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The frequency of black bear ( Ursus americanus) sightings, vehicle collisions, and nuisance incidents in the coastal region of South Carolina has increased over the past 4 decades. To develop the statewide Black Bear Management and Conservation Strategy, the South Carolina Department of Natural Resources needed reliable information for the coastal population. Because no such data were available, we initiated a study to determine population density and genetic structure of black bears. We selected 2 study areas that were representative of the major habitat types in the study region: Lewis Ocean Bay consisted primarily of Carolina Bays and pocosin habitats, whereas Carvers Bay was representative of extensive pine plantations commonly found in the region. We established hair snares on both study areas to obtain DNA from hair samples during 8 weekly sampling periods in 2008 and again in 2009. We used genotypes to obtain capture histories of sampled bears. We estimated density using spatially explicit capture-recapture (SECR) models and used information-theoretic procedures to fit parameters for capture heterogeneity and behavioral responses and to test if density and model parameters varied by year. Model-averaged density was 0.046 bears/km2 (SE = 0.011) for Carvers Bay and 0.339 bears/km2 (SE = 0.056) for Lewis Ocean Bay. Next, we sampled habitat covariates for all locations in the SECR sampling grid to derive spatially explicit estimates of density based on habitat characteristics. Addition of habitat covariates had substantial support, and accounted for differences in density between Carvers Bay and Lewis Ocean Bay; black bear density showed a negative association with the area of pine forests (4.5-km2 scale) and a marginal, positive association with the area of pocosin habitat (0.3-km2 scale). Bear density was not associated with pine forest at a smaller scale (0.3-km2), nor with major road density or an index of largest patch size. Predicted bear densities were low throughout the coastal region and only a few larger areas had high predicted densities, most of which were centered on public lands (e.g., Francis Marion National Forest, Lewis Ocean Bay). We sampled a third bear population in the Green Swamp area of North Carolina for genetic structure analyses and found no evidence of historic fragmentation among the 3 sampled populations. Neither did we find evidence of more recent barriers to gene exchange; with the exception of 1 recent migrant, Bayesian population assignment techniques identified only a single population cluster that incorporated all 3 sampled areas. Bears in the region may best be managed as 1 population. If the goal is to maintain or increase bear densities, demographic connectivity of high-density areas within the low-density landscape matrix is a key consideration and managers would need to mitigate potential impacts of planned highway expansions and anticipated development. Because the distribution of black bears in coastal South Carolina is not fully known, the regional map of potential black bear density can be used to identify focal areas for management and sites that should be surveyed for occupancy or where more intensive studies are needed. © 2012 The Wildlife Society. [ABSTRACT FROM AUTHOR]