1 INRA, UMR 0985 ESE, INRA/Agrocampus Ouest, Ecotoxicologie et Qualité des Milieux Aquatiques, 65 rue de Saint-Brieuc, Rennes, France INRA, UE 1036 U3E, Unité Expérimentale d’Ecologie et Ecotoxicologie Aquatique, 65 rue de Saint-Brieuc, Rennes, France 3 ISAE, Institut en Santé Agro-Environnement, France References – [1] Besnard et al Molecular Ecology Resources, Permanent Genetic Resource Note 13: [2] Leinonen et al Nature Reviews Genetics 14, 179–190. [3]Bonnin et al Genetics 143, 1795–1805. [4] Bouétard et al PLoS ONE, 9: e [5] Whitlock. M.C., Guillaume, F Genetics 183, 1055–1063. Objectives  Investigate the evolutionary potential of pesticide tolerance in populations of a non-target species  Estimate the relative influence of neutral versus selective forces on genetic variation in tolerance Context = ecological risk assessment (ERA) of pesticides Biological relevance of standard toxicity testing: importance of intraspecific variation Long-term impact: incorporation of genetic and evolutionary criteria to future risk assessment procedures Questions and global approach Genetics of copper tolerance? description of within- and between-population variation Population genetic divergence? comparison of tolerance patterns to neutral genetic divergence Species vs population level relevance of a standard test? proposal of an assessment method Objectives  Investigate the evolutionary potential of pesticide tolerance in populations of a non-target species  Estimate the relative influence of neutral versus selective forces on genetic variation in tolerance Context = ecological risk assessment (ERA) of pesticides Biological relevance of standard toxicity testing: importance of intraspecific variation Long-term impact: incorporation of genetic and evolutionary criteria to future risk assessment procedures Questions and global approach Genetics of copper tolerance? description of within- and between-population variation Population genetic divergence? comparison of tolerance patterns to neutral genetic divergence Species vs population level relevance of a standard test? proposal of an assessment method Common-garden experiment (8 Lymnaea stagnalis populations, North-Western Europe)  Population neutral divergence, 14 microsatellite loci [1]: Global F ST = 0.388; 95% CI = [0.354;0.430]  Estimation of copper tolerance (CuSO 4, 5H 2 O): global LC 50 estimation from a range finding test based on a balanced pool of each population and family representatives (8 concentrations, mg/L). exposure of 8 families (F1s) per population to global 48h-LC50 ( 3 replicate groups of 10 individuals per familiy) CONCLUSIONS  Strong population genetic divergence in copper tolerance, consistent with neutral differentiation  Divergence pattern inconsistent with homogenizing selection, i.e., with the condition required to safely extrapolate population-level results to the species level  Need to account for intra-specific variation in standard toxicity testing: Q ST -F ST approach applicable to this context. CONCLUSIONS  Strong population genetic divergence in copper tolerance, consistent with neutral differentiation  Divergence pattern inconsistent with homogenizing selection, i.e., with the condition required to safely extrapolate population-level results to the species level  Need to account for intra-specific variation in standard toxicity testing: Q ST -F ST approach applicable to this context. Acknowledgements - Work funded by INRA-ONEMA Action « Phylogeny and Polluosensitivity ». Rearing and experimentations performed at INRA U3E, Rennes. Acknowledgements - Work funded by INRA-ONEMA Action « Phylogeny and Polluosensitivity ». Rearing and experimentations performed at INRA U3E, Rennes. Lymnaea stagnalis Cliché M. Collinet (INRA) Control Marie-Agnès Coutellec 1, Jessica Côte 1, Anthony Bouétard 1, Yannick Pronost 2, Maïra Coke 2, Anne-Laure Besnard 1, Fabien Piquet 3, Thierry Caquet 1 Genetic variation of Lymnaea stagnalis tolerance to copper: a test of selection hypotheses and its relevance for ecological risk assessment with: genetic variance between (Vb) and within populations (Vw) inbreeding coefficient (f)  Genetic variance decomposition: Q ST approach [2-4] Observed patternTheoretical Evolutionary Expectation Consistency with toxicity assessment at the species level Q ST = F ST Neutral divergence (no selection involved)If F ST significant: NO Q ST > F ST Divergent selection (local adaptation)NO Q ST < F ST Homogenizing selection or trait canalizationYES population: ModelAIC logLikelihood ratio test “model j vs model i ”: P-value (df) Copper sensitivity M1 = Y ~ treatment + size+ (1|population/family)784.7 M2 = Y ~ treatment + size (1|population) (M2 vs M1) M3 = Y ~ treatment + size (1|family)801.3<0.001 (M3 vs M1) M4 = Y ~treatment + (1|population/family) (M4 vs M1) M5 = Y ~size + (1|population/family)1047.9<0.001 (M5 vs M1) Shell size M1 = size ~ treatment + (1|population/family)474.1 M2 = Y ~ treatment + (1|population)549.9<0.001 (M2 vs M1) M3 = Y ~ treatment + (1|family)486.9<0.001 (M3 vs M1) M4 = Y ~1 + (1|population/family) (M4 vs M1) Table 2. Summary of statistical tests performed on shell size and observed 96-h mortality. Generalized linear mixed effects models compared with a LogLikelihood ratio test (P-value). AIC = Akaike information criterion (in bold: best model). Trait and statistical model Observed Q ST - F ST Neutral Q ST - F ST 95%CI Left p-value Right p-value Copper sensitivity M 96h ~ treatment+size+(1|pop/fam)0.024[-0.247;0.214] Shell size Size ~ treatment + (1|pop/fam)-0.195[-0.251;0.228] Figure 1. L. stagnalis population reaction norm to copper. Mean percent survival (SE) calculated over 8 families per population. Table 3. Summary of the Q ST -F ST analyses performed on L. stagnalis sensitivity to copper (M 96h = number of dead snails after 96-h exposure) and shell size. Left and right p-values give the probability for the observed difference between Q ST and F ST to fall within the 95% CI of the expected neutral distribution. P F ST ) [5]. Table 1. Summary of theoretical hypotheses testable under the Q ST -F ST approach. Homogenizing selection is a prerequisite for toxicity result extrapolation from population (or single strain) to the species level.