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Estimating population extinction thresholds with categorical classification trees for Louisiana black bears
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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5779663/ https://www.ncbi.nlm.nih.gov/pubmed/29360863 http://dx.doi.org/10.1371/journal.pone.0191435 |
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author | Laufenberg, Jared S. Clark, Joseph D. Chandler, Richard B. |
author_facet | Laufenberg, Jared S. Clark, Joseph D. Chandler, Richard B. |
author_sort | Laufenberg, Jared S. |
collection | PubMed |
description | 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 ([Image: see text] ) was the best predictor of population persistence. Specifically, population persistence was estimated to be ≥95% over 100 years when [Image: 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. |
format | Online Article Text |
id | pubmed-5779663 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-57796632018-02-05 Estimating population extinction thresholds with categorical classification trees for Louisiana black bears Laufenberg, Jared S. Clark, Joseph D. Chandler, Richard B. PLoS One Research Article 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 ([Image: see text] ) was the best predictor of population persistence. Specifically, population persistence was estimated to be ≥95% over 100 years when [Image: 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. Public Library of Science 2018-01-23 /pmc/articles/PMC5779663/ /pubmed/29360863 http://dx.doi.org/10.1371/journal.pone.0191435 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication. |
spellingShingle | Research Article Laufenberg, Jared S. Clark, Joseph D. Chandler, Richard B. Estimating population extinction thresholds with categorical classification trees for Louisiana black bears |
title | Estimating population extinction thresholds with categorical classification trees for Louisiana black bears |
title_full | Estimating population extinction thresholds with categorical classification trees for Louisiana black bears |
title_fullStr | Estimating population extinction thresholds with categorical classification trees for Louisiana black bears |
title_full_unstemmed | Estimating population extinction thresholds with categorical classification trees for Louisiana black bears |
title_short | Estimating population extinction thresholds with categorical classification trees for Louisiana black bears |
title_sort | estimating population extinction thresholds with categorical classification trees for louisiana black bears |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5779663/ https://www.ncbi.nlm.nih.gov/pubmed/29360863 http://dx.doi.org/10.1371/journal.pone.0191435 |
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