Convergence of an infinite dimensional stochastic process to a spatially structured trait substitution sequence

TL;DR

This paper analyzes how a spatially structured stochastic population process converges to a trait substitution sequence as population size increases and mutation rate decreases, using advanced probabilistic methods.

Contribution

It establishes the convergence of an individual-based spatial model to a jump process in infinite-dimensional space, with detailed large deviation and extinction time analyses.

Findings

Convergence of microscopic process to a trait substitution sequence.

Large deviation principles around deterministic limits.

Bounds on exit and extinction times for population states.

Abstract

We consider an individual-based spatially structured population for Darwinian evolution in an asexual population. The individuals move randomly on a bounded continuous space according to a reflected brownian motion. The dynamics involves also a birth rate, a density-dependent logistic death rate and a probability of mutation at each birth event. We study the convergence of the microscopic process when the population size grows to $+\infty$ and the mutation probability decreases to $0$. We prove a convergence towards a jump process that jumps in the infinite dimensional space of the stable spatial distributions. The proof requires specific studies of the microscopic model. First, we examine the large deviation principle around the deterministic large population limit of the microscopic process. Then, we find a lower bound on the exit time of a neighborhood of a stationary spatial distribution. Finally, we study the extinction time of the branching diffusion processes that approximate small size populations.