure 35. Non-metric multidimensional scaling plot summarising the total macroinvertebrate

communities from each site from 1980 to 2012 (site centroids). ....................................................... 56

Figure 36. Site 800 macroinvertebrate community structure. ............................................................. 59

Figure 37. Site 801 macroinvertebrate community structure ............................................................. 59

Figure 38. Site 804 macroinvertebrate community structure .............................................................. 60

Figure 39. Site 808 macroinvertebrate community structure ............................................................. 60





Figure 44. 2 stage MDS on 5 year block data indicating similarity of site trajectories ......................... 63

List of tables

Table 1. River Murray Biological (Macroinvertebrate) Monitoring Program site locations. ................. 8

Table 2. Descriptions of each of the nodes (variables) depicted in the causal diagram. ..................... 16

Table 3. ANOVA table for dbRDA model with space and time as predictors. ...................................... 21

Table 4. ANOVA table for dbRDA model ............................................................................................... 23

Table 5. GAM models fitted to environmental variables. ..................................................................... 25

Table 6. ANOSIM results of site pairwise comparisons ........................................................................ 57

Table 7. Spearman rank correlation coefficient results from 2stage MDS ........................................... 63

1

Executive summary The River Murray Biological (Macroinvertebrate) Monitoring Program (hence forth Murray

Monitoring Program) is of international significance. The macroinvertebrate data generated during

the past 33 years from the Murray Monitoring Program encapsulated a period of major climatic,

hydrological, water quality and consumptive use changes. It is rare internationally to have a

consistently sampled data set spanning such major variations in the prevailing climate.

The aim of this study was to analyse data from the Murray Monitoring Program, collected over the

period 1980 to 2012, to identify the drivers of the macroinvertebrate community and suggest how

this information could be used to inform the Basin Plan. This report presents an analysis and

interpretation of the 33 years of Murray Monitoring Program data and includes an additional 3 years

to that reported in Cook et al. (2011) and includes data from the post drought period, 2011 to

2012. Also, macroinvertebrate, water quality, discharge and a range of climatic variable data from

the 1980–2012 monitoring period has been analysed and modelled using structural equation

modelling techniques. This report also include developments in structural causal modelling in

conjunction with multivariate statistical methods for analysing community data with the specific

purpose of building and testing a causal model that could in the future be used to set ecosystem

targets, evaluate management actions, and contribute to the ongoing evolution of the Basin Plan.

The analysis indicated that river flows influenced alkalinity, pH, EC, nitrate (and nitrite), phosphate

and turbidity. In turn, these variables accounted for slightly less than half of the explained variation

in the macroinvertebrate community composition, with the remainder being attributable to other

spatial and temporal processes for which no data were available. The causal diagram that was

constructed as part of this project provided some hypotheses regarding the nature of these other

processes. Specifically, these processes are thought to include the morphology of the river and river

basin (i.e., channel width, depth, and elevation), land use (including riparian vegetation cover), solar

radiation and the operation of water storages.

The previous analysis conducted by Cook et al. (2011)suggested that community composition at sites

along the river became more dissimilar with increasing spatial separation consistent with the River

Continuum Concept (Vannote et al. 1980). While this trend remains evident it is now clear that the

dominant spatial pattern in the community is quadratic in nature, with communities at sites at either

end of the river being more similar to each other than they are to communities at midway sites. In

addition, it is evident that this quadratic pattern began to diminish over time, with the structure of

the communities at the either end of the river converging towards those in the midsection. These

patterns appear to be related to rainfall and perhaps climatic factors more generally.

The community structure changes involved a shift towards small bodied, rapid lifecycle,

opportunistic taxa with broad tolerances for water quality and hydrological conditions and are often

associated with disturbed or harsh environments. The sites of the mid-Murray, to which the sites at

either end of the river system converged, are within the semi-arid zone of the catchment and were

already dominated by tolerant taxa and consequently underwent relatively little change during the

drought period. The transition of the macroinvertebrate communities of the temperate zones of the

catchment, to one reflecting that of the semi-arid zone, is consistent with the climatic conditions and

2

reduced water availability during the drought. Site 800 located at Biggara in the upper Murray is in

relatively pristine condition with no river regulation impacts. This site also underwent some

significant changes in community structure during and after the drought period. This supports the

finding suggesting that climate has been a major driver of the community structure changes

observed in the River Murray.

These results provide some insight into the potential changes likely to occur in the biotic

communities of the River Murray under the predicted climate change scenarios of higher frequency

and intensity of drought. The analysis of the additional data from the 2010 flood period and

following two years suggested that there was evidence of a long term cycle in community

composition with some evidence that communities may be returning to a prior state. These changes

appeared to be climate related. This suggested that the macroinvertebrate communities of the River

Murray may be resilient to major hydrological disturbance. However, insufficient time has passed

since the end of the drought for this cyclical pattern to be confirmed.

Recommendations As a result of the current study we make the following recommendations:

Continue to develop and update the causal model. This will require obtaining:

a) data from MDBA on storage releases and diversions within the catchment of each

monitoring site for the period 1980–2012.

b) data from the Australian Collaboration on Land Use and Management for the sub

catchments of the Murray–Darling Basin over the period 1980–2012, and produce suitable

land use metrics for the catchment of each monitoring site.

c) catchment level data for each monitoring site on rainfall, evaporation, temperature, and

solar radiation for the period 1980–2012.

Test and use an updated model to make predictions regarding the status that the

macroinvertebrate community would have attained under various hydrological scenarios (as

determined by MDBA) throughout the 1980–2012 period and translate this information into

performance targets for the macroinvertebrate community.

Use data from the ongoing River Murray Biological (Macroinvertebrate) Monitoring Program to

evaluate the effectiveness of management actions and enhance system knowledge

Assess River Murray Biological (Macroinvertebrate) Monitoring Program data for a cyclical

pattern in macroinvertebrate community structure in 2015 and or 2020.

3

Introduction The River Murray and its wetlands are unique in flora and fauna, with six sites (the River Murray

Channel; Barmah–Millewa Forest; Gunbower–Koondrook–Perricoota Forest; Hattah Lakes; Chowilla

Floodplain and Lindsay–Wallpolla Islands; Lower Lakes, Coorong and Murray Mouth) chosen as icon

sites for their high ecological, cultural, recreational, heritage and economic value and with most

listed as internationally significant wetlands under the Ramsar convention (MDBA 2011). The lower

River Murray, downstream of Lake Hume, has also been listed as an ‘endangered ecological

community’ in NSW due to degradation in a range of biological and environmental conditions and

the likelihood of the community becoming extinct in its current state if the threatening processes

continue (NSW DPI 2007).

Achieving the Murray-Darling Basin Authority’s (MDBA) Basin Plan objectives requires an

understanding of river ecosystem function and how this has been affected by the way the system is

currently managed. To achieve this, sound ecological and scientific understanding of the effects of

flow and water quality on ecological communities is required. Long term data collected over a range

of hydrological and water quality conditions are pivotal to this understanding. River Murray

Biological (Macroinvertebrate) Monitoring Program has been conducted for over 33 years and has

provided an ecological data set that is unique by world standards. Long term data sets enable

natural seasonal and inter-annual variation in biological communities to be distinguished from

longer term changes, which may be a result of anthropogenic disturbance, climate change or

management interventions (eg: Daufresne et al. 2003; Daufresne et al. 2007; Durance and Ormerod

2007; Chessman 2009).

The Murray Monitoring Program was established to ‘systematically sample and record the aquatic

macroinvertebrate populations of the rivers in such a way as to provide a substantial long-term

biological record to complement the existing physical and chemical data already being collected, and

so provide an additional aid to detecting and interpreting changes in water quality and

environmental conditions in the River Murray and its tributaries(Bennison et al. 1989). The biological

monitoring commenced in July 1980 and was conducted at 14 sites. Eleven sites were along the

River Murray and single sites were on three of its tributaries; the Mitta Mitta River, the

Murrumbidgee River and the Darling River (Bennison et al. 1989). In 1986, the program was reduced

to seven sites (Figure 1). In the 2006 summer, an additional site was added in the upper Murray at

Biggara, to provide data from an unregulated site.

The review conducted by Cook et al (2011) analysed the data from 1980 through to 2009,

encapsulating a period of major changes in climate, hydrology, water quality and consumptive use.

In particular the period from 2006 to the beginning of 2010 was a period of particularly intense

drought and reduction in water availability throughout the Murray–Darling basin. The review

indicated that there had been some major changes in the macroinvertebrate communities which

were consistent throughout the River Murray system, including:

an increase in the taxa diversity,

an increase in abundance,

a decrease in diversity and abundance of the Ephemeroptera taxa,

http://www.mdba.gov.au/programs/tlm/icon_sites/river_murray_channel

http://www.mdba.gov.au/programs/tlm/icon_sites/river_murray_channel

http://www.mdba.gov.au/programs/tlm/icon_sites/barmah_millewa

http://www.mdba.gov.au/programs/tlm/icon_sites/hattah_lakes

http://www.mdba.gov.au/programs/tlm/icon_sites/chowilla_floodplain

http://www.mdba.gov.au/programs/tlm/icon_sites/chowilla_floodplain

4

an increase in diversity and abundance of the Diptera and the other non-insects (excluding

the Crustacea and Mollusca),

a decrease of seasonal variability within each site,

a decrease in the inter-annual variability within each site,

a clear directional shift in the community structure at all sites indicating increasing,

dissimilarity of the community structure between earlier and later periods,

periods of major change and ‘flipping’ of community structure, typically around 1994,

no recovery to the prior state following major flooding in the early 1990s.

Cook et al. (2011) indicated that the majority of the increased diversity and abundance was due to

tolerant, opportunistic taxa that are able to attain high population density when conditions are

favourable for them. These taxa are often associated with reduced flow conditions, such as those

experienced during the 2000s, or other environmental stressors. These altered environmental

conditions are consistent with changes associated with the major drought conditions and reduced

water availability experienced throughout the Murray–Darling Basin, particularly during the 2000s,

and are possibly due to a combination of water management, land management and major climate

shifts.

The latter years of the period encapsulated in Cook et al. (2011) were characterised by an intense

dry period often termed the millennium drought. The drought was broken by a period of extensive

flooding throughout the Murray–Darling system during 2010 and 2011. Major hydrological events

are considered key forces structuring biological communities. An aim of this report is to assess how

the macroinvertebrate communities have responded to this transition from drought to a period of

flooding and if there has been any recovery in the macroinvertebrate communities.

Macroinvertebrates are commonly used to monitor water quality and river health. The group

comprises a wide range of organisms, offering the possibility of detecting responses to different

environmental stresses (Hellawell 1986). In particular, aquatic macroinvertebrates have life-history

strategies intimately linked to the physical and chemical characteristics of the habitat in which they

exist (Townsend et al. 1997; Lytle and Poff 2004; Daufresne et al. 2007). Assessing the relationships

between the macroinvertebrate communities and the hydrological and water quality conditions has

typically been problematic due to the multitude of interacting factors which may impact on

individuals, species and assemblages. Causal modelling is a relatively new technique for determining

causal relationships among biological communities and environmental conditions which could

ultimately be used as a predictive tool to assess potential outcomes from management decisions

and major climatic and hydrological events.

Causal modelling

Structural causal modelling (Pearl 1995; Pearl 2000; Shipley 2000; Shipley 2000; Pearl 2009) evolved

from Bayesian networks (Pearl 1988) and structural equation modelling (Wright 1921; Wright 1934;

Haavelmo 1943). Bayesian networks are popular in natural resource management because of their

utility as a communication and decision support tool (McCann et al. 2006; Pollino and Henderson

2010) and because conditional probability is an effective language for conveying uncertain causal

knowledge. For various reasons, including the fact that functional causal models are believed to be a

5

more natural language for expressing causal knowledge, structural equations are now preferred over

conditional probabilities for describing causal mechanisms (Pearl 1998).

Structural equation modelling (SEM) has been applied to macroinvertebrate data previously (Urban

and Bernhardt 2011; Bizzi et al. 2012), but because SEM is generally limited to data that conform to

linear functional forms and multivariate normal distributions these models have been restricted to

using univariate biotic indices that have been derived from multivariate species data. Recent

advances in causal modelling (Pearl 2000; Shipley 2000), however, have overcome the strict

assumptions underpinning SEM. Structural equation models can now be built and tested using

methods that are commonly used in multivariate ecological research, such as distance-based

redundancy analysis (dbRDA) and canonical correspondence analysis (CCA) (Paul and Anderson in-

press; Paul et al. unpublished). This has paved the way for building SCMs using these more

informative multivariate techniques.

The process of structural causal modelling is depicted in Figure 1. Essentially, SCM is a technique for

translating a causal diagram (conceptual model) into a statistical model for the purposes of testing

causal hypotheses from observational (non-experimental) data and predicting the effects of

interventions. The causal diagram is the linchpin of the method. It expresses expert knowledge in a

qualitative format that is then translated into a set of statistical models and independence

relationships, which are used to check the causal assumptions expressed in the causal diagram.

Building a causal model can involve an iterative process of adjusting the causal diagram on the basis

of results from model testing.

Confounding is the prime concern in any observational study, such as the Murray Monitoring

Program. Importantly, the causal diagram is also used to assess the potential for confounding and

guide the choice of variables (covariates) that need to be observed in order to control confounding

bias.

The aim of this project is to maximize the value of the MDBA's Murray Monitoring Program within

the adaptive management context of the Basin Plan by establishing causal links between actions,

drivers, and the macroinvertebrate community. Specifically, the aim is to utilise developments in

SCM to build a causal model that will help to explain the spatiotemporal patterns in the

macroinvertebrate community that have been observed in the 33 year data record (1980–2012). A

spatiotemporal analysis that models the spatial (and temporal) patterns explicitly should be more

informative than an analysis that treats distance from source as a nominal (categorical) variable (i.e.

an analysis that simply groups the data by site), as was done in the previous analysis (Cook et al.

2011). The underlying causal diagram (conceptual model) will guide future research needed to

address knowledge gaps. Importantly, the causal model could in the future be integrated with the

MDBA's hydrological scenarios (that were developed to inform the sustainable diversion limits used

in the Basin Plan) to set ecosystem targets for the evaluation of management actions and ongoing

evolution of the Basin Plan.

