Phytoplankton components and their relation to Descriptors D1 …anemoneproject.eu/wp-content/uploads/files/Workshop_pdf... · 2019. 6. 25. · SOUTH EUROPEAN SEAS (2013-2015, )

Phytoplankton components and

their relation to Descriptors D1 and

D5 assessment and GES definition

in Mediterranean Sea

Kalliopi PagouResearch Director

HCMR-IO

ANEMONE workshop 19-10 June 2019, Istanbul, Turkey

The story:

Within the frame of MSFD implementation in the Southern European Seas and the scientific

support offered to the Competent Authorities and stakeholders of the countires, a series of DG

ENV funded projects have been and will be implemented, which included work on phytoplankton

components regarding the Biodiversity and Eutrophication Descriptors (D1 & D5):

INTEGRATED REGIONAL MONITORING IMPLEMENTATION STRATEGY IN THE

SOUTH EUROPEAN SEAS (2013-2015, www.iris-ses.eu)

ACTION PLANS FOR INTEGRATED REGIONAL MONITORING PROGRAMMES,

COORDINATED PROGRAMMES OF MEASURES AND ADDRESSING DATA AND

KNOWLEDGE GAPS IN MEDITERRANEAN SEA (2015-2017, www.actionmed.eu)

SUPPORT MEDITERRANEAN MEMBER STATES TOWARDS IMPLEMENTATION OF

THE MARINE STRATEGY FRAMEWORK DIRECTIVE NEW GES DECISION AND

PROGRAMMES OF MEASURES AND CONTRIBUTE TO REGIONAL

/SUBREGIONAL COOPERATION (2019-2021, www.medregion.eu)

SUPPORT MEDITERRANEAN MEMBER STATES TOWARDS COHERENT ANDCOORDINATED IMPLEMENTATION OF THE SECOND PHASE OF THE MSFD (2017-2019,www.medcis.eu)

Among IRIS-SES aims were:

• To prepare a catalogue and meta-data base (NIMRD &TUBITAK) and a comprehensive analysis of the existingmonitoring programs related to:

• European Directives• Regional Conventions• National legislation• Projects

• To assess the contribution of these programs to meetMSFD needs.

Cyprus France Greece Italy Spain Turkey Bulgaria Romania TurkeyD Programme UNEP/MAP BSC

1,4,6

Birds ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

Mammals ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

Fish ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

Seabed habitatsPhytobenthos ✓ ✓ ✓

✓✓ ✓ ✓ ✓

Seabed habitatsZoobenthos ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

Water column habitatsPhytoplankton ✓ ✓ ✓

✓✓ ✓ ✓ ✓

Water column habitatsZooplankton ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

2 Non Indigenous Species ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

3Commercial Fish and Shellfish ✓ ✓ ✓ ✓ ✓ ✓ ✓

5 Eutrophication ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

7 Hydrographical changes ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

8

Contaminants in water ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

Contaminants in sediments ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

Contaminants in biota ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

9 Contaminants in seafood ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

10 Marine Litter ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

11 Energy&Noise ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

Water column habitats, phytoplankton: A) Maps of monitoring stations, B) Density of sampling (number of stations within each grid cell), C) Frequency of sampling (average frequency of the stations within each cell)

and D) Sampling effort (number of stations*average frequency of sampling)

FOLLOWING, SOME OUTPUTS FROM IRIS-SES PROJECT REGARDING EXISTING PROGRAM SARE SUMMARIZED:

• MEDIAS surveys potential improvements: description, opportunities and difficulties

• MEDPOL eutrophication monitoring as MSFD JMP: structure of thereport

• MEDPOL pollutants: suggestions of improvement

• WFD monitoring: Usefulness for MSFD requirements

• MPAs monitoring programs in the Med, analysis of current stateand suggestions for improvement

• MEDITS pilot project: adaptation to MSFD requirements

•Descriptor 1: Biological diversity (ongoing pelagic

trawl, additional night plankton tows…)

•Descriptor 2: Non-indigenous species diversity (ongoing

pelagic trawl, additional night plankton tows…)

•Descriptor 3: Populations of commercial fish / shell fish

•Descriptor 4: Food webs (from Pel. Trawls samples)

•Descriptor 5: Eutrophication (additional plankton sampling)

•Descriptor 7: Alteration of hydrographical conditions (CTDs)

•Descriptor 8: Contaminants in sediments (additional

dredges)

•Descriptor 9: Contaminants in biota (from Pel.Trawl samples)

•Descriptor 10: Marine litter (additional plankton tows for

microplastics)

Magdalena Iglesias, IRIS_SES; Mayo 2015

Thus, in the prospected shelf areas, MEDIAS surves

could potentially address, at relatively low cost (extra

team and crew for night work, additional fuel).

In green: directly addressed; in red, potentially adressed from actually available samples; in

blue, potentially adressed through feasible complementary additional sampling

Identification of the “need of information”The ongoing revision of the Decision 2010/44/EC on GES criteria and of MSFD Annex III should be taken into account

Information collection strategy

Requirement with respect to the data

Monitoring strategy

An analitical method based on satellite images useful fordetermining the representativitiy of the sampling sites is proposed .A guidance for the collection of samples for nutrients, chlorophyll and phytoplankton is proposed to guarantee the inter-comparability ofthe data among the national monitoring programs (position of thesampling station, depth) .

Different analytical techniques for estimating chlorophyll a concentration in laboratory are compared (spectrophotometric vs. fluorimetric method). The use of an unique technique is proposed.Adoption of common statistical criteria for reporting data and evaluating the effort of sampling is proposed

The use of the proposed JMP program to collect information forother descriptors is discussedDifferent proposals for optimising samplings are presented.

