Wisconsin Fishery Project
Development of New Generation ‘Mixture-Aware’ Stock Analysis Models Based on Catch-at-Age Data for Lake Whitefish in Lake Michigan
January 2022 - December 2023
- Great Lakes Fishery Commission
Statistical catch-at-age (SCAA) models are used across the Great Lakes to assess lake whitefish stocks and harvest levels. Unless auxiliary data (e.g., tag-recovery, genetic) are included, SCAA models only estimate a single recruitment time-series that represents the aggregation of all stocks contributing individuals to the modeled harvest region, limiting the model’s ability to assess recruitment trends and other dynamic rates for individual stocks. Given frequent occurrence of lake whitefish mixed-stock fisheries, the ability to use SCAA models to track dynamics of individual stocks based on mixed-stock harvests would be valuable for management purposes. Genetic advances have reduced the costs of large-scale mixed stock analyses (MSAs) while increasing discriminatory power, generating data that could be easily incorporated in SCAA models to account for stock-specific dynamics. We are using an MSA based on genomic data to inform an empirical evaluation of the effects and feasibility of integrating stock-specific harvest contributions into lake whitefish SCAA models. This project is a collaboration between the Wisconsin Department of Natural Resources, Michigan Department of Natural Resources, NOAA, US Fish and Wildlife Service, Michigan State University, and the Sault Ste. Marie Tribe of Chippewa Indians. The developed models could serve as the basis for a new generation of 'mixture-aware' statistical catch-at-age models that produce more accurate estimates of stock-specific recruitment than current approaches.