  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
datasets.
