Using genetic data to estimate diffusion rates in heterogeneous landscapes

TL;DR

This paper introduces a new method combining genetic data and reaction-diffusion models to accurately estimate how populations disperse across heterogeneous landscapes, aiding ecological management.

Contribution

It develops a mechanistic-statistical framework using stochastic differential equations and genetic data to estimate space-dependent diffusion rates in complex environments.

Findings

Successful estimation of diffusion parameters in simulated heterogeneous landscapes

Higher genetic differentiation improves estimation accuracy

Finite sample size limits estimation precision beyond a certain differentiation level

Abstract

Having a precise knowledge of the dispersal ability of a population in a heterogeneous environment is of critical importance in agroecology and conservation biology as it can provide management tools to limit the effects of pests or to increase the survival of endangered species. In this paper, we propose a mechanistic-statistical method to estimate space-dependent diffusion parameters of spatially-explicit models based on stochastic differential equations, using genetic data. Dividing the total population into subpopulations corresponding to different habitat patches with known allele frequencies, the expected proportions of individuals from each subpopulation at each position is computed by solving a system of reaction-diffusion equations. Modelling the capture and genotyping of the individuals with a statistical approach, we derive a numerically tractable formula for the likelihood function associated with the diffusion parameters. In a simulated environment made of three types of regions, each associated with a different diffusion coefficient, we successfully estimate the diffusion parameters with a maximum-likelihood approach. Although higher genetic differentiation among subpopulations leads to more accurate estimations, once a certain level of differentiation has been reached, the finite size of the genotyped population becomes the limiting factor for accurate estimation.