ECOSTATIspra, 20.-21. March 2012

Eastern Continental GIG Phytoplankton

• Countries involved• IC common types• Dataset available• IC option• National methods • Pressure selection• Boundary setting• Pressure-response relationships • Country effect investigation • Results of comparability analysis • Reference community description • IC feasibility and compliance check• Flaws• Explanations

Outline

HURO

BG

Eastern Continental GIG (established in February, 2008)

  Countries and experts involvedBulgaria: Mina Asenova, Boril Zadneprovski, EEA (Maya Stoineva)

Hungary: Gábor Borics (GIG lead), CER

Romania: Gabriel Chiriac, Stefan Miron, Levente Nagy, ROWATER)

Common IC type Type characteristics MS sharing IC common type

EC1 Lowland very shallow hard water

Altitude (m. a.s.l.) <200mDepth (mean depth, m) <3m Conductivity (uS/cm) 300-1000 Alkalinity (meq/l) 1-4 Surface area (km2) <10

Bulgaria, Hungary, Romania

EC2 Lowland very shallow, very high

alkalinityhard water

Altitude (m. a.s.l.) <200m Depth (mean depth, m) <3m Conductivity (uS/cm) >1000 Alkalinity (meq/l) >4-5 Surface area (km2) <10

Hungary

EC3 Middle altitude shallow

Altitude (m. a.s.l.) 200-800m Depth (mean depth, m) <6m Conductivity (uS/cm) 200-1000 Alkalinity (meq/l) 1-4 Surface area (km2) <10

Bulgaria, Romania

EC4 Middle altitude deep

Altitude (m. a.s.l.) 200-800m Depth (mean depth, m) <6m Conductivity (uS/cm) 200-1000 Alkalinity (meq/l) 1-4 Surface area (km2) <10

Bulgaria, Romania

EC5 High altitude deep

Altitude (m. a.s.l.) >800m Depth (mean depth, m) >6m Conductivity (uS/cm) 200-1000Alkalinity (meq/l) 1-4 Surface area (km2) <10

Bulgaria, Romania

Common IC types

         

Pressure data HU RO BG

Group Parameter    

TP 273 77 0Chemistry TN 273 77 0  COD    273 77  0 

Lakeuse Intensity of fishing 20 8 0  (fish stock)  

Lakeyears 50 18 0

Database

Bulgaria could not provide phytoplankton and stressor data, therefore, BG did not participate in the phytoplankton intercalibration.

Option 3: direct comparison of the HU and RO national metrics

IC Option

s

iik FpQ

1

),(

3×2 aChlQ EQREQR

HLPI

Composition metric:

Biomass metric (Chl-a):

Multimetric index:

3×2 aChlQ EQREQR

HLPI

3×2 aChlQ EQREQR

HLPI

Hungarian phytoplankton index

Romanian phytoplankton index•Number of taxa (TAX) 5%•Relative biomass abundance of cyanobacteria (CYANO) 20%•Total biomass (BIO) 20%•Chlorophyll-a (CHL) 50%•Diversity index /Shannon-Wiener/ (ID) 5%

The calculation formula:

0.05×TAX+0.2×CYANO+0.2×BIO+0.5×CHL+0.05×ID = ROmultimetric index

National methods

Bloom metricSince neither the evenness nor the relative abundance of cyanobacteria seemed to be applicable in the EC-GIG as bloom metric, the use of absolute abundance of cyanobacteria is proposed.

Cyanobacteria biomass < 10mg/l: the values of the national metrics should be applied Cyanobacteria biomass > 10mg/l:

National EQR > 0.6 The EQR should be reduced by 0.2National EQR < 0.6 No change

TP CHL-a relationship for  Hungarian standing waters 

Stressor selection

TP/Chl-a relationship for the intercalibration dataset

y = 0,4119x + 0,5243R2 = 0,107

0,00

0,50

1,00

1,50

2,00

2,50

3,00

3,50

1,50 2,00 2,50 3,00 3,50 4,00

Log TP

Log

Chl

-a

B o x P l o t (te st 1 4 v*1 4 8 c)

M e d ia n 2 5 % -7 5 % No n -O u tl i e r Ra n g e

1 2 3

cso p

-1 0 0

0

1 0 0

2 0 0

3 0 0

4 0 0

5 0 0

6 0 0

TPát

lag

B o x P l o t (te st 1 4 v*1 4 8 c)

