Front Propagation and Clustering in the Stochastic Nonlocal Fisher Equation

TL;DR

This paper investigates front propagation in the stochastic nonlocal Fisher equation, comparing stochastic and deterministic models, revealing different spreading mechanisms and velocities depending on population density and interaction range.

Contribution

It introduces a comparison between stochastic and deterministic models with cutoff, highlighting new spreading behaviors and velocities in nonlocal Fisher dynamics.

Findings

Large population density leads to constant velocity wave propagation.

Spreading velocity is lower than classical Fisher velocity.

Small populations spread via cluster division with exponentially decaying velocity.

Abstract

The nonlocal Fisher equation is a diffusion-reaction equation with a nonlocal quadratic competition, which describes the reaction between distant individuals. This equation arises in evolutionary biological systems, where the arena for the dynamics is trait space, diffusion accounts for mutations and individuals with similar traits compete, resulting in partial niche overlap. It has been found that the (non-cutoff) deterministic system gives rise to a spatially inhomogeneous state for a certain class of interaction kernels, while the stochastic system produces an inhomogeneous state for small enough population densities. Here we study the problem of front propagation in this system, comparing the stochastic dynamics to the heuristic approximation of this system by a deterministic system where the linear growth term is cut off below some critical density. Of particular interest is the nontrivial pattern left behind the front. For large population density, or small cutoff, there is a constant velocity wave propagating from the populated region to the unpopulated region. As in the local Fisher equation, the spreading velocity is much lower than the Fisher velocity which is the spreading velocity for infinite population size. The stochastic simulations give approximately the same spreading velocity as the deterministic simulation with appropriate birth cutoff. When the population density is small enough, there is a different mechanism of population spreading. The population is concentrated on clusters which divide and separate. This mode of spreading has small spreading velocity, decaying exponentially with the range of the interaction kernel.