CENTRAL BALTIC LAKE GIG – PHYTOLANKTON

1. Methods and required BQE parameters

Full Taxonomic Frequency and Member BQE Combination Biomass composition and intensity of algal meth rule of metrics State abundance blooms od Bloom metric is included as proportion of Relative proportion cyanobacteria are BE-FL Yes Biomass (chlorophyll a) of cyanobacteria Worst metric score included as a metric and intensity is measured by chlorophyll a BE-W No natural lakes Draft metric will contain a bloom (Yes Draft Metric metric is included draft Indicator species as biomass of Weighted average DK Biomass (chlorophyll a) metho and biovolume cyanobacteria are using a score d) cyanobacteria included as a metric together with chlorophyll a Bloom metric is Phytoplankton assessed using compound quotient, evenness and Pielou's index of EE Yes Chlorophyll a community Avg metric scores evenness Description of description which communities includes cyanobacteria Cyanobacteria are Weighted average indirectly assessed of the metric as the MCS composition metric MBA biomass metric MCS specific FR is indicative of Yes based on Chl-a composition metric elevated levels of cyanobacteria as these taxa all have low MCS scores Cyanobacteria blooms are assessed. Intensity is assessed using biomass and Chlorophyll a, Total Algal classes, PTSI maximum Weighted average DE biovolume , maximum (Phytoplankton-Taxa- metric scores Yes biovolume Seen-Index) chlorophyll. In addition cyanobacteria have high scores in the indicator taxa (PTSI) IE Two summer samples of chlorophyll a are scored per year (n = 6 per reporting period) in one of the Average of the metrics and this Yes Chlorophyll a IPI index two normalised may reflect blooms. The abundance of EQRs bloom forming cyanobacteria are also assessed twice per year

LT Intensity of blooms are assessed using Relative proportions maximum of Annual mean of Cyanobacteria, chlorophyll a. Also Avg of metric Yes concentration of Chl-a Bacillariophyta and total biovolume and scores Chrysophyta the proportion of cyanobacteria are included as a metric LV Bloom metric is Phytoplankton assessed using compound quotient, evenness and Pielou's index of Yes Chlorophyll a community Avg metric scores evenness Description of description which communities includes cyanobacteria NL Yes Chlorophyll a Metric species Bloom metric is Avg metric scores composition: metric based on list of based on algae nuisance species blooms each with a threshold value for cell density and an associated EQR value PL Biomass and the Chlorophyll a Relative proportion propotion of Yes Avg metric scores Total biomass of Cyanobacteria cyanobacteria are measured directly. UK Use the proportion current Relative proportion of cyanobacteria are Chlorophyll a method Yes of nuisance assessed. Intensity Worst metric score Cyanobacteria is measured using chlorophyll a UK Cyanobacteria are proposed indirectly assessed method as the PTI composition metric Chlorophyll a Plankton Trophic Yes is indicative of Avg metric scores Index elevated levels of cyanobacteria as these taxa all have high PTI scores

Explanations : why frequency and intensity of algal blooms are not covered ?

All member states have metrics that detect elevated levels of cyanobacteria which are generally regarded as indicators of algal blooms.

