Applying spatially explicit capture–recapture models to estimate black bear density in South Carolina

Wildlife Society Bulletin
By: , and 

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Abstract

Population density is an important component of wildlife management decisions, but can be difficult to estimate directly for an itinerant, wide‐ranging species such as the American black bear (Ursus americanus ). In South Carolina, USA, where there has been growth in black bear populations and bear–human‐conflict reports during the past several decades, managers need robust estimates of population size to inform management strategies. We used maximum‐likelihood capture–recapture models, using hair snares to collect DNA samples, to estimate density and abundance for a harvested population of black bear in northwestern South Carolina during 2013 to 2014. Models were tested in a spatially explicit framework using the secr package in Program R. Black bear density was estimated at 0.133 bears/km2 (SE = 0.034) in 2013 and 0.179 bears/km2 (SE = 0.043) in 2014. Black bear abundance in our study area was estimated to be 586 bears (SE = 95) in 2013 and 680 bears (SE = 128) in 2014, which are 2–3‐fold lower than previous estimates. We suggest that these estimates be considered a baseline for state biologists to employ in the population's management and in developing future harvest‐regulation strategies. Overall our study highlighted the potential for model choice to influence density estimates, and we concluded that spatially explicit models were appropriate for this study because geographic closure could not be assumed.

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Publication type Article
Publication Subtype Journal Article
Title Applying spatially explicit capture–recapture models to estimate black bear density in South Carolina
Series title Wildlife Society Bulletin
DOI 10.1002/wsb.1007
Volume 43
Issue 3
Year Published 2020
Language English
Publisher The Wildlife Society
Contributing office(s) Northern Rocky Mountain Science Center
Description 8 p.
First page 500
Last page 507
Country United States
State south Carolina
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