Summing up, the eutrophication monitoring strategy of MEDPOL has been analysedaccording to the application of the so-called “monitoring cycle” that consists of discriminating between what information and data are required and how the data are collected, following the next steps (Zanpoukas et al. 2014, JRC):, and the following recommendations are proposed:

1. CONDITIONS (SUBSTRATE, CURRENT DIRECTION AND SPEED2. BENTHOS3. PRIORITY SUBSTANCES

QUALITY ELEMENT WFD PARAMETER MSFD DESCRIPTOR

PARAMETERS & INDICATORS

HYDROMORPHOLOGICAL SUBSTRATECURRENT VELOCITY, SPEED

D6, D7, D1 GRANULOMETRYCURRENT VELOCITY, SPEED

PHYSICOCHEMICAL CONDITIONS

NUTRIENT CONDITION, TEMPERATURE, SALINITY, TRANSPARENCY, TN, TP, DISSOLVED OXYGEN

D1, D5, D7 NUTRIENT CONDITION, TEMPERATURE, SALINITY, TRANSPARENCY, TN, TP, DISSOLVED OXYGEN, PARTICULATES, EI INDEX

BIOLOGICAL ELEMENTS PHYTOPLANKTON, BENTHIC MACROINVERTEBRATES, MACROALGAE, ANGIOSPERMS

D1, D2, D5, D6

BENTHIC MACROINVERTEBRATES BENTIX, MULTIMETRIC , MACROALGAE (EEIc) PREI

SPECIFIC CONTAMINANTS PRIORITY SUBSTANCES CHEMICAL STATUS ANNEX X

D8 TRACE METALS AND PRIORITY SUBSTANCES

The report on the applicability of WFD monitoring systems to

MSFD descriptors have identified the following links

From: Biological Quality Elements acquired through the WFD monitoring

address several Descriptors of the MSFD (Simboura et al. 2015):

Main aim of ActionMed:

The main aim of ActionMed was to support and improve the

implementation of the MSFD across the Mediterranean focusing

on the needs of its five steps, in close collaboration with the

RSC (UNEP/MAP) and its Ecosystem Approach.

The project:

❖ reviews the initial assessment, the GES definition and the

environmental target setting in 2018, (Art. 8, 9, 10) with emphasis on

biodiversity,

❖ develops integrated, coordinated and financially sustainable regional

Action Plans (short, mid-term, long-term) and best practices for

monitoring programmes (Art. 11), and programmes of measures (Art.

13), test their implementation and finally

❖ supports the establishment of an information Management System to

fill data gaps for Mediterranean marine waters.

• Within ActionMed we performed a review on how EU MSs deal with biodiversity indicators in

their IAs, identifying the commonalities and differences between nationally selected indicators,

by considering important information sources with respect to biodiversity

descriptors/indicators, GES, and targets, such as EU research projects (i.e. PERSEUS,

DEVOTES, EMBOS etc.).

• Best practices of biodiversity tools of other regional approaches (i.e. OSPAR biodiversity

assessment, HELCOM Biodiversity Assessment Tool (BEAT); MARMONI Marine Biodiversity

Assessment Tool etc) were also considered, in order to categorize indicators and produce an

indicator inventory.

• The produced inventory of biodiversity descriptors, being a freely available e-catalogue,

has been published for open consultation on the ActionMed webportal

(http://actionmed.eu/explore-the-electronic-catalogue-on-biodiversity-descriptor/).

• Advanced functions of the catalogue, linking descriptors/indicator to metrics for computational

purposes were available by the end of the project on the LifeWatch-ITA webportal, with

application to case studies.

• Also we collected information to build study cases in the biodiversity databases of marine

LifeWatch Virtual Research Environments (VREs), to be made available as in kind

contributions by LifeWatch-ITALY and LifeWatch-GREECE, in order to develop experimental

tests of uncertainty associated to biodiversity indicators currently used by MSs or scientific

networks.

• The case studies have been applied on an idealised transect from inland brackish water to the

open sea, to take also into account the natural gradient of nutrients, with decreasing

concentrations along increasing distance from the coastal source of freshwater/anthropogenic

effluents and nutrient input.

• Finally, a GIS tool related to Biodiversity (D1) and Seafloor Integrity (D6) descriptors

developed as a toolbox, in order to assess the degree of vulnerability of the benthic habitats

to human stressors. The design of the GIS tool has been based on the outcomes of the

UNEP/MAP Biodiversity online working group, the HELCOM Baltic Sea Impact (2010) and the

OSPAR BH3 indicator addressing the pressures causing physical damage to the seafloor

habitats. Thus, the main concept of the GIS tool is to relate the sensitivity of

Mediterranean priority habitats to the pressure type and intensity.

Electronic catalogue on Biodiversity Descriptor

➢ The IRIS-SES GIS toolboxes.

D5 – Eutrophication and

D8, D9 - Contaminants

➢ Improvements of the IRIS-SES toolboxes

further improvements of the existing desktop

version

development of the web version

➢ Designing the biodiversity GIS tool/toolbox

In ActionMed we engaged to:

As additional deliverable to the case studies task of ActionMed, a web-

tool for phytoplankton dataset has been produced and available on the

ActionMed website (http://actionmed.eu/).

The webtool is able to calculate a large number of phytoplankton

community indicators and provides support for correlation, similarity and

time series analysis.

