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Identifying species threatened with local extinction in tropical reef fisheries using historical reconstruction of species occurrence

Identifying the species that are at risk of local extinction in highly diverse ecosystems is a big challenge for conservation science. Assessments of species status are costly and difficult to implement in developing countries with diverse ecosystems due to a lack of species-specific surveys, specie...

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Autores principales: Buckley, Sarah M., McClanahan, Tim R., Quintana Morales, Eréndira M., Mwakha, Victor, Nyanapah, Jatieno, Otwoma, Levy M., Pandolfi, John M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6373906/
https://www.ncbi.nlm.nih.gov/pubmed/30759107
http://dx.doi.org/10.1371/journal.pone.0211224
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author Buckley, Sarah M.
McClanahan, Tim R.
Quintana Morales, Eréndira M.
Mwakha, Victor
Nyanapah, Jatieno
Otwoma, Levy M.
Pandolfi, John M.
author_facet Buckley, Sarah M.
McClanahan, Tim R.
Quintana Morales, Eréndira M.
Mwakha, Victor
Nyanapah, Jatieno
Otwoma, Levy M.
Pandolfi, John M.
author_sort Buckley, Sarah M.
collection PubMed
description Identifying the species that are at risk of local extinction in highly diverse ecosystems is a big challenge for conservation science. Assessments of species status are costly and difficult to implement in developing countries with diverse ecosystems due to a lack of species-specific surveys, species-specific data, and other resources. Numerous techniques are devised to determine the threat status of species based on the availability of data and budgetary limits. On this basis, we developed a framework that compared occurrence data of historically exploited reef species in Kenya from existing disparate data sources. Occurrence data from archaeological remains (750-1500CE) was compared with occurrence data of these species catch assessments, and underwater surveys (1991-2014CE). This comparison indicated that only 67 species were exploited over a 750 year period, 750-1500CE, whereas 185 species were landed between 1995 and 2014CE. The first step of our framework identified 23 reef species as threatened with local extinction. The second step of the framework further evaluated the possibility of local extinction with Bayesian extinction analyses using occurrence data from naturalists’ species list with the existing occurrence data sources. The Bayesian extinction analysis reduced the number of reef species threatened with local extinction from 23 to 15. We compared our findings with three methods used for assessing extinction risk. Commonly used extinction risk methods varied in their ability to identify reef species that we identified as threatened with local extinction by our comparative and Bayesian method. For example, 12 of the 15 threatened species that we identified using our framework were listed as either least concern, unevaluated, or data deficient in the International Union for the Conservation of Nature red list. Piscivores and macro-invertivores were the only functional groups found to be locally extinct. Comparing occurrence data from disparate sources revealed a large number of historically exploited reef species that are possibly locally extinct. Our framework addressed biases such as uncertainty in priors, sightings and survey effort, when estimating the probability of local extinction. Our inexpensive method showed the value and potential for disparate data to fill knowledge gaps that exist in species extinction assessments.
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spelling pubmed-63739062019-03-01 Identifying species threatened with local extinction in tropical reef fisheries using historical reconstruction of species occurrence Buckley, Sarah M. McClanahan, Tim R. Quintana Morales, Eréndira M. Mwakha, Victor Nyanapah, Jatieno Otwoma, Levy M. Pandolfi, John M. PLoS One Research Article Identifying the species that are at risk of local extinction in highly diverse ecosystems is a big challenge for conservation science. Assessments of species status are costly and difficult to implement in developing countries with diverse ecosystems due to a lack of species-specific surveys, species-specific data, and other resources. Numerous techniques are devised to determine the threat status of species based on the availability of data and budgetary limits. On this basis, we developed a framework that compared occurrence data of historically exploited reef species in Kenya from existing disparate data sources. Occurrence data from archaeological remains (750-1500CE) was compared with occurrence data of these species catch assessments, and underwater surveys (1991-2014CE). This comparison indicated that only 67 species were exploited over a 750 year period, 750-1500CE, whereas 185 species were landed between 1995 and 2014CE. The first step of our framework identified 23 reef species as threatened with local extinction. The second step of the framework further evaluated the possibility of local extinction with Bayesian extinction analyses using occurrence data from naturalists’ species list with the existing occurrence data sources. The Bayesian extinction analysis reduced the number of reef species threatened with local extinction from 23 to 15. We compared our findings with three methods used for assessing extinction risk. Commonly used extinction risk methods varied in their ability to identify reef species that we identified as threatened with local extinction by our comparative and Bayesian method. For example, 12 of the 15 threatened species that we identified using our framework were listed as either least concern, unevaluated, or data deficient in the International Union for the Conservation of Nature red list. Piscivores and macro-invertivores were the only functional groups found to be locally extinct. Comparing occurrence data from disparate sources revealed a large number of historically exploited reef species that are possibly locally extinct. Our framework addressed biases such as uncertainty in priors, sightings and survey effort, when estimating the probability of local extinction. Our inexpensive method showed the value and potential for disparate data to fill knowledge gaps that exist in species extinction assessments. Public Library of Science 2019-02-13 /pmc/articles/PMC6373906/ /pubmed/30759107 http://dx.doi.org/10.1371/journal.pone.0211224 Text en © 2019 Buckley et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Buckley, Sarah M.
McClanahan, Tim R.
Quintana Morales, Eréndira M.
Mwakha, Victor
Nyanapah, Jatieno
Otwoma, Levy M.
Pandolfi, John M.
Identifying species threatened with local extinction in tropical reef fisheries using historical reconstruction of species occurrence
title Identifying species threatened with local extinction in tropical reef fisheries using historical reconstruction of species occurrence
title_full Identifying species threatened with local extinction in tropical reef fisheries using historical reconstruction of species occurrence
title_fullStr Identifying species threatened with local extinction in tropical reef fisheries using historical reconstruction of species occurrence
title_full_unstemmed Identifying species threatened with local extinction in tropical reef fisheries using historical reconstruction of species occurrence
title_short Identifying species threatened with local extinction in tropical reef fisheries using historical reconstruction of species occurrence
title_sort identifying species threatened with local extinction in tropical reef fisheries using historical reconstruction of species occurrence
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6373906/
https://www.ncbi.nlm.nih.gov/pubmed/30759107
http://dx.doi.org/10.1371/journal.pone.0211224
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