Fisheries scientists can predict fish stock abundance over time using mathematical models that help inform management decisions. However, complexity and variation in ocean conditions and species interactions make this challenging and sometimes imprecise. In a previous project supported by the Lenfest Ocean Program, Dr. Stephan Munch, University of California, Santa Cruz and NOAA Southwest Fisheries Science Center, cultivated empirical dynamic modeling (EDM), an alternative modeling approach that could improve the accuracy of such predictions for fisheries with short-lived species and a long time series of data. He also developed empirical dynamic programming (EDP) alongside EDM as a method to generate optimal harvest policies from the approach.
In this study, Dr. Munch, in collaboration with the NOAA Southeast Fisheries Science Center, will build on his previous work to apply EDM and EDP to pink, white, and brown shrimp in the Gulf of Mexico by:
- Using EDM to update and enhance the fit of the previous stock synthesis (SS) model;
- Analyze pink, white, and brown shrimp fisheries with both the EDM and updated SS methods to generate optimal harvest policies from each approach; and
- Conduct a management strategy evaluation (MSE) for both policies to compare the effectiveness of each after 20 years.
Using outputs from the EDM/EDP approach, researchers will develop several policies specific to scenarios under a changing environment and evaluate their success by conducting additional MSEs to approximate the optimal harvest policy for changing conditions.