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Lakes Northern GIG Phytoplankton (comp) / Eutrophication

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Lakes Northern GIG Phytoplankton (comp) / Eutrophication

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  1. FI: Ansa Pilke and Liisa Lepistö, Finnish Environment InstituteNO: Dag Rosland, Norwegian National Pollution Control Authority Robert Ptacnik, NIVA, Anne Lyche Solheim, NIVA/JRCSE: Mikaela Gönzci, Swedish EPA and Eva Willèn, SLU UK: Geoff Phillips and Sian Davies, Environmental Agency for England and WalesIE: Deirdre Thierney, and Wayne Trodd, Irish EPA Lakes Northern GIG Phytoplankton (comp) / Eutrophication

  2. Common metric: % Cyanobacteria, defined as % of total phytoplankton biovolume: • All Cyanobacteria, excluding Chroococcales, but including Microcystis. • The following genera are included: • Achroonema, Anabaena, Aphanizomenon, Cylindrospermopsis, Gloeotrichia, Limnothrix, Lynbya, Oscillatoria, Phormidium, Planktolyngbya, Planktothrix, Pseudanabaena, Tychonema, Microcystis, Woronichinia. • Only late summer samples used, max 4 obs./lake

  3. proportion Cyanobacteria proportion Cyanobacteria No difference between humic types No difference between clearwater types But clearwater types different from humic types

  4. Types aggregated to two major types: Clear Humic LN8, not enough data

  5. Setting reference conditions • Using median of values from ref. lakes • 170 ref.lakes from clearwater types • 40 ref.lakes from humic lake types • Ref. values: • Clearwater lakes: 1% Cyanobacteria • Humic lakes: 2% Cyanobacteria • These values are also consistent with response curves for these two major types

  6. Setting boundaries – starting point • Could we use the response curves and agreed chla boundaries directly? • Response curves not useful because: • If using the agreed G/M chlorophyll boundaries, the corresponding % Cyanobacteria was so low (<5% for all types) that this would not represent any real change in the taxonomic composition of the phytoplankton community, and thus not be compliant with the normative definitions. • Also the differences between the ref. value, H/G and G/M boundaries would be so small (1, 2 and 5% for Clearwater lakes, and virtually no difference for humic lakes due to a flat reponse curve, see Annex C), that it would be impossible to distinguish the different classes due to the uncertainty of analyses. • Thus a different approach was developed

  7. Setting boundaries – new probabilistic approach • Divided all late summer samples (July – Sept.) into two groups: • reference lakes with chla lower than the mean H/G boundary (< 4 µg/L in clear lakes and < 5 µg/L humic lakes) • impacted lakes (from moderate to bad status) with chla higher than the mean G/M boundaries (> 7 µg/L in clear lakes and > 9 µg/L humic lakes) • Box-plots used to show the statistical distribution of samples (proportion of observations) exceeding different values of % Cyanobacteria. • Such box-plots were made for ref. lakes and for impact lakes for each major lake type. • Different values of % Cyano were tested to find which ones that best would separate the reference samples and impact samples for the two major lake types.

  8. 0 0.2 0.4 0.6 0 0.1 0.2 0.3 clearwater Probability Ref lakes = REF Impacted lakes = no-R humic Probability

  9. Setting G/M boundaries • Decided which value of % Cyanobactreria that could be used as the G/M boundary for each major lake type. Three criteria were used to make this decision: • the mean probability of observations exceeding a certain value of % Cyanobacteria had to be close to zero for reference samples. This is based on the need for managers to be able to distinguish reference sites from clearly impacted sites (< good status) with a very high probability. • the mean probability of observations exceeding a certain value of % Cyanobacteria had to be significantly different between reference samples and impact samples. • the boundary value should be high enough to be compliant with the normative definitions, e.g. the % Cyanobacteria in the impacted sites should represent a real change in the taxonomic composition of the phytoplankton, and also represent a real risk for undesirable secondary impacts, such as Cyanotoxins. There was a general expert agreement within the NGIG group that this value should be at least 20% Cyanobacteria.

  10. 0 0.2 0.4 0.6 0 0.1 0.2 0.3 clearwater Probability Ref lakes = REF Impacted lakes = no-R humic Probability

  11. Setting boundaries – H/G boundaries and EQRs • H/G boundary was judged from the need to distinguish ref. sites from impacted sites with an uncertainty in phytoplankton composition analyses that is at least 20%. The difference between the G/M and H/G boundary thus must be at least 20%. • The final step was to calculate the EQRs. To avoid too low EQRs we normalized the ratio, using the following formula: EQR = (1- boundary value) / (1-ref.value).

  12. Results (preliminary) Boundary Clearwater lakes Humic lakes Ref value 1% 2% H/G value 5% 10% H/G EQR 0.96 0.92 G/M value 25% 35% G/M EQR 0.76 0.66

  13. Next steps untill July • Testing common metric vs. national metrics for SE (ready now) and UK (expected ready in early May) • New NGIG meeting in Oslo 25th May to discuss results of the tests and accept, adjust or reject the common metric and the preliminary boundaries • Revise the milestone report before ECOSTAT in July

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