2.6 Analyses of population genomic structure
We performed a principal component analysis (PCA) using the Bioconductor package SNPRelate (Zheng et al., 2012); https://www.bioconductor.org/) to summarize population genomic structure. We used the program fastStructure to estimate the number of genetically distinct populations within the sampled P. auritus range. We tested a range of K values (where K denotes the number of inferred populations) from 1 to 10. The script “choose.py” included in the FastStructure package was used to determine the best estimate of K that maximizes the marginal likelihood. We also calculated pairwise estimates of FST(Weir & Cockerham, 1984) among sites and among K populations inferred from FastStructure using VCFtools. To test for an IBD effect, a Mantel test was used to assess the correlation between pairwise FST values and geographic distance. Mantel tests were run with 999,999 permutations using VEGAN2.2-1 in R (Oksanen et al., 2018) and are reported using both raw FST and transformed FST/(1-FST) distances, as well as both raw Euclidian geographic distance and log-transformed Euclidian distances (Rousset, 1997; Slatkin, 1995).