6

Figure 1. The process of structural causal modelling

Aims The specific aims of this report are to:

develop a structural causal model to assess the potential environmental drivers of changes

in the macroinvertebrate community structure,

assess changes in the macroinvertebrate community structure, diversity and abundance of

the macroinvertebrate assemblages in the River Murray following the breaking of the

millennium drought in 2010,

assess distribution, abundance of key taxa in relation to discharge and water quality

parameters,

provide some discussion of the implications for the implementation of the Basin Plan.

7

Methods

Study sites

The monitoring program samples seven sites distributed throughout the River Murray from Biggara

in the upper Murray to Woods Point near where the River Murray enters Lake Alexandrina in South

Australia, and one site is located on the lower Darling River at Burtundy (Bennison et al. 1989; Cook

et al. 2011; MDFRC 2012)(Figure 2; Table 1). Seven of the sites have been monitored since 1980.

These seven sites are Jingellic (site 801) upstream of Lake Hume; Yarrawonga (site 804) downstream

of the impoundment; Euston (site 808) also immediately downstream of an impoundment; Lock 9

(site 811) which is in the weir pool and is a lake environment; and Burtundy (site 810) on the Darling

River, the largest tributary. Two sites, Murtho Park (site 812) and Woods Point (site 814) are in the

South Australian section of the River Murray, with the Woods Point site immediately upstream of

Lake Alexandrina. An additional site was added in the upper Murray at Biggara (site 800) in 2006 to

include an unregulated, near to pristine, site closer to the source of the River Murray.

Site 800

This sampling site is located at the edge of Kosciusko National Park, approximately 30 km

downstream of Tom Groggin Station but upstream of the agriculture conducted in the valley

(S 36°20.457’ E 148°03.360’). The riparian zone width is greater than 30 m on both banks and is

composed of native species with only a small amount of exotic shrubs and ground covers. The

longitudinal extent of the riparian vegetation on both banks is continuous. The structural

composition of both banks is largely gum trees and shrubs and a small proportion of ground covers.

There is no obvious catchment erosion and the only local point source of pollution is a gravel road

leading to the site. There are no dams or barriers present upstream of the site. Survey reach is

150 m long and about 20 m wide. Banks are shallow with the channel width only 5 m wider than the

stream width. Substrate is mainly composed of boulders and cobbles with a small proportion of

pebbles, gravel, sand and silt. There are few snags and/or coarse particulate organic matter at the

site. Current velocity of the reach is generally medium/moderate or fast to very fast flow with a

small proportion with slow to no obvious flow.

Site 801

The sampling site is on the River Murray, 25 km downstream of Jingellic (S 35°57.748’ E 147°30.517’)

immediately upstream of the maximum extent of Lake Hume and well below the junction of the

Swampy Plains and Indi Rivers (River Murray). The survey reach is 150 m long and about 55 m wide.

Banks are moderately steep with the channel width about 15 m wider than stream width. Substrate

is mainly composed of sand with some gravel, pebbles and clay/silt. There are some large snags and

other woody debris present at the site. Current velocity of the reach is generally medium/moderate

or fast to very fast flow with a small proportion with slow to no obvious flow. The riparian zone is

10 m wide on the Victorian bank and only 2 m on the NSW bank and is composed of mainly native

species with some exotic ground covers. The longitudinal extent of the riparian vegetation on the

Victorian bank is continuous, on the NSW bank it is reduced to occasional clumps. The structural

composition of the Victorian bank is mainly trees, ground covers and a small amount of shrubs

whereas the NSW bank is mainly composed of a moderate amount of trees and small amount of

shrubs and ground covers.

8

Figure 2. Map of the River Murray Biological (Macroinvertebrate) Monitoring Program monitoring sites

Table 1. River Murray Biological (Macroinvertebrate) Monitoring Program site locations.

Site

No. River Location

Distance from source

(km)

GDA 94

Latitude

Longitude

800 Murray upstream of Biggara 103 S 36°20.457’ E 148°03.360’

801 Murray downstream of Jingellic 258 S 35°57.748’ E 147°30.517’

804 Murray downstream of Yarrawonga Weir 527 S 36°00.524’ E 145°57.571’

808 Murray downstream of Euston Weir 1389 S 34°35.403’ E 142°45.190’

810 Darling Tulney Point, Burtundy 2607

S 33°45.010’ E 142°15.580’

811 Murray upstream of Lock 9 at Cullulleraine 1737 S 34°11.081’ E 141°36.204’

812 Murray Murtho, upstream of Renmark 1910 S 34°06.8396’ E 140°81.1894’

814 Murray Woods Point, downstream of Murray Bridge

2416 S 35°13.966’ E 139°24.895’

9

Site 804

The sampling site is on the River Murray, 4 km below Yarrawonga Weir, Yarrawonga (36°00.524S’;

145°57.571’ E). Site 804 is the first site within the Murray Plains and mostly cleared for grazing with

some forestry activity on the NSW bank. The Victorian bank is very steep and composed of red clay.

The substrate is mainly composed of sand and clay/silt. Other stream features include a moderate

amount of willow roots, some filamentous algae and loose silt lying on substrate. There are some

coarse particulate organic matter and snags and other woody debris. Current velocity of the reach is

medium/moderate and there are no pools present at the site. The riparian zone width is 5 m on both

banks and is composed of mainly exotic trees and ground covers. There is a high density of willows

(Salix spp.) on the Victorian bank and the longitudinal extent of riparian vegetation is continuous,

whereas on the NSW bank it is reduced to occasional clumps.

Site 808

The sampling site is on the River Murray, 3 km downstream of Euston Weir, Euston (34°35.403’S;

142°45.190’E). The stream banks are steep with the channel width about 50 m wider than the

stream width. Substrate is mainly composed of sand with some clay/silt. Other stream features

include some submerged macrophytes and some moss, filamentous algae and loose silt lying on

substrate. There is a moderate amount of snags and other woody debris. The current velocity of the

reach is medium/moderate flow and there are no pools present at the site. The riparian zone is 30 m

wide on both banks and is composed of native trees and shrubs and some exotic ground cover. The

longitudinal extent of the riparian vegetation of the Victorian bank is continuous whereas on the

NSW bank it is semicontinuous. The structural compositions of both banks are mainly trees with

some shrubs and ground covers.

Site 810

The sampling site is on the Darling River at Burtundy, (33°45.010’ S; 142°15.580’E). The site is

directly downstream of a small weir, with the surrounding area mostly cleared for grazing and

intensive horticulture (vineyards). Banks are steep with the channel width about 20 m wider than

the stream width. Substrate is mainly composed of clay/silt and sand. Other stream features include

a moderate amount of loose silt lying on substrate and a stand of emergent macrophytes. There is a

moderate amount of snags and other woody debris present at the site. Current velocity is zero to

slow flow and the reach is basically one long pool. The riparian zone is 5 m wide for both banks and

is composed of native trees and shrubs and a large proportion of exotic ground cover. The

longitudinal extent of the riparian vegetation on the Victorian bank is semi-continuous whereas the

NSW bank is continuous. The structural composition of the Victorian bank is mainly trees with some

shrubs and groundcovers whereas the structural composition of the NSW bank is composed of only

trees.

Site 811

The sampling site is on the River Murray, 1 km upstream of Lock 9 Weir and is a lentic environment

due to being within the weir pool (34°11.081’S; 141°36.204’E). The survey reach is 150 m long and

the stream width is approximately 170 m. Banks are shallow with the channel width the same as the

stream width. Substrate is composed mostly of sand with some clay/silt. Other stream features

include some macrophytes and the presence of filamentous algae and loose silt lying on substrate.

10

There is a small amount of coarse particulate organic matter, snags and other woody debris. The

riparian zone is 30 m wide on both banks and is composed of native trees and shrubs with some

exotic ground cover. The Victorian bank has a large proportion of trees, a small amount of shrubs

and a moderate amount of ground covers. The NSW bank is composed almost entirely of trees.

Site 812

The site at Murtho is about 12 km north of Paringa, via Renmark (S 34o 04.106’, E 140o 48.668’). This

site represents the gorges section of the Mallee region. The River Murray meanders through a large

floodplain valley, which is embanked by a 15–30 m high limestone cliff on the south southeast side;

the river flows close to the cliff at this site on the southern bank. In this region and on the western

bank there are many elongated wetlands, with native vegetation dominated by Eucalyptus

camaldulensis, Acacia stenophylla and some exotics (e.g. Salix babylonica). Bank vegetation cover is

high (>80%) and Phragmites, Azolla and Vallisneria are the most common macrophytes found at this

site.

Site 814

The sampling site is on the River Murray at Woods Point, about 16 km south of Murray Bridge at the

end of Craton Lane (S 35o 13.966, E 139o 24.895’). The site is located in a small irrigation pumping

bay. The site represents the lowest site on the River Murray and is about 25 km north of Lake

Alexandrina. The influence of flow is considerably reduced at the new site, as well as an increase in

the riparian vegetation of Weeping willows (Salix babylonica).

Site 814 has been relocated twice. The original site was located at the Woods Point boat ramp,

which became unsuitable, due to vandalism and tampering of the artificial substrate samplers (ASS),

and was subsequently moved 2.5 km downstream in autumn 1998. Due to irrigation works at this

second site the site was moved in June 2008 to its present site 1 km further downstream (AWQC,

2011).

Monitoring Methods Monitoring sites were sampled in winter (May–June) and summer (October–December). A

combination of artificial substrate samplers (ASS) and sweep net (SN) sampling were used to assess

the macroinvertebrate communities at each site.

An artificial substrate sampler (ASS) is a device placed in an aquatic ecosystem to assess colonisation

by indigenous organisms. Each ASS consisted of a cylinder of black plastic “Gutterguard” (mesh size

10 mm), 180 mm high and 240 mm in diameter containing one and a quarter new, commercially

available onion bags (total area 1000 x 420 mm = 0.42 m²) as substratum. A clean river rock is used

for ballast. The bottom of the basket is secured to the cylinder using a nylon cord which is also used

to seal the top of the ASS when it is pinched closed. The design of the ASS:

provides unrestricted and uniform space for colonisation to burrowing, drifting and actively

swimming organisms,

provides for a complete range of water movement regimes,

ensures the surfaces are suitable for the development of periphyton and collection of

detritus and sediment, thus providing food as well as shelter for the macroinvertebrates,

11

ensures it is relatively independent of local substrata characteristics,

operate effectively in all types of riverine habitats (Bennison et al. 1989).

The ASS is placed on the river bed at depths less than 1.5 m to ensure that it remains within the

photic zone and is subsequently colonisationed by macroinvertebrates. The ASS is retrieved after a

deployment period of six weeks.

A single sweep net sample is collected from all of the stream’s major habitats (e.g. macrophytes, leaf

packs, snags, water surface) at a site to collect invertebrates that are not expected to colonise the

ASS (Bennison et al. 1989), thereby providing a more complete assessment of site diversity.

In the laboratory, each sample is sub sampled using a Marchant sub sampler (Marchant 1989) and

sorted to order prior to identification. Macroinvertebrates are identified to species level where

possible and enumerated. The exceptions to this are the Bryozoa, Nematoda, Nemertea (to phylum),

Oligochaeta, Hirudinea, Polychaeta (to class) and Acarina (to order).

Report methods

This report is in two parts:

The development of a causal model to assess causal relationships among macroinvertebrate

assemblages and environmental conditions.

Assessment of macroinvertebrate communities in response to the transition from drought to

flood and determine if there has been recovery in the macroinvertebrate communities.

Structural causal modelling Structural causal modelling (Pearl 1995; Pearl 2000; Shipley 2000; Shipley 2000; Pearl 2009) involves

the following:

Describing the (composite) causal hypothesis in a causal diagram (conceptual model).

Determining from the causal diagram whether confounding can be controlled with the

available data (i.e. whether the causal effect of interest is “identifiable”) and, if not,

pinpointing those variables for which data are needed.

Translating the causal diagram into a set of structural equations and conditional

independence (d-separation) statements.

Fitting and checking statistical models for the set of structural equations.

Using the fitted structural equations to test the conditional independencies entailed by the

causal diagram (i.e. the missing links in the causal diagram).

As noted in the Introduction, there were a number of potentially confounding variables for which no

data were available, so the effect of discharge on macroinvertebrates was not identifiable and there

were no conditional independence relationships among the observed variables that could enable

testing of the causal structure. However, it was possible to derive the set of structural equations for

the incomplete model, and these equations still had some explanatory power that demonstrated the

potential of this method for the Murray Monitoring Program.

12

Data analysis

The square root transformed multivariate species abundance data were first transformed into a set

of principal co-ordinate (PCO) axes, via the Bray-Curtis dissimilarity measure, where the PCO scores

represented the state of the community at a point in time and space. If there are systematic

patterns in the community data they are usually revealed in the first few PCO axes, where each

nontrivial PCO axis reflects an underlying environmental gradient (Gauch et al. 1977; Gauch 1982;

Faith et al. 1987; ter Braak and Prentice 1988; Legendre and Legendre 2012). Various diagnostic

methods were used to help identify the number of potentially nontrivial PCO axes (Paul and

Anderson in-press), including the broken stick method (Frontier 1976), bootstrapped eigenvalue-

eigenvector method (Jackson 1993), and the holistic and conditional random permutation methods

(Paul and Anderson in-press).

Spatiotemporal patterns in the “nontrivial” PCO axes were then explored via space-time plots with

locally weighted scatterplot smoothers (LOWESS) overlayed and correlations among PCO axes and

environmental variables were explored with scatterplot matrices. Species scores (which are

averages of the PCO sample scores weighted by the species abundance) were produced for a subset

of PCO axes. The species scores can be thought of as indicating the “optimum” position of each

taxon along the underlying environmental gradient (ter Braak and Prentice 1988). The Pearson

correlations between the PCO axis and each environmental variable are provided above each plot to

assist with the interpretation of the underlying gradient.

