EASTERN CONTINENTAL LAKE GIG PHYTOPLANKTON (14 March 2014) Table of contents

Transcription

1 1 EASTERN CONTINENTAL LAKE GIG PHYTOPLANKTON (14 March 2014) Table of contents Description of member states assessment methods HU. 2 Hungarian classification method for phytoplankton in lakes... 2 Summary... 2 Introduction... 2 Scheme of the phytoplankton metric development in Hungary... 3 Lake typology EC GIG lake types... 3 Details of sampling and sample processing Metric descriptions, biomass metric Metric descriptions, composition metric.. 4 Combination of metrics to whole quality element results... 6 Application of the bloom metric.. 7 Uncertainty Literature Romanian classification method for phytoplankton in lakes... 8 Summary...8 Introduction...8 Metrics included in the Romanian phytoplankton assessment system... 8 Boundary setting Combination of metrics to whole quality element results Literature Stressor metric relationships Compliance checking development Reference values and class boundaries in the EC GIG Selection of reference lakes for phytoplankton Common database.. 20 Boundary comparison Investigation of Country effect on the results of the assessment... 21

2 2 Annex 1 - Description of Member states assessment methods 1. Hungary - Hungarian classification method for phytoplankton in lakes Summary This document outlines how status was assigned for the biological quality element lake phytoplankton and how boundaries were assigned in Hungary. The metrics included in the intercalibrated Hungarian lake phytoplankton assessment method are the chlorophyll a metric (as a proxi for phytoplankton biomass) the taxonomic composition metric (Q index) and the absolute abundance of cyanobacteria as bloom metric. None of the lakes fulfilled the reference criteria applied by Hungary, therefore, alternative benchmark lakes were selected. These lakes can be considered as high quality lakes for phytoplankton. Based on descriptive statistics of these lake group HG boundaries were set for Biomass (Chl-a) and Composition (Q)T metric. The Chl-a GM boundary was set by the Chl-a secchi transparency relationship. GM boundary for the composition metric (Q) was based on the relationship between the stressor and Q with respect to normative definitions of the WPD at points of ecological change. The other boundaries were set by the median values of the heavily impacted lake population and using equidistant categories. The Hungarian classification method for phytoplankton in lakes is used to assess eutrophication and general anthropogenic loads. Introduction In Hungary, during the phytoplankton-based lake-quality assessment three parameters are considered: a biomass metric (chlorophyll-a), a composition metric (Q index) (Padisák et al., 2006) and as a bloom etric the absolute abundance of cyanobacteria. The 's for these parameters are normalized so that their boundaries and class widths are on the same scale and then combined by taking the weighted average of the biomass and of the composition metrics (Fig. 1). The Bloom metric is considered if the absolute abundance of cyanobacteria exceeds 10 mg/l concentration. The Hungarian lake phytoplankton method assesses eutrophication pressure and general anthropogenic load. This document summarizes the lake phytoplankton metrics and the process of boundary setting elaborating for the ECGIG lake type EC1.

3 3 Scheme of the phytoplankton metric development in Hungary Chlorophyll-a metric Composition metric Boundary setting and normalisation Boundary setting and normalisation Chl-a Composition Metric combination Hungarian lake phytoplankton index HLPI Absolute abundance of cyanobacteria Exceeding 10mg/l: reduction of the HLPI by 0.2 Fig. 1. Phytoplankton metrics and their contributions in the Hungarian Lake Phytoplankton Index. Lake typology Table 1.EC GIG lake types Common IC type Type characteristics MS sharing IC common type EC1 Lowland very shallow hard-water EC2 Lowland very shallow but very high alkalinity Altitude <200m Depth< 6m Conductivity (µs/cm Alkalinity 1-4 (meq/l HCO3) Altitude <200m Depth< 6m Conductivity >1000 (µs/cm) Alkalinity >4 (meq/l HCO3) EC3 Altitude m Depth <6m Conductivity (µS/cm) Alkalinity 1-4 (meq/l HCO3) EC4 Altitude m Depth>6m Conductivity (µS/cm) Alkalinity 1-4 (meq/l HCO3) EC5 Reservoirs? Altitude ?m Depth>6m Conductivity (µS/cm) Alkalinity 1-4 (meq/l HCO3) HU Yes RO Yes HU Yes RO No HU- No RO -Yes HU- No RO -Yes HU- No RO -Yes For lakes in the Pannonian Ecoregion a top-down lake typology was developed (Szilágyi et al. 2008). Besides the obligatory descriptors of System A (shown above), two other factors were considered i.e.

