03 lyche solheim lake assessment

7/31/2019 03 Lyche Solheim Lake Assessment

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Conference, date, place

Lakes assessment:suitability of BQEs along gradients of

eutrophication and hydromorphological pressures

Anne Lyche Solheim, NIVAPhytoplankton experts: Laurence Carvalho2, Geoff Phillips3, Ute Mischke4, GiuseppeMorabito5, Gbor Borics6, Birger Skjelbred1, Marko Jrvinen7, Stina Drakare8, Tiina Noges9,Peeter Noges9, Stephen Thackeray2, Claire McDonald2, Christophe Laplace-Treyture18,

Macrophyte experts: Agnieszka Kolada10, Martin Sndergaard11, Nigel Wilby12, SeppoHellsten7, Bernard Dudley2, Fraucke Ecke8, Marit Mjelde1, Vincent Bertrin18

Macroinvertebrate experts: Martin Pusch4, Ralph Clarke13, Ken Irvine14,15, Angelo

Solimini16, Jukka Aroviita7, Oliver Miler4, Elaine McGoff8, Jrgen Bhmer17,

Fish experts: Erik Jeppesen11, Torben L. Lauridsen11, Christine Argillier18, StephaniePedron18,20, Simon Causs18, Murielle Gevrey18, Sandra Brucet19, Kerstin Holmgren8,

Matthias Emmrich4, Thomas Mehner4, Julien De Bortoli18, Ian Winfield2, Pietro Volta5, AtleRustadbakken1, Martti Rask21,

Cross-BQE Statisticians: Jannicke Moe1 , Mike Dunbar2

Generalists and coordinators: Sandra Poikane19 , Christian Feld22, Daniel Hering22


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WISER final conference, 25-26 Jan 2012, Tallin, Estonia

Outline

Objectives

Datasets and methodology Main results for each BQE Cross BQE comparisons Key messages and recommendations Future challenges


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Objectives for WISER work on Lakes

identify and develop suitable biological indicators /metrics for assessing status of European lakes

according to WFD requirements

propose common metrics sensitive to eutrophicationand hydro-morphological pressures, to support theWFD intercalibration process

quantify uncertainty for each biological qualityelement as a basis for recommendations on WFDmonitoring design


4/30

Datasetsbiggest in Europe and the world?

Existing data from 21 countries

with harmonised taxonomy

New data from 26-51 lakes25 lakes sampled for all BQEs in 2009

(and 2010)


BQE # Water-bodies

Phytoplankton 6 927*

Macrophytes 1 575

Macroinvertebrates 227

Fish

Abio&cpressuredata

forallwaterbodies

1 632

*taxonomic data available from ca. 1500 water bodies


5/30

Methodology

Metric development/testing:

Statistical data analyses

Multivariate methods(CCA & others)

Univariate regressions(linear & non-linear)

Uncertainty analyses:

Sampling of each BQE accordingto standardised methodology

Statistically stratified samplingdesign used for all BQEs

Using WISERBUGS software toquantify uncertainty components



6/30

Phytoplankton main results and impacts

Results

New common metricdeveloped for taxonomic

composition: Phytoplankton

Trophic Index (PTI)

New bloom metric developed:Cyanobacteria biovolume

Low spatial uncertainty,temporal uncertainty more

important

Impacts

PTI metric used in IC in CentralBaltic and Northern GIGs

Cyanobacteria biovolume adoptedas national metric by several

countries (Spain,Italy, Norway,UK) Both metrics can be used together

with chlorophyll a for whole BQEassessment and to set more WFD

compliant nutrient targets

Uncertainty results can be used toimprove the design of WFD

monitoring programmes, esp.

concerning sampling frequency



7/30

Phytoplankton Taxonomic composition metric:

Phytoplankton trophic index (PTI)

PTI scores had a significant typespecific relationship with phosphorusthat was linear in the range of 2

100 gP L-1. The lack of response of the phytoplankton

community > 100 gP L-1 highlights the

importance of reducing lake P

concentrations to below this level.

