Avery Hoffman, Orutsararmiut Native Council Summer Youth Representative, conducts harvest surveys at Bethel area fish camps and attempts to recruit local fishermen to participate in the Chinook Salmon Age-Sex-Length Sampling Program. Here, Avery is showing a subsistence sampler how to properly sample his subsistence-caught Chinook.

Community-Based Monitoring: Using Participatory Modeling to Empower Community Engagement in Salmon Science

Trust is central to successful implementation of fisheries management decisions in Western Alaska. A Kuskokwim River citizen science effort is ongoing to empower communities to collect their own information and provide it to tribal, state, and federal managers. The information gathered guides decisions about when to allow or restrict fishing. This two-way exchange of information is intended to build mutual respect and trust, leading to better, and less controversial decisions.

Community-Based Monitoring (CBM) is increasingly contributing to the management of natural resources in rural Alaska. There are two dimensions to our story of CMB on the Kuskokwim River. The first is more technical: how do we define the most valuable information needed for guiding wise and sustainable management of subsistence fisheries? The second is more social: how do we build relationships that engender mutual respect and trust between the community and fishery managers on a foundation of information collection?

To address both of these issues, we identified intersections between fisheries managers seeking information for better decision making, and the capacity of stakeholders to monitor information from within their own communities. We incorporated community-sourced information into decision-making models to better inform fish harvest decisions, also support the exchange of knowledge between two world views of salmon management.

To define critical information needs, we applied simulation models of salmon fisheries to an approach known as “Value of Information” analysis. As we reduce the level of uncertainty with better information (green line → orange line), both the preferred decision (peak of each curve), and the value associated with the decision, increase.

Value of Information Model

We applied simulation models of salmon fisheries questions, such as harvest decisions, to a variety of VOI models, with input from community-based monitoring efforts (Fig. 1). Improvement in decision-making was measured in terms of forecasted outcomes that relate to objectives of stakeholders. Fisheries managers make difficult decisions amid many uncertainties. The “Value of Information” (VOI) analysis is a model-based approach developed to support critical thinking about complex decision-making. The VOI model allows us to ask: How can we identify the uncertainties that increase our ability to make the best decision? The strength of the model lies in the ability to identify which questions deserve the most focus from managers with limited time and resources.



The Community Monitoring Team at NCEAS in Santa Barbara.
Image Credit: Ginger Gillquist

Elijah Lindley delivering fish caught from Bethel Test Fishery to elders.
Image Credit: Janessa Esquible

Anna Pavila, an Alaska Native Science & Engineering Program student, conducts harvest surveys in Bethel area fish camps. Image Credit: Janessa Esquible

Alissa Rogers conducting surveys at Bethel area fish camps.
Image Credit: ONC

Principal Investigator

Michael L. Jones

Principal Investigator
Department of Fisheries and Wildlife Quantitative, Michigan State University

In his position with the Partnership for Ecosystem Research and Management (PERM), Michael’s research focuses on fish population dynamics and ecology, resource management, and simulation modeling. He is especially interested in how Structured Decision Making methods can lead to better management outcomes, especially when they involve stakeholder engagement.

Links of Interest


How A Massive Dataset and A Set of Rural Communities Are Helping to Sustain Alaska’s Salmon: Podcast: Alaska’s Exceptional Salmon Data


Learn about the Community Engagement Working Group