4 4 coverage of emergent macrophytes and water regime, i.e. astatic and perennial lakes. (Those lakes were considered astatic that occasionally dry up). This typology contains 16 lake types (Table 2.). Table 2. Hungarian national lake types. (Grey shade indicates thae types that can be pooled in the EC1 lake type (Borics et al., 2014)) Type coda Size (km 2 ) Average depth (m) Lake bed material Macrophyton coverage (%) Water regime Number of lakes 1 <1.0 <1.0 organic > 66% astatic 1 2 < organic > 66% perennial 3 3 < organic < 66% perennial 1 4 <1.0 <1.0 soda > 66% astatic 7 5 <1.0 <1.0 soda < 66% astatic 7 6 < soda > 66% perennial 1 7 < soda < 66% perennial soda < 66% perennial soda > 66% perennial 1 10 <1.0 <1.0 calcareous > 66% astatic 2 11 <1.0 <1.0 calcareous < 66% astatic 1 12 < calcareous > 66% perennial 7 13 < calcareous < 66% perennial calcareous < 66% perennial 5 15 > calcareous < 66% perennial 1 16 >10 3 calcareous < 66% perennial 1 Details of sampling and sample processing Integrated samples from the photic layers (2.5 Secchi depth) of the lakes are taken typically at the deepest part of the lakes. Sampling frequency is normally 4 samples per year in the vegetation period (May-September). Dedepending on lake size spatial replication with more stations should be considered. Sestonic chlorophyll-a concentration is measured spectrophotometrically and corrected for phaeophytin (ISO 10260:1992). For microscopic analyses samples are preserved on the spot by Lugol s solution. Qualitative and quantitative analysis of phytoplankton is performed using inverted microscope, according to the of Utermöhl s method (Utermöhl 1958). Phytoplankton biovolume determination is based on the calculation of the volume of each units from appropriate geometric formulae (Hillebrandt et al.,1999). Metric descriptions Biomass metric In Hungary, sestonic Chl-a concentration is used as biomass metric. Reference Chl-a value was considered equal with the H/G boundary (18µgl -1 ). This value is the observed (growing season) mean of Chl-a in the benchmark lake population. H/G boundary setting is based on the Chl-a Secchi transparency relationship.

5 SECCHI transparency (cm) chl-a Fig. 2 Chlorophyll-a Secchi depth relationshipin EC-1 lakes. The red line indicates the fitted GAM model. The arrow indicates the chl-a concentration above which SD >120 cm cannot be expected. Since depth of the photic layer can be approximated by 2.5 Secchi depth, in those cases when SD <120cm reduction of the oxygen content in the bottom of a 3ms deep water column can be expected. Chl-a >40μgl l, SD <120cm (oxygen depletion occurs at ~3ms depth). Because of its ecological significance Chl-a =40μgl -1 value was proposed as G/M boundary. M/P boundary (Chl-a = 75μgl -1 ) was based on the upper quartile of the vegetation period mean chl-a values in the impacted lake category. P/B (Chl-a =100μgl -1 ) boundary was set by expert judgement. Table 3. chl-a boundaries Quality classes Chlorophyll-a boundaries (µl -1 ) boundaries HIGH GOOD MODERATE POOR BAD > 100 < 0.2

