A global dataset of inland fisheries expert knowledge

Gretchen L. Stokes, Abigail J. Lynch, Simon Funge-Smith, John Valbo‐Jørgensen, T. Douglas Beard, Benjamin S. Lowe, Jesse P. Wong, Samuel J. Smidt

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

8 Citas (Scopus)

Resumen

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.

Idioma originalEnglish
Número de artículo182
PublicaciónScientific data
Volumen8
N.º1
DOI
EstadoPublished - dic. 2021
Publicado de forma externa

Nota bibliográfica

Funding Information:
We thank the survey respondents, including members of the American Fisheries Society, collaborators and staff with the Food and Agriculture Organization of the United Nations (FAO), and members of the InFish network (http://infish.org/). We also thank Wei-en Wang, So-Jung Youn, and Moonhyuk Choi for their assistance translating surveys; and USGS reviewer Sarah Endyke for constructive feedback. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1842473. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Publisher Copyright:
© 2021, The Author(s).

ASJC Scopus Subject Areas

  • Statistics and Probability
  • Information Systems
  • Education
  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences

PubMed: MeSH publication types

  • Dataset
  • Journal Article
  • Research Support, Non-U.S. Gov't

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