2. National reference conditions

Member State Methodology used to derive the reference conditions

Expert knowledge for taxonomic metric, chlorophyll from Intercalibration phase 1(based on GIG BE-FL reference sites BE-W Not relevant because all the "lakes" in Wallonia are HMWB Reference condition is derived from the Intercalibration results (based on GIG reference sites), Expert DK knowledge for taxonomic metric Existing near-natural reference sites, expert knowledge, historical data, least disturbed conditions, EE modelling (extrapolating model results), Reference condition for chlorophyll is derived from the Intercalibration results (based on GIG reference sites) For chl-a (MBA): RC are site specific, based on a mathematical model between chl-a and mean depth, FR For composition metric (MCS): RC are specific per macro lake type, based on ref sites (median value of MCS on ref site per macro lake type) or site specific (when not enough ref sites in macro lake type), based on a mathematical model (regression with TP) Existing near-natural reference sites and palaeolimnological studies, Reference condition for chlorophyll DE is derived from the Intercalibration results (based on GIG reference sites) IE Existing near-natural reference sites, selected by expert opinion and also through palaeolimnological validation. LCB1 reference for chlorophyll taken from phase 1 IC results. Reference condition for LCB2 taken from combination of expert judgement and near-natural reference sites. LT Existing near-natural reference sites, expert knowledge, historical data, least disturbed conditions (to be checked) LV Existing near-natural reference sites, expert knowledge, adopted from EE method NL Expert knowledge, historical data, least disturbed conditions, modeling (extrapolating model results); no actual existing natural sites in lakes, reference for chlorophyll taken from phase 1 IC results PL Existing near-natural reference sites, expert knowledge, least disturbed conditions, reference for chlorophyll taken from phase 1 IC results UK Existing near-natural reference sites, expert knowledge, modeling (extrapolating model results), reference for chlorophyll taken from phase 1 IC results

3. National boundary setting

Member State Methodology used to set class boundaries

Chl-a boundaries taken from IC (based on species composition shift, secondary effects of eutrophication BE-FL and compared with equidistant division of EQR gradient), % Cyano – expert judgement BE-W No natural lakes Boundaries taken from the Intercalibration results (based on species composition shift, secondary effects DK of eutrophication and compared with equidistant division of EQR gradient). Draft taxonomic metric boundaries derived using expert judgement Based on the biological response to the anthropogenic pressure. GM boundary: break point EE where the share of tolerant and sensitive species is more or less equal. MBA: Boundary value H/G is the Prediction Interval of 90th % of the model. Other boundaries are based on the pressure-impact model with log(TP), equal size classes (same distance as between ref and H/G limit). FR MCS: boundary value H/G is the 95th % of the distribution on ref sites or the prediction interval of 95th % of the model with log(TP) when not enough ref sites in the macro lake type. Other boundaries are based on the pressure-impact model with log (TP), equal size classes (same distance as between ref and H/G limit). Boundaries set using deviations from the reference status along the scale of the German DE LAWA-Index and a TP-derived Index and expert judgment, adjusted by the Intercalibration exercise. Boundaries selected using discontinuities along a pressure gradient IE Using discontinuities in the relationship of anthropogenic pressure and the biological response (breakpoints in species composition changes and secondary effects) LT For taxonomic composition and chlorophyll a, boundaries were equal division of the EQR gradient. LV Taken from the EE method, compared with LV data. NL Chl-a boundaries taken from the Intercalibration results (based on species composition shift, secondary effects of eutrophication and compared with equidistant division of EQR gradient), species composition metric threshold values set by expert judgement, linked to equal EQR thresholds (0.2,0.4,0.6,0.8) PL Correlation between TP, chlorophyll a, SD and biomass following Carlson`s procedure. Carlson`s index value = 70 was treated as poor/bad boundary. The other boundaries were established based on the frequency of particular values within earlier adopted chlorophyll a classification system. UK Chl-a boundaries taken from the Intercalibration results (based on species composition shift, secondary effects of eutrophication and compared with equidistant division of EQR gradient) Percentage of cyanobacteria boundaries set from expert judgement based on conclusions from NGIG phase 1 IC work. Boundaries for proposed new PTI metric based on proportion of sensitive and tolerant taxa 4. Pressures-response relationships Pressures addressed by the MS assessment methods Member Pressure Metrics tested Pressure Strenght of relationship State indicators Regression R2=0.78 for TP, 0.58 for TN, from BE-FL Chl-a EU TP, TN 0.52 to 0.81 for types (all GIG common dataset, > 1000 lakes) Spearman correlation TP 0.44 Non-parametric quantile regressions sign for BE-W % Cyano EU TP quantiles 0.50, 0.75, 0.90 and 0.95 (WISER common dataset, 602 lakes CB, 6 BE) Regression R2=TP 0.78, TN 0.58, TN and TP DK Chl-a EU TP, TN 0.77 from 0.52 to 0.81 for types (all GIG common dataset, > 1000 lakes) Multimetrics Catchment Regression R2=0,53, p<0.00001 EE EU index* IPLAC Regression R2 0.62 (n=112 FR and 22 DE and FR EU TP (multimetric) AT) TP, LAWA Correlation R to TP: 0.82 for LAL3 and LAL4, Multimetric index (incl DE EU 0.67 LCB1 and 0.48 LCB2, to LAWA index 0.90 PSI chl-a, TP, LCB1 and 0.91 LCB2 SD) Total Correlation R ranging from 0.55 to 0.77 for LAL3, biovolume, DE EU TP 0.70 to 0.85 for LAL4, 0.42 to 0.82 for LCB1, 0.28 Algal classes, to 0.51 for LCB2 PTSI IE Regression R2=0.78 for TP, 0.58 for TN, from Chl-a EU TP, TN 0.52 to 0.81 for types (all GIG common dataset, > 1000 lakes) IE Regression R2 = 0.67 (p <0.0001, n=129). For IPI index EU TP independent data set: R2 = 0.62 (p <0.0001, n=30) LT Lakes < 3m deep (n=30) TN 0.2 and TP 0.6 (p<0.05); 3 -9m deep (n=44): TN 0.6 and TP 0.4; Annual mean EU TP, TN lakes > 9m deep (n=26): TN 0.3 and TP 0.2 (all chl-a p<0.05)