The webtool allows:

1) the analysis of the datasets described in a study case;

2) the upload of phytoplankton datasets organized according to the

LIFEWATCH standards

We embedded layer coastlines & Marine Regions

Automatic input as predefined polygon features; it facilitates index calculations for the Eutrophication tool

Eutrophication Status Toolbox

D5 tool: How it looks

2. Index

Selection

3. Table

selection

o Western Mediterranean

o Eastern Mediterranean

o Black Sea

Chl-α, Nutrients (PO4,

NO3, NH4 etc), EI,

TRIX

Table contains required parameters

for the selected index calculation

4. Table to Feature

class

Coordinate system selection by user

5. Feature class

projection

Workflow Scheme (I)

1. Area

Selection

station lon lat PO4 NO3 …

S1

S2

S3

S4

S5

…

XY Table required for the

processing of the

selected index calculation

Point Feature class for

raster datasets creation

6. Field Selection

7. Interpolation parameters Input

Workflow Scheme (II)

9. Index calculation

10. Model output

PO4, NO3,

NO2, NH3, Chl-

α

Used interpolation method:

spline with barriers

Raster resolution entered by user

as output raster datasets cell size

Raster

algebra to

apply the

index formula

(if needed)

8. Raster Datasets creation

PO4 grid,

NO3 grid,

NO2 grid,

NH3 grid

Chl-α

grid

EI model outputSaronikos gulf

Input No 1:Eutrophication indices criteria for Greece/Eastern Mediterranean

Chl-α indexProposed scaling Classes according to

requirements of WFD

< 0.1 High

0.1 - 0.41 Good

0.4 - 0.61 Moderate

0.6 - 2.211 Poor

> 2.211 Bad

Eutrophication

IndexProposed scaling Eutrophication

status

Classes according to

requirements of WFD

< 0.04 High

0.04 - 0.38 Oligotrophy Good

0.38 - 0.85 Mesotrophy Moderate

0.85 - 1.51 Eutrophication Poor

> 1.51 Bad

TRIXProposed

scaling

Eutrophication

status

Classes according to

requirements of WFD

2 - 4 Ultra oligotrophic High

4 - 5 Oligotrophy Good

5 -6 Mesotrophy Moderate

6 - 8 Eutrophic Poor

> 8 Dystrophic Bad

➢ The required table is a simple Excel file which contains:➢ longitude and latitude for each sampling station. ➢ the concentrations of all required parameters

➢ Values of all parameters should be mean integrated for both water column and time.➢ Mean integrated values can be computed within Excel utilizing pre-

defined macro commands.

name lon lat PO4_μM NO2_μM NO3_μM NH4_μM Chl-a DO DIN_mgL-1 PO4_mgL-1

S7 23.59083 37.92367 0.083 0.230 0.255 0.350 0.740 2.072 32.647 7.928

S43 23.58717 37.87783 0.033 0.177 0.096 0.145 0.208 3.034 16.700 3.134

S11 23.63833 37.87267 0.033 0.166 0.101 0.098 0.266 3.073 15.664 3.140

S16 23.70067 37.78717 0.035 0.299 0.285 0.069 0.196 0.638 32.691 3.341

S13 23.45500 37.84083 0.047 0.214 0.061 0.155 0.193 3.045 16.401 4.478

S8 23.53333 37.88333 0.050 0.217 0.096 0.153 0.382 3.118 18.651 4.784

S3 23.58333 37.95000 0.066 0.214 0.678 0.683 0.924 1.637 64.152 6.286

Excel file for the calculation of Eutrophication status indices

Input No 2: data

D5: Eutrophication status examples(chl-α index)

Saronikos Gulf - Greece

1998-1999

2000 - 2001

2001 - 2002

2002 - 2003

2005 - 2006

2007 - 2008

2008 - 2009

DESIGNING THE BIODIVERSITY

GIS TOOL/TOOLBOX

the main objectives

➢ Assessment of Environmental Status related to Biodiversity - MSFD D1

➢ Unified platform for the Mediterranean Sea take into account the sub-regions according to IECS

➢ Addressed to scientists, decision makers

➢ Simple tool & user friendly environment

➢ Calculation and visualization of

- habitats sensitivity related to pressure intensity (? )

- relevant Parameters or Indices according to predefined thresholds (? )

the materials

➢ Model builder – ArcGIS 10.3

➢ Toolboxes – ArcGIS 10.3

➢ Python Programming Language 2.7

the product

➢ Toolbox for ArcGIS Desktop and Server (?)

Habitats sensitivity related to pressure intensity

Based on

➢ the draft list of species–habitats by UNEP (WG420_3 and 4 Annex I)

including

➢ the priority habitats–species according the WG420_3 and 4 Annex I

concerning

➢ the main pressures according the WG420_3 and 4 Annex I

Activity 1:“Systematic solutions of current gaps and needs in relation to articles 8, 9, 10 of the MSFD. Focus on biodiversity”

Subtask 1.1.1: General overview of MSFD

Participants: UoA (lead), UTH, Univ of Salento, HCMR, IEO, NIB, UNEP/MAP, CORILA, CNR-ISMAR

Analysis of MSFD reporting on Descriptors 1, 4, 6 and 7 in the Mediterranean; Defining GES and Establishing targets in the first

MSFD cycle.

Descriptors 5, 8 and 9; examining the heterogeneity of methodological approaches and standards among

Mediterranean MSs in the 2012 MSFD reporting exercise.