M e d i a n 2 5 % -7 5 % No n -O u tl i e r R a n g e

1 2 3

cso p

1 0

2 0

3 0

4 0

5 0

6 0

7 0

8 0

9 0

1 0 0

KO

Iátla

g

B o x P lo t (te st 1 3 v*1 4 8 c)

M e d ia n 2 5 % -7 5 % No n -O u tl i e r Ra n g e

1 2 3

cso p

-1

0

1

2

3

4

5

6

TNát

lag

TP: 94 and 250µgl-1 TN: 1310 and 2370µgl-1 COD: 31.83 and 50mgl-1

3

2

1

Whole lake population

Regional experts

Good quality lakes

Bad quality lakes

Benchmark criteria

Criteria for bad quality lakes

Benchmark lakes (I)

Impacted lakes (II)

Heavily impacted lakes (III)

I II III

Fx=3

Fx=2

Fx=1

TP, TN, COD

Lake characterisation

a

b c

MAS

Chl-a

Fig. 1 Flow chart of the main steps of setting reference Chl-a concentrations for naturally eutrophic shallow lakes. a: selection of benchmark, impacted and heavily impacted lakes; b: translation of the chemical characteristics into factor numbers using the median values of the benchmark and heavily impacted lakes; c: calculation of the anthropogenic stressor (MAS), and using the metric (MAS = 1) for estimating reference Chl-a. (dots indicate vegetation period mean Chl-a data)

4FFFF MAS use-LakeCODTNTP

Calculation of the multimetric stressor

1: Benchmark 2: Impacted 3: Heavily impacted

Pressure selection

y = 2,3042e1,4816x

R² = 0,5087

0

100

200

300

400

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

Chl

-a

MAS

Stressor-metric (Chl-a) relationship(lake year data)

Benchmark selection criteria

1. absence of major point sources in catchment,2. no (or insignificant) artificial modifications of the

shore line,3. complete zonation of the macrophytes in the littoral

zone,4. no mass recreation (camping, swimming, rowing),5. no or low fishing activity (fishstock < 50kg/ha)

• Latitude Longitude•Atkai-Holt Tisza alsó vége, Algyő 46o 23' 04. 03" 20o 11' 09. 32"•Egyek-Kócsi Tározó, Górés 47o 34' 04. 65" 20o 56' 12. 08"•Morotvaközi holt meder, Egyek 47o 39' 51. 07" 20o 56' 57. 13"•Snagov 44o 42' 50. 81" 26o 09' 54. 30"•Szelidi-tó 46o 37' 26. 04" 19o 02' 29. 77"•Szöglegelői Holt Tisza 48o 04' 15. 51" 21o 27' 41. 14"•Tiszadobi Holt-Tisza, Darab Tisza 48o 01' 24. 18" 21o 11' 24. 03"•Tiszadobi Holt-Tisza, Falu-Tisza 48o 01' 09. 18" 21o 10' 56. 68"•Tiszadobi Holt-Tisza, Malom-Tisza kanyar 48o 01' 13. 72" 21o 11' 26. 53"•Tiszadobi Holt-Tisza, Malom-Tisza úszóláp 48o 00' 44. 05" 21o 12' 32. 91"

10 lakes with 25 lake-year data

Benchmark lakes

  B o x P lo t (te st 1 4 v*1 4 8 c)

M e d i a n 2 5 % -7 5 % No n -O u tl i e r Ra n g e

1 2 3

cso p

-5 0

0

5 0

1 0 0

1 5 0

2 0 0

2 5 0

3 0 0

klor

ofil

átla

g

 

Chlorophyll-a in the benchmark, impacted and heavily impacted lake populations

Bad

Poor

Moderate

Good

High

 0.8

 0.6

 0.4

 0.2

 

Setting boundaries for the HU composition metric

Boundary setting

         

         

         

         

         

H

G

M

P

B

Setting boundaries for the Romanian metric

HU and RO EQRs for the entire dataset

y = -0,3464x + 1,3391R2 = 0,4801

y = -0,2888x + 1,2905R2 = 0,39

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

0,80

0,90

1,00

1,00 1,20 1,40 1,60 1,80 2,00 2,20 2,40 2,60 2,80

MAS

EQR

HU EQR

RO EQR

Lineáris (HUEQR)Lineáris (ROEQR)

Pressure-response relationships

Significant country effects were not found in relationships between national EQR data vs pressure (MAS),

Country effect investigation

Country effect investigation

The average absolute class difference is 0.291. The average class difference is 0.255Results of the comparison using the calculation sheet proposed by the JRC are shown below .

