Data_MER120022R1

To evaluate the utility of different methods for genetic barrier detection, we used data sets from Landguth et al. (2010), who conducted spatially–explicit, individual–based genetic divergence simulations in the program CDPOP (Landguth & Cushman 2010). Landguth et al. (2010) simulated genotypes for 1 000 individuals of an animal species within a study landscape of 70 x 100 km. Simulations were initiated with 30 loci and 30 alleles maximum per locus (resulting in 900 total possible alleles and mean Ho = 0.967), a k–allele mutation rate of 0.0005 in a two–sex mating structure with sex assigned randomly with equal probability (see Landguth et al. 2010 for details). Landscape resistances to movement were homogeneous and controlled by isolation-by-distance on either side of a complete (i.e., impermeable) linear barrier that bisected the landscape into a western and eastern half (500 individuals on either side). We used data from 10 independent Monte Carlo simulations and under two dispersal distances. In the first scenario (10k), the maximum simulated dispersal distance was 10 kilometres, while in the second scenario (60k) the dispersal distance was set to 60 kilometres. These scenarios use the two most extreme dispersal distances simulated by Landguth et al. (2010) and correspond to species exhibiting short– versus long–range dispersal relative to the spatial extent of the study area. Because we were interested in testing the performance of methods for inferring recent barriers to gene flow, we applied the methods only to the first 20 generations after barrier imposition. There are then 20 generations of raw genotypes for each individual stored in files called grid{generation}.csv. In addition, genetic distance matrices are calculated for each generation using proportion of shared alleles and stored in files called Gdmatrix{generation}.csv.