6 6 Using linear transformation Chl-a values are converted to the normalized scale with equal class widths and standardized class boundaries, where the HG, GM, MP, and PB boundaries are 0.8, 0.6, 0.4, 0.2, respectively. (Details of the Hungarian Chl-a boundary settings for EC1 lakes are presented in Annex 1.) Composition metric (Q k) Assessment is based on the quantitative phytoplankton data. The applied composition metric is based on the Assemblage index (Q) published by Padisák et al. (2006). Q is given as Q k s i 1 ( p F), pi: the relative contribution of the i th assemblage to the total biomass, F: is a factor number that evaluates the given assemblage in the given lake type. This metric is based on the evaluationof functional groups (FG) of algae. The FG scores (F )were given by considering the distribution of the FGs along the combined stressor values. Factor values S1 S2 SN YPh H1 G J M C P T X1 LM W1 W2 Q D Y E K LO WS MP A B N Z X3 X2 F U V The Q index value is automatically calculated for each analysed phytoplankton sample in the phytoplankton database of the Hungaria Biological Survey Database. i Boundaries for the composition metric were set by median values of the high quality impacted and heavily impacted lakes populations. Each metric are converted to the normalized scale with equal class widths and standardized class boundaries, where the HG, GM, MP, and PB boundaries are 0.8, 0.6, 0.4, 0.2, respectively. HIGH GOOD MODERATE POOR BAD Fig.3 Normalization of composition metric values Combination of metrics to whole quality element results Comparing the data distribution and the strength of the relationships between the composition and biomass metrics, the biomass metric seems better predictor of the ecological state. Therefore during the combination of the two metrics weighted average of the values was proposed. Q 2 Chl a HLPI 3 HLPI: Hungarian lake phytoplankton index Q : normalised of the composition metric Chl-a : normalised of the biomass (Chlorophyll-a metric).

7 7 Regression analysis showed a strong non linear relationship between the HLPI and the multimetric stressor values (Fig. 9). Application of the bloom metric The WFD requires that the the frequency and intensity of algal blooms are considered during phytoplankton based quality assessment. Since the term water bloomis not clearly defined in the hydrobiological literature several approach have been proposed, e.g. evenness, relative and or absolute abundance of cyanobacteria. Since neither the evennes nor the relative abundance of cyanobacteria seem to be applicable in the EC-GIG as bloom metric, the use of absolute abundance of cyanobacteria has been proposed in Hungary. Cyanobacteria biomass < 10mg/l: the values of the national metrics should be applied Cyanobacteria biomass > 10mg/l: National > 0.6 The should be reduced by 0.2 National < 0.6 No change Uncertainty. The calculated values show high variability especially in the higher range of stressors,therefore lake- year data are calculated. Nevertheless lake-year results might also show considerable variabilty, therefore mean of s calculated for the three consecutive years should be considered during asssessment. Literature Borics G, Nagy L, Miron S, Grigorszky I, László-Nagy Z, Lukács BA, G -Tóth L, Várbíró G, What factors affect phytoplankton biomass in shallow eutrophic lakes? HYDROBIOLOGIA 714: pp Borics G., B.A. Lukács, I. Grigorszky, Z.L. Nagy, L. G-Tóth, Á. Bolgovics, S. Szabó, J. Görgényi & G. Várbíró Phytoplankton based shallow lake types in the Carpathian basin: steps towards a bottomup typology. Fundamental and Applied Limnology ACCEPTED Hillebrand, H., Dürselen, C. D., Kirschtel, D., Pollingher, U. & Zohary, T., 1999: Biovolume calculation for pelagic and benthic microalgae. J. Phycol. 35: Padisák, J., I. Grigorszky, G. Borics, É. & Soróczki-Pintér, Use of phytoplankton assemblages for monitoring ecological status of lakes within the Water Framework Directive: the assemblage index. Hydrobiologia 553, pp Szilágyi, F., Ács, É., Borics, G., Halasi-Kovács, B., Juhász P., Kiss, B., Kovács, T., Müller, Z., Lakatos, G., Padisák, J., Pomogyi, P., Stenger-Kovács, C., Szabó, K. É., Szalma, E. & Tóthmérész, B., 2008: Application of Water Framework Directive in Hungary: Development of biological classification systems. Water Sci. Technol. 58: Utermöhl, H., 1958: Zur Vervollkommnung der quantitative Phytolankton-Methodik. Mitt. Int. ver. Theor. Angew. Limnol. 9: CEN SFS-CEN 15204, Water quality Guidance on the enumeration of phytoplankton using inverted microscopy (Utermöhl technique).