The level of acceptable bias is smaller than proposed +/-0.25 value, therefore no additional boundary harmonisation is needed.

Results of comparability analysis

Boundary MP GM HG  MaxMs A 0,4 0,6 0,8 1A on scale of B 0,499049 0,67219981 0,8453508 1,016578MS B 0,4 0,6 0,8 1B on scale of A 0,339159 0,54059218 0,7420253 0,944934A bias on B EQR 0,03609991 0,0226754B bias  on A EQR -0,02970391 -0,0289874A bias on A EQR 0,02970391 0,0289874B bias on B EQR -0,03609991 -0,0226754A boundary bias as classes on B 0,20848797 0,1309575A boundary bias as classes on A 0,14851954 0,1449368B Boundary bias as classes on A -0,14746291 -0,1428594B Boundary bias as classes on B -0,18049953 -0,1133771A average bias 0,17850375 0,1379472B average bias -0,16398122 -0,1281183A excess as classesA harmonised boundary no change no changeB excess as classesB harmonised boundary no change no change

Reference community descriptionPhytoplankton composition of the lakes in reference status can be characterised by the frequency distribution of the functional groups having higher factor numbers (F7-9) These functional groups (F=9) are A: Urosolenia longiseta, Cyclotella comensis; B: Aulacoseira subarctica, A. islandica; N: Cosmarium spp., X2 Rhodomonas; X3: Chrysococcus spp.; U Uroglena spp. (F=7) D: Fragilaria acus, Stephanodiscus hantzschii, Y: large size cryptomonads; Lo: dinoflagellates; MP: meroplanctic diatoms; K: Aphanothece spp. The relative abundance of these taxa has to be higher than 80%. Algae that belong to functional groups that have factor values F=5; C: Stephanodiscus neoastraea, Aulacoseira ambigua; W2 Trachelomonas spp.; P: Aulacosira granulata, Fragilaria crotonensis; Q: Goniostomum semen, G, latum can also be present but rarely dominate the phytoplankton. The ratio of those taxa that are considered as undesirable in this lake type lower than 30% These groups are the J: Chlorococcalean green algae, the bloom-forming cyanobacteria like M: Microcystis, H1: Anabaena and Aphanizomenon spp. Sn: Cylindrospermopsis raciborskii, S1: Limnothrix spp. Planktothrix spp. S2: Spirulina spp. Arthrospira spp. Biomass expressed in chlorophyll-a can fluctuate during the vegetation period, but the annual mean Chl-a maxima does not exceed 50µgl-1. The mean Chl-a value in the growing season is less than 25 µgl-1. Secchi transparency usually higher than 1.5ms.Blooms do not occur. Decrease of the oxygen concentration might occur towards the deeper layers, but oxygen depletion never develops.Normalised value of the HLPI > 0,8.

IC feasibility check• Typology

-intercalibration is feasible for EC1 lake-type

• Assessment concept• -comparable in terms of habitats, (photic layer is sampled)

• Relationship with pressure -both national methods have good relationship with

pressure (when applying national methods to IC dataset)

Representative sampling ✓All relevant data covered by the sampling ✓Taxonomic level meets adequate confidence and precision ✓Five classes of ecological status ✓All parameters indicative of the BQE ✓WFD-compliant national boundary setting ✓Adapted to common intercalibration types ✓Results in EQR✓Type-specific near-natural reference conditions?

Compliance criteria

Contradiction• Contradiction: there are not reference lakes but most of the lakes are

assessed as good or high quality.• There are reference lakes, or

boundaries are not set correctly.

• Identification of the alternative benchmark lakes on the gradient of impact is not clear.

• Separation of the alternative benchmark lakes from reference lakes is not clear.

• Good status boundary is closer to the median of heavily impacted lakes as to the median of benchmark lakes

.

.

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EQR

MAS

Tasks in EC-GIG

• Data collection (more data for the lower quality classes.)

• Overview of the H/G and G/M boundaries.• Setting more stringent boundaries.• Clear separation of the bechmark and reference

lakes needed.• Updating reference community description.