Low alkalinity lakes (red) had lowerPTI scores than high alkalinity lakes

(green) at the same level ofphosphorus.

Eutrophic taxa are less dominant in lowalkalinty lakes than in high alkalinity lakesat same TP level

For more info, see poster by G. PhillipsWISER final conference, 25-26 Jan 2012, Tallin, Estonia

L N2 b L N 2 a L N 1 L CB 1 L C B2C ar te ri a - 0, 48 0 10 4S S S VS VSS po nd yl os i um - 0, 48 0 6 43 S S S VS VS

C h ry s oc h ro m u li n a - 0 ,4 7 2 2 2 2S S S VS VS

C hr ys oc oc cu s - 0, 46 8 2 6 S S S V S VS

R ha bd od er ma - 0, 44 8 6 00 S S S V S VS

Q ua dr ig ul a - 0, 43 6 1 07 7S S S VS VSOo cystis -0,405 68 S S S VS VS

R ad io cy st is - 0, 33 1 8 3T S S VS VS

Eunotia -0,318 2 57 T S S VS VS

Synura -0,316 18 T S S V S VS

Pinnula ri a -0,290 322 T S S VS VS

S ph ae ro c ys t is - 0, 27 7 7 99 T S S S VSAsterionella -0,227 775 T S S S VS

C yc lo te ll a - 0, 20 9 3 9T S S S VS

P un ct ic ul at a - 0, 16 3 4 86 T S S S VS

C hl am yd oc ap sa - 0, 13 9 1 10 1 T S S S V S

P er id in iu m - 0, 12 5 2 5 0T S S S VSM ou ge ot ia - 0, 11 2 3 53 T S S S VS

Ankyra -0,071 167 T T S S VS

G on yo st om um - 0, 06 9 8 7T T S S VS

Peridini opsis -0,057 46 T T S S S

Xanthid ium -0,055 123 T T S S S

R ap hi do ce li s 0 ,0 08 5 6T T S S SW or on ic hi ni a 0 ,0 43 4 21 T T S S S

F ra gi la ri a 0 ,3 17 1 47 T T T S S

C ya no di ct yo n 0 ,3 18 2 0T T T S S

Katodinium 0,343 50 T T T S S

Cym bell a 0,353 82 T T T S SC er at iu m 0 ,5 83 1 05 VT T T T S

Navicu la 0,687 98 VT VT T T T

Eudorina 0,694 64 VT VT T T T

P la nc to ne ma 0 ,7 30 6 16 V T VT T T T

Closteri um 0,732 44 VT VT T T T

P la nk t os ph a er ia 0 ,7 55 2 1 VT VT T T TAnabaena 0,984 26 VT V T VT T T

Volvox 1,032 4 1VT V T VT T T

Go le nk in ia 1 ,0 53 8 5VT V T VT T T

Treuba ri a 1,054 27 VT VT VT T T

C oe la st ru m 1 ,0 78 1 58 V T VT V T T T

Diatoma 1,082 41 VT V T VT T TLyngbya 1,345 25 VT VT VT VT T

C hl or el la 1 ,3 73 4 50 VT VT VT VT T

P la nk to th ri x 1 ,4 16 3 21 V T VT V T V T V T

S te ph an od is cu s 1 ,4 27 1 1 V T V T V T V T V T

Ulothrix 1,430 1 90 V T V T V T V T VTL im no th ri x 1 ,4 41 6 1V T V T V T V T VT

P se ud a na ba en a 1 ,5 70 1 69 V T V T V T V T VT

O sc il la to ri a 1 ,5 75 1 3V T V T V T V T VT

Aphanizomenon 1,595 58 VT VT VT VT VTN it zs ch ia 1 ,6 74 2 1 V T V T VT VT VT

Melo sira 1,711 1 0 V T V T V T V T VTG ol en k in io ps i s 1 ,7 52 1 43 V T V T V T V T VT

