Concentration in selection-mutation models: error estimates and asymptotic expansions

TL;DR

This paper analyzes an integro-differential model of population evolution, providing asymptotic expansions for small mutations, linking mathematical solutions to biological trait distributions and moments.

Contribution

It rigorously derives a first-order asymptotic expansion of the solution, connecting Hamilton-Jacobi methods to biological trait moments.

Findings

First-order asymptotic expansion of the solution

Approximation of phenotypic density moments

Connection between mathematical and biological quantities

Abstract

In this paper, we study an integro-differential equation which describes the evolutionary dynamics of a population structured by a phenotypic trait. This population undergoes asexual reproduction, competition, selection, and mutation. We provide an asymptotic analysis of the model, assuming that the mutations have small effects. A standard approach for the analysis of the qualitative properties of the solutions of such an equation is to apply a logarithmic transformation, which yields a Hamilton-Jacobi equation with constraint. When the reproduction term is a concave function of the trait, it has been established that the solution is classical. We rigorously derive a first-order asymptotic expansion of the solution. This expansion allows us to approximate the moments of the phenotypic density. This result establishes a connection between the approximations of the phenotypic density obtained via the Hamilton-Jacobi approach and relevant biological quantities, which are more suitable from a modeling perspective.