Distance-based redundancy analysis (dbRDA) was used to model the community data as a function

of the environmental variables and space (distance from source) and time (Legendre and Anderson

1999; Paul and Anderson in-press; Paul et al. unpublished). Prior to this, however, the data were

modelled as a function of space and time only to help select appropriate functional forms for these

two variables. The results of exploratory analyses were instrumental in the choice of functional

forms for environmental variables and space and time. The assumption of exchangeability, which

underpins the permutation tests used in dbRDA, was checked by estimating the Mantel correlogram

(Legendre and Legendre 2012) for the full set of PCO residual column vectors, and by plotting the

residuals versus fitted values for the first few PCO axes.

Generalised additive models were used for all other structural equations in the causal model.

Residual diagnostic plots and AIC guided the choice of link functions and error distributions in these

models.

Statistical analyses were performed using the R language and software environment for statistical

computing and graphics (R Development Core Team 2013). The following R packages were used:

‘vegan’ (Oksanen et al. 2013), ‘BiodiversityR’ (Kindt and Coe 2005), ‘mgcv’ (Wood 2006), and ‘car’

(Fox and Weisberg 2011).

13

Transition from drought

Due to changes in taxonomy and staffing over the past 30 years it was necessary to standardise the

taxonomy of the data set to the pre 1985 taxonomy to ensure the integrity of the analysis. In most

cases this was to genera; however, with some groups such as the Caenidae mayflies it was necessary

to bring the data to family level. It is for this reason that diversity values may differ from previous

reviews of the Murray Monitoring Program. Data were also summarised by major group, which

included the major insect orders plus the non-insect groups— Crustacea and Mollusca. In addition, a

range of other primitive invertebrate, non-insect groups, for which the taxonomy is poorly known,

were combined into a single grouping called ‘non-insect others’. This group included: Acarina,

Spongillidae, Hydrozoa, Collembolla, Oligochaeta, Hirudinae, Gordioidae, Nematoda, Nemertea,

Tardigrada, Temnocephala and Tricladida.The total taxa richness and the proportion of the total

richness within each major group was calculated by combining the taxa from both ASS and SN

samples from a site. Relative abundances and statistical analyses of changes in richness and total

abundance were generated from the ASS data only. Multivariate community structure analysis was

conducted on the abundant taxa which were determined as the taxa that occurred in at least 10% of

the samples at any one site (Bennison et al. 1989) and in this study numbered 125 taxa including site

800 and 114 taxa excluding site 800 as per Cook et al. (2011).

For each site, a range of indices was calculated from the macroinvertebrate data to enable

comparison among and within sites over time. These included: total taxon richness (diversity);

abundance; Ephemeroptera, Plecoptera and Trichoptera (EPT) diversity and abundance; functional

feeding group (FFG) composition; and multivariate community structure. These indices were

calculated and summarised as means and standard errors for each seasonal sampling and presented

graphically for visual inspection of patterns.

Statistical analyses were conducted using the Permanova+ v1.0.3 add-on to the PrimerE v6 statistical

package. A permanova analysis was conducted to assess temporal changes within each site

individually using a two factor design (site*5 year period). Data was summarised into 5 year periods

with sample day replicates averaged and this value used as replicates in the analysis of the 5 year

period. Pairwise comparison was used to determine which time periods (5 year period) differed. An

assessment of community structure variability, as carried out in Cook et al. (2011), was not

conducted as insufficient data was available from the post drought period to enable a valid

comparison.In addition, multivariate community structure among 5 year periods at a site was

represented graphically with non-metric multidimensional scaling (nMDS) using the PrimerE V6

statistical package (Clarke and Warwick 2001).

14

Results

Causal modelling

Causal diagram

The preliminary causal diagram in Figure 3 encapsulates the geomorphic, climatic and anthropogenic

causal processes thought to affect the macroinvertebrate community over space and time in the

Murray Monitoring Program. A brief description and justification for the nodes and arrows in Figure

3 is outlined in Table 2.

The green-filled nodes in Figure 3 signify the variables that have been consistently recorded in the

monitoring program. Some variables such as alkalinity, nitrate and orthophosphate (the

orange-filled nodes) were recorded only occasionally and were not included in the modelling of the

macroinvertebrate community composition.

From the causal diagram in Figure 3 it is clear that there are potentially confounding variables for

which data is needed in order to obtain unbiased estimates of the effects of certain variables on the

macroinvertebrate community and its proximal causes. These are the red-filled nodes in Figure 3.

Storage releases, diversions and land use at the catchment scale are potentially confounding

because they influence discharge as well as nutrient loads in the causal diagram. For example, in

order to estimate the effect that discharge has on nutrient concentrations and macroinvertebrates it

would be necessary to adjust for the confounding effects of storage releases and land use.

Furthermore, storage releases and diversions (in the catchment of each monitoring site) are two of

the most important variables for the model because these are the targets for intervention.

Other variables identified in Figure 3—such as substrate composition, CPOM:FPOM (the ratio of

coarse to fine particulate organic matter) and current velocity—would need to be measured in the

future if achieving a fuller explanation of the changes in the macroinvertebrate community is

desired.

Despite the absence of data on some important variables, the available data were used in this

project to develop a spatiotemporal causal model (although incomplete, technically a “non-

identifying” model) using the multivariate species data to demonstrate the feasibility of this method.

This model will still have some explanatory power, though its value as a predictive tool will be

limited until data on the potentially confounding variables is incorporated.

15

Figure 3. Causal diagram for the River Murray Biological (Macroinvertebrate) Monitoring Program, where the green nodes signify variables that have been recorded

consistently, the orange nodes indicate variables for which the data is sparse, and the red nodes indicate variables for which data is required.

16

Table 2. Descriptions of each of the nodes (variables) depicted in the causal diagram of Figure 3.

Node Description

Algae

Algal biomass (chlorophyll-a) is influenced by phosphate and nitrate concentrations, storage releases (Baldwin et al. 2010), light level (Dauta et al. 1990; Solovchenko et al. 2008), and current velocity (Whitford and Schumacher 1961). Algae provide habitat structure for macroinvertebrates and have been shown to influence the abundance and body size of macroinvertebrates (Downes et al. 1998).

Alkalinity, inorganic nutrients, salt, sediment, and organic loads

Loads may be influenced by geomorphology, storage releases (Baldwin et al. 2010), land use (Allan 2004), riparian vegetation (Osborne and Kovacic 1993; Daniels and Gilliam 1996), and rainfall (Schulz 2001). Macroinvertebrate production has been shown to respond to these physical and chemical properties (Krueger and Waters 1983; Hart et al. 1991; Boulton and Lake 1992; Buss et al. 2004).

Alkalinity, phosphate, nitrate, salinity, turbidity, and CPOM:FPOM

These are influenced by their respective loads and the discharge (Johnson et al. 1969).

Catchment area, channel width & depth

The catchment area, channel width, and channel depth increase with distance from source. As such they play a role in determining channel form (Resh et al. 1988) and in turn the availability of suitable habitat for macroinvertebrates(Malmqvist and Otto 1987; Fuller and Rand 1990; Mérigoux and Dolédec 2004).

Climate Climate is a latent variable that is manifested in the temperature, rainfall, and solar radiation variables. Climate in the Murray–Darling Basin varies systematically both spatially and temporally (Chiew et al. 2008), hence the arrows from the space and time nodes to the climate node.

Current velocity Current velocity is determined by the discharge, channel width and depth, and elevation. Current velocity plays an important role in determining habitat suitability for macroinvertebrates (Ciborowski and Craig 1989; Bunn and Arthington 2002).

Demand Demand for water will vary with land use, catchment area, climate, and allocations and are adjusted according to storage levels.

Discharge Discharge is influenced by catchment area, land use, rainfall, and (net) storage releases and can affect the abundance, diversity and distribution of macroinvertebrates (Cobb et al. 1992; Growns and Growns 2001; Robinson 2012).

Dissolved oxygen Dissolved oxygen is influenced by the organic load and algal biomass. Macroinvertebrates are directly influenced by concentration of DO (Connolly et al. 2004).

Diversions The volume of water extracted for consumptive use. Diversions influence flow rate and can affect salt loads.

Elevation Height above sea level, which decreases with distance from source, influences physical properties such as current velocity and substrate type, thereby effecting macroinvertebrate community composition.

Land use % Land use percentage in the catchment of each monitoring site. Land use will vary systematically with elevation and geomorphology (hence the connection to the space node), and with time.

Macroinvertebrate community composition

The macroinvertebrate community is influenced by water temperature (Vannote and Sweeney 1980), pH(Allard and Moreau 1987; Courtney and Clements 1998), algal biomass (Downes et al. 1998), salinity (Hart et al. 1991), dissolved oxygen (Connolly et al. 2004), turbidity (Henley et al. 2000; Van de Meutter et al. 2005), CPOM:FPOM (Vannote et al. 1980; Boulton and Lake 1992), and substrate composition (Buss et al. 2004).

Net storage release The volume of water released from storages in the catchment of each monitoring site less the volume diverted for

17

Node Description

consumptive uses.

pH pH is influenced by alkalinity and algal biomass and can has a direct influence on macroinvertebrate communities (Courtney and Clements 1998).

Rainfall Rainfall in the catchment for each monitoring site influences discharge as well as loadings of organics, sediments, and salt.

Riparian vegetation The riparian vegetation in the catchment of each monitoring site can influence organic load (Osborne and Kovacic 1993) and light availability.

Salt interception There are 18 salt interception schemes in the Murray-Darling catchment. Salt interception schemes reduce salt loading and maintain salinity levels by intercepting saline groundwater.

Space (dist) The distance of each monitoring site from the source of the River Murray.

Storage level Water storage levels will vary spatially and temporally.

Storage releases Outflows from storages in the catchment of each monitoring site. Storage releases are determined by the operational rules governing demand.

Substrate Substrate composition is influenced by the current velocity (Bunn and Arthington 2002).

Temperature and solar radiation

Regional air temperature and solar radiation influence local water temperatures, which effect macroinvertebrate community composition (Vannote and Sweeney 1980).

Time Samples were taken biannually (June and December) each year from June 1980 to December 2009, and “Time” represents the chronological sequence of sampling occasions (1, 2,…, 60).

Water temperature Water temperature at a monitoring site is influenced by the air temperature, light level, and possibly storage releases. Water temperature has been shown to influence macroinvertebrate community composition (Vannote and Sweeney 1980).

Exploratory analyses for the community data

Scree plots showing the results of the PCO diagnostics are given in Figure 4. These results suggest there are between 2

and 12 nontrivial axes out of a total of 268. The first six of these (which account for 32% of the total variation) are

plotted as a function of space (distance from source) and time in Figure 5. The next six, which account for another 10%

of the total variation, are shown plotted in Figure 6. In each of these graphs the Pearson correlations between the PCO

axis and the environmental variables are provided above the graph. The full set of species scores for the first six PCO

axes are provided in Appendix I.

18

Figure 4. Scree plots for the PCO analysis with (A) the broken-stick model, (B) 95% bootstrap confidence intervals for eigenvalues,

(C) 95% permutation confidence intervals from a null model of no structure, where percentages explained by each axis are taken

holistically as a fraction of the total variation; and (D) as in (C), except percentages explained by each axis are taken conditionally

as a fraction of the variation remaining after removing that which is explained by prior axes.

19

Figure 5. Space-time plots for PCO axes 1-6 with locally weighted scatterplot smoother (LOWESS) lines overlayed. Species scores

for a subset of taxa are plotted on the secondary y-axis, and Pearson correlations between each PCO axis and the environmental

variables are given above each graph.

20

Figure 6. Space-time plots for PCO axes 7-12 with locally weighted scatterplot smoother (LOWESS) lines overlayed. Species scores

for a subset of taxa are plotted on the secondary y-axis, and Pearson correlations between each PCO axis and the environmental

variables are given above each graph.

21

Modelling community data as a function of space and time

Overlaid on the space–time plots in Figure 7 are the regression lines corresponding to the dbRDA

regression equations:

2 2 3

1 2 3 4 5

2 3

6 7 8

2 2 2 2 2

9 10 11

j j j j j j

j j j

j j j j

PCO dist dist time time time

dist time dist time dist time

dist time dist time dist time

(1)

where PCOj is the jth rank-

terms in the model account for the quadratic relationship between community composition and

distance from source (particularly apparent in PCO1), the polynomial (wiggly) trend over time, and the

interaction between the two. All parameter estimates were statistically significant at the 0.05 level

(Error! Reference source not found.Table 3), and the dbRDA model explained 23% of the total variation.

There is some evidence from residual diagnostic plots that Site 804 (distance from source=527 km) does

not entirely conform with the quadratic spatial component of the model, but adding third degree

polynomial terms for distance reduced the (pseudo) BIC. In addition, there may be some evidence of a

systematic temporal pattern in the residuals for PCO5, but overall the correlations in the Mantel

correlogram were very low (about 0.02 or less).

Table 3. ANOVA table for dbRDA model with space and time as predictors.

Source Df Var F N.Perm Pr(>F)

dist 1 12.757 21.6066 99 0.01 **

dist2 1 9.751 16.5151 99 0.01 **

time 1 7.434 12.5918 99 0.01 **

time2 1 1.641 2.7794 99 0.01 **

time3 1 2.645 4.4799 99 0.01 **

dist×time 1 2.88 4.8778 99 0.01 **

dist×time2 1 1.915 3.2442 99 0.01 **

dist×time3 1 1.874 3.1736 99 0.01 **

dist2×time 1 2.794 4.732 99 0.01 **

dist2×time2 1 0.916 1.5522 99 0.03 *

dist2×time3 1 1.03 1.7445 99 0.01 **

residual 257 151.735

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

22

Figure 7. Space-time plots for PCO axes 1–6 with predictions (lines) from the dbRDA model with space and time

as predictors.

23

Modelling community data as a function of environmental variables

In accord with the relationships depicted in the causal diagram (Figure 3), the macroinvertebrate

community composition was modelled with dbRDA as a function of pH, EC, turbidity, water

temperature, discharge, rainfall, and both distance and time. Scatter plots of PCOs against

environmental variables gave no reason to transform the environmental variables, and distance and

time were included in the dbRDA model in the same form as in Equation (1). All terms in the dbRDA

model were statistically significant at the 0.05 level, and the model explained 28% of the total

variation—the environmental variables accounted for slightly less than half of this, while the remainder

was due to other spatial and temporal processes. As per the spatiotemporal model in the previous

section there was some evidence of systematic patterns in the residuals of PCO3 and 5. Fitted axes are

overlaid on the space–time plots of the first six PCO axes in Figure 8. Shown above each plot is the

percentage of the explained variation accounted for by each variable in the model (obtained by fitting

separate linear models to each PCO axis).

