Population differentiation and genetic structure
To infer genetic differentiation parameters, haplotypes grouped by continent or sub-region were considered to comprise distinct geographic populations. We assessed the genetic differentiation among geographic populations by computing the gene differentiation statistic developed by Nei and Chesser (1983), an allele frequency-based approach that relies on estimates of genetic differentiation among geographic sub-populations. We further used Hudson et al. (1992)’s statistical test, a simple non-parametric method based on Monte Carlos permutations. That method, compared to the traditional Chi-square analysis of genetic differentiation estimates, helped understand whether the geographical populations are genetically different from one another. In addition, genetic differentiation among populations was estimated by computing a distance matrix based on the number of mutational steps between haplotypes (Nst) and by using haplotype frequencies (Gst). Phylogeographical structure was tested based on the difference between GST and NST using PERMUT 2.0 (Pons & Petit, 1996; Chiu et al., 2013) with 1000 permutations. In contrast to Gst, Nst considers sequence differences between the haplotypes. Thus, Nst > Gst indicates that closely related haplotypes are observed more often in a given geographical area than would be expected by chance (Pons & Petit, 1996; Burban et al., 1999; Grivet, 2002; Guicking et al., 2011; Chiu et al., 2013; Chávez-Pesqueira & Núñez-Farfán, 2016; Sun et al., 2019). Following Templeton (1996), we tested the null hypothesis of homogeneity of nucleotide mutations using Fisher’s exact test to investigate haplotypic differentiation within the overall population. We also performed Fu’s Fs(Fu, 1997) to analyze the expansion level of the population under the hypothesis of selective neutrality and population equilibrium. Tajima’s D test also was implemented for comparison with the Fu’s Fs .