P an do ri na 1 ,7 63 2 7V T V T V T V T VT

M ic ro cy st is 1 ,7 88 5 6 V T VT V T V T VT

Phacus 1,912 15 VT VT VT VT VT

Euglena 2,095 51 V T V T V T V T VT

C y li n dr o sp e r mo p s is 2 , 12 1 2 4 V T V T V T V T VT

PTI

Optima Nrecords

LakeType

Genus


8/30

Phytoplankton bloom intensity metric:

Cyanobacteria biovolume

Cyanobacterial blooms aresevere in enriched lakes

across Europe

Risk of exceedance of WHO healthalert threshold (biovolume 2mm3 L-1)

10% exceedance at 20 g L-1 TP 30% exceedance at 40 g L-1 TP



9/30

Metric scores vary largely between lakes - significantlyrelated to eutrophication pressure for IC recommended

metrics (chla, PTI, cyanobacteria)

Within-lake sampling- and analytical variability areminor for integrated samples in euphotic pelagic zone


Metric Country Waterbody Station Sample Analyst Error

(sub-

sample)

Total

within

Total

between

Optimal predictor

Chlorophylla 0 0.96 0.01 0.01 - 0.02 0.04 0.96 TP, depth, latitude

PTI 0 0.88


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Macrophytes main results and impacts

Results

Common metrics suitable foreutrophication are: Taxonomic composition metrics:

ICM and Ellenberg index

Abundance proxy metrics: max.colonisation depth and % cover Metric suitable for

hydromorphological pressure:

The macrophyte water levelfluctuation index (FI, NO)

Uncertainty: largest variability foundbetween stations within a lake

Impacts

The ICM has been used forintercalibration in Northern and

Central Baltic GIG

The abundance metrics arepromising, but need furtherimprovement in field methodology

The Water level fluctuation indexis a promising tool to set true

biological boundaries for good

ecological potential for heavilymodified water bodies

to reduce uncertainty in ecologicalstatus assessment formacrophytes, several stations or

increased station area should besampledWISER final conference, 25-26 Jan 2012, Tallin, Estonia


11/30

Eutrophication metrics for macrophytes

taxonomic composition

Intercalibra&onCommonMetricforlakemacrophytes(ICM),calculatedusing

averageofunweightedspeciesscores

(scaling110)basedonarithme&cmean

TPinlakes(leE)wheretheyoccur

LogTP (ug/L)

ICM

BE

EE

FI

IE

LT

LV

NL

NO

PL

RO

SE

UK

0,0 0,5 1,0 1,5 2,0 2,5 3,0 3,50

1

2

3

4

5

6

7

8

9

10

LogTP (ug/L)

Ellen

berg

Index

0,0 0,5 1,0 1,5 2,0 2,5 3,0 3,50

1

2

3

4

5

6

7

8

9

ICM r2 = 0.52

Ellenberg r2 = 0.47

llenbergNindex,calculatedusing

averageofunweightedspeciesscores

(scaling110),andexpertbased

indicatorvalues(speciesscores)

Bothmetrics,butesp.llenbergindexhave

lessresponsetoTPwhenTPis>100g/l



12/30

Eutrophication metrics for macrophytes

abundance proxy: Cmax and % cover


Maximum colonisation depth is apromising abundance metric for

macrophytes : Log Cmax = 0.84 0.17*log TP (0.17) -0.27*log

Colour (0.21) + 0.19*log Z_max (0.07),

% cover shows a decrease atincreasing nutrient concentrations

r2=0.45, N= 233


13/30

Macrophytes Water Level Fluctuation index

WLc correlated very well withwinter drawdown in storagereservoirs in Northern countries

Aquatic macrophytes in Finnish, Swedish and Norwegianlakes sensitive and tolerant to water level regulation



