Data from: Examining the full effects of landscape heterogeneity on spatial genetic variation: a multiple matrix regression approach for quantifying geographic and ecological isolation

Understanding the effects of landscape heterogeneity on spatial genetic
variation is a primary goal of landscape genetics. Ecological and
geographic variables can contribute to genetic structure through
geographic isolation, in which geographic barriers and distances restrict
gene flow, and ecological isolation, in which gene flow among populations
inhabiting different environments is limited by selection against
dispersers moving between them. Although methods have been developed to
study geographic isolation in detail, ecological isolation has received
much less attention, partly because disentangling the effects of these
mechanisms is inherently difficult. Here, I describe a novel approach for
quantifying the effects of geographic and ecological isolation using
multiple matrix regression with randomization. I explored the parameter
space over which this method is effective using a series of
individual-based simulations and found that it accurately describes the
effects of geographic and ecological isolation over a wide range of
conditions. I also applied this method to a set of real-world datasets to
show that ecological isolation is an often overlooked but important
contributor to patterns of spatial genetic variation and to demonstrate
how this analysis can provide new insights into how landscapes contribute
to the evolution of genetic variation in nature.