Table 4. ANOVA table for dbRDA model that includes environmental variables as well as space and time.

Source Df Var F N.Perm Pr(>F)

pH 1 7.101 12.5241 99 0.01 **

ec 1 6.581 11.607 99 0.01 **

turb 1 1.754 3.0943 99 0.01 **

wtemp 1 4.613 8.1355 99 0.01 **

flow 1 2.318 4.0883 99 0.01 **

rain 1 2.638 4.653 99 0.01 **

dist 1 4.771 8.4145 99 0.01 **

dist2 1 5.18 9.1353 99 0.01 **

time 1 6.148 10.8432 99 0.01 **

time2 1 1.519 2.6793 99 0.01 **

time3 1 2.028 3.5761 99 0.01 **

dist×time 1 2.608 4.6005 99 0.01 **

dist×time2 1 2.057 3.6273 99 0.01 **

dist×time3 1 1.782 3.1433 99 0.01 **

dist2×time 1 2.217 3.9097 99 0.01 **

dist2×time2 1 0.804 1.4175 99 0.05 *

dist2×time3 1 0.938 1.6538 99 0.02 *

residual 251 142.315

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

24

Figure 8. Space–time plots for PCO axes 1–6 with predictions (lines) from the dbRDA model that includes environmental variables

as well as space and time. Shown above each plot is the percentage of the explained variation accounted for by each variable in

the model (obtained by fitting separate linear models to each PCO axis).

25

Models for environmental variables

Environmental variables were modelled as a function of their predecessors in accordance with the

causal diagram (Figure 3) using Generalised Additive Models (GAMs). Following the notation of Wood

(2006) the models that were fitted are listed in Table 5. Predictions from these models are overlaid on

the space–time plots in Figure 9 and

Figure 10.

Table 5. GAM models fitted to environmental variables.

GAM Distribution Explained deviance

1 2 3 41 ,E alk f flow f dist f time f dist time

Gamma 0.871

1 2 3 4

5

1

,

E pH f alk f wtemp f dist f time

f dist time

Gamma 0.824

1 2 3 4

5

1

,

E ec f flow f rain f dist f time

f dist time

Gamma 0.971

1 2 3 4

5 6

log

,

E turb f ec f flow f rain f dist

f time f dist time

Gamma 0.846

1 2 3 4log ,E nox f flow f dist f time f dist time

Gaussian 0.616

1 2 3 4log ,E frp f flow f dist f time f dist time

Inverse Gaussian

0.310

1 2 3 41 ,E wtemp f temp f dist f time f dist time

Gamma 0.870

1 2 3 4 ,E flow f rain f dist f time f rain dist

Inverse Gaussian

0.531

1 2 3log ,E rain f dist f time f dist time

Gamma 0.383

1 2log cos 2 / 2E temp time f dist f time

Gaussian 0.929

26

Figure 9. Space–time plots for alkalinity, pH, conductivity, turbidity, nitrate + nitrite, and filterable reactive

phosphorus, with predictions (lines) from the GAMs overlaid.

27

Figure 10. Space–time plots for (air) temperature, water temperature, rainfall, and discharge, with predictions

(lines) from the GAMs overlayed.

28


Water quality and discharge

Environmental variables were summarised using principle components analysis (PCA) and combined into

5-year blocks. Vectors on the PCAs indicate the direction in which the environmental variables are

related to the sample periods. At all sites the period associated with the drought 2000–2010 period

were at one extreme of the PCA plot typically separating out along PCA 1 and associated with decreased

discharge and in most cases reduced EC (site 814 elevated EC). There was a clear shift in the

environmental parameters following the onset of wet conditions during the 2010s and the

environmental conditions became more similar to the periods prior (1980–1999) to the intense drought

of the 2000–2009 period (Figure 11; Figure 12; Figure 13; Figure 14; Figure 15; Figure 16; Figure 17).

Figure 11. Principal components analysis (PCA) summarising environmental variables for site 801 during each 5

year period. Vector length and direction indicates the contribution of each environmental variable to the PC

axis.

-6 -4 -2 0 2 4

PC1

-2

0

2

4

PC

2

5 year period

80-84

85-89

90-94

95-99

00-04

05-09

10-12

Discharge

EC

Water Temp

Turbidity

pH

29



axis.

-4 -2 0 2 4

PC1

-4

-2

0

2

4P

C2

DischargeEC

Water Temp

TurbiditypH

5 year period

80-84

85-89

90-94

95-99

00-04

05-09

10-12

30



axis.

-3 -2 -1 0 1 2

PC1

-2

-1

0

1

2

3

4P

C2

Discharge

EC

Turbidity

5 year period

80-84

85-89

90-94

95-99

00-04

05-09

10-12

31



axis.

-4 -2 0 2 4 6

PC1

-2

0

2

4P

C2

Discharge

EC

Water Temp

Turbidity

pH 5 year period

80-84

85-89

90-94

95-99

00-04

05-09

10-12

32

Figure 15. Principal components analysis (PCA) summarising environmental variables for site 811during each 5


axis.

-4 -2 0 2 4

PC1

-2

0

2

4P

C2

Discharge

EC

Water Temp

Turbidity

pH5 year period

80-84

85-89

90-94

95-99

00-04

05-09

10-12

33



axis.

-4 -2 0 2 4

PC1

-2

0

2

4P

C2

Discharge

ECWater Temp

Turbidity

pH

5 year period

80-84

85-89

90-94

95-99

00-04

05-09

10-12

34



axis.

-4 -2 0 2 4

PC1

-2

0

2

4P

C2 Discharge

EC

Water Temp

Turbidity

pH

5 year period

80-84

85-89

90-94

95-99

00-04

05-09

10-12

35

Macroinvertebrates – total community composition

A total of 225 taxa (based on 1985 taxonomy) were collected (artificial substrate and sweep net

samples) and identified as part of the Murray Monitoring Program between 1980 and 2012. Total taxa

richness was greatest at site 801, with 153 taxa. Among all other sites total taxa richness was similar,

ranging from 106 taxa at site 811 to 117 taxa at site 800 (Figure 18). The taxa richness increased during

the 1990s and 2000s at all sites but was reduced during the 2010s (Figure 19). However, this reduction

in taxa richness is likely due to the lower number of sampling events so far during the 2010s. The

taxonomic group contributing most to the diversity were the Diptera (true flies) at all sites (Figure 20).

The Ephemeroptera, Plecoptera and Trichoptera (EPT) taxa contributed to approximately 42% of the

taxa richness at site 800, 23% at site 801 and between 12% and 15% at all other sites. Coleoptera

typically contributed around 15% at all sites other than site 800 in which Coleoptera contributed

approximately 7%. The contribution of Crustacea to the total diversity increased downstream ranging

from approximately 2% at site 800 to 10% at site 814.

Figure 18. Total number of taxa collected from each site from 1980–2012 ( site 800 2006–2012) using artificial

substrate samplers and hand net.

Site

800 801 804 808 810 811 812 814

Ta

xa

ric

hn

ess

0

20

40

60

80

100

120

140

160

36

Figure 19. Total number of taxa collected from each site in each decade (as per Cook et al. 2011) between 1980–

2012 (site 800 2006–2012) using artificial substrate samplers and hand net.

Site

800 801 804 808 810 811 812 814

Taxa r

ichness

0

20

40

60

80

100

120

140

1980s

1990s

2000s

2010s

37

Figure 20. Relative contribution of each major group to the total taxa richness at a site using artificial substrate

samplers and hand net for the period 1980–2012 (site 800 2006–2012).

Macroinvertebrates – artificial substrate samples

Taxa richness

Mean taxa richness (TR) remained relatively consistent between 2006 and 2012 at site 800 with no shift

in response to altered climatic conditions in 2010 (Figure 21). At site 801 TR underwent a rapid increase

between 1992 and 1994 generally remaining at the increased levels until 2006 when TR decreased and

has remained steady through to 2012 (Figure 21). TR at site 804 underwent a steady increase from 1993

through to 2006 and has then remained steady (Figure 21). TR at site 808 underwent a slight decrease

in 1993, then increased through to 2009 and has subsequently remained relatively steady (Figure 21).

Sites 810 and 811 TR has undergone slight variation over time reaching maximum in 2008 and 2010

respectively (Figure 22). TR increased from 1995 and 1994 at sites 812 and 814 respectively (Figure 22).

At both sites TR peaked in 2006 before undergoing a slight reduction at both sites.

Site

800 801 804 808 810 811 812 814

% d

ivers

ity

0

20

40

60

80

100

Ephemeroptera

Plecoptera

Trichoptera

Diptera

Coleoptera

Hemiptera

Odonata

Other insects

Mollusca

Crustacea

Non-insect others

38

Figure 21. Artificial substrate sampler taxa richness (mean ± 1SE) for each sampling trip from 1980 to 2012 for

sites 800, 801, 804 and 808.

801

78 80

82

84

86

88

90

92

94

96

98

00

02

04

06

08

10

12

14

0

10

20

30

40

800

04

06

08

10

12

14

Ta

xa

ric

hn

ess

0

5

10

15

20

25

30

35

40

804

Date

78 80

82

84

86

88

90

92

94

96

98

00

02

04

06

08

10

12

14

Ta

xa

ric

hn

ess

0

10

20

30

40

808

Date

78 80

82

84

86

88

90

92

94

96

98

00

02

04

06

08

10

12

14

0

10

20

30

40

39

Figure 22. Artificial substrate sampler taxa richness (mean ± 1SE) from each sampling trip from 1980 to 2012 for

sites 810, 811, 812 and 814.

810

78 80

82

84

86

88

90

92

94

96

98

00

02

04

06

08

10

12

14

Ta

xa

ric

hn

ess

0

10

20

30

40

811

78 80

82

84

86

88

90

92

94

96

98

00

02

04

06

08

10

12

14

0

10

20

30

40

812

Date

78 80

82

84

86

88

90

92

94

96

98

00

02

04

06

08

10

12

14

Ta

xa

ric

hn

ess

0

10

20

30

40

814

Date

78 80

82

84

86

88

90

92

94

96

98

00

02

04

06

08

10

12

14

0

10

20

30

40

40

Community abundance

Mean total abundance (mean abundance) from the ASS was typically consistent over time at site 800

with the exception of two peaks in December 2009 and June 2011 (Figure 23). Mean abundance at site

801 generally underwent a steady increase from 1994 through to 2007 followed by a slight decrease in

abundance (Figure 23). Mean abundance at site 804 underwent a general increase from 1996 through

to 2008 followed by a slight decrease (Figure 23). Mean abundance at site 808 was variable during the

study period fluctuating between periods of higher and lower abundance (Figure 23). Abundance at site

810 remained largely consistent over time, however there were distinct periods when abundance was

substantially higher (Figure 24). Abundance fluctuated through a relatively narrow range during the

study period at Site 811 with occasional periods of elevated abundance (Figure 24). At site 812, mean

abundance appeared to undergo an increase after 1994 which was maintained until 2009 when

abundance decreased slightly (Figure 24). Similarly, at site 814 mean abundance increased in 1994 and

appeared to decrease slightly in 2010 (Figure 24).

41

Figure 23. Artificial substrate sampler abundance (mean ± 1SE) for each sampling trip from 1980 to 2012, for

sites 800, 801, 804 and 808.

801

78 80

82

84

86

88

90

92

94

96

98

00

02

04

06

08

10

12

14

0

1000

2000

3000

4000

5000

6000

7000

800

04

06

08

10

12

14

Ab

un

da

nce

0

5000

10000

15000

20000

804

Date

78 80

82

84

86

88

90

92

94

96

98

00

02

04

06

08

10

12

14

Ab

un

da

nce

0

1000

2000

3000

4000

5000

6000

7000

808

Date

78 80

82

84

86

88

90

92

94

96

98

00

02

04

06

08

10

12

14

0

2000

4000

6000

8000

10000

42

Figure 24. Artificial substrate sampler abundance (mean ± 1SE) from each sampling trip from 1980 to 2012 for

sites 810, 811, 812 and 814.

810

78 80

82

84

86

88

90

92

94

96

98

00

02

04

06

08

10

12

14

Ab

un

da

nce

0

1000

2000

3000

4000

5000

6000

7000

811

78 80

82

84

86

88

90

92

94

96

98

00

02

04

06

08

10

12

14

0

1000

2000

3000

4000

5000

6000

7000

812

Date

78 80

82

84

86

88

90

92

94

96

98

00

02

04

06

08

10

12

14

Ab

un

da

nce

0

1000

2000

3000

4000

5000

6000

7000

814

Date

78 80

82

84

86

88

90

92

94

96

98

00

02

04

06

08

10

12

14

0

1000

2000

3000

4000

5000

6000

7000

43

EPT taxa richness

The taxa richness of the Ephemeroptera, Plecoptera and Trichoptera (EPT richness) remained relatively

stable through time at most sites (Figure 25; Figure 26). However, EPT richness was highly variable at

site 801, ranging from 1 EPT taxa to 9 taxa. However, there was no consistent pattern over time at this

site. There was a slight increase in mean EPT richness post 1994 at sites 812 and 814. EPT richness

dropped to the lower end of the range post 2010 at most sites.

Figure 25. Artificial substrate sampler Ephemeroptera, Plecoptera and Trichoptera (EPT) richness (mean ± 1SE)

for each sampling trip from 1980 to 2012 for sites 800, 801, 804 and 808.

801

78 80

82

84

86

88

90

92

94

96

98

00

02

04

06

08

10

12

14

0

2

4

6

8

10

12

14

808

Date

78 80

82

84

86

88

90

92

94

96

98

00

02

04

06

08

10

12

14

0

2

4

6

8

10

12

14

804

Date

78 80

82

84

86

88

90

92

94

96

98

00

02

04

06

08

10

12

14

EP

T r

ich

ne

ss

0

2

4

6

8

10

12

14

800

04

06

08

10

12

14

EP

T r

ich

ne

ss

0

2

4

6

8

10

12

14

44

Figure 26. Artificial substrate sampler Ephemeroptera, Plecoptera and Trichoptera (EPT) richness (mean ± 1SE)

from each sampling trip from 1980 to 2012 for sites 810, 811, 812 and 814.

