A Bayesian Approach to the Stock Assessment of the Big Skate Raja binoculata in British Columbia's Groundfish Fishery

In 1996, a targeted fishery for big skate (Raja binoculata) began in North Hecate Strait (NHS) and Queen Charlotte Sound (QCS) off the coast of British Columbia, Canada. The targeted fishery uses both bottom trawl and longlining gear; currently a 597-tonne soft cap exists on big skate catch in Hecate Strait but no cap exists in Queen Charlotte Sound. A targeted fishery for big skate, like those for other elasmobranchs, is of particular concern to fishery managers because of the characteristic life history traits that make these species subject to overfishing / population collapse: longevity, slow growth, low fecundity and late maturation. Information on the population structure and biological parameters of these two big skate stocks is limited. Using commercial catch data (1996-2009), commercial trawl catch and effort data (1996-2009) and Department of Fisheries and Oceans (DFO) survey abundance indices (1985-2009), we developed a Graham-Schaefer biomass dynamic model for each big skate stock (NHS and QCS). A tagging study targeting big skate was undertaken by DFO from 2003-2006. Length and age measurements from the recaptured individuals were used to fit a von Bertalanffy growth function and obtain estimates of L-infinity, the asymptotic length, and growth rate, k. Through the use of life history invariant equations, we used the estimates of L-infinity and k to inform the priors of the parameters required to run our model, namely the intrinsic growth rate, r, and carrying capacity, K. Our research will inform managers of the value for the intrinsic growth rate of the population; this can be used to calculate management targets (e.g., maximum sustainable yield) for each stock and can therefore be used as a reference point for the fishery. Our model can be used to show how the abundance of big skate has changed since the fishery started and can also provide an estimate of current abundance. Additionally, the use of Bayesian statistics allowed us to incorporate prior knowledge and ultimately make probability statements about values of r and K. The information provided from our research can be used by fishery managers to aid in the future management decisions of the big skate fishery.