Bayesian sample assignment
To assign individuals to genetic clusters, a two-step approach was taken. First, genotypes for all North American samples were added to the dataset presented in Andersen et al. (2019a), and the probability of assignment (Q ) of sampled individuals to one of two distinct genetic clusters (K ) representing either pure winter moth or pure Bruce spanworm was calculated using Structure v.2.3.2 (Falush, Stephens, & Pritchard 2003; Pritchard, Stephens, & Donnelly, 2000). These analyses were based on the analysis of 12 polymorphic microsatellite loci that co-amplify between the two species, and ten independent analyses were run using the admixture model, correlated allele frequencies, and default settings, with random starting values, runtimes of 1,000,000 generations, and burn-in periods of 100,000 generations. Results were then summarized across runs using Clumpak (Kopelman, Mayzel, Jakobsson, Rosenberg, & Mayrose, 2015), and hybrid individuals were identified as those receiving scores of Q < 0.75 to both the Bruce spanworm and the winter moth genetic clusters. Hybrid individuals were then removed from the dataset, and the filtered dataset (≥ 20 polymorphic loci for each individual) was used to estimate values of Q for all individuals (both native and introduced regions) for values of K= 2 throughK = 14 in Structure using the run parameters described above. To determine the optimal number of clusters present in the dataset, the ∆K statistic (Evanno, Regnaut, & Goudet, 2005) was calculated in StructureHarvester (Earl & vonHoldt, 2012), and independent runs were again summarized for major and minor partition schemes using Clumpak.
For each value for ∆K with a distinct peak representing a positive rate of change identified using StructureHarvester, the summarized ‘popfile’ of cluster membership coefficients for the major mode calculated in Clumpak was used to create a distance matrix using the ‘dist’ function in R v. 4.0.0 (R Core Team, 2020). The resulting matrices were then used to calculate ‘NeighborNet’ networks using SPLITSTREE v.4.14.2 (Huson & Bryant, 2006), and the outputs were examined to identify geographic patterns.