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1INRA, UMR 0985 ESE, INRA/Agrocampus Ouest, Ecotoxicologie et Qualité des Milieux Aquatiques, 65 rue de Saint-Brieuc, 35042 Rennes, FranceINRA, UE 1036 U3E, Unité Expérimentale d’Ecologie et Ecotoxicologie Aquatique, 65 rue de Saint-Brieuc, 35042 Rennes, France
3ISAE, Institut en Santé Agro-Environnement, France
References – [1] Besnard et al. 2013. Molecular Ecology Resources, Permanent Genetic Resource Note 13: 158-159.[2] Leinonen et al. 2013. Nature Reviews Genetics 14, 179–190.[3]Bonnin et al. 1996. Genetics 143, 1795–1805.[4] Bouétard et al. 2014. PLoS ONE, 9: e106670[5] Whitlock. M.C., Guillaume, F. 2009. Genetics 183, 1055–1063.
ObjectivesÞ Investigate the evolutionary potential of pesticide tolerance in populations of a non-target speciesÞ 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 variationLong-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)Þ Population neutral divergence , 14 microsatellite loci [1]: Global FST = 0.388; 95% CI = [0.354;0.430]Þ Estimation of copper tolerance (CuSO4, 5H2O):
• global LC50 estimation from a range finding test based on a balanced pool of each population and family representatives (8 concentrations, 0 - 2.56 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: QST-FST approach applicable to this context.
Acknowledgements - Work funded by INRA-ONEMA Action « Phylogeny and Polluosensitivity ». Rearing and experimentations performed at INRA U3E, Rennes.
Lymnaea stagnalis
Cliché M. Collinet (IN
RA)
Control
Marie-Agnès Coutellec1, Jessica Côte1
, Anthony Bouétard1, Yannick Pronost2, Maïra Coke2, Anne-Laure Besnard1, Fabien Piquet3, Thierry Caquet1
Genetic variation of Lymnaea stagnalis tolerance to copper: a test of selection hypotheses and its
relevance for ecological risk assessment
wb
bST VVf
VfQ
2)1(
)1(
with: genetic variance between (Vb) and within populations (Vw) inbreeding coefficient (f)
Þ Genetic variance decomposition: QST approach [2-4]
Observed pattern Theoretical Evolutionary Expectation Consistency with toxicity assessment at the species level
QST = FST Neutral divergence (no selection involved) If FST significant: NO
QST > FST Divergent selection (local adaptation) NO
QST < FST Homogenizing selection or trait canalization YES
Control Copper exposure40
50
60
70
80
90
100
AMS
SAN
BIE
PUT
EMM
BUX
HED
REN
Surv
ival
(%)
population:
Model AIClogLikelihood ratio test
“modelj vs modeli”:P-value (df)
Copper sensitivity M1 = Y ~ treatment + size+ (1|population/family) 784.7 M2 = Y ~ treatment + size (1|population) 789.7 0.008 (M2 vs M1)M3 = Y ~ treatment + size (1|family) 801.3 <0.001 (M3 vs M1)M4 = Y ~treatment + (1|population/family) 793.5 0.001 (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) 475.6 0.062 (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 ObservedQST - FST
NeutralQST - FST
95%CI
Leftp-value
Rightp-value
Copper sensitivity
M96h ~ treatment+size+(1|pop/fam) 0.024 [-0.247;0.214] 0.631 0.369
Shell size Size ~ treatment + (1|pop/fam) -0.195 [-0.251;0.228] 0.085 0.915
Figure 1. L. stagnalis population reaction norm to copper. Mean percent survival (SE) calculated over 8 families per population.
Table 3. Summary of the QST-FST analyses performed on L. stagnalis sensitivity to copper (M96h = number of dead snails after 96-h exposure) and shell size. Left and right p-values give the probability for the observed difference between QST and FST to fall within the 95% CI of the expected neutral distribution. P< 0.025 is indicative of homogenizing (left value, QST<FST) or divergent selection (right value, QST>FST) [5].
Table 1. Summary of theoretical hypotheses testable under the QST-FST approach. Homogenizing selection is a prerequisite for toxicity result extrapolation from population (or single strain) to the species level.