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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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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. |
format | Online Article Text |
id | pubmed-8285391 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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