811

78 80

82

84

86

88

90

92

94

96

98

00

02

04

06

08

10

12

14

0

2

4

6

8

10

12

14

814

Date

78 80

82

84

86

88

90

92

94

96

98

00

02

04

06

08

10

12

14

0

2

4

6

8

10

12

14

812

Date

78 80

82

84

86

88

90

92

94

96

98

00

02

04

06

08

10

12

14

EP

T r

ich

ne

ss

0

2

4

6

8

10

12

14

810

78 80

82

84

86

88

90

92

94

96

98

00

02

04

06

08

10

12

14

EP

T r

ich

ne

ss

0

2

4

6

8

10

12

14

45

EPT abundance

Mean EPT abundance was highly variable through time at sites 801, 808 and 810. EPT abundances were

highest from the mid-1990s through to the early 2000s at sites 810, 811 and 812 and during the 2000s at

site 801 and site 804 (Figure 27; Figure 28). No clear temporal pattern was apparent at any of the other

sites. No noticeable shift in EPT abundances was apparent post 2010.

Figure 27. Artificial substrate sampler Ephemeroptera, Plecoptera and Trichoptera (EPT) abundance (mean ±

1SE) for each sampling trip from 1980 to 2012 for sites 800, 801, 804 and 808.

801

78 80

82

84

86

88

90

92

94

96

98

00

02

04

06

08

10

12

14

0

500

1000

1500

2000

2500

808

Date

78 80

82

84

86

88

90

92

94

96

98

00

02

04

06

08

10

12

14

0

500

1000

1500

2000

2500

804

Date

78 80

82

84

86

88

90

92

94

96

98

00

02

04

06

08

10

12

14

Ab

un

da

nce

0

500

1000

1500

2000

2500

800

04

06

08

10

12

14

Ab

un

da

nce

0

1000

2000

3000

4000

5000

15000

20000

25000

30000

46

Figure 28. Artificial substrate sampler Ephemeroptera, Plecoptera and Trichoptera (EPT) abundance (mean ±

1SE) from each sampling trip from 1980 to 2012 for sites 810, 811, 812 and 814.

811

78 80

82

84

86

88

90

92

94

96

98

00

02

04

06

08

10

12

14

0

500

1000

1500

2000

2500

814

Date

78 80

82

84

86

88

90

92

94

96

98

00

02

04

06

08

10

12

14

0

500

1000

1500

2000

2500

812

Date

78 80

82

84

86

88

90

92

94

96

98

00

02

04

06

08

10

12

14

Ab

un

da

nce

0

500

1000

1500

2000

2500

810

78 80

82

84

86

88

90

92

94

96

98

00

02

04

06

08

10

12

14

Ab

un

da

nce

0

500

1000

1500

2000

2500

47

Proportion of taxa richness in each major group

The proportion of the taxa richness contributed by each of the major groups remained largely similar

following the transition from drought conditions to flooding post 2010 at all sites, with the exception of

site 814 where the relative contribution to total taxa richness made by Diptera continued to increase

and the contribution to the taxa richness made by Crustacea increased following two decades of decline

(Figure 29; Figure 30).

48

Figure 29. Artificial substrate sampler proportional contribution of taxa richness in each major group to the

total TR in each decade from 1980 to 2012 for sites 800, 801, 804 and 808.

800

1980s 1990s 2000s 2010s

% T

R

0

20

40

60

80

100

Ephemeroptera

Plecoptera

Trichoptera

Odonata

Diptera

Coleoptera

Hemiptera

Other insect

Mollusca

Crustacea

NIO

801

1980s 1990s 2000s 2010s

0

20

40

60

80

100

804

Decade

1980s 1990s 2000s 2010s

% T

R

0

20

40

60

80

100

808

Decade

1980s 1990s 2000s 2010s

0

20

40

60

80

100

49

Figure 30. Artificial substrate sampler proportional contribution of taxa richness in each major group to the

total TR in each decade from 1980 to 2012 for sites 810, 811, 812 and 814.

810

1980s 1990s 2000s 2010s

% T

R

0

20

40

60

80

100

Ephemeroptera

Plecoptera

Trichoptera

Odonata

Diptera

Coleoptera

Hemiptera

Other insect

Mollusca

Crustacea

NIO

811

1980s 1990s 2000s 2010s

0

20

40

60

80

100

812

Decade

1980s 1990s 2000s 2010s

% T

R

0

20

40

60

80

100814

Decade

1980s 1990s 2000s 2010s

0

20

40

60

80

100

50

Proportion of total abundance contributed by each major group

From the 1980s through to the 2000s there has been a general decrease in the contribution of EPTs to

the total abundance and an increase in the contribution of Diptera and the non-insect others (Figure 31;

Figure 32). Following the return to wetter conditions in 2010 the contribution of the non-insect others

continued to increase at all sites except for site 812. The contribution of Diptera decreased at all sites

with the exception of site 804 where the contribution remained steady. The contribution of EPT taxa

decreased at sites 801, 804, 811; increased at site 812 and underwent little change at sites 800, 808 and

810. Shifts in the contributions of EPTs were mostly due to the Ephemeroptera.

The Crustacea which were initially a major contributor to the total abundance at sites 812 and 814.

They underwent subsequent decline in relative abundance during the 1990s and 2000s and remained

largely unchanged post 2010.

51

Figure 31. Artificial substrate sampler proportional contribution of the abundance in each major group to the

total abundance, in each decade from 1980 to 2012 for sites 800, 801, 804 and 808.

800

Decade

1980s 1990s 2000s 2010s

% a

bu

nd

an

ce

0

20

40

60

80

100

Ephemeroptera

Plecoptera

Trichoptera

Odonata

Diptera

Coleoptera

Hemiptera

Other insect

Mollusca

Crustacea

NIO

801

Decade

1980s 1990s 2000s 2010s

% a

bu

nd

an

ce

0

20

40

60

80

100

804

Decade

1980s 1990s 2000s 2010s

% a

bu

nd

an

ce

0

20

40

60

80

100808

Decade

1980s 1990s 2000s 2010s

% a

bu

nd

an

ce

0

20

40

60

80

100

52

Figure 32. Artificial substrate sampler proportional contribution of the abundance in each major group to the

total abundance, in each decade from 1980 to 2012 for sites 810, 811, 812 and 814.

810

1980s 1990s 2000s 2010s

% a

bu

nd

an

ce

0

20

40

60

80

100

Ephemeroptera

Plecoptera

Trichoptera

Odonata

Diptera

Coleoptera

Hemiptera

Other insect

Mollusca

Crustacea

NIO

811

1980s 1990s 2000s 2010s

0

20

40

60

80

100

812

Decade

1980s 1990s 2000s 2010s

% a

bu

nd

an

ce

0

20

40

60

80

100814

Decade

1980s 1990s 2000s 2010s

0

20

40

60

80

100

53

Functional feeding group proportional abundance

Gathering collectors were the most abundant functional feeding group (FFG) present at all sites followed

by the predators (Figure 33; Figure 34). Other FFGs generally contribute less than15% at all sites. The

proportional contribution of collector gatherers typically increased downstream to a maximum at site

812, with a slight decrease in relative abundance at site 814. Filtering collectors were most abundant at

site 800. Noticeable shifts in relative abundance of the FFGs occurred post 2010 at sites 800 801, 810,

811, 812 and 814.

54

Figure 33. Artificial substrate sampler functional feeding group (FFG) relative abundance summarised for each

decade 1980 to 2012, for sites 800, 801, 804 and 808.

800

1980s 1990s 2000s 2010s

% a

bu

nd

an

ce

0

20

40

60

80

100

Filter collector

Gathering collector

Predator

Scraper

Shredder

801

1980s 1990s 2000s 2010s

0

20

40

60

80

100

804

Decade

1980s 1990s 2000s 2010s

% a

bu

nd

an

ce

0

20

40

60

80

100808

Decade

1980s 1990s 2000s 2010s

0

20

40

60

80

100

55

Figure 34. Artificial substrate sampler functional feeding group (FFG) relative abundance summarised for each

decade 1980 to 2012, for sites 810, 811, 812 and 814.

810

1980s 1990s 2000s 2010s

% a

bu

nd

an

ce

0

20

40

60

80

100

Filtering collector

Gathering collector

Predator

Scraper

Shredder

811

1980s 1990s 2000s 2010s

0

20

40

60

80

100

812

Decade

1980s 1990s 2000s 2010s

% a

bu

nd

an

ce

0

20

40

60

80

100814

Decade

1980s 1990s 2000s 2010s

0

20

40

60

80

100

56

Multivariate analysis – comparison of sites

Multivariate analysis was conducted on the most abundant taxa (as per methods above) from the ASS

samples. Multivariate analyses were conducted on data summarised into 5 year periods. Permanova

analysis revealed that there was a significant difference in community structure among sites and 5 year

period and that there was a significant interaction between sites and 5 year period. Pairwise tests

revealed that, with a few exceptions, sites remained significantly different within each 5 year period. On

occasions sites 808, 810 and 811 were not significantly different. ANOSIM results indicated that site 801

and site 811 had the most dissimilar community structure and sites 808, 810 and 811 had the most

similar community structure of all sites (low R statistic indicates high similarity, values closer to 1

indicate greater dissimilarity). The sites 812 and 814 were more similar to the upstream site, site 801,

than the sites located in the mid-Murray region (Figure 35 Table 6).

Figure 35. Non-metric multidimensional scaling plot summarising the total macroinvertebrate communities

from each site from 1980 to 2012 (site centroids) using the ASS only.

Site800

801

804

808

810

811

812

814

2D Stress: 0.01

57

Table 6. ANOSIM site pairwise comparison results indicating R statistic and significance levels (R statistic range –

1 to +1, values closer to +1 indicate greater degree of separation of sites).

Sites R Statistic Level %

801, 804 0.352 0.1

801, 808 0.591 0.1

801, 810 0.622 0.1

801, 811 0.738 0.1

801, 812 0.509 0.1

801, 814 0.664 0.1

804, 808 0.397 0.1

804, 810 0.425 0.1

804, 811 0.5 0.1

804, 812 0.314 0.1

804, 814 0.501 0.1

808, 810 0.13 0.1

808, 811 0.24 0.1

808, 812 0.141 0.1

808, 814 0.569 0.1

810, 811 0.25 0.1

810, 812 0.229 0.1

810, 814 0.661 0.1

811, 812 0.141 0.1

811, 814 0.499 0.1

58

Multivariate analysis – comparison time periods within sites

The MDS plot for Site 800 indicated an annual trajectory from left to right (Figure 36). The drought

years of 2008 and 2009 overlay and indicated high similarity of macroinvertebrate communities.

Permanova analysis revealed that there was a significant difference in the macroinvertebrate

communities from drought to flood years.

The MDS plot for site 801 (Figure 37) indicated a trajectory of change from the left to right hand side of

the MDS plot. Permanova analysis revealed that there was a significant difference among 5 year periods

and pair wise comparisons indicated that there were no significant differences between the 05-09 and

10-12 periods. However, community structure was significantly different between 10-12 and all other 5

year periods.

The MDS plot for site 804 indicated a similar trajectory through time as site 801 (Figure 38). The 10-12

post flooding period was significantly different from all other 5 year periods.

The MDS plot for site 808 indicated that there was a major shift in community structure from the 80-84

to the 85-89 period, followed by a shift back towards the initial community of the 80-84 period then

further shifts in community structure through to the 10-12 period (Figure 39). Permanova analysis and

pairwise comparisons indicated that the 10-12 period was significantly different from all other 5 year

periods.

The MDS plot for site 810 indicated that the period from 1985 to 2004 had similar community structure

which was quite different from the 80-84, 05-09 and 10-12 periods (Figure 40). Permanova analysis

revealed that the 80-84 and 85-89 periods were not significantly different from the 10-12 period; all

other 5 year periods were significantly different from the 10-12 period.

The MDS plot of site 811 indicated high level of similarity among five year periods from the 80-84

through to the 95-99 period (Figure 41). Permanova analysis revealed that there was no significant

difference between the 80-84 and 10-12 period and significant differences between 10-12 period and all

other 5 year periods.

The MDS plot for site 812 indicated trajectory from left to right on the MDS plot with some reversal

during the 10-12 period (Figure 42). Permanova analysis revealed that there was no significant

difference between the 10-12 period and the 85-89 and the 90-94 periods. There was a significant

difference between 10-12 period and all other 5 year periods.

The MDS plot for site 814 indicated a similar trajectory of change to site 812 with some reversal in

trajectory during the 10-12 period (Figure 43). However, Permanova analysis indicated that all 5 year

periods remained significantly different.

59

Figure 36. Site 800 macroinvertebrate community structure summarised as year centroids.

Figure 37. Site 801 macroinvertebrate community structure summarised as 5 year period centroids.

Year2006

2007

2008

2009

2010

2011

2012

2D Stress: 0

5 year period80-84

85-89

90-94

95-99

00-04

05-09

10-12

2D Stress: 0.01

60



2D Stress: 05 year period80-84

85-89

90-94

95-99

00-04

05-09

10-12

2D Stress: 0.035 year period80-84

85-89

90-94

95-99

00-04

05-09

10-12

61



2D Stress: 0.015 year period80-84

85-89

90-94

95-99

00-04

05-09

10-12


85-89

90-94

95-99

00-04

05-09

10-12

62

Figure 42. Site 812 macroinvertebrate community structure summarised as 5 year periods centroids.


2D Stress: 0

5 year period80-84

85-89

90-94

95-99

00-04

05-09

10-12


85-89

90-94

95-99

00-04

05-09

10-12

63

Multivariate analysis – comparison site trajectories

A two stage MDS analysis indicated that trajectories of change in the macroinvertebrate community

structure over the 33 year period were similar for the sites 801, 804, 812 and 814 (Figure 44; Table 7).

Sites 808, 810 and 811 had trajectories of change in community structures which were dissimilar from

these sites. Trajectories of sites 810 and 811 were moderately similar.

Figure 44. A 2stage MDS on 5 year block data indicating similarity of site trajectories from 1980–2012.

Table 7. Spearman rank correlation coefficient results from 2stage MDS on 5 year block data. Values closer to +1

indicate greater degree of similarity.