LT Lakes < 3m (n=30) TN 0.3 and TP 0.4; 3 - 9m Max chl-a: EU TP, TN deep (n=44): TN 0.4 and TP 0.5; lakes > 9 m (n=26): TN 0.4 and TP 0.1 (all p<0.05) LT Spearman correlation TP 0.44 Non-parametric quantile regressions sign for % Cyano EU TP quantiles 0.50, 0.75, 0.90 and 0.95 (WISER common dataset, 602 lakes CB, 6 BE) LT % Bacillario+ EU TP tested but not sign relationships found (n=26) Chrysophyta LV Regression R2=0.78 for TP, 0.58 for TN, from Chl-a EU TP, TN 0.52 to 0.81 for types (all GIG common dataset, > 1000 lakes) LV Evenness EU BV Correlation R=0.41 (p< 0.001) index LV Correlation - BV R=0.18 , PCQ-chl 0.40 (1st type), PCQ EU BV, chl 0.34 (2nd type), p< 0.01 LV Community EU BV Correlation: com description -BV R=0.36, p< 0.01 description NL Regression R2=0.78 for TP, 0.58 for TN, from Chl-a EU TP, TN 0.52 to 0.81 for types (all GIG common dataset, > 1000 lakes) NL Correlations: For M11/M14 and M25/M27 (depth Metric TP, TN, chl- 1.5 m) EQR -TP 0.48-0.55, TN 0.44-0.73, chl-a species EU a, chl-a:P 0.67-0.81, chl-a:P 0.37-0.53, for M16/M20/M21 composition (depth 6m) chl-a 0.69, chla:P 0.68 PL Chl-a EU TP, TN Correlation TP 0.64, TN 0.40 (all p <0.05), n=39 PL Biomass of EU TP, TN Correlation TP 0.55, TN 0.44 (all p <0.05), n=39 phytoplankton PL % Cyano EU TP, TN Correlation TP 0.49, TN 0.39 (all p <0.05), n=39 UK Regression R2=0.78 for TP, 0.58 for TN, from Chl-a EU TP, TN 0.52 to 0.81 for types (all GIG common dataset, > 1000 lakes) UK Spearman correlation TP 0.44 Non-parametric quantile regressions sign for % Cyano EU TP quantiles 0.50, 0.75, 0.90 and 0.95 (WISER dataset, 602 lakes CB ) UK proposed PTI metric EU TP Correlation TP 0.59 (p<0.001, n=?? ) metric *Inhabitant density per water volume of the lake + % of natural and agricultural land use + number of domestic animals per water volume + secondary pollution