Development and Testing of a New Method for Selecting Among Stock Assessment Models

Michael Wilberg, Chesapeake Biological Laboratory, UMCES

Abstract

Selecting among competing stock assessment models remains an important problem in fisheries management because stock assessments provide estimates of population size, fishing mortality rates, and safe harvest levels.  Models with different descriptions of biological and fishery processes often are developed over the course of an assessment, with no a priori reason to choose one model over another. These differences in models can have a large effect on estimates of key management quantities (e.g., spawning stock biomass and fishing mortality). Therefore, objective methods to select among models are necessary. In the proposed work we will develop a novel statistical method for selecting the best model from a suite of several competing assessment models.  Additionally, we will use computer simulations to test how well the method works under a range of conditions.