Data from: A comparison of regression methods for model selection in individual-based landscape genetic analysis

Anthropogenic migration barriers fragment many populations and limit the
ability of species to respond to climate-induced biome shifts.
Conservation actions designed to conserve habitat connectivity and
mitigate barriers are needed to unite fragmented populations into larger,
more viable metapopulations, and to allow species to track their climate
envelope over time. Landscape genetic analysis provides an empirical means
to infer landscape factors influencing gene flow, and thereby inform such
conservation actions. However, there are currently many methods available
for model selection in landscape genetics, and considerable uncertainty as
to which provide the greatest accuracy in identifying the true landscape
model influencing gene flow among competing alternative hypotheses. In
this study, we used population genetic simulations to evaluate the
performance of seven regression-based model selection methods on a broad
array of landscapes that varied by the number and type of variables
contributing to resistance, the magnitude and cohesion of resistance, as
well as the functional relationship between variables and resistance. We
also assessed the effect of transformations designed to linearize the
relationship between genetic and landscape distances. We found that linear
mixed effects models had the highest accuracy in every way we evaluated
model performance, however, other methods also performed well in many
circumstances, particularly when landscape resistance was high and the
correlation among competing hypotheses was limited. Our results provide
guidance for which regression-based model selection methods provide the
most accurate inferences in landscape genetic analysis and thereby best
inform connectivity conservation actions.