A multi‐method approach for analyzing hierarchical genetic structures: a case study with cougars Puma concolor

Abstract

Genetic data are increasingly used to describe the structure of wildlife populations and to infer landscape influences on functional connectivity. To accomplish this, genetic structure can be described with a multitude of methods that vary in their assumptions, advantages and limitations. While some methods discriminate distinct subpopulations separated by sharp genetic boundaries (i.e. barrier detection or clustering methods), other methods estimate gradient genetic structures using individual‐based genetic distances. We present an analytical framework based on individual ancestry values that combines these different approaches and can be used to a) test for local barriers to gene flow and b) evaluate effects of landscape gradients through individual‐based genetic distances that account for hierarchical genetic structure. We illustrate the approach with a data set of 371 cougars Puma concolor from a 217 000 km2 study area in Idaho and western Montana (USA) that were genotyped at 12 microsatellite loci. Results suggest that cougars in the region show a complex, hierarchical genetic structure that is influenced by a local barrier to gene flow (an urban population cluster connected by high traffic volumes), different landscape features (the Snake River Plain, forested habitat), and geographic distance. Our novel approach helped to elucidate the relative influence of these factors on different hierarchical levels of population structure, which was not possible when using either clustering methods or standard genetic distances. Results obtained with our analytical framework highlight the need for multi‐scale management of cougars in the region and show that landscape heterogeneity can create complex genetic structures, even in generalist species with high dispersal capabilities.