801 804 808 810 811 812

804 0.92

808 0.49 0.70

810 0.03 0.22 0.49

811 0.15 0.25 0.19 0.68

812 0.71 0.77 0.62 0.18 0.02

814 0.83 0.83 0.52 0.21 0.19 0.90

801

804

808

810

811

812

814

2D Stress: 0.01

64

Discussion

Causal modelling

This project has demonstrated that it is feasible to build a spatiotemporal causal model for the River

Murray Biological (Macroinvertebrate) Monitoring program using multivariate species data. There are

clear spatiotemporal patterns in both the macroinvertebrate community composition and the available

environmental variables. Although the absence of data on some important variables has meant that the

causal structure was untestable, the fitted structural equations were consistent with the relationships

depicted in the causal diagram (Figure 3).

The present analysis indicates that discharge influences alkalinity, pH, EC, nitrate (and nitrite),

phosphate, and turbidity. In turn, these variables accounted for slightly less than half of the explained

variation in the macroinvertebrate community composition, with the remainder being attributable to

other spatial and temporal processes for which no data were available. Given the absence of data on

some of the variables depicted in the causal diagram (Figure 3) only a partial explanation for the

patterns in community composition can be given here.

The spatial pattern in the space–time plots in PCO1 (see Figure 8) displays a distinctive quadratic spatial

pattern at the beginning of the time series, with sites at either end of the river (distances 258, 527,

1910, and 2416 km, corresponding to sites 801, 804, 812 and 814 respectively) being more similar with

respect to community composition than they are with sites midway along the river (distances 1389 and

1737 km, corresponding to sites 808 and 811 respectively). Cook et al. (2011) suggested that the

principal trend in community composition along the river was monotonic, consistent with the River

Continuum Concept (Vannote et al. 1980), with sites becoming increasingly different with increasing

spatial separation. While this trend remains evident in PCO2 (and may in part be due to the trend in EC)

it is now clear that the dominant spatial pattern in the community (evident in PCO1) is quadratic in

nature. This quadratic pattern diminishes over time with sites at the extremes becoming more like the

mid-Murray sites. These patterns appear to be related to rainfall and perhaps climatic factors more

generally.

Cook et al. (2011) indicated that there was a step change in community composition around the year

1994. The present analysis substantiates this change in community composition but suggests that this

period represented a turning point, instead of a step change, in community composition from which

communities underwent a period of sustained unidirectional change. In addition, the inclusion of data

from the 2010 flood and the two years following indicated that this unidirectional shift may have ceased

and that there may well be evidence for a long term cycle in community composition (particularly in

PCO1, where the cycle seems to be climate related), with some components of the communities

changing in the direction back towards a prior state. However, insufficient time has passed since the

end of the drought for this cyclical pattern to be confirmed.

The macroinvertebrate community variability identified in PCO1 was correlated with rainfall, pH and

water temperature but most of the variance in PCO1 seems to be related to other spatial and temporal

65

processes (see causal diagram in Figure 3). The convergence in PCO1 over time does appear to concord

with the convergence in rainfall at all sites over time (Figure 10). While not as pronounced, there is a

similar concordance with the pattern in water temperature. Such a result is consistent with other long

term studies where climate is a major driver of macroinvertebrate variability at broad spatial scales

(Collier 2008;Pace et al. 2013).

There is also some agreement between the spatial pattern in pH seen in Figure 9 and the spatial pattern

in PCO1, although there appears to be a downward trend in pH at Site 801 (distance 258 km). According

to the causal diagram (Figure 3), pH is influenced by water temperature and alkalinity. In fact, the water

temperature component of the pH Generalised Additive Model (not shown) indicated that pH decreased

with increasing temperature and decreasing alkalinity. Temperature is often a driver of

macroinvertebrate communities (Vannote and Sweeney 1980). The increase in water temperature over

the period of study and the low alkalinity at Site 801 would explain the downward trend in pH. An

increase in water temperature is consistent with the intensification of the drought conditions and

associated reduction in water availability, reduced flows and higher air temperatures from the mid-

1990s through to 2010.

Overall, it may be reasonable to speculate that the community structure changes identified in PCO1

reflect climatic variation over space and time, and variation in solar radiation (which was not recorded in

the data) may account, at least in part, for the variance explained by the spatial and temporal

components of the PCO1 model. Climate is often cited as a major overarching factor influencing aquatic

communities at broad spatial scales (Collier 2008). It should be noted that the climate data used in this

analysis were taken from weather stations that were near to the monitoring sites. It would probably be

more appropriate to use climate measures at the catchment scale than local scale. Catchment scale

data may be better able to account for the spatiotemporal patterns observed in PCO1.

The macroinvertebrate changes identified in PCO2 (Figure 8) indicated an increasing separation of sites

along the length of the River Murray and a decrease in this separation over time. These changes are

likely to reflect the natural longitudinal transition in macroinvertebrate communities in streams

(Vannote et al. 1980) and also appear to be related to changes in water quality, in particular conductivity

and to a lesser degree pH and turbidity. These results suggest that the community variability identified

in PCO2 may be responding to the reduction of in-stream salinity due to the salt interception scheme,

the drought conditions, Darling River inflows and changes in land management practices. These might

have contributed to altered saline water inflows, turbidity and pH in the River Murray over the past 33

years.

The macroinvertebrate community structure variation identified by the remaining PCO axes are related

to a range of water quality, climatic and seasonal conditions. The relative contribution of each in

explaining the overall changes in community structure decreased with each successive axis. PCO3

displayed a spatial pattern and strong seasonal pattern which appear to be related to temperature

(Figure 4). PCO4 also displayed a spatial pattern and strong seasonal pattern, partly explained by water

66

temperature, although most of the variation in PCO4 is explained by other spatial and temporal

processes (Figure 8). PCO5 (Figure 8) appeared to be responding in part to the effects of turbidity and

discharge (probably via its effect on current velocity), although the patterns in PCO5 for Sites 801 and

804 (distances 258 and 527 km) seem to be the reverse of the patterns exhibited at other sites. A

substantial proportion of the explained variability is accounted for by other spatial and temporal

processes. PCO6 (Figure 8) displayed a predominantly temporal trend which may in part be related to

electrical conductivity.

The analysis by Cook et al. (2011) noted that community patterns were only partially explained by the

measured environmental variables, and that there were clearly other factors at work. The present

analysis, by explicitly including space and time in the model, indicates that the available environmental

variables accounted for slightly less than half (45%) of the explained variation in community

composition, with the remainder being attributable to other spatial and temporal processes. The causal

diagram (Figure 3) that was constructed as part of this project, which is based on a substantial review of

the literature, provided some hypotheses regarding the nature of these other processes. Specifically,

these processes are thought to include the morphology of the river and river basin (i.e. channel width

depth, and elevation), land use (including riparian vegetation cover), solar radiation and the operation of

water storages.

These analyses have successfully identified and modelled the responses of the macroinvertebrate

communities to the key water quality and climatic factors. There were significant relationships between

the macroinvertebrate community changes and the water quality and climatic variables. The individual

PCOs have indicated how particular components of the community structure respond to changes in the

water quality and climatic variables. While a fuller explanation of the patterns in community

composition may be possible in the future by expanding the list of variables observed, it is not essential

for being able to predict the effects of changes to the hydrologic regime within the river. It will be

necessary, however, to include in the model catchment level data on storage releases, diversions, and

land use for each of the monitoring sites before the model can be used to reliably predict the effects of

interventions. All three of these variables are necessary because they are potentially confounding, but

storage releases and diversions are particularly important variables because they are the targets for

interventions.

With the inclusion of catchment level data on storage releases, diversions, land use and climate, it

should be possible to build and test a model that could be used to explore the effects on the

macroinvertebrate community of interventions to storage releases and diversions, thus maximising the

value of the MDBA’s long term monitoring investment. The MDBA has already undertaken hydrologic

modelling to determine sustainable diversion limits (MDBA 2012). The scenarios that were explored as

part of that work, which included the scenario of “without development” (a near-natural condition

scenario), could be used as input to a future causal model of the macroinvertebrate community. This

would enable predictions to be made regarding the status of the community had these scenarios been in

place throughout the 1980–2012 period. In this way the macroinvertebrate causal model could be used

67

to contribute to the evolution of the Basin Plan through the setting of management objectives, the

development of performance targets, and the evaluation of management actions, in a manner

consistent with the MDBA’s framework for ecosystem monitoring and evaluation (Davies et al. 2009).


Since the production of the report by Cook et al. (2011), an additional 2.5 years of monitoring

encompassing five sampling periods has been added to the monitoring data set. The period from 2010

has been characterized by the breaking of the millennium drought and subsequent major flooding

throughout the Murray–Darling Basin in 2010 and 2011. Major hydrological events, such as severe

droughts and floods, are considered to be one of the most important factors structuring aquatic

communities and have often been associated with major shifts in the aquatic communities (Daufresne et

al. 2007).

The Murray Monitoring Program has identified a total of 225 taxa (based on 1985 taxonomy) to date.

This is an additional 14 taxa to that reported by Cook et al. (2011) but includes the additional site at

Biggara (site 800). Total taxa richness for the 1980 to 2012 period was greatest at site 801 (153 taxa)

and was similar among all other sites ranging from 106 taxa at site 811 to 117 taxa at site 800. As

identified in Cook et al. (2011) the taxa richness and abundance generally increased throughout the

1990s and 2000s at all sites. To date the total number of taxa collected has been lower during the 2010s

relative to the 2000s. However, this is attributed to there being only 2.5 years of samples contributing

to the 2010s data set so far.

The taxonomic group contributing most to the diversity at each site was the Diptera (true flies). The

Ephemeroptera, Plecoptera and Trichoptera (EPT) taxa contributed approximately 42% of the taxa

richness at site 800, 23% at site 801 and between 12% and 15% at all other sites. Coleoptera typically

contributed around 15% at all sites other than site 800 in which Coleoptera contributed approximately

7%. The contribution of Crustacea to the total diversity increased with distance downstream, ranging

from approximately 2% at site 800 to 10% at site 814. These patterns are similar to the patterns

identified in Cook et al. (2011) with the additional 2.5 years of sampling not altering the overall relative

proportions of the taxa.

Cook et al. (2011) identified a trajectory of change throughout the 30 year period, which indicated a

unidirectional shift in the community away from the initial state identified in the 1980s when the

monitoring program began. During this period there was no indication of a cyclical pattern in

community structure change. In addition to this gradual shift, Cook et al. (2011) considered changes

which occurred at around 1994 following major flooding in the MDB, as a potential step change in

community structure, possibly associated with this major climatic event. Rather than a step change,

analyses carried out during the causal model development in this study, identified this period as a

turning point from which the drift in community structure occurred. The period following the flooding of

the early 1990s has been characterized by a gradual drying, culminating in an intense drought period

68

during the late 2000s. The recent data that was available for this project, which included the 2010 flood

and the two years following, suggests evidence of a long term cycle in community composition.

The drift in macroinvertebrate community structure that occurred during the 1990s and 2000s was

consistent with reduced water availability, loss of flowing habitat and reduced water quality. In

particular, the diversity and abundance of taxa within theDiptera and the non-insect others increased.

These taxa are generally rapid lifecycle, small bodied, tolerant and opportunistic taxa which are typically

associated with disturbed habitat or habitats with poor water quality and or flow conditions (Rapport et

al. 1985; Ledger et al. 2011) Such changes in community structure are almost universally being identified

where climatic, hydrological and water quality disturbance are impacting upon lowland rivers around

the world (Thomson et al. 2012; Floury et al. 2013; Pace et al. 2013) Interestingly, in the present study

the diversity and abundance of taxa increased over this period. This indicated that few taxa were lost

from the system as a result of the changing conditions. However, the contribution of many taxa to the

total abundance of the system was reduced as new taxa, better adapted to the poor hydrological

conditions and or reduced water quality, were able to colonise and in many cases dominate the

macroinvertebrate communities.

The drift in community structure was most pronounced at the upper and lower Murray sites (sites 801,

804, 812 and 814), with the mid Murray sites undergoing the least change. The mid Murray sites were

already dominated by tolerant opportunistic taxa, which were increasing in diversity and abundance at

all other sites. Consequently, all sites were becoming more similar to each other over time. The 2 stage

MDS analysis carried out in this study confirmed that sites at either end of the River Murray were

undergoing similar changes in community structure with high correlation among their trajectories, and

that their trajectories were dissimilar from sites 808, 810 and 811. This corroborates the findings of the

casual analysis where PCO1 and PCO2 indicated a trajectory of sites at either end of the Murray towards

the community of the mid Murray.

The analysis revealed that the trajectories towards the sites within the semi-arid zone of the mid Murray

were consistent with the changes in the prevailing climate; warmer temperature, reduced rainfall and

water availability. Changes which were most pronounced at either end of the catchment where the

climate is typically more temperate. Drought is considered as a ramp disturbance where conditions

gradually become harsher over time (Lake 2000; Lake 2003). There was a general intensification of

drought conditions and reduction of water availability in the River Murray system during the 2000s and

this intensification is reflected in the gradual but continued trajectory of the macroinvertebrate

community towards one that is characteristic of harsh climates.

Similar changes in macroinvertebrate community structure to those in the River Murray are also being

identified throughout the world where climatic, hydrological and water quality disturbance are

impacting upon lowland rivers (Collier 2008; Thomson et al. 2012; Floury et al. 2013; Pace et al. 2013)

Specialist taxa or taxa of cooler flowing waters are increasingly being replaced by opportunistic taxa that

can survive and thrive under a broad range of water quality and hydrological conditions. Rapport et al.

(1985) suggested that systems under stress will favour species that are best adapted to the new harsher

69

environmental conditions. Typically these taxa are exotics or locally uncommon species that tend to

displace native abundant species, are more opportunistic in character and are shorter-lived. The causal

modelling component of this report indicated that the pattern of drift in community structure during the

1990s and 2000s was consistent with changing climatic conditions and general reduction of water

availability and discharge in the River Murray over this period. The similarity among the trajectories of

the macroinvertebrate communities at either end of the River Murray system is consistent with the

effect of some broad scale climatic driver, which is a pattern identified by Collier (2008) and Pace et al.

