| Summary: | Reviewed Intrapopulation variability in resource acquisition (i. e., niche variation) influences population dynamics, with important implications for conservation planning. Spatial analyses of niche variation within and among populations can provide relevant information about ecological associations and their subsequent management. We used stable isotope analysis and kernel- weighted regression to examine spatial patterns in a keystone consumer- resource interaction: salmon (Oncorhynchus spp.) consumption by grizzly and black bears (Ursus arctos horribilis, n = 886; and Ursus americanus, n = 557) from 1995 to 2014 in British Columbia (BC), Canada. In a region on the central coast of BC (22,000 km(2)), grizzly bears consumed far more salmon than black bears (median proportion of salmon in assimilated diet of 0.62 and 0.06, respectively). Males of both species consumed more salmon than females (median proportions of 0.63 and 0.57 for grizzly bears and 0.06 and 0.03 for black bears, respectively). Black bears showed considerably more spatial variation in salmon consumption than grizzlies. Protected areas on the coast captured no more habitat for bears with high- salmon diets (i.e., proportions >0.5 of total diet) than did unprotected areas. In a continental region (similar to 692,000 km(2)), which included the entire contemporary range of grizzlies in BC, males had higher salmon diets than females (median proportions of 0.41 and 0.04, respectively). Highsalmon diets were concentrated in coastal areas for female grizzly bears, whereas males with high- salmon diets in interior areas were restricted to areas near major salmon watersheds. To safeguard this predatorprey association that spans coastal and interior regions, conservation planners and practitioners can consider managing across ecological and jurisdictional boundaries. More broadly, our approach highlights the importance of visualizing spatial patterns of dietary niche variation within populations to characterize ecological associations and inform ...
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