14/30

Macrophytes uncertainty

Spatial variability is high,but can be reduced byincreasing the number of

stations and transects



15/30

Macroinvertebrates main results and impacts

Results:

A new multimetric index isdeveloped for HyMo pressure: The littoral macroinvertebrate

shore-line modification index (LIM)

Macroinvertebrates in thelittoral zone are less suited

than the botanical BQEs to

detect eutrophication pressure

Spatial variability is high,requiring sampling many

replicates for each level of

pressure

Impacts:

A new assessment tool formorphological alterations of the

shores of natural lakes is now

applied in Germany and can be

applied in other countries

Eutrophication pressure is notspecifically assessed with littoral

macroinvertebrates

To reduce uncertainty asampling protocol is

recommended



16/30

2.Addi&onalquan&ta&vees&ma&onusing

theLakeHabitatSurveyprotocol

High alterationReferenceIntermediate

alteration

Es#ma#onofstresslevel1.samplingsitesgroupedinto3levelsofmorphological

altera&on

Rowan, J.S. 2008. Lake Habitat Survey in the United Kingdom. Field survey guidance manual. Version4. Dundee, The Scotland and Northern Ireland forum for environmental research (SNIFFER) & Scottish

Natural Heritage (SNH)



17/30

basedonsamplesfrom51lakes

-compositesampling(LIMCO)and

-habitat-specificsampling(LIMHA)

Differen&a&onofmul&metricwasdone

dependingonregionduetobiogeogr.

MMI Spearmans

Rho

Region Metrics

LIMCO 0.700.490,440.47

DE+DK

ItalySE+FIIE+UK

MEAN of (Gath&Coll%AC + MargalefDiv + Chiro%AC +No.EPTCBOtaxa)MEAN of (rK-relation + MargalefDiv + Odon% + no.ETOtaxa)MEAN of (Lithal%AC + no.famil + Crusta%AC + no.Odontaxa)MEAN of (GathColl% + MargalefDiv + Dipt%AC + no.ETOtaxa)

LIMHA 0.720.400,440.71

DE+DK

Italy

SE+FI

IE+UK

STONES: MEAN of (Gath&Coll%AC + MargalefDiv + Coleopt%AC +No.EPTCBOtaxa)SAND: MEAN of (%TypePsa + ShanWienDiv + Oligo%AC + EPTtaxa%)MACROPHYTES: MEAN of (Predat% + Evenness + Coleopt%AC +

EPTCBOtaxa%)STONES: MEAN of (SwimmDiv%AC + ShanWienDiv + no.famil +EPTCBOtaxa%)

Li7oralInvertebrateMul#metric(LIM)forshorelinemodifica#ons



18/30

Littora

lInverte

bra

teM

ultime

tricIndex

base

do

ncompos

itesa

mp

les

(LIMCO

)

Pressure-response-rela#onshipsofassessmenttool

Li7oralInvertebrateMul#metricIndexbasedoncompositesamples(LIMCO)



19/30

Further improvement

Merging data from all four regions together Finding the overall best single metrics to be

combined to a multimetric applicable for

larger parts of Europe



20/30

Fish main results and impacts

Results: Impacts: Fish seem less suited than otherBQEs to detect single pressures at

the pan-European scale, but canbe used regionally

Hydroacoustics provide cost-effective assessment of fishabundance

Fish species can be goodindicators of climate change

A multimetric Fish index respondingto eutrophication is developed,

consisting of:

