A BAYESIAN-APPROACH TO STOCK ASSESSMENT AND HARVEST DECISIONS USING THE SAMPLING IMPORTANCE RESAMPLING ALGORITHM

Scientific advice to fishery managers needs to be expressed in probabi listic terms to convey uncertainty about the consequences of alternati ve harvesting policies (policy performance indices). In most Bayesian approaches to such advice, relatively few of the model parameters used can be treated as uncertain, and deterministic assumptions about popu lation dynamics are required; this can bias the degree of certainty an d estimates of policy performance indices. We reformulate a Bayesian a pproach that uses the sampling/importance resampling algorithm to impr ove estimates of policy performance indices; it extends the number of parameters that can be treated as uncertain, does not require determin istic assumptions about population dynamics, and can use any of the ty pes of fishery assessment models and data. Application of the approach to New Zealand's western stock of hoki (Macruronus novaezelandiae) sh ows that the use of Bayesian prior information for parameters such as the constant of proportionality for acoustic survey abundance indices can enhance management advice by reducing uncertainty in current stock size estimates; it also suggests that assuming historic recruitment i s deterministic can create large negative biases (e.g., 26%) in estima tes of biological and economic risks of alternative harvesting policie s and that a stochastic recruitment assumption can be more appropriate .