8 8 2. Romania -Romanian classification method for phytoplankton in lakes Summary This document outlines how status is assigned for the biological quality element phytoplankton and how boundaries were initially assigned in Romania. The metrics included in the Romanian phytoplankton assessment method are two biomass metrics (chlorophyll-a, and the measured biovolume of algae), two composition metrics (taxon number, shannon diversity) and relative abundance of cyanobacteria as bloom metric (Fig. 1). Metric boundaries were set separately with respect to the normative definitions of the WFD at points of ecological change. Since reference lakes cannot be found in the region alternative benchmark lakes were selected, and used for setting H/G boundaries. The 's for these parameters are normalized so that their boundaries and class widths are on the same scale and then combined by taking the weighted average of the five metrics. The Romanian lake phytoplankton method assesses eutrophication pressure and general anthropogenic load. Introduction The assessment method described below, based on the communities of planktonic algae is used for the natural lakes and complies with the requirements of the Water Framework Directive. The phytoplankton is sensitive to several pressures e.g.: nutrients supply, organic pollution, general degradation, lake use, fishing activities. The method description took into account the main pressures to which the communities of phytoplanktonic algae respond. Method description Details of sampling and sample processing Integrated samples from the photic layers (2.5 Secchi depth) of the lakes are taken typically at the deepest part of the lakes. Sampling frequency is normally 4 samples per year in the vegetation period (May-September). Dedepending on lake size spatial replication with more stations should be considered. Chlorophyll a is determined following extraction using spectrophotometric analysis and corrected for phaeophytin (ISO 10260:1992). For microscopic analyses samples are preserved on the spot by Lugol s solution. Qualitative and quantitative analysis of phytoplankton is performed using inverted microscope, according to the of Utermöhl s method (Utermöhl 1958). Phytoplankton biomass determination is based on the calculation of the volume of each units from appropriate geometric formulae (Hillebrandt et al.,1999). Metrics included in the Romanian phytoplankton assessment system The metrics selected in order to assess the ecological status of the natural lakes, based on the phytoplankton, are: number of taxa, biomass, chlorophyll a, numerical abundance of cyanophyceae and the Shannon s diversity. The description of the metrics and of the calculation formula are shown below. Number of taxa A numerical value that represents the taxa (specific and supraspecific) identified in a water sample. This is the most simple diversity metric, in which the relative abundance of taxa are not considered. Shannon s diversity (Shannon, 1948) H S p ln p i i 1

9 9 S = number of species; p i = number of individuals of the i th species related to the whole number of individuals from the water sample. Biovolume Represents the actual biovolume of the algae in a water sample, expressed in mm 3 l -1. Phytoplankton biovolume determination is based on the calculation of the volume of each units from appropriate geometric formulae (Hillebrandt et al.,1999). Chlorophyll a Represents the concentration of this pigment in a water sample, expressed in µgl -1. Bloom metric In Romania relative abundance of the cyanobacteria is used as bloom metric. It represents the relative abundance of cyanobacteria related to the whole biomass of algae in the water sample. It is expressed as percentage (%). Chlorophyll-a Chl-a Biovolume Biovolume Taxon number Shannon diversity Boundary setting and normalisation Taxon number Shannon diversity Metric combination Romanian lake phytoplankton index Relative abundance of cyanobacteria Relative abundance of cyanobacteria Fig. 4. Phytoplankton metrics and their contributions in the Romanian Lake Phytoplankton Index. Boundary setting Metric boundaries are set by considering relevant ecological change and by expert opinion. Chla metric is applied by both HU and RO, therefore, in the EC GIG common boundaries were set fort this metric. Reference Chl-a value was considered equal with the H/G boundary (18µgl -1 ). This value is the observed (growing season) mean of Chl-a in the benchmark lake population. H/G boundary setting is based on the Chl-a Secchi transparency relationship.