(2013). Further to this, the causal modelling also indicated that EC, turbidity and rainfall were

significant drivers of the shifts in community structure throughout the river system. Pace et al. (2013)

found that streams over a broad geographic scale were responding similarly to large scale climatic

variables. However, at local or site scale, macroinvertebrate assemblages were responding to the local

conditions such as water quality and habitat suitability. Similarly, Collier (2008) found that climate was a

key factor determining the stability and persistence of macroinvertebrate assemblages at a site but that

land use determined site specific variability in macroinvertebrate communities. The interaction between

broad spatial scale drivers, such as climate and localised drivers such as water quality and potentially

land use was highlighted by the causal modelling analysis in the current report.

Despite the changes in community composition in the River Murray identified by Cook et al. (2011) it

does appear that few, if any, species have been lost from the system and that, given the appropriate

conditions, the River Murray system may possess resilience to major hydrological stress. Analyses

associated with the causal modelling indicated that there may have been a cessation in the direction of

the multivariate trajectories post 2010. A shift in the community trajectories would be consistent with

changes in the climatic and in-stream environmental conditions of the River Murray. It appeared that

the trajectories of some individual components of the community structure have altered since the onset

of wetter conditions post 2010 and the trajectory may be back towards a prior state and may indicate a

long term cyclical pattern in community structure. In particular, community abundance appeared to be

no longer increasing and the contribution of the Diptera to the community abundance has decreased at

most sites. Before the return of wetter conditions, taxa richness may have begun to decline as

prolonged harsh conditions reduced the prevalence of some of the more sensitive taxa. However, not

all of the changes identified in Cook et al. (2011) have ceased. The contribution of the non-insect others

has continued to increase across all sites and the reduction in the contribution of the EPT taxa (in

particular the Ephemeroptera) has continued at sites 801 and 804.

An analysis of the macroinvertebrate communities at Site 800 was not included in Cook et al. (2011) as

this site had only been sampled since 2006. In addition, it is for this reason that it was not included in

the causal model analysis for this report. However, site 800 may be expected to have a natural response

to the drought and subsequent flood period as it has an intact riparian zone and is in an unregulated

section of the River Murray. Analysis of the drought period (2006 to summer 2010) and the post-

drought period (winter 2010 – 2012) indicated that Site 800 showed little change in some community

indices such as taxa richness, total abundance, and EPT diversity and abundance following the breaking

of the drought. It appears that these components of the community structure were resistant to the

70

hydrological shifts. The near natural state and intact riparian zone may confer some level of resistance

and resilience to hydrological and climatic disturbance (Fritz and Dodds 2004; Thomson et al. 2012).

However, there was a small change in the functional feeding group composition and multivariate

analyses revealed that the community structure of the two most intense drought years (2008, 2009)

were very similar and were distinct from all other years. The post-drought period also had significantly

different communities to that prior. These finding support the results from the causal modelling which

suggested that climate and associated reduction in water availability, water quality and habitat changes,

was influencing macroinvertebrate communities throughout the catchment.

Macroinvertebrate communities have been shown to recover rapidly from short term (seasonal and

annual) droughts (Boulton and Lake 1992). However, few studies have investigated recovery from such

a protracted period of drying as that experienced in the River Murray since the mid-1990s. This

monitoring program has offered a rare and internationally significant opportunity to evaluate how the

biota of the River Murray responds to extreme climatic and hydrological conditions. Studies on long

term data sets from overseas have identified long term cyclical patterns, often associated with large

scale climatic shifts, usually linked to large scale climatic drivers such as the North Atlantic Oscillation

(Bradley and Ormerod 2001). Studies in Europe have indicated that macroinvertebrates may show long

term resilience to poor water quality, evidenced by improvements in macroinvertebrate communities

with the reduction in industrial discharges to rivers and river rehabilitation throughout the UK and

France (Vaughan and Ormerod 2012; Floury et al. 2013). This study did not include enough post drought

data to assess whether the long term drift of the macroinvertebrate communities has halted. However,

such an assessment will be vital if we are to understand the long term dynamics of River Murray biotic

communities and their resilience to short and long term climate variation. Assessing community

resilience and response to returning greater water availability will be critical in light of projected

changes to flow management as part of the Basin Plan. Further analysis of the River Murray monitoring

data and development of the causal model will greatly enhance our understanding of the dynamics of

the biotic communities of the River Murray and highlights the value of long term monitoring programs.

Progress towards recommendations of Cook et al. (2011) The current study has made progress towards some of the recommendations outlined in

Cook et al. (2011), specifically:

Recommendation 3

Investigate the biological response following the breaking of the extreme drought conditions by

analysing data over the 2009, 2010, 2011 and 2012 sampling periods. Inclusion of the unregulated site

800 from the upper River Murray would be recommended for this, as sampling of this site incorporated

this transitional period.

71

Recommendation 4

A review of the current monitoring program to determine the most appropriate means for addressing

monitoring and management requirements into the future, whilst maintaining the integrity of the

monitoring data to date. Possible issues to investigate include the following:

a) Additional monitoring sites within unregulated sections of the River Murray.

b) Additional sites on tributary streams, where possible unregulated tributary streams, to assist with

disentangling management, land use and climatic influences on biological condition.

c) Influence of dams and weirs, which could include sites above and below impoundments.

Recommendation 7

Potential projects which address specific management questions and requirements developed through

consultation with key partners. Key issues which may be investigated include the following:

a) Projects which are specifically designed to assess the biological response to management, land use

and climate shifts.

b) Projects which aim to determine the mechanisms driving the resistance and resilience of the

biological communities e.g. water availability, water quality, habitat, water management.

c) Drivers of system productivity, food resource quantity and quality e.g. the effect of turbidity and

salinity on lower River Murray productivity and structure of the macroinvertebrate community.

In particular this study addressed Recommendation 3 (for an analysis of data for the 2009–2012

monitoring period and the inclusion of data from the unregulated site 800) in order to investigate

biological responses following the breaking of the extreme drought periods. The inclusion of these data

in the present analysis proved to be informative, with the results suggesting a cyclical nature to the

changes in macroinvertebrate assemblages over longer timeframes.

In addition to Recommendation 3, the current study has made progress towards and provides

information for Recommendations 4, 7a, and 7b. One of the primary advantages of developing the

causal model is that it highlights those areas where data are currently not collected but if collected

would provide the causal model with greater predictive power and hence improved water resource

management. As the causal model is developed the biological responses of communities to

management actions can be better predicted. This predictive power can be used to address the

recommendation given in 7a. The causal models also provide inferences as to what the driving

mechanisms behind macroinvertebrate community resilience and resistance are, as per

Recommendation 7b.

72

Conclusion This project successfully demonstrated that a structural causal model can be effectively constructed to

model and predict macroinvertebrate dynamics in the River Murray utilising the Murray Monitoring

Program data in conjunction with the water quality monitoring program and River Murray flow data.

With the inclusion of additional data on storage releases, diversions and land use the causal model will

be able to model and predict biotic responses to flow management decisions enabling the testing of a

range of scenarios for expected outcomes. This will be particularly useful in light of the implementation

of the Basin Plan.

Importantly, the spatiotemporal analysis undertaken for this project, which modelled the spatial and

temporal patterns explicitly, was more informative than the analyses that treat distance from source as

a nominal (categorical) variable (i.e. analyses that simply group data by site), as was done in the

previous analysis (Cook et al. 2011). This causal model described the nature of the spatiotemporal

variability of the macroinvertebrate communities within the River Murray and the contributions of the

available environmental data to the variability. Changes in flow of the River Murray and its water

quality changes such as EC, water temperature and turbidity were significantly correlated with changes

in macroinvertebrate communities. However, the overarching driver of biotic change would appear to

be climate which in turn has impacted on flow and water quality in the River Murray.

This analysis has also indicated that the unimodal trajectories of the macroinvertebrate communities

towards tolerant, small, rapid lifecycle, generalist taxa identified by Cook et al. (2011) prior to the

breaking of the drought in 2010 may in fact be part of a cyclical trajectory. There is some evidence that

the return to wetter conditions in 2010 is associated with a change to the trajectories of some

components of the macroinvertebrate community. However, insufficient time has elapsed since the end

of the drought to assess whether there is a long term cyclical pattern occurring or simply a temporary

halt to the long term drift in community structure. These results indicate that the macroinvertebrate

communities of the River Murray may well have high level of resilience to major hydrological events.

However, these results also highlight the potential changes in the River Murray biota that may be

expected under the predicted climate change scenario of higher frequency of droughts and associated

reduced water availability.

The long term data set generated by the MDBA Murray Monitoring Program is essential for

understanding and discerning between short and long term variability of biotic communities.

Consistency of sampling through the diverse range of climatic conditions will enable an understanding of

biotic variability. Prior to the millennium drought the monitoring program had not encompassed such

extremes of climate. In the last decade the Murray Darling Basin has experienced a 1:1000 year drought

followed by a 1:100 year flood. Data detailing the ongoing community response will be of international

significance and of immense scientific and managerial interest and value.

73

Appendix I Sorted species scores for PCO1–6 PCO1

Microvelia Austroargiolestes Batrachomatus Cordulephya -0.48912 -0.48236 -0.47889 -0.44263 Lancetes Antiporus Kingolus Physa -0.37949 -0.34522 -0.33948 -0.33716

Atriplectides Berosus Euastacus Notriolus -0.3177 -0.31201 -0.27762 -0.27171

Austrolimnius Austrogomphus Triaenodes Austroaeschna -0.26646 -0.25779 -0.24959 -0.23788

Anisocentropus Dinotoperla Sphaeriidae Atalophlebia -0.23693 -0.23427 -0.2076 -0.20471

Archichauliodes Leptoperla Tipulidae Cheumatopsyche -0.19591 -0.17894 -0.16911 -0.1635

Ulmerochorema Taschorema.complex Micronecta Djalmabatista -0.13754 -0.09332 -0.09199 -0.09098 Simsonia Nososticta Sternopriscus Isidorella -0.07795 -0.07143 -0.06943 -0.0679

Coloburiscoides Nousia Coxelmis Asmicridea -0.05489 -0.05472 -0.05118 -0.04877 Paratya Stenochironomus Rhadinosticta Austrosimulium

-0.03876 -0.035 -0.03167 -0.02487 Agapetus Rheocricotopus Riekoperla Apocordulia -0.02224 -0.02111 -0.01846 -0.00247 Oecetis Lingora Thienemanniella Matasia

0.004676 0.022031 0.023584 0.026625 Austrophlebiodes Amarinus Paracladopelma Nemertea

0.046436 0.056569 0.061199 0.082666 Amphipoda Macrobrachium Ecnomus Empididae

0.083607 0.100514 0.108729 0.111859 Ablabesmyia Chironomus Pentaneura Polypedilum

0.122303 0.122947 0.12782 0.144154 Hemicordulia Dicrotendipes Rheotanytarsis Caenidae

0.144577 0.153623 0.156087 0.157138 Parakiefferiella Hirudinea Potomopyrgus Sminthuridae

0.157383 0.159871 0.160216 0.163588 Orthotrichia Apsectrotanypus Ferrissia Heterias

0.164804 0.166378 0.171239 0.172213 Corbiculina Paratrichocladius Triplectides Tachaea

0.17408 0.175016 0.175238 0.178894 Ischnura Cherax Glyptophysa Cryptochironomus 0.179896 0.181537 0.188341 0.189934

Hydroptila Hydra Kiefferulus Cricotopus 0.192115 0.19539 0.195876 0.197635 Procladius Nanocladius Caridina Tanytarsus 0.202145 0.208634 0.211171 0.212582 Acarina Austrochiltonia Coelopynia Nematoda

0.215323 0.216692 0.234432 0.235345 Cladopelma Parachironomus Hellyethira Cloeon

0.235894 0.241496 0.244752 0.247756 Temnocephala Ceratopogonidae Oligochaeta Larsia

0.248961 0.250776 0.251353 0.252151 Offadens Cladotanytarsus Paratanytarsus Paralimnophyes 0.255264 0.255937 0.25717 0.258508 Dugesiidae

0.26212

74

PCO2 Agapetus Berosus Cheumatopsyche Austrophlebiodes -3.34E-01 -3.29E-01 -3.24E-01 -3.21E-01

Taschorema.complex Djalmabatista Ulmerochorema Coloburiscoides -3.20E-01 -3.17E-01 -3.16E-01 -3.14E-01

Nousia Riekoperla Asmicridea Rheocricotopus -3.13E-01 -3.09E-01 -3.06E-01 -3.05E-01

Leptoperla Austrosimulium Thienemanniella Triaenodes -2.83E-01 -2.82E-01 -2.76E-01 -2.74E-01 Notriolus Austroaeschna Dinotoperla Austrolimnius -2.67E-01 -2.63E-01 -2.60E-01 -2.43E-01

Atalophlebia Apocordulia Archichauliodes Cordulephya -2.37E-01 -2.31E-01 -2.29E-01 -2.28E-01

Lingora Austrogomphus Anisocentropus Simsonia -2.26E-01 -2.22E-01 -2.22E-01 -2.14E-01

Rhadinosticta Rheotanytarsis Euastacus Austroargiolestes -2.12E-01 -1.96E-01 -1.88E-01 -1.80E-01 Tipulidae Paracladopelma Atriplectides Oecetis -1.76E-01 -1.75E-01 -1.65E-01 -1.64E-01 Nososticta Batrachomatus Empididae Parakiefferiella -1.57E-01 -1.57E-01 -1.50E-01 -1.46E-01

Sternopriscus Caenidae Lancetes Stenochironomus -1.45E-01 -1.40E-01 -1.38E-01 -1.23E-01 Ecnomus Ablabesmyia Polypedilum Tanytarsus -1.22E-01 -1.18E-01 -1.11E-01 -1.07E-01

Larsia Microvelia Temnocephala Dicrotendipes -9.94E-02 -9.61E-02 -9.60E-02 -9.33E-02

Cryptochironomus Coelopynia Pentaneura Nanocladius -8.88E-02 -8.77E-02 -8.66E-02 -8.27E-02

Paratrichocladius Sphaeriidae Kingolus Parachironomus -8.05E-02 -7.64E-02 -6.98E-02 -6.65E-02

