Benchmarking STRUCTURE against DAPC in population genetics

We used STRUCTURE [1 (link),2 (link)] as a benchmark for the performance of DAPC. We analysed all simulated datasets with STRUCTURE v2.1, using the admixture model with correlated allele frequencies to determine the optimal number of genetic clusters and to assign individuals to groups. Computations were performed on the computer resources of the Computational Biology Service Unit at Cornell University (http://cbsuapps.tc.cornell.edu/). For each run, results were based on a Markov Chain Monte Carlo (MCMC) of 100,000 steps, of which the first 20,000 were discarded as burn-in. Analyses were ran with numbers of clusters (k) ranging from 1 to 8 for the island and hierarchical island models (Figure 2a-b), from 1 to 15 for the hierarchical stepping stone (Figure 2c), and from 1 to 30 for the stepping stone (Figure 2d). Ten runs were performed for each k value. We employed the approach of Evanno et al. [57 (link)] to assess the optimal number of clusters. In order to assess assignment success, STRUCTURE was run by enforcing k to its true value. Individuals were assigned to clusters using CLUMPP 1.1.2 [58 (link)], which allows to account for the variability in individual membership probabilities across the different runs. To obtain results comparable to DAPC, individuals were assigned to the cluster to which they had the highest probability to belong.