10 SECCHI transparency (cm) chl-a Fig. 5 Chlorophyll-a Secchi depth relationshipin EC-1 lakes. The red line indicates the fitted GAM model. The arrow indicates the chl-a concentration above which SD >120 cm cannot be expected. Since depth of the photic layer can be approximated by 2.5 Secchi depth, in those cases when SD <120cm reduction of the oxygen content in the bottom of a 3ms deep water column can be expected. Chl-a >40μgl l, SD <120cm (oxygen depletion occurs at ~3ms depth). Because of its ecological significance Chl-a =40μgl -1 value was proposed as G/M boundary. M/P boundary (Chl-a = 75μgl -1 ) was based on the upper quartile of the vegetation period mean chl-a values in the impacted lake category. P/B (Chl-a =100μgl -1 ) boundary was set by expert judgement. Boundaries for other metrics were based by expert opinion. The proposed boundaries are shown in table X Table... Proposed boundaries for the metrics Metrics Reference value High ecological status Good ecological status Moderate ecological status Poor ecological status Bad ecological status *Chl-a (µgl -1 ) (max.) >100 Biomass (max.) > 35 Number of taxa (min.) < 7 Shannon diversity (min.) 2,56 2,32 2,07 1,79 1,32 <1,32 Relative abundance of cyanobacteria (%) (max.) > 70 *The Chl-a boundaries are common (HU and RO) boundaries.

11 11 Metric combination The proposed threshold values were translated into normalised values and were combined. In case of EC-1 lakes, the importance and sensitivity of the selected metrics is different, therefore different weighting factors have to be applied. The following weighting factors were proposed: Number of taxa (TAX) 5% Numerical abundance of cyanobacteria (CYANO) 20% Biomass (BIO) 20% Chlorophyll a (CHL) 50% Diversity index Shannon-Wiener (ID) 5% The calculation formula is: Romanian multimetric index = 0.2 BIO+0.5 CHL TAX+0.05 ID +0.2 CYANO Literature Borics G, Nagy L, Miron S, Grigorszky I, László-Nagy Z, Lukács BA, G -Tóth L, Várbíró G, What factors affect phytoplankton biomass in shallow eutrophic lakes? Hydrobiologia 714: pp CEN SFS-CEN 15204, Water quality Guidance on the enumeration of phytoplankton using inverted microscopy (Utermöhl technique). Borics G, Lukács BA, Grigorszky I, László-Nagy Z, G-Tóth L, Bolgovics Á, Szabó S, Görgényi J, Várbíró G., Phytoplankton-based shallow lake types in the Carpathian basin: steps towards a bottomup typology Fundamental and Applied Limnology In press: p. &4) Hillebrand, H., Dürselen, C. D., Kirschtel, D., Pollingher, U. & Zohary, T., 1999: Biovolume calculation for pelagic and benthic microalgae. J. Phycol. 35: Shannon, C. E., A mathematical theory of communication. The Bell System Technical Journal 27: Utermöhl, H., 1958: Zur Vervollkommnung der quantitative Phytolankton-Methodik. Mitt. Int. ver. Theor. Angew. Limnol. 9: 1 38.

12 12 Appendix 1 Stressor metric relationships Fig. 6. Changes of Chl-a in relation to various stressors (relationship was calculated for the intercalibration database)

13 13 Fig. 7. Changes of Chl-a in relation to land use categories (a-d), lake depth (e) and in the benchmark impacted and heavily impacted lake categories (f).

