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Qualitative and Quantitative Fisher Perceptions to Complement Natural Science Data for Managing Fisheries
Author(s) -
Murphy Robert,
Cunningham Curry,
Harris Bradley P.,
Brown Caroline
Publication year - 2021
Publication title -
fisheries
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.725
H-Index - 79
eISSN - 1548-8446
pISSN - 0363-2415
DOI - 10.1002/fsh.10568
Subject(s) - sustainability , natural resource , value (mathematics) , resource (disambiguation) , environmental resource management , perception , data collection , natural resource management , fishery , business , data science , ecology , computer science , sociology , environmental science , biology , computer network , social science , machine learning , neuroscience
Sustainably managing fisheries for ecological and social objectives in the current era of rapid environmental change requires that managers, scientists, and fishery stakeholders work together to find solutions to complex problems. Recognizing that multiple forms of knowledge generation exist and focusing on the strengths of different ways‐of‐knowing can facilitate a more holistic understanding of these problems. Here, we illustrate the value in both natural and social science‐generated information for informing our understanding of a rapidly changing social–ecological system. Using data collected through an in‐person and online survey, we show that methods for quantifying user perceptions can facilitate the integration of social data with traditional scientific data collection efforts. Specifically, the synthesis of a Bayesian lifecycle model (Cunningham et al. 2018) for Chinook Salmon Oncorhynchus tshawytscha and quantified fisher perceptions suggests that natural science efforts and resource users largely agree on many of the factors that may be contributing to the observed declines in abundance of Chinook Salmon in Alaska. A qualitative assessment of the perceptions of resource users revealed benefits to incorporating stakeholders in science and management, including the ability to identify regional issues or trends potentially unrealized by natural science approaches. Our study highlights the synergistic value of multiple sources of ecological information for sustainably managing fishery systems.