Art9 /DESCRIPTOR 1Criteria Indicators

1.1. Species distribution

Distributional range (1.1.1) Distributional pattern within range, where appropriate (1.1.2)

Area covered by the species (for sessile/benthic species) (1.1.3) X

1.2. Population size Population abundance and/or biomass, as appropriate (1.2.1)

1.3. Population condition

Population demographic characteristics (e.g. Body size or age class structure, sex ratio, fecundity rates, survival/mortality rates) (1.3.1)

Population genetic structure, where appropriate (1.3.2) X

1.4 Habitat distribution Distributional range (1.4.1) Distributional pattern (1.4.2)

1.5 Habitat extent Habitat area (1.5.1)Habitat volume (1.5.2)

1.6 Habitat condition

Condition of the typical species and communities (1.6.1)

Relative abundance and/or biomass, as appropriate (1.6.2)

Physical, hydrological and chemical conditions (1.6.3)

1.7 Ecosystem structure Composition and relative proportions of ecosystem components (habitats & species) (1.7.1)

➢All Med MSs defined GES for D1 following COMDEC 2010/477/EU mentioning at least one component from species, habitats or ecosystem levels to varying levels of detail; level of coherence is moderate. ➢green most popular /red: least popular➢ Low Level of Integration with other Directives➢Low level of Regional/ International Cooperation

.

Criteria Indicators

5.1 Nutrients levels

Nutrients concentration in the water column (5.1.1)

Nutrient ratios (silica, nitrogen and phosphorus), where appropriate (5.1.2)

5.2 Direct effects of nutrient enrichment

Chlorophyll concentration in the water column (5.2.1)

Water transparency related to increase in suspended algae, where relevant (5.2.2)Abundance of opportunistic macroalgae (5.2.3)

Species shift in floristic composition such as diatom to flagellate ratio, benthic to pelagic shifts, as well as bloom events of nuisance/toxic algal blooms (e.g. cyanobacteria) caused by human activities (5.2.4)

5.3 Indirect effects of nutrient enrichment

Abundance of perennial seaweeds and seagrasses (e.g. fucoids, eelgrass and grass) adversely impacted by decrease in water transparency (5.3.1)Dissolved oxygen, i.e. changes due to increased organic matter decomposition and size of the area concerned (5.3.2).

• All MSs reported under D5 incorporating the definition provided by COM Dec 2010/477/EU; level of coherence MODERATE

• Colour coded; Most popular

DESCRIPTOR 5

Activity 1:“Systematic solutions of current gaps and needs in relation to articles 8, 9, 10 of the MSFD. Focus on biodiversity” Subtask 1.1.1 General overview of MSFD

GAPS▪Lack of methodological approach▪ Lack of thresholds and baselines▪ Lack of data or availability of fragmented data▪Lack of knowledge and/or full understanding of characteristics/compounds

and

NEEDS▪Need of regular and specific monitoring programs and impact assessment studies▪ Need of a regional approach in regards to monitoring ▪Need to establish threshold values and determine baselines ▪ Need to integrate EU legislation ▪Need to collaborate in the framework of the Barcelona Convention▪Need to expand our knowledge about detected issues ▪Need of research programmes

Activity 1:“Systematic solutions of current gaps and needs in relation to articles 8, 9, 10 of the MSFD. Focus on biodiversity” Subtask 1.1.3. Indicator study case

➡Study cases on two biological elements:➡Phytoplankton;

➡Benthic macroinvertebrates.

➡Uncertainty analysis on:➡Methodological uncertainty;

➡GES assessment uncertainty

➡Data availability from:➡ActionMed partners;

➡LifeWatch data resources


Phytoplankton study case

Multimetrics

Composition metrics


Phytoplankton study case

DISTRIBUTIONTAXONOMIC RATIOTAXONOMIC DIVERSITY

AC T I V I TY 2Gaps, Needs and Actions to

implement for national and regional

monitoring for MSFD programmes

(Art. 11)

Activity 2 Deliverables:

➢ Georeferenced Database containing relevant data on national MSFD

monitoring programs proposals submitted by Mediterranean EU countries.

➢ Report on gaps of national MSFD monitoring programs proposals and

evaluation of their coherence at regional level.

➢ Report on pilot action on the integration of automated continuous

monitoring systems (moorings) and offshore plankton pelagic sampling,

addressing descriptors 1, 4 and 10 within routine mesoscale hydrographic

surveys.

➢ Report on pilot study on the development of reliable indicators, parameters

and thresholds for GES determination within descriptor 1, based on key

protected species monitoring in infralittoral bottoms.

➢ Mid and long term Action Plans for overcoming the detected gaps and

inconsistencies, and ensure the coherence among Mediterranean MSs MSFD

monitoring plans.

PHYTOPLANKTON INDICATORS AND ECOLOGICAL QUALITY ASSESSMENT

ACCORDING TO THE MSFD AND WFD REQUIREMENTS:

A REVIEW FOR EASTERN MEDITERRANEAN COASTAL WATERS .

Ioanna Varkitzi, Kalliopi Pagou, Georgia Assimakopoulou

HCMR, Institute of Oceanography

Many of the phytoplankton and zooplankton indicators can

respond quickly to changes in the environment and therefore give

fast feedback about changes in food webs and ecosystems, “early

warning indicators”.

However, the number of operational phytoplankton indicators for

the Mediterreanean Sea is very low.

On the other hand, in NE Atlantic for example, phytoplankton,

benthic invertebrates and fish are the three biological components

for which there is the highest number of operational indicators

available.

In Eastern Mediterranean waters a lot of work has been done since the

1990s towards an ecological evaluation system based on the

pressure-impact relationship.

Pressure = Nutrients, indices etc and

Impact = Phytoplankton biomass (as chl-a), abundance etc

EutrophicationScale

LUSI IndexEutrophication

IndexOthers

INTRODUCTION FOR GREECE AND CYPRUS FROM MEDGIG REPORT

GR and CY waters

Med water bodies are categorized with hydrographical and physicochemical

characteristics, due to their heterogeinity.

