Monitoring the effective population size of a brown bear ( Ursus arctos) population using new single‐sample approaches

• Published in: Molecular Ecology
• Main Authors: SKRBINŠEK, TOMAŽ, JELENČIČ, MAJA, WAITS, LISETTE, KOS, IVAN, JERINA, KLEMEN, TRONTELJ, PETER
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2012
• Subjects: Ursus arctos
• Online Access: http://dx.doi.org/10.1111/j.1365-294x.2011.05423.x; https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fj.1365-294X.2011.05423.x; https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-294X.2011.05423.x

Abstract The effective population size ( N e ) could be the ideal parameter for monitoring populations of conservation concern as it conveniently summarizes both the evolutionary potential of the population and its sensitivity to genetic stochasticity. However, tracing its change through time is difficult in natural populations. We applied four new methods for estimating N e from a single sample of genotypes to trace temporal change in N e for bears in the Northern Dinaric Mountains. We genotyped 510 bears using 20 microsatellite loci and determined their age. The samples were organized into cohorts with regard to the year when the animals were born and yearly samples with age categories for every year when they were alive. We used the Estimator by Parentage Assignment (EPA) to directly estimate both N e and generation interval for each yearly sample. For cohorts, we estimated the effective number of breeders ( N b ) using linkage disequilibrium, sibship assignment and approximate Bayesian computation methods and extrapolated these estimates to N e using the generation interval. The N e estimate by EPA is 276 (183–350 95% CI), meeting the inbreeding‐avoidance criterion of N e > 50 but short of the long‐term minimum viable population goal of N e > 500. The results obtained by the other methods are highly consistent with this result, and all indicate a rapid increase in N e probably in the late 1990s and early 2000s. The new single‐sample approaches to the estimation of N e provide efficient means for including N e in monitoring frameworks and will be of great importance for future management and conservation.