Assessing genomic differentiation
We generated maximum-likelihood phylogenies for complete mitochondrial
genomes and autosomal SNPs, with and without the inclusion of an
outgroup, using RAxML (GTRCAT model, 10,000 bootstrap replicates,
random starting seed; Stamatakis 2014). It should be noted that
phylogeny inference using highly variable data (e.g., SNPs) can induce
acquisition bias resulting in longer branch lengths (Leaché et
al. 2015). Phylogenies were visualized in FigTree v1.2.2
(http://tree.bio.ed.ac.uk/software/figtree/).
Principal component analyses (PCA) were run and visualized using theSNPrelate (Zheng et al. 2012) R v3.3.4 library (R
Development Core Team 2008). Diversity statistics
(FIS , FST, FS or
relatedness2 statistics, nucleotide diversity (π), and Tajima’sD ) were calculated in VCFtools (Commands: –het,
–weir-fst-pop, –relatedness2, –window-pi, –TajimaD,
respectively), with π and D calculated in 100 bp window
intervals. F2 statistics were generated from the
compute_moment_stats and compute_most_additive_trees functions in
MixMapper (Lipson et al. 2013) with 1000 bootstraps and SNP
blocks of 100 (1 per 100 bp) in MatLab 2018 (The MathWorks, Inc.,
Natick, Massachusetts, USA).