Among Med MSs, only coastal waters of GR and CY belong to the Eastern Mediterranean

basin Type III E (mean salinity >37.5).

MEDGIG Intercalibration exercise between these 2 countries goes on.

Description SP FR IT HR SI GR CY

Type IHighly influenced by freshwater

inputX X

Type IIModerately influenced by

freshwater inputX X X X X

Type III W Not influenced by freshwater input X X X X

Type III E Not influenced by freshwater input X X

Trophic status

Parameter OligotrophicLower

mesotrophic

Upper

mesotrophicEutrophic

Ν-ΝΟ3 (μΜ) <0.62 0.62 - 0.65 0.65 - 1.19 >1.19

Ν-ΝΗ4 (μΜ) <0.55 0.55 - 1.05 1.05 - 2.20 >2.20

Ρ-ΡΟ4 (μΜ) <0.07 0.07-0.14 0.14 - 0.68 >0.68

Chlorophyll-a (μg/L) <0.10 0.10 - 0.60 0.60 - 2.21 >2.21

Phytoplankton

abundance (cells/L)<6 103

6 103 - 1.5

105

1.5 105 - 9.6

105>9.6 105

Parameter values derived from a gradient of sites with high to bad status.

PCA (with log-normal transf.) to define the class boundaries.

Four step scale.

Most widely used eutrophication assessment method in east med

Eutrophication scale

Ignatiades et al. (1992), Karydis (1999), Pagou et al. (2002). M

E

T

H

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D

1

In order to fit to the WFD five-step ecological status scale,

Chl-a range was adapted by Simboura et al. (2005).

Eutrophication

scale

Chlorophyll-a

(μg/l)

Ecological

Status

Oligotrophic < 0.1 High

Lower mesotrophic 0.1 – 0.4 Good

Mesotrophic 0.4 – 0.6 Moderate

Higher mesotrophic 0.6 – 2.21 Poor

Eutrophic >2.21 Bad

RC and boundaries for Type III-E waters in terms of Chl-a (μg/l) and EQR.

Type of waters Type III-E

Ref. Conditions (annual average Chl-a μg/l) 0.08

Boundaries for Chl-a

(annual avg Chl-a μg/l)H/G 0.1

GES threshold G/M 0.4

Failed >0.4

Boundaries for EQR H/G 0.8

G/M 0.2

Failed <0.2

M

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1

Another methodology tested for the BQE phytoplankton in GR and CY waters was

based on the pressure-impact rational,

using LUSI Index (Flo et al. 2011) to estimate pressures

and

a phytoplankton biomass indicator to estimate impacts,

i.e. Chlorophyll-a in sea water,

in the frame of intercalibration exercises, which are in progress.

Dataset involved 30 sites from the WFD network in GR and

17 sites from CY from both pristine and impacted coastal water bodies.

For LUSI Index (Flo et al., 2011), the CORINE 2000 (EEA, 2013) was

used,

and Chl-a was expressed in P90th percentile values in μg/l (after log-

transf). Linear model significant at 95% confidence level.M

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2

Estimated coefficient

Intercept 0.05061

LUSI 0.03643

R-squared 0.101533

Correlation

coefficient

0.31864

Goodness of LM fitted approximately 10% and the correlation coefficient indicated a

weak relationship between the variables LUSI and Chla

Plot of Fitted Model

Lusi

Ch

la t

ran

sf

0 1 2 3 4

0

0,2

0,4

0,6

0,8Greece and Cyprus

y = 0.03643 * x + 0.05061

R2 = 0.101533

P<0.05


Lusi

Ch

la t

ran

sf

0 1 2 3 4

0

0,2

0,4

0,6

0,8Greece and Cyprus

y = 0.03643 * x + 0.05061

R2 = 0.101533

P<0.05

Chl-a transformed = 0.03643*LUSI + 0.05061


LUSI

Ch

la t

ran

sf

0 1 2 3 4 5

0

0,1

0,2

0,3

0,4Greece

y = 0.03628 * x + 0.01177

R2 = 0.2025

P<0.01


LUSI

Ch

la t

ran

sf

0 1 2 3 4 5

0

0,1

0,2

0,3

0,4Greece

y = 0.03628 * x + 0.01177

R2 = 0.2025

P<0.01M

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2

Only GR data

Intercept 0.01177

LUSI 0.03628

R-squared 0.2025

Correlation coefficient 0.4126

Goodness of LM fit approximately 20% and the correl. coef. indicates

a relatively weak relationship between the variables LUSI and Chla

Chl-a transformed = 0.03628*LUSI + 0.01177

M

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2

Only CY data

Intercept 0.070

LUSI 0.013

R-squared 0.332

Goodness of LM fit approximately 33% and indicates

a relatively weak relationship between the variables LUSI and Chla

Chl-a Transformed = 0.070*LUSI + 0.013

EI score Ecological Status

< 0.04 High

0.04 - 0.38 Good

0.38 - 0.85 Moderate

0.85 - 1.51 Poor

> 1.51 Bad

M

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3

EI score Eutrophication Status

0.04-0.38 Oligotrophic

0.37-0.87 Mesotrophic

0.83-1.51 Eutrophic

Ecological and eutrophication assessment in coastal waters based on

Eutrophication Index (ΕΙ) by Primpas et al. (2010)

EI = 0.261*NO3 + 0.296*NO2 + 0.275*NH3 + 0.279*PO4 + 0.214*Chl-a

GES threshold 0.38

In three Mediterranean coastal areas with different ecological conditions:

1) Adriatic, Gulf of Trieste

2) E. Mediterranean, Saronikos Gulf, Aegean Sea

3) Black Sea, Bay of Varna.

