Genetic analyses
Mme7 is a sex-linked marker and it deviated from Hardy Weinberg Equilibrium (HWE), therefore, we coded it as a missing allele for females and juveniles in further analyses (Rasner et al., 2004; Rodríguez-Bardía et al., 2022). We genotyped 76 individuals, 28 males from ‘period 1’, 25 males from ‘period 2’, 14 females from ‘period 1’, and 9 females from ‘period 2’. We calculated expected heterozygosity (\(H_{e}\)), observed heterozygosity (\(H_{o}\)), allelic richness (\(A_{r}\)), and inbreeding coefficients (\(F_{\text{IS}}\)) for each population and time period (e.g., MTV period 1 and MTV period 2). We estimated confidence intervals for FIS values using 9999 bootstraps as implemented in the “hierfstat” package in R (R Core Team, 2020). To compare differences in heterozygosity for the same populations between time periods we used z-scores from a Wilcoxon signed rank test as implemented in the “coin” package in R (R Core Team, 2020). To estimate changes in genetic structure among populations over a decade, we calculated Nei’s coefficient (\(G_{\text{st}}\)) among populations within time periods, using 9999 bootstrap values to estimate confidence intervals. We assumed that nonoverlapping confidence intervals indicated significant differences in genetic structure between periods. We also tested for differences in genetic diversity between time periods using an analysis of molecular variance (AMOVA) with 9999 permutations using the “poppr” package in R (R Core Team, 2020). We clustered individuals in each time period into the most likely configuration of clusters based on allele frequency similarity using the Bayesian clustering algorithms implemented in the program STRUCTURE V.2.3.4 (Pritchard et al., 2000). To determine the most probable number K, we utilized a method implemented in a study that used data from some of the same populations (Rodríguez-Bardía et al., 2022). Thus, we used correlated allele frequencies and the admixture model with 300 000 Markov chains, and a burning of 30 000 chains. We grouped individuals in 1 to 6 clusters with 20 repetitions for each cluster (Rodríguez-Bardía et al., 2022). To determine the most likely number of K clusters we used Structure Harvester 0.6.94 (Evanno et al., 2005). After determining the most likely number of K cluster, we conducted another structure analysis with 1 000 000 Markov chains and a burn-in of 100 000 chains to assure proper chain mixture (Rodríguez-Bardía et al., 2022).