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PLoS One. 2018 Jan 23;13(1):e0191435. doi: 10.1371/journal.pone.0191435. eCollection 2018.

Estimating population extinction thresholds with categorical classification trees for Louisiana black bears.

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Department of Forestry, Wildlife and Fisheries, University of Tennessee, Knoxville, Tennessee, United States of America.
U.S. Geological Survey, Southern Appalachian Research Branch, Northern Rocky Mountain Science Center, University of Tennessee, Knoxville, Tennessee, United States of America.
Warnell School of Forestry and Natural Resources, University of Georgia, Athens, Georgia, United States of America.


Monitoring vulnerable species is critical for their conservation. Thresholds or tipping points are commonly used to indicate when populations become vulnerable to extinction and to trigger changes in conservation actions. However, quantitative methods to determine such thresholds have not been well explored. The Louisiana black bear (Ursus americanus luteolus) was removed from the list of threatened and endangered species under the U.S. Endangered Species Act in 2016 and our objectives were to determine the most appropriate parameters and thresholds for monitoring and management action. Capture mark recapture (CMR) data from 2006 to 2012 were used to estimate population parameters and variances. We used stochastic population simulations and conditional classification trees to identify demographic rates for monitoring that would be most indicative of heighted extinction risk. We then identified thresholds that would be reliable predictors of population viability. Conditional classification trees indicated that annual apparent survival rates for adult females averaged over 5 years ([Formula: see text]) was the best predictor of population persistence. Specifically, population persistence was estimated to be ≥95% over 100 years when [Formula: see text], suggesting that this statistic can be used as threshold to trigger management intervention. Our evaluation produced monitoring protocols that reliably predicted population persistence and was cost-effective. We conclude that population projections and conditional classification trees can be valuable tools for identifying extinction thresholds used in monitoring programs.

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