Spreading speed of locally regulated population models in macroscopically heterogeneous environments

TL;DR

This paper analyzes the spreading speed of a lattice branching random walk with local competition in large-scale heterogeneous environments, deriving an explicit ODE for the front's position in a specific asymptotic limit.

Contribution

It establishes the asymptotic behavior of the population front in a heterogeneous environment as parameters tend to their limits, connecting to the Fisher-KPP model and highlighting non-commuting limits.

Findings

Rescaled front position converges to an explicit ODE.

Limits $ ext{ε} o 0$ and $K o iginfty$ do not commute.

Connection to Fisher-KPP equation in the large $K$ limit.

Abstract

We consider a certain lattice branching random walk with on-site competition and in an environment which is heterogeneous at a macroscopic scale $1/\varepsilon$ in space and time. This can be seen as a model for the spatial dynamics of a biological population in a habitat which is heterogeneous at a large scale (mountains, temperature or precipitation gradient\ldots). The model incorporates another parameter, $K$, which is a measure of the local population density. We study the model in the limit when first $\varepsilon\to 0$ and then $K\to\infty$. In this asymptotic regime, we show that the rescaled position of the front as a function of time converges to the solution of an explicit ODE. We further discuss the relation with another popular model of population dynamics, the Fisher-KPP equation, which arises in the limit $K\to\infty$. Combined with known results on the Fisher-KPP equation, our results show in particular that the limits $\varepsilon\to0$ and $K\to\infty$ do not commute in general. We conjecture that an interpolating regime appears when $\log K$ and $1/\varepsilon$ are of the same order.