Outlier loci detection
The “all-species” and species-specific datasets analyzed in Genepop
were also run through Bayescan v.2.1 to identify loci under divergent
selection (Foll, 2012). Parameters of the Markov chain Monte Carlo
algorithm were set to 20 pilot runs of 5000 iterations. Afterward, a
burn-in of 50,000 iterations followed by 50,000 iterations were used for
estimation with a thinning interval of 10. Jeffrey’s scale was used to
interpret selection per loci (Jeffreys, 1998). Loci with a log10
value > 0.5 are considered to have “substantial” evidence
of selection and those with a log10 value > 1.0 have
“strong” evidence of selection. To identify loci under selection
across clusters another new SNP dataset was generated by filtering to
include only those occurring in all five clusters and 75% of the
individuals within each cluster. The nucleotide sequences for each locus
found to be under selection were submitted for an alignment search in
the InsectBase and Flybase databases (Thurmond et al., 2019; Yin et al.,
2016).