Monthly resolution in one station per site for the years 2000-2012.

Assessment on the basis of EI.

Time evolution of environmental status

over the years 2000-2012 based on EI in

the frame of Perseus project, HCMR.

M

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3

Gulf of Trieste

R2 = 0,0499

0

1

2

3

4

5

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

YearE

nvir

on

men

tal sta

tus High

Good

Moderate

Poor

Bad

The inter-annual Environmental status in the Gulf of Trieste was Moderate.

The overall trend of quality was slightly decreasing.

M

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H

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D

3

The inter-annual Environmental status in Saronikos Gulf was Moderate.

The overall trend of quality was clearly increasing.

Saronikos Gulf

R2 = 0,7238

0

1

2

3

4

5

6

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

Year

High

Good

Moderate

Poor

Bad

M

E

T

H

O

D

3

The inter-annual Environmental status in the Bay of Varna was Moderate.

The overall trend of quality was slightly increasing.

Bay of Varna

R2 = 0,0921

0

1

2

3

4

5

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

Year

High

Good

Moderate

Poor

Bad

M

E

T

H

O

D

3

M

E

T

H

O

D

3

Testing EI in a river influenced coastal area.

Maliakos Gulf monitoring from April 2014 to September 2015

monthly or bimonthly, KRIPIS project, HCMR

CONCLUSIONS

Chlorophyll α is the most commonly used metric for phytoplankton communities,

because it is a simple and integrative measure of the phytoplankton community

response to nutrient enrichment.

Eutrophication index also seems to be quite efficient for Εastern Mediterranean

waters assessment and consistent with chl-a assessment.

CONCLUSIONS

However, phytoplankton community structure gives different information.

For example, an increase in Chl-α due to eutrophication is usually accompanied by

changes in phytoplankton community structure:

total abundance, species richness, and evenness.

The structure of phytoplankton communities seems to be more robust to the

changes due to eutrophication than chlorophyll a. Maybe the use of multimetric

indices combining various phytoplankton metrics is appropriate for an integrated

water quality assessment.

Using a holistic ecosystem-integrated approach to assess the environmental status of Saronikos Gulf, Eastern Mediterranean.

A. Pavlidou, N. Simboura, Κ. Pagou, G. Assimakopoulou, V. Gerakaris, I. Hatzianestis, P. Panayotidis, M. Pantazi, N. Papadopoulou, S. Reizopoulou, C. Smith, M. Triantaphyllou, M. C. Uyarra, I. Varkitzi, V. Vassilopoulou, C. Zeri, A. Borja

MEDCIS Final Scientific Conference 30 January 2019, Brussels

Within Activity 2 of MEDCIS Project, several tasks and subtasks intend to collateinformation and propose tools to assess the environmental status under theMSFD. In relation to this, one of the Milestones was MS2.2 “A specific andadapted case study for the Mediterranean in NEAT software” that could beused as model to be extended for the whole Mediterranean.

The case study was chosen to be the Saronikos Gulf (Greece), and we testedthe Nested Environmental status Assessment Tool (NEAT; Borja et al., 2016).

Introduction

Scope: To test NEAT’s applicabilityunder different circumstancesand demonstrate to scientists,managers and stakeholders(those implementing the MSFD)that NEAT tool is able to catchspatial and temporal variations,in which the program ofmeasures should be based.

1984-1994. Construction of WWTP-First Stage : Primary Treatment

1999-2004. Second Stage : Advanced Secondarybiological achieving suspended solids and organic load reduction by about 93% and total nitrogen reduction by about 80% in comparison with influent loads. Part of the effluent undergoes filtration (through sand-filters) and disinfection (by means of UV devices) so as to be reused as process water for the facilities on PsyttaliaIsland.

2007: Construction of sludge thermal drying unit.

The Psittalia WWTP is the main wastewater treatment plant in the greater Athens area with capacity of 5.600.000 p.e., receiving an average wastewater flow of approximately 750.000 – 800.000 m3/d.

PsyttaliaA: Until 1994: No TreatmentB: 1995-2004: Primary TreatmentC: 2005-2016: Advanced Secondary Treatment

• There are enough spatial and long-termtemporal data, from 9 ecosystemcomponents.

• 24 indicators and 8 descriptors of theMSFD, as well as pressures.

Why does the Saronikos Gulf provide the proper case study for the NEAT application?

NEAT was applied to Saronikos Gulf by testing:

• Different SAUs,

•Ecological components and

•Indicators.

The method can be used to aggregate all indicators of a SAU and show the status divided among the different ecosystem components of the SAU.

NEAT ASSESSMENT METHOD

SARONIKOS CASE STUDY

WWTP

SAUs:Psitallia outfallInner Saronikos GulfOuter Saronikos GulfElefsis BayWestern Basin

S7: at Psitallia sewage outfall

S8: 6.81 Km southwest of Psittalia

S13: 15.3 Km southwest of Psittalia

S11: 7.44 Km southeast of Psittalia and

S16: 18.1 Km southeast of Psittalia

S1, S2: Elefsis BayS25: Outer Saronikos Gulf


WWTP

S7-Psittalia Sewage Outfall: Heavily PollutedInner Saronikos Gulf: Moderately PollutedOuter Saronikos: Slightly PollutedWestern Saronikos: Moderately PollutedElefsis Bay: Heavily Polluted

Pressure Index classification:

Pressure Index (P.I.) estimated in: Simboura et al. (2016) and Pavlidou et al. (2015).

