SNP analyses
Genetic similarity among individuals and populations was visualized
using ordination (Principal Coordinates Analysis [PCoA]; Gower,
1966), using individuals as entities and loci as attributes and
implemented by the gl.pcoa and gl.pcoa.plot functions of dartR. For our
phylogenetic analysis we constructed SNP genotypes for each individual
by concatenating only the variable bases from each SNP locus into a
single partition. A few loci had the SNP removed with the adaptor,
because of chance matching of the adaptor sequence to the terminal
region containing the SNP. These loci were removed prior to
concatenation. Heterozygous SNP positions were represented by the
standard ambiguity codes. We generated a phylogenetic tree using Maximum
likelihood (ML) applied to concatenated sequences. ML analyses were
conducted using RAxML 8.2.12 (Stamatakis 2014) on the CIPRES cluster
(Miller, Pfeiffer & Schwartz, 2010) using the model GTRCAT and
searching for the best-scoring ML tree using the model GTRGAMMA in a
single program run, with bootstrapping set to finish based on the
autoMRE majority rule criterion. The tree was imported to Mega 7.0.18
(Kumar, Stecher & Tamura, 2016), formatted and mid-point rooted. To
assist with identifying potential introgressed individuals,
heterozygosity was calculated in R using the command “het <-
rowMeans(as.matrix(gl)==1, na.rm=T)” followed by “write.csv (het,
file=”het.csv”)”.
The diagnosability of lineages and candidate species was assessed by
calculating the number of pairwise fixed differences (both absolute and
allowing a 5% tolerance for shared alleles at each locus) and the
associated probabilities that such values could arise through sampling
error alone (dartR command gl.fixed.diff; parameter tloc = 0 or tloc =
0.05; see Unmack et al., 2022 for rationale and methods involved).