Using resiliency, redundancy, and representation in a Bayesian belief network to assess imperilment of riverine fishes
Rattachement africain : us. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Abstract Conservation prioritization frameworks are used worldwide to identify species at greatest risk of extinction and to allocate limited resources across regions, species, and populations. Conservation prioritization can be impeded by ecological knowledge gaps and data deficiency, especially in freshwater species inhabiting highly complex aquatic ecosystems. Therefore, we developed a flexible approach that calculates a species' imperilment risk based on the conservation principles of resiliency, redundancy, and representation (i.e., the “three R's”). Our approach organizes data on species traits, distributions, population connectivity, and threats within a Bayesian belief network capable of predicting resiliency and redundancy within representative ecological settings. Empirical data and expert judgment inform the model to provide robust and repeatable risk assessments for rare and data‐deficient species. The model calculates resiliency at hierarchical spatial scales from distributional trends and population strength. Redundancy is estimated from the connectivity and quantities of extant populations. Resiliency, redundancy, and species' inherent vulnerability based on species traits collectively estimate extirpation risk within each unique ecological setting. Extirpation risks across ecological settings characterize representation and are aggregated to estimate global imperilment risk. We demonstrate the model's utility with Piebald Madtom (Noturus gladiator), a species petitioned for listing under the U.S. Endangered Species Act. Our results revealed that resiliency, redundancy, and extirpation risks can vary spatially across the species' range while identifying populations where additional sampling could disproportionally reduce uncertainty in estimated global imperilment risk. Our approach could standardize and expedite conservation status assessments, identify opportunities for early management intervention of at‐risk species and populations, and strategically reduce uncertainty by focusing monitoring and research on priority information gaps.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Using resiliency, redundancy, and representation in a Bayesian belief network to assess imperilment of riverine fishes
- Date Crossref
- 01/01/2024
- Éditeur
- Wiley
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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United States Geological Survey pays non établi dans la noticeOrganisme public
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Mississippi State University Department of Wildlife pays non établi dans la noticeUniversité ou école supérieure
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University of Wisconsin–La Crosse pays non établi dans la noticeUniversité ou école supérieure
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Columbia Environmental Research Center U.S. Geological Survey pays non établi dans la noticeStructure de recherche
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United States Fish and Wildlife Service pays non établi dans la noticeOrganisme public
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Mississippi Cooperative Fish and Wildlife Research Unit Mississippi State Mississippi USA U.S. Geological Survey pays non établi dans la noticeStructure de recherche
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Department of Biology and River Studies Center University of Wisconsin‐La Crosse La Crosse Wisconsin USA pays non établi dans la noticeUniversité ou école supérieure
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University of Wisconsin-La Crosse Department of Biology and River Studies Center pays non établi dans la noticeUniversité ou école supérieure
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U.S. Fish and Wildlife Service pays non établi dans la noticeInstitution
United States Geological Survey, Department of Wildlife — Mississippi State University et University of Wisconsin–La Crosse, avec 6 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.