A preliminary stock assessment in SSRUS 486A, G: A Bayesian and CPUE based biomass dynamic model

The present report analyses data regarding CPUE, tagging and catch and their contribution to estimate abundance in a simple biomass dynamic model for both species of toothfish in northern area of 48.6 (SSRU 48.6A, 48.6G). Antarctic and Patagonian toothfish presented a high proportion with sets in which both species are present, and thus, an analysis of catch intention was implemented by using multivariate statistical approach of the catch composition. Standardisation of CPUE was done by using Generalised Additive Models (GAM) considering temporal and location factors. Tagging data was analysed in terms number of tagged and recovered individuals, and tag size-overlap. Catch reported and assumed IUU were combined to produce a time series of catches between 1991 and 2013.  A general biomass dynamic model in Bayesian framework using standardised CPUE and catches (considering assumed IUU) was implemented in each species of toothfish. Tagging data was not used in any of the toothfish models, because of the low number of recovered individuals and low tag size-overlap. Modelling abundance in both species is highly dependent on priors for estimated parameters and information in CPUE and catches contributed little to the abundance estimates. This indicates that other modelling framework should be implemented in order to have unbiased abundance estimates. Due to high importance of tagging data in stock assessment model, it is highly recommendable to continue with the collection of the data by scientific observed and tagging and release program. Bayesian data-poor models seem to be appropriated to treat data with little information of abundance such as presented here.