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).