Stressor Metric (HLPI) relationship(lake year data)

y = -0,331x + 1,2513R2 = 0,5369

0,0

0,2

0,4

0,6

0,8

1,0

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

MAS

HLP

I

Stressor response relationship with more stringent boundaries

Lake categories along the stressor

0,0

0,2

0,4

0,6

0,8

1,0

1,0 1,2 1,4 1,6 1,8 2,0 2,2 2,4 2,6 2,8 3,0

MAS

EQR

Benchmark Impacted Heavily impacted

Stressor Metric (HLPI) relationship(2 - 5 years mean)

y = -0,3178x + 1,2151R2 = 0,5964

0,0

0,2

0,4

0,6

0,8

1,0

1,0 1,1 1,2 1,3 1,4 1,5 1,6 1,7 1,8 1,9 2,0 2,1 2,2 2,3 2,4 2,5 2,6 2,7 2,8 2,9

MAS

HLP

I

Stressor response relationship (with more stringent boundaries)

0,00

0,20

0,40

0,60

0,80

1,00

1,00 1,50 2,00 2,50 3,00

EQR

MAS

Lake categories along the stressor(2-5 years  mean data)

Reference lakes are separated

Acknowledgements

This work was financially supported by• the HU Ministry of Rural Development,• the RO Ministry of Environment and Water•Hungarian Academy of Sciences

BG – Mina Asenova

BG – Maya Stoineva

HU – Gábor Várbíró

RO – Gabriel Chiriac

RO – Stefan Miron

RO – Levente Nagy

Thanks for your attention!

y = 0,0229x + 0,8496R2 = 0,8847

0

1

2

3

4

5

6

7

8

0 50 100 150 200 250 300

Secchi transparency (cm)

Dept

h of

oxi

gen

depl

etio

n (m

)

Relationship between the secchi transparency and the depth of oxigen depletion (<2mg/l)

y = 174,28x-0,3387

R2 = 0,1266

0

50

100

150

200

250

300

0 25 50 75 100 125 150 175 200 225 250

Chl-a (ug /l)

Secc

hi d

epth

(cm

)

Relationship between the chl-a and Secchi transparency

40ugl-1

•Number of taxa (TAX) 5%•Relative biomass abundance of cyanobacteria (CYANO) 20%•Biomass (BIO) 20%•Chlorophyll-a (CHL) 50%•Diversity index Shannon-Wiener (ID) 5%

The calculation formula is:

0.05×TAX+0.2×CYANO+0.2×BIO+0.5×CHL+0.05×ID = ROmultimetric index

p Chl-a Chl EQR SH div SH  EQRCyano metric

Taxa number pH

conductivity Secchi NH4 TP TN COD

TP metric

TN metric

COD metric

lake-use Q EQR

Normalised Q

Chl EQR HLPI

NEW RO EQR

0,01 MAS YES YES NO NO NO NO NO NO NO YES NO YES YES YES YES YES YES YES YES YES YES YES0,05 MAS YES YES NO NO NO NO YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES0,1 MAS YES YES NO NO NO NO YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES

r 0,41 -0,69 -0,25 -0,07 -0,04 -0,13 0,33 0,30 -0,38 0,40 0,37 0,51 0,58 0,79 0,41 0,81 0,82 -0,39 -0,42 -0,69 -0,67 -0,57

Correlation matrix (MAS and different metrics)

Latitude LongitudeEgyek-Kócsi Tározó, Górés 47o 34' 04. 65" 20o 56' 12. 08"Tiszadobi Holt-Tisza, Falu-Tisza 48o 01' 09. 18" 21o 10' 56. 68"Tiszadobi Holt-Tisza, Malom-Tisza úszóláp 48o 00' 44. 05" 21o 12' 32. 91"

Lakes considered reference

050

100150

1 2 3

Whole lake population

Regional experts

Good quality lakes

Bad quality lakes

Benchmark criteria

Criteria for bad quality lakes

Benchmark lakes (I)

Impacted lakes (II)

Heavily impacted lakes (III)

I II III

Fx=3

Fx=2

Fx=1

TP, TN, COD

Lake characterisation

a

b c

MAS

Chl-a

Fig. 1 Flow chart of the main steps of setting reference Chl-a concentrations for naturally eutrophic shallow lakes. a: selection of benchmark, impacted and heavily impacted lakes; b: translation of the chemical characteristics into factor numbers using the median values of the benchmark and heavily impacted lakes; c: calculation of the anthropogenic stressor (MAS), and using the metric (MAS = 1) for estimating reference Chl-a. (dots indicate vegetation period mean Chl-a data)

4FFFF MAS use-LakeCODTNTP

Relationship between the landuse categories and the Chl-a EQR

2-5 years’ average

Pressure-response relationship