Opportunities and impediments for use of local data in the management of salmon fisheries
Sarah C. Inman, Human Centered Design and Engineering
Janessa Esquible, Orutsararmiut Traditional Native Council
Michael L. Jones, Michigan State University, Department of Fisheries and Wildlife, Quantitative Fisheries Center
William R. Bechtol, Bechtol Research
Brendan Connors, Institute of Ocean Sciences, Fisheries and Oceans Canada
Full Text: HTML
Data availability challenges the management of small-scale fisheries in large river basins. One way to circumvent the challenges of data collection is to rely on local stakeholders who are well-positioned to collect data that can inform management through community-based monitoring (CBM). Although science and management has increasingly considered opportunities for community involvement in scientific research, the efficacy of these programs are rarely assessed. We describe a current CBM initiative in the Kuskokwim River Basin of western Alaska. We then explore how existing approaches for incorporating local involvement in fisheries research and management measure against claims made by CBM programs to understand pathways for data utility for decision makers and approaches to capacity building and meaningful engagement of local citizens. We identify major gaps in the CBM literature and explore one of these gaps through an interview-based study of public participation in the Kuskokwim. We find that the CBM program intent to collect high quality data was complemented by increasing trust in data stewards. Ultimately, through our interview findings we illustrate how definitions of local engagement differ, how CBM data is used by decision makers, and how trust in data is dependent on trust in data stewards and the infrastructure that supports that stewardship.
Alaskan salmon; community-based monitoring; coproduction of scientific knowledge; natural resource management
Copyright © 2021 by the author(s). Published here under license by The Resilience Alliance. This article is under a Creative Commons Attribution-NonCommercial 4.0 International License. You may share and adapt the work for noncommercial purposes provided the original author and source are credited, you indicate whether any changes were made, and you include a link to the license.