Physa Cherax Ischnura Orthotrichia -5.72E-02 -5.19E-02 -5.02E-02 -4.83E-02

Cladopelma Procladius Antiporus Corbiculina -4.59E-02 -4.53E-02 -4.28E-02 -4.17E-02

Kiefferulus Hirudinea Coxelmis Offadens -3.67E-02 -3.31E-02 -3.11E-02 -3.06E-02

Micronecta Cladotanytarsus Apsectrotanypus Hemicordulia -2.90E-02 -2.28E-02 -1.85E-02 -1.57E-02 Hydroptila Ceratopogonidae Hellyethira Sminthuridae -7.66E-03 -3.26E-03 -6.41E-05 2.03E-02

Oligochaeta Cloeon Tachaea Cricotopus 2.28E-02 2.43E-02 2.80E-02 2.84E-02

Hydra Paratya Macrobrachium Caridina 3.02E-02 3.32E-02 4.45E-02 5.10E-02

Nematoda Nemertea Paratanytarsus Austrochiltonia 5.65E-02 5.76E-02 6.21E-02 7.71E-02

Triplectides Matasia Chironomus Paralimnophyes 1.25E-01 1.31E-01 1.72E-01 1.89E-01

Amphipoda Dugesiidae Heterias Glyptophysa 1.95E-01 2.01E-01 2.01E-01 2.05E-01 Ferrissia Acarina Potomopyrgus Amarinus 2.10E-01 2.75E-01 3.49E-01 3.90E-01 Isidorella 4.31E-01

75

PCO3

Nousia Ulmerochorema Riekoperla Asmicridea

-4.12E-01 -3.83E-01 -3.75E-01 -3.69E-01 Thienemanniella Taschorema.complex Coloburiscoides Leptoperla

-3.68E-01 -3.63E-01 -3.53E-01 -3.51E-01 Agapetus Austrosimulium Lingora Austrophlebiodes -3.45E-01 -3.38E-01 -3.37E-01 -3.36E-01

Cheumatopsyche Dinotoperla Triaenodes Notriolus -3.34E-01 -3.25E-01 -3.19E-01 -3.07E-01 Berosus Djalmabatista Austroaeschna Simsonia

-2.97E-01 -2.92E-01 -2.89E-01 -2.70E-01 Anisocentropus Austrolimnius Austroargiolestes Atriplectides

-2.67E-01 -2.62E-01 -2.48E-01 -2.33E-01 Cordulephya Physa Glyptophysa Archichauliodes

-2.19E-01 -2.11E-01 -2.07E-01 -2.00E-01 Potomopyrgus Tipulidae Rheocricotopus Acarina

-1.99E-01 -1.97E-01 -1.95E-01 -1.92E-01 Isidorella Atalophlebia Lancetes Austrogomphus -1.89E-01 -1.88E-01 -1.82E-01 -1.79E-01

Batrachomatus Ferrissia Oecetis Paralimnophyes -1.74E-01 -1.66E-01 -1.54E-01 -1.53E-01

Dugesiidae Hydroptila Sphaeriidae Nemertea -1.44E-01 -1.43E-01 -1.43E-01 -1.40E-01 Microvelia Amarinus Sminthuridae Apsectrotanypus -1.38E-01 -1.29E-01 -1.21E-01 -1.16E-01 Heterias Rheotanytarsis Triplectides Kingolus

-1.16E-01 -1.05E-01 -1.04E-01 -1.01E-01 Antiporus Micronecta Cricotopus Oligochaeta -9.80E-02 -8.44E-02 -8.24E-02 -7.99E-02 Nematoda Empididae Parakiefferiella Rhadinosticta -7.16E-02 -6.82E-02 -6.66E-02 -6.51E-02

Hydra Pentaneura Tanytarsus Corbiculina -6.29E-02 -5.91E-02 -5.83E-02 -5.32E-02

Chironomus Caenidae Paracladopelma Paratanytarsus -5.32E-02 -5.29E-02 -4.96E-02 -4.25E-02

Ceratopogonidae Paratrichocladius Austrochiltonia Apocordulia -3.36E-02 -3.28E-02 -3.04E-02 -2.68E-02

Hellyethira Hirudinea Cladotanytarsus Polypedilum -2.12E-02 -1.41E-02 -1.08E-02 -6.38E-03

Hemicordulia Ablabesmyia Temnocephala Larsia -1.60E-03 -6.77E-04 -5.75E-04 6.61E-06

Cryptochironomus Caridina Cloeon Cladopelma 3.75E-03 3.96E-03 7.39E-03 1.37E-02

Amphipoda Offadens Coxelmis Dicrotendipes 1.63E-02 2.05E-02 2.40E-02 2.89E-02 Ecnomus Nanocladius Procladius Ischnura 2.91E-02 3.63E-02 4.06E-02 4.59E-02

Parachironomus Coelopynia Cherax Paratya 4.86E-02 5.06E-02 5.27E-02 5.29E-02

Stenochironomus Matasia Sternopriscus Tachaea 5.70E-02 5.75E-02 6.23E-02 6.55E-02

Kiefferulus Euastacus Nososticta Macrobrachium 7.04E-02 7.27E-02 8.17E-02 8.84E-02

Orthotrichia 8.93E-02

76

PCO4 Temnocephala Macrobrachium Cloeon Offadens

-0.20407 -0.15533 -0.13444 -0.13204 Dinotoperla Cordulephya Ulmerochorema Taschorema.complex

-0.10447 -0.09633 -0.0962 -0.09139 Parakiefferiella Asmicridea Leptoperla Ceratopogonidae

-0.09137 -0.0887 -0.08595 -0.08333 Tachaea Parachironomus Austrosimulium Caenidae -0.08251 -0.07944 -0.07659 -0.07517

Oligochaeta Nousia Riekoperla Sminthuridae -0.07216 -0.07206 -0.06984 -0.06873 Berosus Coloburiscoides Lingora Agapetus -0.06842 -0.06805 -0.06773 -0.06366

Austrolimnius Triaenodes Cheumatopsyche Thienemanniella -0.06109 -0.05797 -0.05758 -0.05218

Austroaeschna Austrophlebiodes Batrachomatus Cricotopus -0.05099 -0.05033 -0.04762 -0.04683

Anisocentropus Notriolus Simsonia Cladotanytarsus -0.04449 -0.0412 -0.04012 -0.03767 Lancetes Atriplectides Kingolus Djalmabatista -0.03493 -0.03392 -0.03165 -0.02719

Austrogomphus Tanytarsus Austroargiolestes Caridina -0.0248 -0.02124 -0.01339 -0.01025 Physa Hirudinea Antiporus Tipulidae

-0.00128 0.001795 0.003861 0.003881 Paratya Cryptochironomus Pentaneura Corbiculina

0.005836 0.00729 0.008343 0.010118 Sphaeriidae Empididae Orthotrichia Microvelia

0.010818 0.012257 0.018256 0.019738 Nanocladius Rheocricotopus Atalophlebia Nematoda

0.024929 0.026851 0.026884 0.029309 Triplectides Larsia Oecetis Stenochironomus

0.034069 0.037834 0.038827 0.041348 Paratanytarsus Austrochiltonia Coelopynia Cladopelma

0.04178 0.043331 0.046793 0.047156 Hemicordulia Chironomus Amphipoda Dugesiidae

0.050575 0.054025 0.056299 0.057579 Hydroptila Archichauliodes Nososticta Kiefferulus 0.058912 0.061927 0.062999 0.064031

Hellyethira Ischnura Matasia Paratrichocladius 0.064529 0.066787 0.07515 0.076664 Euastacus Nemertea Apsectrotanypus Polypedilum 0.079849 0.090733 0.093879 0.095269

Paracladopelma Procladius Coxelmis Cherax 0.095768 0.096401 0.098056 0.100437 Isidorella Potomopyrgus Ablabesmyia Amarinus 0.105628 0.112281 0.117682 0.119834

Paralimnophyes Ferrissia Rheotanytarsis Acarina 0.120717 0.127125 0.133051 0.134979

Dicrotendipes Micronecta Heterias Ecnomus 0.136633 0.138782 0.152852 0.163956

Hydra Glyptophysa Rhadinosticta Apocordulia 0.164062 0.165178 0.191784 0.20502

Sternopriscus 0.24025

77

PCO5 Austrophlebiodes Agapetus Nousia Taschorema.complex

-0.24478 -0.24256 -0.20781 -0.20536 Coloburiscoides Riekoperla Asmicridea Djalmabatista

-0.20535 -0.2014 -0.19758 -0.19738 Rheocricotopus Thienemanniella Ulmerochorema Austrosimulium

-0.19672 -0.18489 -0.18001 -0.17093 Lingora Cheumatopsyche Apsectrotanypus Paracladopelma

-0.16872 -0.15499 -0.1304 -0.11743 Simsonia Apocordulia Ecnomus Atalophlebia -0.10649 -0.10174 -0.09983 -0.09522

Leptoperla Archichauliodes Notriolus Empididae -0.09157 -0.08807 -0.08697 -0.08483 Tipulidae Parakiefferiella Rhadinosticta Triaenodes -0.08178 -0.07585 -0.07448 -0.06905

Austroaeschna Matasia Amphipoda Isidorella -0.06569 -0.06313 -0.06237 -0.06145

Rheotanytarsis Dinotoperla Austrolimnius Nanocladius -0.061 -0.06034 -0.05812 -0.05531

Heterias Pentaneura Caenidae Macrobrachium -0.05101 -0.0445 -0.02993 -0.02684

Tanytarsus Amarinus Temnocephala Sternopriscus -0.02541 -0.02345 -0.01944 -0.01909

Nososticta Corbiculina Cryptochironomus Oecetis -0.01624 -0.01041 -0.00905 -0.00856 Coxelmis Orthotrichia Atriplectides Chironomus -0.00706 -0.00682 -0.00664 0.000194 Hirudinea Euastacus Berosus Anisocentropus 0.00094 0.00622 0.010956 0.013892

Sminthuridae Hydra Stenochironomus Ablabesmyia 0.014501 0.021049 0.024159 0.025058

Hemicordulia Austrogomphus Austrochiltonia Oligochaeta 0.032353 0.033918 0.039488 0.040508

Austroargiolestes Nematoda Nemertea Potomopyrgus 0.050869 0.052686 0.052817 0.053645 Tachaea Triplectides Parachironomus Paratya 0.059736 0.059747 0.065613 0.067565

Micronecta Cladotanytarsus Acarina Hydroptila 0.072255 0.072477 0.073909 0.074905

Cricotopus Glyptophysa Cladopelma Procladius 0.074937 0.075024 0.077988 0.083245

Polypedilum Dugesiidae Ferrissia Paralimnophyes 0.088251 0.093807 0.093822 0.099263 Offadens Dicrotendipes Coelopynia Ceratopogonidae 0.100449 0.103673 0.105921 0.111364

Cherax Cloeon Cordulephya Physa 0.119947 0.121279 0.123732 0.124316 Caridina Paratanytarsus Sphaeriidae Larsia

0.128353 0.130829 0.13208 0.133766 Paratrichocladius Kingolus Microvelia Ischnura

0.16038 0.161716 0.163491 0.16654 Kiefferulus Hellyethira Lancetes Antiporus 0.171019 0.171045 0.191921 0.206053

Batrachomatus 0.235483

78

PCO6 Archichauliodes Paratrichocladius Tipulidae Rhadinosticta

-0.12951 -0.12928 -0.09846 -0.08703 Caridina Chironomus Lancetes Apsectrotanypus -0.07111 -0.06457 -0.06418 -0.06216

Pentaneura Cherax Paracladopelma Kiefferulus -0.05903 -0.0587 -0.05507 -0.04367

Microvelia Polypedilum Euastacus Ceratopogonidae -0.03997 -0.03924 -0.0369 -0.03673 Isidorella Coelopynia Offadens Nemertea -0.03574 -0.03033 -0.02918 -0.02822

Austrolimnius Nososticta Ablabesmyia Acarina -0.02809 -0.02273 -0.02261 -0.02255

Nematoda Micronecta Austroargiolestes Atalophlebia -0.02194 -0.02062 -0.01877 -0.01816

Hydra Stenochironomus Apocordulia Atriplectides -0.01707 -0.01684 -0.0155 -0.01391

Cladopelma Austroaeschna Sternopriscus Kingolus -0.01057 -0.01006 -0.00676 -0.00577

Djalmabatista Oecetis Procladius Paratya -0.00343 -0.00122 -0.00091 0.000513 Cloeon Triplectides Dicrotendipes Lingora

0.00127 0.005215 0.006837 0.007004 Hellyethira Tachaea Larsia Antiporus 0.009398 0.010054 0.011977 0.01214

Sminthuridae Potomopyrgus Notriolus Thienemanniella 0.012952 0.013583 0.014877 0.01641

Macrobrachium Parakiefferiella Heterias Dugesiidae 0.016934 0.018004 0.018125 0.019093

Austrophlebiodes Nousia Physa Triaenodes 0.020159 0.021077 0.021848 0.02273

Glyptophysa Ecnomus Simsonia Cryptochironomus 0.022878 0.025909 0.026089 0.027152 Hirudinea Hemicordulia Ischnura Cordulephya 0.030151 0.031547 0.03486 0.035315

Parachironomus Austrosimulium Ferrissia Sphaeriidae 0.036114 0.037924 0.03831 0.038388

Dinotoperla Agapetus Coxelmis Batrachomatus 0.041952 0.043407 0.043989 0.045076 Matasia Cheumatopsyche Oligochaeta Corbiculina

0.047361 0.047498 0.047592 0.048526 Anisocentropus Rheocricotopus Leptoperla Austrogomphus

0.049629 0.049783 0.056616 0.057745 Coloburiscoides Empididae Hydroptila Amarinus

0.057786 0.067743 0.073146 0.07442 Temnocephala Paratanytarsus Orthotrichia Nanocladius

0.075129 0.077227 0.07766 0.081098 Tanytarsus Riekoperla Caenidae Paralimnophyes 0.082988 0.083005 0.083374 0.087096

Asmicridea Rheotanytarsis Taschorema.complex Berosus 0.089785 0.089856 0.097088 0.099395

Ulmerochorema Cladotanytarsus Cricotopus Austrochiltonia 0.115994 0.121486 0.140055 0.170457

Amphipoda

79

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