Improving Assessments of North Pacific Crab Stocks through Enhanced Modelling of Growth

• Main Author: Andre Punt
• Format: Dataset
• Language: unknown
• Published: Research Workspace 2016
• Subjects: Lithodes aequispinus; golden king crab; North Pacific Research Board; Bering Sea; Pacific; Golden King Crab; Alaska; Aleutian Islands
• Online Access: https://search.dataone.org/view/10.24431_rw1k5aa_20210709T210913Z

Stock assessment methods for many invertebrate stocks, including crab stocks in the Bering Sea of Alaska, rely on size-structured population dynamics models. A key component of these models is the size-transition matrix, which specifies the probability of growing from one sizeclass to another after a certain period of time. This project developed an approach for computing size-transition matrices that explicitly allows for individual variation in growth. The approach has been coded and a method identified for estimating the parameters of the sizetransition matrix using maximum likelihood. Three variants of this new approach have been included in the Generalized Modeling for Alaskan Crab Stocks (GMACS) package. This approach for constructing size-transition matrices assumes growth parameters differ among individuals. Other approaches such as the ‘traditional’ and ‘platoon’ method, make different assumptions regarding growth variability. Simulation tests compared the ability of these approaches to estimate size-transition matrices for scenarios similar to those related to conducting assessments for golden king crab in the Aleutian Islands region of Alaska. The simulations explored the effects of whether growth differs among individuals and the data available. Data were used from Alaska Department of Fish and Game between 1991 and 2006. No estimation method performed best for all scenarios examined. Importantly, the number of size-classes in the size-transition matrix and how the tag-release data used for parameter estimation were collected heavily dictate performance. The numerical integration method that allows L∞ to vary among individuals and smaller size-classes in the assessment were found to be preferred. The Gmacs package can be downloaded from the following Github website: https://github.com/seacode/gmacs.