ECOSTAT Ispra, 20.-21. March 2012 Eastern Continental GIG Phytoplankton.
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Transcript of ECOSTAT Ispra, 20.-21. March 2012 Eastern Continental GIG Phytoplankton.
![Page 1: ECOSTAT Ispra, 20.-21. March 2012 Eastern Continental GIG Phytoplankton.](https://reader035.fdocuments.us/reader035/viewer/2022081514/5a4d1ade7f8b9ab059975d0f/html5/thumbnails/1.jpg)
ECOSTATIspra, 20.-21. March 2012
Eastern Continental GIG Phytoplankton
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• 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
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HURO
BG
Eastern Continental GIG (established in February, 2008)
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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)
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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
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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.
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Option 3: direct comparison of the HU and RO national metrics
IC Option
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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
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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
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TP CHL-a relationship for Hungarian standing waters
Stressor selection
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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
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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
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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)
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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)
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• 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
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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
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0.8
0.6
0.4
0.2
Setting boundaries for the HU composition metric
Boundary setting
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H
G
M
P
B
Setting boundaries for the Romanian metric
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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
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Significant country effects were not found in relationships between national EQR data vs pressure (MAS),
Country effect investigation
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Country effect investigation
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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
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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.
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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)
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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
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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
.
.
0,00
0,20
0,40
0,60
0,80
1,00
1,0 1,5 2,0 2,5 3,0
EQR
MAS
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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.
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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
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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
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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)
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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
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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
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Thanks for your attention!
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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)
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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
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•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)
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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
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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
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Relationship between the landuse categories and the Chl-a EQR
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2-5 years’ average
Pressure-response relationship