14 14 Table 4. Regression equations for log 10 chlorophyll-a concentration as a function of potential descriptor variables. Variables equation log depth x log TP x log TN x log COD x; log NO3-N x; log NH4-N x log PO4-P x log ph x log Electrical x conductivity urban areas x; intensive agriculture x; nonintensive x; agriculture forests and natural wetlands x;

15 15 metric s; HU composition metric (lake-year data) y = -0,1702x + 0,98 R² = 75 0,5 1,5 2,0 2,5 3,0 3,5 4,0 metric s; HU composition metric (lake data) y = -0,1644x + 0,9646 R² = 0,3145 0,5 1,5 2,0 2,5 3,0 3,5 4,0 metric s; RO biomass metric (lake-year data) y = -344x + 1,1071 R² = 0,3474 0,5 1,5 2,0 2,5 3,0 3,5 4,0 metric s; RO biomass metric (lake data) y = -462x + 1,1237 R² = 567 0,5 1,5 2,0 2,5 3,0 3,5 4, metric s; Chl-a metric (lake-year data) 0 y = -94x + 1,2178 R² = ,5 1,5 2,0 2,5 3,0 3,5 4,0 metric s; Chl-a metric (lake data) y = -0,3141x + 1,2461 R² = 983 0,5 1,5 2,0 2,5 3,0 3,5 4, Taxa number (lake-year data) 20 y = -3,267x + 36, R² = ,5 1,5 2,0 2,5 3,0 3,5 4, Relationship between the combined stressor values and Taxa number (lake data) 20 y = -2,4353x + 34, R² = ,5 1,5 2,0 2,5 3,0 3,5 4,0 1,2 metric s; Taxa number (lake-year data) y = -19x R² = 049 0,5 1,5 2,0 2,5 3,0 3,5 4,0 metric s; Taxa number (lake data) y = -015x R² = 5E-05 0,5 1,5 2,0 2,5 3,0 3,5 4,0 Fig 8. Relationship between the combined stressor and metric s

16 RO RO HU HU 16 1,2 1 metric s; Diversity metric (lake-year data) y = -706x + 0,7282 R² = ,5 1,5 2,0 2,5 3,0 3,5 4,0 metric s; Diversity metric (lake data) y = -601x + 0,7226 R² = 52 0,5 1,5 2,0 2,5 3,0 3,5 4,0 Relative abundance of cyanobacteria (lake-year data) y = 0,1126x R² = 0,1602 0,5 1,5 2,0 2,5 3,0 3,5 4,0 Relative abundance of cyanobacteria (lake data) y = 0,1292x - 0,112 R² = 193 0,5 1,5 2,0 2,5 3,0 3,5 4,0 metric s; Relative abundance of Cyanobacteria (lakeyear data) y = -0,124x + 1,1075 R² = 0,1337 0,5 1,5 2,0 2,5 3,0 3,5 4,0 Relationship between the combined stressor values and metric s; Relative abundance of Cyanobacteria (lake data) y = -0,1494x + 1,15 R² = 152 0,5 1,5 2,0 2,5 3,0 3,5 4,0 Relationship between the stressor and HU s lake-year data (blue marks indicate reference lakes, red ones the others) 2,0 3,0 4,0 Combined Stressor Relationship between the stressor and HU s aggregated years (blue marks indicate reference lakes, red ones the others) 2,0 3,0 4,0 Combined Stressor Relationship between the stressor and RO s lake-year data (blue marks indicate reference lakes, red ones the others) 2,0 3,0 4,0 Combined Stressor Relationship between the stressor and RO s aggregated years (blue marks indicate reference lakes, red ones the others) 2,0 3,0 4,0 Combined Stressor Fig 9. Relationship between the combined stressor and HU and RO s s

