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A global dataset of inland fisheries expert knowledge

Inland fisheries and their freshwater habitats face intensifying effects from multiple natural and anthropogenic pressures. Fish harvest and biodiversity data remain largely disparate and severely deficient in many areas, which makes assessing and managing inland fisheries difficult. Expert knowledg...

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Autores principales: Stokes, Gretchen L., Lynch, Abigail J., Funge-Smith, Simon, Valbo‐Jørgensen, John, Beard, T. Douglas, Lowe, Benjamin S., Wong, Jesse P., Smidt, Samuel J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8285391/
https://www.ncbi.nlm.nih.gov/pubmed/34272376
http://dx.doi.org/10.1038/s41597-021-00949-0
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author Stokes, Gretchen L.
Lynch, Abigail J.
Funge-Smith, Simon
Valbo‐Jørgensen, John
Beard, T. Douglas
Lowe, Benjamin S.
Wong, Jesse P.
Smidt, Samuel J.
author_facet Stokes, Gretchen L.
Lynch, Abigail J.
Funge-Smith, Simon
Valbo‐Jørgensen, John
Beard, T. Douglas
Lowe, Benjamin S.
Wong, Jesse P.
Smidt, Samuel J.
author_sort Stokes, Gretchen L.
collection PubMed
description Inland fisheries and their freshwater habitats face intensifying effects from multiple natural and anthropogenic pressures. Fish harvest and biodiversity data remain largely disparate and severely deficient in many areas, which makes assessing and managing inland fisheries difficult. Expert knowledge is increasingly used to improve and inform biological or vulnerability assessments, especially in data-poor areas. Integrating expert knowledge on the distribution, intensity, and relative influence of human activities can guide natural resource management strategies and institutional resource allocation and prioritization. This paper introduces a dataset summarizing the expert-perceived state of inland fisheries at the basin (fishery) level. An electronic survey distributed to professional networks (June-September 2020) captured expert perceptions (n = 536) of threats, successes, and adaptive capacity to fisheries across 93 hydrological basins, 79 countries, and all major freshwater habitat types. This dataset can be used to address research questions with conservation relevance, including: demographic influences on perceptions of threat, adaptive capacities for climate change, external factors driving multi-stressor interactions, and geospatial threat assessments.
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spelling pubmed-82853912021-07-23 A global dataset of inland fisheries expert knowledge Stokes, Gretchen L. Lynch, Abigail J. Funge-Smith, Simon Valbo‐Jørgensen, John Beard, T. Douglas Lowe, Benjamin S. Wong, Jesse P. Smidt, Samuel J. Sci Data Data Descriptor Inland fisheries and their freshwater habitats face intensifying effects from multiple natural and anthropogenic pressures. Fish harvest and biodiversity data remain largely disparate and severely deficient in many areas, which makes assessing and managing inland fisheries difficult. Expert knowledge is increasingly used to improve and inform biological or vulnerability assessments, especially in data-poor areas. Integrating expert knowledge on the distribution, intensity, and relative influence of human activities can guide natural resource management strategies and institutional resource allocation and prioritization. This paper introduces a dataset summarizing the expert-perceived state of inland fisheries at the basin (fishery) level. An electronic survey distributed to professional networks (June-September 2020) captured expert perceptions (n = 536) of threats, successes, and adaptive capacity to fisheries across 93 hydrological basins, 79 countries, and all major freshwater habitat types. This dataset can be used to address research questions with conservation relevance, including: demographic influences on perceptions of threat, adaptive capacities for climate change, external factors driving multi-stressor interactions, and geospatial threat assessments. Nature Publishing Group UK 2021-07-16 /pmc/articles/PMC8285391/ /pubmed/34272376 http://dx.doi.org/10.1038/s41597-021-00949-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) applies to the metadata files associated with this article.
spellingShingle Data Descriptor
Stokes, Gretchen L.
Lynch, Abigail J.
Funge-Smith, Simon
Valbo‐Jørgensen, John
Beard, T. Douglas
Lowe, Benjamin S.
Wong, Jesse P.
Smidt, Samuel J.
A global dataset of inland fisheries expert knowledge
title A global dataset of inland fisheries expert knowledge
title_full A global dataset of inland fisheries expert knowledge
title_fullStr A global dataset of inland fisheries expert knowledge
title_full_unstemmed A global dataset of inland fisheries expert knowledge
title_short A global dataset of inland fisheries expert knowledge
title_sort global dataset of inland fisheries expert knowledge
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8285391/
https://www.ncbi.nlm.nih.gov/pubmed/34272376
http://dx.doi.org/10.1038/s41597-021-00949-0
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