Use of state–space modeling with a Bayesian approach to estimate target reference points for green sea urchin (Strongylocentrotus droebachiensis) stocks in the Queen Charlotte Strait region, British Columbia, Canada

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Abstract This study develops a state–space modeling and Bayesian approach to a biomass dynamic model of green sea urchins ( Strongylocentrotus droebachiensis ) in British Columbia, Canada. The objective is to determine limit and target reference points for harvest quota options for this fishery, and to assess the degree of risk associated with these target reference points (in which risk is defined as the probability that the target reference point will include the limit reference point when all uncertainties are considered). These uncertainties include random variability both in catch per unit effort and in annual biomass dynamics, and uncertainties in catch and effort data in the developing years of this fishery. In addition, the approach uses fishery-independent survey data as auxiliary information to index green urchin population abundance. Compared with the traditional methods of fitting the biomass dynamic model, this new approach incorporates more realistic error structures. The resulting probability distribution for the limit reference point (maximum sustainable yield, MSY) is likely to reflect the true uncertainties about MSY more closely, and can be used to measure the risk of exceeding MSY for setting fishing quotas for this green urchin stock. It is concluded that the current practice of selecting target reference points for this population at 25–50% of MSY has a probability of including the limit reference point (MSY) of