Chinook harvest-population diversity tradeoffs

Salmonid Research, Monitoring, and Evaluation (RM&E)

Project ID1503
Recovery Domains -
Start Date07/15/2015
End Date04/30/2019
Last Edited07/28/2021
1 - 1


The project developed an integrated age-structured multi-population state-space spawner-recruit model and fitted it to data from 13 Chinook salmon populations from the Kuskokwim drainage in western Alaska. Clear evidence was found of population diversity in the system where productivity and carrying capacity can vary by as much as 3-fold and 18-fold among populations, respectively. Simulation testing of the model illustrated that it was largely unbiased with respect to leading parameters, abundance states, and derived biological reference points, whereas more commonly applied regression-based approaches showed substantial bias. The state-space model was used to parameterize closed-loop simulations that evaluated how well alternative harvest policies meet Chinook population diversity and fishery objectives in the Kuskokwim. Chinook population diversity gives rise to asymmetric trade-offs among fishery and conservation objectives whereby foregoing relatively small amounts of mixed-stock harvest resulted in relatively large increases in the chances of ensuring equitable access to Chinook (i.e., meeting tributary goals) and nearly eliminated the risk of weak stock extirpation. We also found that harvest policies focused on meeting minimum subsistence needs were unlikely to jeopardize long-term prospects for basin wide sustainable use. The fishery and biological performance of alternative harvest policies, and the magnitude of resulting trade-offs, were moderately sensitive to potential future changes in population productivity and capacity and to uncertainty in the underlying drivers of recruitment variation. This approach provides a general framework for characterizing salmon population diversity in large river basins and evaluating harvest-population diversity trade-offs among alternative harvest policies within them.

The project was slightly overspent due to indirect expenses from the sub-awardee being added to the award that were not originally budgeted for. The project end dates were changed from July 31, 2017 to September 30, 2018 and then again to April 30, 2019.

The reconstruction of sub-stock brood tables, which informed all subsequent analyses were more complicated than originally anticipated because the project investigators had to pursue GIS analyses to scale up index estimates of Chinook spawners from each sub-drainage of the Kuskokwim River.

In addition, the time series methodology for quantifying stock-recruitment parameters for each sub-stock was a challenge to develop and apply. As a result, the project investigators were a few months behind schedule with it’s development and application.

Lastly, the stakeholder workshops became significantly more engaged (4 instead of 2 workshops) and took considerably longer to occur than originally planned for. While this allowed for more meaningful engagement with Kuskokwim stakeholders, it also meant the project investigators were behind schedule when it came to completing Objectives 1, 2, 4, 5, 6, & 7.

A final data management expense was allowed for this award adding an additional $1,420.61 to the final total.

Project Benefit    

Our final report will describe estimated tradeoffs between long-term sustainable fishery yield and population diversity across a range of mixed-harvest rates for the Kuskokwim Chinook population complex and how these tradeoffs determine (1) the mixed-population harvest rate necessary to ensure no individual population within the complex are overexploited, (2) the proportion of population that would be predicted to be overexploited when yield is maximized for the complex as a whole, (3) the sensitivity of the predicted proportion of populations overfished to changes in mixed population harvest rates, and lastly (4) measures of the potential value of maintaining biodiversity under current and changing environmental conditions. Our report will also describe an optimal harvest policy for Kuskokwim Chinook that will take into account the tradeoffs described above, persistent changes in productivity and fishery objectives. Lastly our report will describe the potential costs of ‘getting it wrong’ when it comes to thinking about the drivers of observed Kuskokwim stock recruitment dynamics.


Metric Completed Originally

Funding Details

In-Kind Donated Labor$53,778
Report Total:$212,395

Project Map


Kuskokwim River watershed    

  • Worksite Identifier: Kuskokwim River watershed
  • Start Date: 07/15/2015
  • End Date: 07/31/2017
Area Description

Location Information

  • Basin:
  • Subbasin:
  • Watershed:
  • Subwatershed:
  • State: Alaska
  • Recovery Domain:
  • Latitude: 60.196319529765425
  • Longitude: -162.3962402343617


  • Un-Named ESU Chinook




  • E.0 Salmonid Research, Monitoring, and Evaluation (RM&E)Y (Y/N)
    •      . . E.0.a RM&E Funding 206,539.00
    •      . . E.0.b
      Complement habitat restoration project
    •      . . E.0.c
      Project identified in a plan or watershed assessment.
    •      . . E.0.d.1 Number of Cooperating Organizations 6
    •      . . E.0.d.2
      Name Of Cooperating Organizations.
      US Fish and Wildlife Service, University of British Columbia, Michigan State University, Biometric Research, LLC., Department of Fisheries and Oceans, Canada, Columbia RIver Inter-Tribal Fish Commission
    •      . . E.0.e.1 Number of reports prepared 1
    •      . . E.0.e.2
      Name Of Report
      Chinook harvest-population diversity tradeoffs Brendan M. Connors 1,† Lew Coggins 2, Benjamin A. Staton 3, Mike Jones 4, Carl Walters 5, and Daniel C. Gwinn 6. 1 Fisheries and Oceans Canada, Sidney, BC 2 US Fish and Wildlife Service, Bethel, AK 3 Columbia River Inter-Tribal Fish Commission, Portland, OR 4 Michigan State University, East Lansing, MI 5 University of British Columbia, Vancouver, BC 6 Biometric Research LLC, Gainesville, FL †; (250) 858-7028 July 5, 2019
    •      . . E.2 ResearchY (Y/N)
      •      . . . . E.2.a Research Funding 206,539.00
      •      . . . . E.2.b.1 Modeling and data analysisY (Y/N)
        •      . . . . . . E.2.b.1.a
          Key issues addressed by modeling and data analysis research