Fine-scale landscape genetics of the American badger (Taxidea taxus): disentangling landscape effects and sampling artifacts in a poorly understood species

Abstract

Landscape genetics is a powerful tool for conservation because it identifies landscape features that are important for maintaining genetic connectivity between populations within heterogeneous landscapes. However, using landscape genetics in poorly understood species presents a number of challenges, namely, limited life history information for the focal population and spatially biased sampling. Both obstacles can reduce power in statistics, particularly in individual-based studies. In this study, we genotyped 233 American badgers in Wisconsin at 12 microsatellite loci to identify alternative statistical approaches that can be applied to poorly understood species in an individual-based framework. Badgers are protected in Wisconsin owing to an overall lack in life history information, so our study utilized partial redundancy analysis (RDA) and spatially lagged regressions to quantify how three landscape factors (Wisconsin River, Ecoregions and land cover) impacted gene flow. We also performed simulations to quantify errors created by spatially biased sampling. Statistical analyses first found that geographic distance was an important influence on gene flow, mainly driven by fine-scale positive spatial autocorrelations. After controlling for geographic distance, both RDA and regressions found that Wisconsin River and Agriculture were correlated with genetic differentiation. However, only Agriculture had an acceptable type I error rate (3-5%) to be considered biologically relevant. Collectively, this study highlights the benefits of combining robust statistics and error assessment via simulations and provides a method for hypothesis testing in individual-based landscape genetics.

Inter-annual maintenance of the fine-scale genetic structure in a biennial plant

“…However, we are confident of our results for a number of reasons. It has been shown that these models are robust to type I errors4142 and do not fail when using sPCA principal components as response variables4043. In addition, ρ was consistently high in our models, and the residuals showed little to no spatial autocorrelation (Table S1), indicating that these models accounted adequately for the spatial autocorrelation.…”

“…Prudence is cautioned in interpreting spatially lagged regressions because little is known about their performance when applied to landscape genetic analyses40. However, we are confident of our results for a number of reasons.…”

“…The PC scores of all other individuals ( G j ) are weighted by the parameter ρ , which accounts for the lack of independence among individuals, and by the cost-distance-weighting matrix ( W ij ). We computed W ij using the inverse spatial distances among individuals because it approximately emulates a spatial autocorrelation under IBD4043. By using site-based measures we were able to determine the environmental factors associated with genetic structure63.…”

High gene flow in the American badger overrides habitat preferences and limits broadscale genetic structure

“…Despite their capability to move large distances, little reliable information exists about the outcome of dispersal and previous regional studies have found contrasting patterns of gene flow (Kierepka et al . ; Kierepka & Latch ). Kierepka et al .…”

“…Kierepka et al . () found panmixia whereas badgers in Wisconsin exhibited isolation by distance and a correlation between agriculture and genetic differentiation (Kierepka & Latch ).…”