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From Individual Stochastic Processes to Macroscopic Models in Adaptive Evolution

Nicolas Champagnat 1 Régis Ferrière 2 Sylvie Méléard 3 
INRIA Lorraine, CRISAM - Inria Sophia Antipolis - Méditerranée , UHP - Université Henri Poincaré - Nancy 1, Université Nancy 2, INPL - Institut National Polytechnique de Lorraine, CNRS - Centre National de la Recherche Scientifique : UMR7502
Abstract : We are interested in modelling Darwinian evolution, resulting from the interplay of phenotypic variation and natural selection through ecological interactions. Our models are rooted in the microscopic, stochastic description of a population of discrete individuals characterized by one or several adaptive traits. The population is modelled as a stochastic point process whose generator captures the probabilistic dynamics over continuous time of birth, mutation, and death, as influenced by each individual's trait values, and interactions between individuals. An offspring usually inherits the trait values of her progenitor, except when a mutation causes the offspring to take an instantaneous mutation step at birth to new trait values. We look for tractable large population approximations. By combining various scalings on population size, birth and death rates, mutation rate, mutation step, or time, a single microscopic model is shown to lead to contrasting macroscopic limits, of different nature: deterministic, in the form of ordinary, integro-, or partial differential equations, or probabilistic, like stochastic partial differential equations or superprocesses. In the limit of rare mutations, we show that a possible approximation is a jump process, justifying rigorously the so-called trait substitution sequence. We thus unify different points of view concerning mutation-selection evolutionary models.
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Submitted on : Tuesday, December 9, 2008 - 4:08:05 PM
Last modification on : Thursday, March 17, 2022 - 10:08:20 AM



Nicolas Champagnat, Régis Ferrière, Sylvie Méléard. From Individual Stochastic Processes to Macroscopic Models in Adaptive Evolution. Stochastic Models, 2008, 24 (S1), pp.2-44. ⟨10.1080/15326340802437710⟩. ⟨inria-00345680⟩



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