1. NGIG lake fish IC ECOSTAT meeting, Ispra 21 March 2012 MIKKO OLIN 1, MARTTI RASK 2, FIONA KELLY 3, KERSTIN HOLMGREN 4 & TRYGVE HESTHAGEN 5 1 University of Helsinki, Department of Environmental Sciences, Finland 2 Finnish Game and Fisheries Research Institute, Evo Fisheries Research Station 3 Inland Fisheries Ireland – Research and Development Division 4 Swedish University of Agricultural Sciences, Department of Aquatic Resources, Institute of Freshwater Research 5 Norwegian Institute for Nature Research

2. L-N-F working history LNF founded as part of Cross-GIG Fish Pilot exercise Sep. 2008 -> Sweden as GIG leader (Kerstin Holmgren) 6 meetings: Sep/2008, Sep/2009, May/2010, Nov/2010, Apr /2011, Nov/2011 During 2008-2011 data collection (Cross-GIG database, Cemagref) In 2009: common data classification with FI and SE methods In May 2010: LNF1 and LNF2 as common IC types, eutrophication as common pressure. Feb. 2011: final version of the Cross-GIG database Until spring 2011: national fish indices for the common data -> SE, updated FI, IE, NO (only 35 lakes) -> preliminary IC results In summer 2011: Sweden gave leadership to Finland (Martti Rask) In Oct. 2011: prelim. IC results (FI, IE) In March 2012: final IC results

3. Finnish method, EQR4 Based reference lakes of 10/12 national lake types (colour, mean depth, area) Data from standard gillnetting Targeting eutrophication pressure Average of four variables (BPUE, NPUE, Cyprinid% and indicator species) Reference values and class boundaries calculated from type-specific reference lakes (except indicator species) Equidistant boundaries: linear response of fish communities to eutrophication (Olin et al. 2002)

4. Irish method, FIL2 13 variables (TOT_BPUE, PERCH_BIO, ROACH_BPUE, NAT_BPUE, RHEO_BIO, SPE_EVEN, PHYT_BIO, BREAM_IND, CYP_BIO, %at2_BIO, RUDD_IND, MAX_L_DOM, LITH_IND) 5-6 variables used in each lake type (Low/high alkalinity, shallow/deep lakes) Data from std gillnetting and fyke nets Targeting eutrophication pressure, general land use Ecological status by using discriminant classification rules, EQR by a generalised linear model (EQR and DA impact classes were cross-tabulated at various cut-points to quantify class boundaries )

5. Swedish method, EQR8 8 variables (n of native species, Simpson’s D: abundance and biomass, Relative biomass, Relative abundance, Mean mass, Piscivorous percids %, Perch / Cyprinids ratio) Data from std gillnetting Targeting multiple pressures (ecolocigal degradation) Reference values by modeling (according to lake altitude, area, max. depth, annual mean air T, location below or above the highest coast line after deglaciation) Boundary setting based on statistical distribution of reference and impacted lakes starting from the setting of G/M boundary Metrics expressed as std residuals from lake-specific reference values (Z-values), transformed to P-values EQR8 = mean of 3-8 P-values

6. Norwegian method, FCI The index requires data on species occurrence and evenness, both historically and present, and species status (losses / changes in abundance) for different categories. Data from interviews, reports, test-fishing, water chemistry, models for species occurrence Targeting mainly acidification pressure FCI = (REF-S CRed -S CInc -S Lost -S Intro )/REF Boundary setting by determining reference conditions, based on unchanged/healthy populations of dominant, subdominant and rare species

7. Compliance All methods are compliant with the WFD requirements *Age structure included indirectly # National lake types $ Models of lake-specific reference values £ Benthic survey nets according to EN 14757 standard *

8. Feasibility Intercalibration is feasible between Finnish, Irish and Swedish methods. Norwegian method: different pressure, problems with data obtaining especially in multispecies communities -> excluded *LNF1: clear water (colour < 30 mg Hg/l), < 40 km 2 lakes; LNF2: humic (colour 30-90 mg Hg/l), < 5 km 2 lakes # structural/functional metrics

9. LNF common data IC dataset of LNF group is based on the data delivered to the cross-GIG database since 2009, total number 1577 lakes Common LNF IC types: LNF1 - dimictic, clear (colour <30 mg Pt/l), <40 km 2 LNF2 – dimictic, humic (colour 30-90 mg Pt/l) <5 km 2 Reduced IC data set: 169 non-acidic and non-limed lakes within common LNF IC types with classification by the three methods

10. Pressure (eutrophication) response Total phosphorus (µg /l) EQR (original) Excluded SE FI IE NO

11. Correlation with PCM Benchmark standardization -> subtraction FI & IE methods correlated with PCM SE method was too different compared to the other two methods -> EQR8 could not be intercalibrated FI method IE method SE method Excluded

12. Method comparison, FIL2 & EQR4 Lake n = 104 (FI&IE lakes, types LNF1&LNF2) BM std -> subtraction IC option 3a Regression characteristics: FI IE FI

13. Method comparison, FIL2 & EQR4 IC-spreadsheet for 2 methods, Option 3: -> boundary biases no boundary adjustments needed Class difference is relatively high (0.5 can’t be reduced without increasing boundary bias High variation is mainly due to: - differences in boundary setting and benchmark data (not fully corrected by benchmark standardization) - biogeographical differences (eg. fish species in BM lakes) - differences in sampling and metrics measured: IE method - fewer gillnets + fyke nets, FI method - only gillnets IE method - 13 detailed metrics, FI method - 4 general metrics

14. Summary & conclusions All LNF methods were WFD-compliant Eutrophication was the main common pressure -> IC against eutrophication pressure SE and NO methods were not intercalibrated due to feasibility problems (weak/no correlation with eutrophication or with PCM) FI and IE methods gave quite similar results -> no adjustments needed -> high variation due to biogeographical and method. reasons SE and NO methods will be used nationally (together with EQR4?) and will be further developed (IC later ?)

15. Thank you!