Improving mesocosm data analysis through individual-based modelling of control population dynamics: a case study with mosquitofish (Gambusia holbrooki)
2012
Beaudouin, Rémy | Ginot, Vincent | Monod, Gilles
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.
Afficher plus [+] Moins [-]Mots clés AGROVOC
Informations bibliographiques
Cette notice bibliographique a été fournie par Fundamental Library of Latvia University of Life Sciences and Technologies
Découvrez la collection de ce fournisseur de données dans AGRIS