Spatial Assessment Units (SAU), habitats, ecosystem components and unique indicators included inNEAT calculations for Saronikos Gulf. The source for GES thresholds (i.e. boundary between goodand moderate) used for each indicator and the related MSFD (Marine Strategy Framework Directive)descriptors (D) are indicated. EQR: Ecological Quality Ratio; GES: Good Environmental Status.

SAUs Area (km2) Habitats Ecosystem

components

Indicators (and related MSFD Descriptor) GES thresholds

(EQR; Bits)

Source

Saronikos Gulf 2973.9 Pelagic and Benthic Alien Species CIMPAL index (D3) 10 Katsanevakis at al., 2016

Psittalia 64.96 Benthic

(sedimentary)

Benthic Fauna BENTIX (D1, D6)

Multimetric BENTIX (D1, D6)

Foraminifera stress index (D1, D6)

Index of size distribution (D1, D6)

3.5

0.60

0.55

0.39

Simboura & Zenetos, 2002

Simboura et al., 2015

Dimiza et al., 2016

Reizopoulou & Nicolaidou, 2007

Inner

416.99 Benthic (rocky) Benthic Vegetation/

macroalgae

Ecological Evaluation Index (D1, D6) 0.48 Orfanidis et al., 2001;2011

Benthic

(sedimentary)

Benthic Vegetation/

Seagrasses

Posidonia Rapid Evaluation Index (D1, D6) 0.55 Gobert et al., 2009

Outer 1296.24 Pelagic Mammals

Fish

% Threatened mammals (D1, D4) % Threatened sharks (D1, D4) % Stocks that meet GES based on fishing mortality (D3) % of stocks that meet GES based on reproductive capacity (D3) % of stocks that meet GES based on reproductive capacity and biomass indices (D3)

0.35 0.35 0.65 0.65 0.65

Pantazi et al., 2015b; Frantzis 2009 Pantazi et al., 2015b; Papaconstantinou. et. al., 2014 Pantazi et al., 2015b; Greek Initial Assessment Report. 2013 Pantazi et al., 2015b; Greek Initial Assessment Report. 2013 Pantazi et al., 2015b; Greek Initial Assessment Report. 2013

Elefsis Bay 71.48 Pelagic Phytoplankton Chlorophyll-a (D5) 0.37 Pagou et al., 2017; GIG, 2014

Western Basin 1124.23

Benthic

(sedimentary)

Sediments

Organic Carbon (D5, D6, D8)

Aliphatic Hydrocarbons (D8)

Polyaromatic Hydrocarbons Σ48 (D8)

0.7 μg g-1

Pantazi et al., 2015a

200 μg g-1

1 μg g-1

Expert Judgment

Expert Judgment

Pelagic Water column

(euphotic zone)

Nitrates (D5)

Phosphates (D5)

Zinc (D8)

Lead (D8)

Copper (D8)

Cadmium (D8)

Physicochemical Quality Index (D5, D7)

Eutrophication Index (D5)

1 μmol L-1

Phillips et al., 2017

0.1 μmol L-1

Phillips et al., 2017

115 nmol L-1

2.6 nmol L-1

8.26 nmol L-1

0.574 nmol L--1

0.62

0.38

Paraskevopoulou et al., 2014




Bald et al, 2005; Simboura et al., 2016

Primpas et al., 2010


Analyses Performed

• Area Analysis: Non-Weighting by SAU

• Area Analysis: Weighting by SAU

• Area Analysis: Filtered by Descriptor.

• Analysis before and after the upgrading the WWTP


The application of NEAT tool in Saronikos Gulf classified the whole Saronikos Gulf basin into GOOD status under both treatments.

Psittalia and Elefsis bay, the most impacted sub-areas of Saronikos Gulf were classified into MODERATE and POOR status, respectively.

Non-Weighting by SAU

Weighting by SAU

Area Analysis: Filtered by Descriptors. Non-weighted by SAU

Organic sediment contamination, benthic fauna and vegetation, mammals and alien species are the most impacted ecological components in Saronikos Gulf.

AIM: Which descriptor is more affected by a human pressure.

• NEAT shows gradients of indicators/status according to the distance from pollution source.

•The NEAT results (weighted or not in relation to SAU area) were consistentwith the known pressures and the pollution intensity in the different SAUsof Saronikos Gulf, as it was expressed by the pressure index which waslinearly correlated with the NEAT at the 95% confidence level.

•NEAT demonstrates the effectiveness of management measures taken (Psittalia WWTP), showing temporal changes.

• NEAT results for Saronikos Gulf are in line with previous assessments of the area according to the known pressures.

• NEAT shows also the Overall Status changes of Saronikos, using long data series (2000-2016) and a large number of indicators and SAUs.

• The results can be shown at different geographical levels, MSFD descriptors and ecosystem components.

