posted on 2024-03-22, 10:43authored byJesús Jurado-Molina, Patricia A. Livingston
No abstracts are to be cited without prior reference to the author.
The most common methods used in stock assessment are VPA and the statistical catch-at-age methods, which permit the reconstruction of the numbers-at-age of exploited stocks and provide estimates of mortality rates for management advice. The difference between these models is the statistical assumptions allowing the fitting of parameters by considering how errors enter into these models and using several data sources for the estimation procedure. Fishery managers are increasingly asked to consider multispecies interactions in their decisions because there is an increasing tendency to recognize that fish populations are not isolated. One option to achieve this goal is the MSVPA that incorporates predation into the virtual population; however, it lacks statistical assumptions. In this work we use a two-species system to incorporate the predation equations from MSVPA into a multispecies statistical catch-at-age model (MSM). Results suggested multispecies statistical model reproduced most of the suitabilities estimated by MSVPA; however, MSM provided a measure of the uncertainty associated with these parameters. MSM is an important step for providing advice to fisheries managers because it incorporates the current tools used in stock assessment such as Bayesian methods and analysis of decision in a multispecies context, helping to establish useful scenarios for management in the eastern Bering Sea.
History
Symposia
2003 ICES Annual Science Conference, Tallinn, Estonia
Session
Theme Session X: Evaluation of Fisheries Management Scenarios and the Supporting Data through Simulation
Abstract reference
X:04
Recommended citation
[Authors]. 2003. Incorporating Predation Interactions In A Statistical Catch At Age Model For A Simplified Predator-Prey System In The Eastern Bering Sea. 2003 ICES Annual Science Conference, Tallinn, Estonia. CM 2003/X:04. https://doi.org/10.17895/ices.pub.25349080