Blue sharks are an important part of the bycatch in international tuna and swordfish fisheries in the North Atlantic. The multifleet logbooks available with fishery data are not considered to be complete given the large number of incidental captures, variation in release status (alive vs. dead) and unreported captures over time. Thus these data alone offer several limitations for stock assessment and population modeling. Furthermore, stock assessment analysis of a highly migratory species such as the blue shark is difficult at best. The complex sexual and life-stage segregation patterns of the population in the North Atlantic make the modeling process even more difficult. Alternative methods are needed to deal with the problem.
This project uses the tag-recapture database of the National Marine Fisheries Service (NMFS) Cooperative Shark Tagging Program (CSTP) to investigate the blue shark population dynamics in the North Atlantic Ocean. The use of tag recapture data is among ICCAT's recommendations for future stock assessment work. Our approach relies on a statistical framework for estimating blue shark movement and fishing mortality rates from the tagging data. The model considers four geographical regions. Blue sharks can stay in the region in which they were tagged and they can move among regions. The parameters of the model are the intra- and inter-region movement probabilities and the catchability coefficients that relate the probabilities of capture and the ICCAT longline fishing effort data. Bayesian estimation methods are used to estimate and quantify the uncertainty associated with the values for these parameters. The dataset of the NMFS CSTP shows potential for use in a blue shark stock assessment.
Contact information:
Alexandre Aires-da-Silva, PhD student
School of Aquatic and Fishery Sciences, University of Washington, Seattle WA
asilva@u.washington.edu
John J. Hoey, Ph.D.
NOAA-NMFS, Naragansett Laboratory
John.Hoey@noaa.gov
Nancy E. Kohler, Ph.D.
NOAA-NMFS, Naragansett Laboratory
Nancy.Kohler@noaa.gov