17 17 Compliance checking Compliance criteria 1. Ecological status is classified by one of five classes (high, good, moderate, poor and bad). 2. High, good and moderate ecological status are set in line with the WFD s normative definitions (Boundary setting procedure) 3. All relevant parameters indicative of the biological quality element are covered (see Table 1 in the IC Guidance). A combination rule to combine para-meter assessment into BQE assessment has to be defined. If parameters are missing, Member States need to demonstrate that the method is sufficiently indicative of the status of the QE as a whole. 4. Assessment is adapted to intercalibration common types that are defined in line with the typological requirements of the WFD Annex II and approved by WG ECOSTAT 5. The water body is assessed against type-specific near-natural reference conditions 6. Assessment results are expressed as s 7. Sampling procedure allows for representative information about water body quality/ ecological status in space and time 8. All data relevant for assessing the biological parameters specified in the WFD s normative definitions are covered by the sampling procedure 9. Selected taxonomic level achieves adequate confidence and precision in classification Compliance checking conclusions HU: Yes RO: Yes HU: Yes RO: Yes HU: Biomass metric and composition metric has been elaborated. The index is the weighted average of these two metrics. As a bloom metric the absolute abundance of cyanobacteria is applied. RO: Composition, biomass and bloom metric is incorporated in the multimetric index. During the calculation of the index different weighting factors are used. The relative abundance of cyanobacteria is one of the composition metrics in the RO multimetric index. HU: Yes RO: Yes HU: Yes RO: Yes HU: Yes RO: Yes HU: Yes RO: Yes HU: Yes RO: Yes HU: Yes RO: Yes It can be concluded that HU and RO methods meet the requirements of the WFD compliant methods

18 Log Chl-a 18 development The nutrient content of EC-1 lakes (even in a natural state) is typically found at a concentration range where the Chl-a = f (Nutrients) models show asymptotic behavior (Phillips et al., 2008) and can be characterised by increased variation. (See the appendix for stressor metric relationships ) Since the nutrients as single variables are not strong predictors of phytoplankton biomass (Borics et al., 2013), combination of various stressors were proposed in Hungary. The possible stressors e.g.: TP, TN, COD, Lake-use were expressed in normalized values (in 0-1 range )and were summed. Since recreational fishing/angling is the most important type of lake use in the region, this stressor was used to create three lake categories: Lake group 1: no fishing/angling activity and no artificial stocking of fish, fish abundance < 50 kg ha -1 ; Lake group 2: moderate fishing/angling activity with occasional artificial fish stocking, fish abundance is between 50 and 200 kg ha -1 ; Lake group 3: intensive fishing/angling, regular fish stocking, fish abundance > 200 kg ha -1. Meedian values of the TP, TN, and COD were calculated for the data in Lake group 1 and Lakegroup 3. (Table3). These values were used as boundaries for transforming the measured concentrations into normalized values. TP (µgl -1) TN (µgl -1) COD (mgl -1) Lakegroup Lakegroup Maximum values Normalised values Using polynomial and/or piecewise linear transformation, each concentration are converted to normalized scale. Lake-use categories were also described by numerical value (LG1:0.33; LG2: 0.66; LG3:1.0). The combined stressor was defined as the sum of the four metrics. Implicitly, the minimum value of the stressor is approximately 0.5, while the maxima is Stressor metric relationship for EC1 lakes y = x R² = Fig.10. Distribution of growing season average chlorophyll-a concentrations along different values of the.

