The population and landscape genetics of the European badger (Meles meles) in Ireland

Abstract

The population genetic structure of free‐ranging species is expected to reflect landscape‐level effects. Quantifying the role of these factors and their relative contribution often has important implications for wildlife management. The population genetics of the European badger (Meles meles) have received considerable attention, not least because the species acts as a potential wildlife reservoir for bovine tuberculosis (bTB) in Britain and Ireland. Herein, we detail the most comprehensive population and landscape genetic study of the badger in Ireland to date—comprised of 454 Irish badger samples, genotyped at 14 microsatellite loci. Bayesian and multivariate clustering methods demonstrated continuous clinal variation across the island, with potentially distinct differentiation observed in Northern Ireland. Landscape genetic analyses identified geographic distance and elevation as the primary drivers of genetic differentiation, in keeping with badgers exhibiting high levels of philopatry. Other factors hypothesized to affect gene flow, including earth worm habitat suitability, land cover type, and the River Shannon, had little to no detectable effect. By providing a more accurate picture of badger population structure and the factors effecting it, these data can guide current efforts to manage the species in Ireland and to better understand its role in bTB.

Functional connectivity and home range inferred at a microgeographic landscape genetics scale in a desert‐dwelling rodent

“…In this context, clusters can be visualized as tools to summarize and understand the data, but recognizing that complex systems are not always subject to this clear-cut representation (Jombart & Collins, 2015). As mentioned above, obtaining multiple values of K using the K-means algorithm has been related to continuously distributed species, but only on a wide geographic extent (Guerrero et al, 2018), hence performing better for island-based models than for continuous models (Jombart & Collins, 2015). Moreover, this algorithm uses a simple measure of group differentiation and is likely to fail to identify the correct number of clusters in complex population models (Jombart et al, 2010).…”

Genomic epidemiology ofMycobacterium bovisinfection in sympatric badger and cattle populations in Northern Ireland

“…Pathogen spread across landscapes is recognised to be an inherently spatial process (Biek and Real, 2010), leading to distinct patterns in pathogen genetic structure. Similarly, free ranging wildlife hosts, exhibit partitioning of their own genetic variation across landscapes (Guerrero et al 2018), for example, isolation by distance (IBD) (Wright, 1943). An appreciation of how these types of landscape-genetic phenomena intersect can help to inform epidemiological investigations in wildlife populations (Biek and Real, 2010).…”

Feral Horse Space Use and Genetic Characteristics from Fecal DNA

“…This is a multivariate method that infers genetic clusters from sequential K‐means clustering and model selection (Jombart et al 2010). Discriminant analysis of principal components is as reliable as Bayesian clustering approaches (Jombart et al 2010, Guerrero et al 2018, Kaczensky et al 2018), is likely to be less affected by uneven sampling and presence of close relatives (Puechmaille 2016), and can be better than Bayesian approaches at characterizing population subdivision (Jombart et al 2010).…”