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A rigorous model study of the adaptive dynamics of Mendelian diploids

Overview of attention for article published in Journal of Mathematical Biology, July 2012
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Title
A rigorous model study of the adaptive dynamics of Mendelian diploids
Published in
Journal of Mathematical Biology, July 2012
DOI 10.1007/s00285-012-0562-5
Pubmed ID
Authors

Pierre Collet, Sylvie Méléard, Johan A. J. Metz

Abstract

Adaptive dynamics (AD) so far has been put on a rigorous footing only for clonal inheritance. We extend this to sexually reproducing diploids, although admittedly still under the restriction of an unstructured population with Lotka-Volterra-like dynamics and single locus genetics (as in Kimura's in Proc Natl Acad Sci USA 54: 731-736, 1965 infinite allele model). We prove under the usual smoothness assumptions, starting from a stochastic birth and death process model, that, when advantageous mutations are rare and mutational steps are not too large, the population behaves on the mutational time scale (the 'long' time scale of the literature on the genetical foundations of ESS theory) as a jump process moving between homozygous states (the trait substitution sequence of the adaptive dynamics literature). Essential technical ingredients are a rigorous estimate for the probability of invasion in a dynamic diploid population, a rigorous, geometric singular perturbation theory based, invasion implies substitution theorem, and the use of the Skorohod M 1 topology to arrive at a functional convergence result. In the small mutational steps limit this process in turn gives rise to a differential equation in allele or in phenotype space of a type referred to in the adaptive dynamics literature as 'canonical equation'.

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Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 3%
Philippines 1 3%
Unknown 31 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 33%
Researcher 7 21%
Professor 5 15%
Student > Master 3 9%
Student > Bachelor 2 6%
Other 0 0%
Unknown 5 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 33%
Mathematics 9 27%
Environmental Science 4 12%
Computer Science 1 3%
Earth and Planetary Sciences 1 3%
Other 1 3%
Unknown 6 18%