CONCLUSIONS

• With NEAT is possible to integrate data from different sources, spatial and temporal scales and from different ecosystem components into a unique value;

• Despite the integration in NEAT there is not a loss in tracking the problems that should be addressed at the indicator, ecosystem component, descriptor or smaller spatial levels;

• This track of the problems is clearly related with the pressures identified and the pressure index used to validate the assessment undertaken using NEAT;

• All of these findings and conclusions could be very useful for managers, policy makers and scientists when deciding the method to use in assessing and communicating the health status

CONCLUSIONS

Pelagic Habitats as Biodiversity component included in the

COMMISSION DECISION (EU) 2017/848

Descriptor 1: Biodiversity

Issues identified:

According to the latest revision of the GES definitions,

all 8 Mediterranean MSs have defined GES at the Biodiversity Descriptor 1 level,

However, not all of them in relation to pelagic habitats and plankton communities

Τhe level of coherence among them is still low for pelagic habitats

MEDCIS contribution:

Issues to be addressed & resources to be used as the e-catalogue on Biodiversity

descriptor developed in ActionMed

•Review the use of existing diversity indicators for different plankton groups in the Mediterranean

•Compare existing approaches of GES definitions for D1

•Practices in other European seas

Focus on plankton indicators for phytoplankton, zooplankton and prokaryotes

Class Order Family Genus Taxa Shape Measurement Internal and Class

Remarks external structures Code

Dinophyceae Peridiniales Protoperidiniaceae Protoperidinium Protoperidinium spp. Double cone Frontal Thecate 50

Dinophyceae Peridiniales Protoperidiniaceae Protoperidinium Protoperidinium spp. Double cone Frontal Thecate 50

Chlorodendrophyceae Chlorodendrales Chlorodendraceae Tetraselmis Tetraselmis spp. Prolate spheroid Unique 93

Chlorodendrophyceae Chlorodendrales Chlorodendraceae Tetraselmis Tetraselmis spp. Prolate spheroid Unique 93

Cryptophyceae Cryptophyceae cone+half sphere Unique flagella 11

Cryptophyceae Cryptophyceae cone+half sphere Unique flagella 11

Dinophyceae Dinophyceae Prolate spheroid lessthan20 Thecate 505

Dinophyceae Dinophyceae Prolate spheroid lessthan20 Thecate 505

Coccolithophyceae Prymnesiales Prymnesiaceae cf. Imantonia sp. Sphere Unique 971

Coccolithophyceae Prymnesiales Prymnesiaceae cf. Imantonia sp. Sphere Unique 971

Chrysophyceae Chromulinales Dinobryaceae Ollicola Ollicola vangoorii Prolate spheroid Unique 99

Mediophyceae Chaetocerotales Chaetocerotaceae Chaetoceros Chaetoceros spp. Prism on elliptic base Valvar 30

Mediophyceae Chaetocerotales Chaetocerotaceae Chaetoceros Chaetoceros spp. Prism on elliptic base Valvar 30

Prymnesiophyceae Prymnesiophyceae Prolate spheroid lessthan20 804

Mediophyceae Chaetocerotales Chaetocerotaceae Chaetoceros Chaetoceros decipiens Prism on elliptic base Girdle 34



Bacillariophyceae Naviculales Naviculaceae Navicula Navicula spp. Prism on elliptic base Valvar 6

Mediophyceae Chaetocerotales Chaetocerotaceae Chaetoceros Chaetoceros wighamii Prism on elliptic base Girdle 39



Mediophyceae Chaetocerotales Chaetocerotaceae Chaetoceros Chaetoceros spp. Prism on elliptic base Girdle 31

Data resourcesFor the phytoplankton case study

Taxonomic check

Numerical check

Uploading

Mapping

Download your file

• Index_Workflow, on Phytoplankton Virtual Research Environment,

provided by LifeWatch Italy (www.lifewatchitaly.eu)

• 8 different indices commonly used in phytoplankton ecology were

calculated

Margalef’s diversity

Shannon - Wiener’s diversity

Taxonomic richness

Simpson’s diversity

Pielou’s evenness

Sheldon’s evenness

BergerParker’s dominance

McNaughton’s dominance

Computation

http://www.lifewatchitaly.eu/

Ph

yto

VR

E

TO

OL

S/S

ER

VIC

ES

Service Panel

Workflow

Explorer

Workflow Diagram

WORKFLOW ORCHESTRATOR

Computation

Index workflow

Index workflow output

We processed

• more than 4000 samples and 85,500 data entries

The 8 different indices common in phytoplankton ecology tested vs.

oeutrophication levels

oseasonality

ospatial axes (latitude and longitude)

osub-regional scale

odepth

odistance from the coast

Output analysis • Distribution of indices across anthropogenic impact levels

❖reference conditions (0)

❖low impact (1)

❖moderate impact (2)

❖heavy impact (3)

• Distribution of indices along spatial axes

non-impacted sites (0, 1, 2) impacted sites (3)

latitude longitude

Output analysis

Conclusions and recommendations from the

phytoplankton case studies in the Med:

• biodiversity indices (as the combination of Shannon’s/Simpson’s diversity and

Sheldon’s evenness indices) can discriminate the level of eutrophication impact on

plankton communities across different coastal environments in the Med

• space-specific thresholds are needed due to the strong variation of biodiversity

indices along longitudinal and latitudinal gradients

• sampling across the whole water column is quite imperative because taxonomic

indices appear to be more constant across different depths and distances from the

coast

• high sampling frequency is very crucial since many indices have bad performances

in discriminating impacted sites in summer (July – August)

• more testing of existing plankton biodiversity indicators with good performances is

needed in order to construct solid thresholds for the Med.

The work will continue in MEDREGION.

Among the objectives are:

• Selection of indicators (for D1, D4, D6) based upon

criteria established in the Commission Decision (EC,

2017).

• Set reference conditions and thresholds (i.e. boundary

between good and moderate status.

• Integration of multiple indicators, criteria, ecosystem

components, and descriptors in multiple temporal and

spatial scales.

• Perform a gap analysis about pollution pressures on

biodiversity in the Mediterranean

Thank you!

Documents

Phytoplankton components and their relation to Descriptors D1 …anemoneproject.eu/wp-content/uploads/files/Workshop_pdf... · 2019. 6. 25. · SOUTH EUROPEAN SEAS (2013-2015, )