DISENTANGLING THE EFFECTS OF GEOGRAPHIC AND ECOLOGICAL ISOLATION ON GENETIC DIFFERENTIATION

TLDR

Genetic isolation can arise from both geographic distance and ecological differences, yet common approaches such as the partial Mantel test have notable limitations. The authors aim to quantify the relative contributions of geographic and ecological distance to genetic differentiation between populations. They model allele frequencies as spatially correlated Gaussian processes whose covariance decreases with geographic and ecological distance, estimating parameters with MCMC and implementing the method, BEDASSLE, in R. Simulation studies and empirical analyses on human and teosinte data demonstrate the method’s utility.

Abstract

Populations can be genetically isolated both by geographic distance and by differences in their ecology or environment that decrease the rate of successful migration. Empirical studies often seek to investigate the relationship between genetic differentiation and some ecological variable(s) while accounting for geographic distance, but common approaches to this problem (such as the partial Mantel test) have a number of drawbacks. In this article, we present a Bayesian method that enables users to quantify the relative contributions of geographic distance and ecological distance to genetic differentiation between sampled populations or individuals. We model the allele frequencies in a set of populations at a set of unlinked loci as spatially correlated Gaussian processes, in which the covariance structure is a decreasing function of both geographic and ecological distance. Parameters of the model are estimated using a Markov chain Monte Carlo algorithm. We call this method Bayesian Estimation of Differentiation in Alleles by Spatial Structure and Local Ecology (BEDASSLE), and have implemented it in a user-friendly format in the statistical platform R. We demonstrate its utility with a simulation study and empirical applications to human and teosinte data sets.