title: Impact assessment predicted by means of genetic agent-based modeling.
abstract: Toxicological effects on ecosystems are complex, and tools that improve our understanding are necessary. Until now, efforts have been ecologically based with no practice of incorporating genetic diversity measurements into risk assessment. However, with the use of geneticists' approaches the information level returned is potentially much higher. The precise genetic consequences of population perturbations result from a complex balance among effects on population substructure, size, and founding events. Hence, there is a need for tools that will support the interpretation of genetic erosion effects in toxicological investigation. Spatially explicit modeling using autonomous agent systems seems to be a promising emerging tool, which can benefit the work of population geneticists, by explicitly incorporating spatiotemporal interactions between the ecology and behavior of individuals and their environment. It is likely that agent-based modeling will be similarly beneficial to toxicologists, and we suggest that combining the agent-based models with toxicology and genetics could have several further useful applications. The combination of agent-based models and genetics is in its infancy, and hence models would be novel in evaluating toxicological impact on genetic composition of populations. Once appropriate validation of both genetic and ecological components is carried out, genetic agent-based models ought to be an appropriate tool to simulate these genetic and ecotoxicological interactions, being sufficiently flexible to mimic real population processes under a range of environmental conditions. Additionally, they can be used to obtain measures for the genetic and demographic status, assessing how different toxicological scenarios affect both genetic and demographic parameters.
authors.affiliation: Department of Ecology and Genetics, Institute of Biological Sciences, University of Aarhus, Build 540, Ny Munkegade, DK-8000 Aarhus C, Denmark. biocp@nf.au.dk
authors.forename: Cino
authors.initials: C
authors.lastname: Pertoldi
authors.forename: Chris
authors.initials: C
authors.lastname: Topping
mesh_terms: D003710:Demography; D014644:Genetic Variation; D005828:Genetics, Population; D023281:Genomics / Q000639:trends; D006801:Humans; D008957:Models, Genetic; D018570:Risk Assessment; D014116:Toxicology / Q000639:trends*