MODELING FOR DATA-LIMITED FISHERIES

Optimizing Bayesian Analysis in Data-Rich Stock Assessments and Management Resources in Data-Limited Fisheries

Washington Sea Grant-supported fellow investigates common fishery stock-assessment methods that may bias managers’ fishery catch decisions.

 

Fellow

Cole Monnahan, University of Washington, Quantitative Ecology and Resource Management

Project Leader

Trevor Branch, University of Washington, School of Aquatic and Fishery Sciences

Co-Project Leader

James Thorson, NOAA Fisheries, Northwest Fisheries Science Center

Project

This project will pursue four objectives: identify methods to decrease runtime for data-rich stock assessments, apply new analysis guidelines to past stock assessments in order to speed up future evaluations and compare management implications of different approaches, identify strategies for the optimal allocation of scarce resources toward data gathering and stock assessment of species that have not been assessed, and evaluate the long-term implications of managing stocks using harvest levels set with data-limited methods.

Publications

Monnahan, C.C., J.T. Thorson, and T.A. Branch. (2017). Faster estimation of Bayesian models in ecology using Hamiltonian Monte Carlo. Methods in Ecology and Evolution 10.1111/2041-210x.12681.10.1111/2041-210x.12681

Stewart, I.J. and C.C. Monnahan. (2016). Implications of process error in selectivity for approaches to weighting compositional data in fisheries stock assessments. Fisheries Research 10.1016/j.fishres.2016.06.018.10.1016/j.fishres.2016.06.018

Annual Reports

2016 Progress Report

2015 Progress Report

2014 Progress Report

2013 Progress Report