research articlejournal article
Convergence of population processes with small and frequent mutations to the canonical equation of adaptive dynamics
Abstract
International audienceIn this article, a stochastic individual-based model describing Darwinian evolution of asexual, phenotypic trait-structured population, is studied. We consider a large population with constant population size characterised by a resampling rate modeling competition pressure driving selection and a mutation rate where mutations occur during life. In this model, the population state at fixed time is given as a measure on the space of phenotypes and the evolution of the population is described by a continuous time, measure-valued Markov process. We investigate the asymptotic behavior of the system, where mutations are frequent, in the double simultaneous limit of large population (K → +∞) and small mutational effects (σK → 0) proving convergence to an ODE known as the canonical equation of adaptive dynamics. This result holds only for a certain range of σK parameters (as a function of K) which must be small enough but not too small either. The canonical equation describes the evolution in time of the dominant trait in the population driven by a fitness gradient. This result is based on an slow-fast asymptotic analysis. We use an averaging method, inspired by (Kurtz, 1992), which exploits a martingale approach and compactness-uniqueness arguments. The contribution of the fast component, which converges to the centered Fleming-Viot process, is obtained by averaging according to its invariant measure, recently characterised in (Champagnat-Hass, 2022)- info:eu-repo/semantics/article
- Journal articles
- Adaptive dynamics
- Canonical equation
- Individual-based model
- Measure-valued Markov process
- Slow-fast asymptotic analysis
- Averaging method
- Centered Fleming-Viot process
- MSC subject classification. Primary 60B10, 60G44, 60G57, 92D10, 92D25, 92D40; Secondary 60F10, 60G10, 60 J35, 60J60, 60J68
- [MATH.MATH-PR]Mathematics [math]/Probability [math.PR]