GENECLASS2 is a software that computes various genetic assignment criteria to assign or exclude reference populations as the origin of diploid or haploid individuals, as well as of groups of individuals, on the basis of multilocus genotype data. In addition to traditional assignment aims, the program allows the specific task of first-generation migrant detection. It includes several Monte Carlo resampling algorithms that compute for each individual its probability of belonging to each reference population or to be a resident (i.e., not a first-generation migrant) in the population where it was sampled. A user-friendly interface facilitates the treatment of large datasets.

Attempts to study the genetic population structure of large mammals are often hampered by the low levels of genetic variation observed in these species. Polar bears have particularly low levels of genetic variation with the result that their genetic population structure has been intractable. We describe the use of eight hypervariable microsatellite loci to study the genetic relationships between four Canadian polar bear populations: the northern Beaufort Sea, southern Beaufort Sea, western Hudson Bay, and Davis Strait-Labrador Sea. These markers detected considerable genetic variation, with average heterozygosity near 60% within each population. Interpopulation differences in allele frequency distribution were significant between all pairs of populations, including two adjacent populations in the Beaufort Sea. Measures of genetic distance reflect the geographic distribution of populations, but also suggest patterns of gene flow which are not obvious from geography and may reflect movement patterns of these animals. Distribution of variation is sufficiently different between the Beaufort Sea populations and the two more eastern ones that the region of origin for a given sample can be predicted based on its expected genotype frequency using an assignment test. These data indicate that gene flow between local populations is restricted despite the long-distance seasonal movements undertaken by polar bears.

Genetic assignment methods use genotype likelihoods to draw inference about where individuals were or were not born, potentially allowing direct, real-time estimates of dispersal. We used simulated data sets to test the power and accuracy of Monte Carlo resampling methods in generating statistical thresholds for identifying F0 immigrants in populations with ongoing gene flow, and hence for providing direct, real-time estimates of migration rates. The identification of accurate critical values required that resampling methods preserved the linkage disequilibrium deriving from recent generations of immigrants and reflected the sampling variance present in the data set being analysed. A novel Monte Carlo resampling method taking into account these aspects was proposed and its efficiency was evaluated. Power and error were relatively insensitive to the frequency assumed for missing alleles. Power to identify F0 immigrants was improved by using large sample size (up to about 50 individuals) and by sampling all populations from which migrants may have originated. A combination of plotting genotype likelihoods and calculating mean genotype likelihood ratios (DLR) appeared to be an effective way to predict whether F0 immigrants could be identified for a particular pair of populations using a given set of markers.