CPUE, BPUE, OMNI Response to pressure is less good

than the botanical BQEs

Hydroacoustic method is promisingto assess fish abundance

No relationship found between fishand HyMo pressure

Large intra-lake variability betweendepth strata means many nets per

lake (or precise hydroacoustics) Fish species are sensitive to

increased temperature with warmwater species increasing and cold

water species decreasingWISER final conference, 25-26 Jan 2012, Tallin, Estonia


21/30

Fish metric response to eutrophication


r = -0,5

% of non-natural land cover in the catchment

Fishind

ex

ALCBECMEDNO

0.0

0.2

0.4

0.6

0.8

1.0

0 10 20 30 40 50 60 70 80 90

Mu

ltime

tricindex

(CP

UE

,BPUE

,OMNI)E

QR

R2 = 0.25


22/30

Main trends in fish response to eutrophication and climate

DENSITY & BIOMASS EutrophicWarm

Lowland lakes

Oligotrophic

Cold

High altitude lakes

RICHNESS & DIVERSITYLarge lakes

Warm

Small lakes

Cold

BODY SIZEHigh altitude lakes

Low seasonality

Lowland lakes

High seasonality



23/30

Fish in lakes and Hydromorphology

Seemingly no relationship between fish community descriptors andhydromorphological alterations. Why?

Fish are not sensitive or they have a high resilience ? Fish are moving Impacts of these pressures is obscured by the effect ofbiological

interactions?

Results are limited to a certain degree of pressure intensity? New analyses focusing on fish sampled by gill nets in the littoral

zone may reveal impacts

HyMo pressure should be better characterised



24/30

Cross BQE comparisons:

Sensitivity to human pressures

BQE Pressure

Best common metrics R2 Rho

Phytoplankton Eutrophication (TP) Chlorophyll a

PTI (tax. comp.)

0.63

0.67

Macrophytes Eutrophication (TP) ICM (tax.comp) 0.52

HyMo (water level fluct.) WLi (tax. Comp) (NO+FI) 0.77

Benthic fauna

(littoral)

Eutrophication (TP) MMI 0.40

HyMo (shore modifications) MMI (LIMCO) (DE+DK)MMI (LIMHA) (DE+DK)

0.70

0.72

Fish Eutrophication (non-naturalland cover)

MMI (CPUE, BPUE, OMNI) 0.25



25/30

Cross BQE comparisons:

Variability based on the field sampling

BQE Major variance component Overall natural +methodological

variability

Phytoplankton Temporal (seasonal) Small (~90%)


*Large within lake variability for fish is less important, as data from all gill nets andall depth strata are merged when analysing fish response to pressure


26/30

Other evidence supporting WISER

results on sensitivity and uncertainty


The WISER common metric results

are supported by a comparison of

93 national lake assessment

methods (Brucet , Birk et al.)showing that correlation

coefficients are higher and less

variable for phytoplankton and

macrophytes than for

macroinvertebrates and fish.


27/30

Key messages for lake assessment

Phytoplankton and macrophytes arerecommended for assessing

eutrophication pressure. Good common

metrics developed in WISER and used forintercalibration can be used also as

national metrics

Littoral BQEs are well suited forassessing HyMo pressures: metrics are

available for macrophytes response towater level fluctuations, and benthic

invertebrates response to morphological

shore-line degradation.

Fish show less clear signals to individualpressures at the European scale, but maybe good indicators at the regional scale.Fish are also good indicators of climate

change: warm water species are

increasing, cold water species decreasing


Phyto-plankton

Macrophytes

Fish

Lake

BiologicalQuality

brates

Benthicinverte

-


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Recommendations for lake monitoring

Phytoplankton monitoring should have sufficientfrequency (monthly) to reduce the temporal variability

Monitoring of littoral BQEs: macrophytes and benthicinvertebrates should have several stations andreplicates for each station

Fish monitoring must include all depth strata withmany gill nets (or whole-lake hydroacoustics)


For all BQEs:

Uncertainty can be further reduced by ensuring effectiveand consistent training in sampling and species

identification across Europe


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Future challenges Linking ecological status in lakes to

ecosystem services

Metrics, reference values and classboundaries should take account of climate

change

Functional metrics across BQEs are neededto improve whole lake assessment, including

top-down control and trophic interactions

Better metrics for Eastern Continental andMediterranean regions needed

More metrics needed for reservoirs (HMWBs)



30/30

Thanks for your attention


03 lyche solheim lake assessment

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