A Hamilton-Jacobi approach to characterize the evolutionary equilibria in heterogeneous environments

TL;DR

This paper develops a Hamilton-Jacobi based method to analyze the distribution of phenotypically structured populations in heterogeneous environments, revealing conditions for monomorphic or dimorphic states with explicit population distribution characterization.

Contribution

It introduces a novel Hamilton-Jacobi approach to determine dominant terms and distribution shapes in evolutionary models with non-zero mutation effects, extending previous asymptotic analyses.

Findings

Population distribution has at most two peaks.

Conditions for monomorphic versus dimorphic populations are explicitly given.

The method connects adaptive dynamics with quantitative genetics.

Abstract

In this work, we characterize the solution of a system of elliptic integro-differential equations describing a phenotypically structured population subject to mutation, selection and migration between two habitats. Assuming that the effects of the mutations are small but nonzero, we show that the population's distribution has at most two peaks and we give explicit conditions under which the population will be monomorphic (unimodal distribution) or dimorphic (bimodal distribution). More importantly, we provide a general method to determine the dominant terms of the population's distribution in each case. Our work, which is based on Hamilton-Jacobi equations with constraint, goes further than previous works where such tools were used, for different problems from evolutionary biology, to identify the asymptotic solutions, while the mutations vanish, as a sum of Dirac masses. In order to extend such results to the case with non-vanishing effects of mutations, the main elements are a uniqueness property and the computation of the correctors. This method allows indeed to go further than the Gaussian approximation commonly used by biologists and makes a connection between the theories of adaptive dynamics and quantitative genetics. Our work being motivated by biological questions, the objective of this article is to provide the mathematical details which are necessary for our biological results [16].