Detecting black bear source–sink dynamics using individual-based genetic graphs

Abstract

Source–sink dynamics affects population connectivity, spatial genetic structure and population viability for many species. We introduce a novel approach that uses individual-based genetic graphs to identify source–sink areas within a continuously distributed population of black bears ( Ursus americanus ) in the northern lower peninsula (NLP) of Michigan, USA. Black bear harvest samples ( n = 569, from 2002, 2006 and 2010) were genotyped at 12 microsatellite loci and locations were compared across years to identify areas of consistent occupancy over time. We compared graph metrics estimated for a genetic model with metrics from 10 ecological models to identify ecological factors that were associated with sources and sinks. We identified 62 source nodes, 16 of which represent important source areas (net flux > 0.7) and 79 sink nodes. Source strength was significantly correlated with bear local harvest density (a proxy for bear density) and habitat suitability. Additionally, resampling simulations showed our approach is robust to potential sampling bias from uneven sample dispersion. Findings demonstrate black bears in the NLP exhibit asymmetric gene flow, and individual-based genetic graphs can characterize source–sink dynamics in continuously distributed species in the absence of discrete habitat patches. Our findings warrant consideration of undetected source–sink dynamics and their implications on harvest management of game species.