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The assessment of toxic effects at biologically and ecologically relevant scales is an important challenge in ecosystem protection. Indeed, stressors may impact populations at much longer term than the usual timescale of toxicity tests. It is therefore important to study the evolutionary response of a population under chronic stress. We performed a 16-generation study to assess the evolution of two populations of the ubiquitous nematode Caenorhabditis elegans in control conditions or exposed to 1.1 mM of uranium. Several generations were selected to assess growth, reproduction, survival, and dose-responses relationships, through exposure to a range of concentrations (from 0 to 1.2 mM U) with all endpoints measured daily. Our experiment showed an adaptation of individuals to experimental conditions (increase of maximal length and decrease of fecundity) for both populations. We also observed an increase of adverse effects (reduction of growth and fertility) as a function of uranium concentration. We pointed out the emergence of population differentiation for reproduction traits. In contrast, no differentiation was observed on growth traits. Our results confirm the importance of assessing environmental risk related to pollutant through multi-generational studies.

Natural populations are chronically exposed to various pollutants over many generations. It is thus crucial to understand and quantify adaptive dynamics of stressed populations in order to increase the relevance of ecotoxicological risk assessment. However, long-term consequences to population exposure are not much studied yet. The present study investigated evolutionary responses of Chironomus riparius populations exposed to uranium (heavy metal pollutant) and to assess the underlying mechanisms. To fulfil our objective, we produced data with organisms exposed to four relevant concentrations of uranium through eight successive generations. We built an individual-based (IBM) model of C. riparius population dynamics to analyse these data and to test several assumptions about the mechanisms involved in the phenotypic changes. The IBM was based on a dynamic energy budget (DEB) model for C. riparius by Pery et al. (2002). DEB models account mathematically for the acquisition and use of energy to describe and predict growth, maintenance, development and reproduction of living organisms. The IBM accounted for the influence of the test conditions on the observations over eight generations and highlighted some trait evolution such as time to emergence and adult size in control conditions. The model was then used to analyse the exposed population data. Our results showed that exposure to uranium led to a phenotypic selection via a differential survival characterised by longer time to emergence and smaller larval maximal size. As a general conclusion, IBMs based on DEB-based modelling developed to analyse multi-generation experiments are very promising for understanding and quantifying long term selection and tolerance mechanisms in a population under toxic stress.

Experimental ecosystems such as mesocosms have been developed to improve the ecological relevance of ecotoxicity test. However, in mesocosm studies, the number of replicates is limited by practical and financial constraints. In addition, high levels of biological organization are characterized by a high variability of descriptive variables. This variability and the poor number of replicates have been recognized as a major drawback for detecting significant effects of chemicals in mesocosm studies. In this context, a tool able to predict precisely control mesocosms outputs, to which endpoints in mesocosms exposed to chemicals could be compared should constitute a substantial improvement. We evaluated here a solution which consists in stochastic modelling of the control fish populations to assess the probabilistic distributions of population endpoints. An individual-based approach was selected, because it generates realistic fish length distributions and accounts for both individual and environmental sources of variability. This strategy was applied to mosquitofish (Gambusia holbrooki) populations monitored in lentic mesocosms. We chose the number of founders as a so-called "stressor" because subsequent consequences at the population level could be expected. Using this strategy, we were able to detect more significant and biologically relevant perturbations than using classical methods. We conclude that designing an individual-based model is very promising for improving mesocosm data analysis. This methodology is currently being applied to ecotoxicological issues.