19 19 Reference values and class boundaries in the EC GIG Reference condition criteria for selection of lake reference sites in Hungary and Romania Reference criteria used by EU countries and specified in the CIS Guidance document No. 14 (Guidance document on the intercalibration process ) were applied. 1) 'Reference site is a site with no or minimal anthropogenic pressure. a) Morphological changes The reference site must be a natural lake. Reference sites should not be affected by any morphological modification that affect the biota or hydrological circumstances. b) Residence time of water In reference conditions there is no change in the natural residence time. c) Shoreline vegetation There is no artificial modification of the shoreline The natural vegetation is preserved, zonation is complete, there are no alien species (or the occurrence of alien species is negligible). d) Land use of the catchment area The percent of natural surfaces of the lake catchment area (after Corine Land Cover) is: >90 % or >70 %, if at least 50 m from the lake there are no agricultural or urban areas (after Corine Land Cover). (Natural surfaces of Corine Land Cover are: 2.3; 3; 4; 5 (except )) e) Lack of point source pollution f) No fishing activities Selection of reference lakes for phytoplankton Lakes that meet all reference criteria were not found in the region. Nevertheless several lakes can be found in the region, which meet all those criteria that can be important from the point of view of phytoplankton.therefore, reference lakes for phytoplankton were selected. During the lake selection the following criteria used to define lakes considered reference for phytoplankton: no major point sources in catchment, complete zonation of the macrophytes in the littoral zone, no (or insignificant) artificial modifications of the shore line, no mass recreation (camping, swimming, rowing, low/moderate fishing (Fish stock <50kg/ha). Based on TP, TN, COD values and intensity of fishing a combined stressor has been developed. The stressor ranges from 0 4. Lakes considered reference have < 1.5 combined stressor value. This means that: Fishing is low. (Fish stock <50kg/ha) Vegetation period mean TP < 115 µgl -1 Vegetation period mean TN < 1550 µgl -1 Data were provided by the regional HU and RO water authorities. In addition, experts from the regional environment agencies were involved in the final decision making. Thus the criteria used consisted of pressure data, impact data, knowledge of biology and chemistry, land-use data in conjunction with expert judgement.

20 20 Common database Hungary and Romania established a common database. Prior to data collection data acceptance criteria were defined. Data acceptance criteria used for the data quality control and results of data acceptance checking process and results Data acceptance criteria 1. Data requirements (obligatory and optional) 2. The sampling and analytical methodology 3. Level of taxonomic precision required and taxalists with codes 4. The minimum number of sites / samples per intercalibration type 5. Sufficient covering of all relevant quality classes per type Data acceptance checking HU: data from the vegetation period RO: data from the vegetation period HU: sampling of the euphotic layer, determination of the absolute and relative abundance of taxa by inverted microscope. RO: sampling of the euphotic layer, determination of the absolute and relative abundance of taxa by inverted microscope Taxa have to be identified to species level One type can be intercalibrated. The database contains 80 lake-year data Data cover a wide range of stressors 6. Other aspects where applicable In order to have data in the heavily impacted lake category, data for lakes that smaller than 50 ha were also considered. The common database contains the following data Biological data Physico- chemical data Data for other pressures Hungary Romania Boundary comparison Boundary MP GM HG Max The level of acceptable bias is smaller than proposed +/-0.25 value, therefore, no additional boundary harmonisation is needed Class diff 3 classes % Class agreement % Class agreement±1 Ms A pre-adj 1 78, A on scale of B post-adj 0,39 62,61 98,26 MS B B on scale of A 0,34 0,55 0,75 0,96 A bias on B 3 2 B bias on A -3-2 A bias on A 3 2 B bias on B -3-2 A boundary bias as classes on B 0,15 9 A boundary bias as classes on A 0,13 0,12 B Boundary bias as classes on A -0,13-0,12 B Boundary bias as classes on B -0,14-8 A average bias 0,14 0,11 B average bias -0,13-0,10 A excess as classes A harmonised boundary no change no change B excess as classes B harmonised boundary no change no change

21 21 Investigation of Country effect on the results of the assessment HU = *X R 2 = p<0.001 RO = *X R 2 = p<0.001 Fig. 11. Global regression of combined stressor against HU_ and RO_s Intercepts and slopes of the global regression of combined stressor against HU_ and RO_ has been tested (separately). No significant country-effects were observed. Systematic deviation of country's data from the global regression was not observed.