APPLICATION OF BAYESIAN DECISION-ANALYSIS TO MANAGEMENT OF A SOCKEYE-SALMON (ONCORHYNCHUS-NERKA) FISHERY

We developed a decision-making framework for management of a sockeye s almon (Oncorhynchus nerka) fishery on the Nass River, British Columbia , that explicitly accounts for uncertainties in (i) the stock-recruitm ent relationship, (ii) annual recruitment, (iii) run timing, and (iv) catchability. The method used Monte Carlo simulation within a decision analysis framework and used Bayesian statistics to calculate probabil ities for parameter values in the Shepherd stock-recruitment model. Th e decision dealt with when to open a fishery, upstream of all normal f ishing areas, that is intended to harvest fish that are considered sur plus to spawning requirements. The optimal decision rule for opening t his fishery depended on (i) the relative importance of different manag ement objectives and (ii) the range of shapes of the stock-recruitment relationship that were admitted as possible within the decision analy sis. The management decision that was optimal if we assumed a dome-sha ped stock-recruitment relationship was not optimal when we admitted th e possibility of other shapes of the relationship. Therefore, given th e variability in salmon stock-recruitment data, uncertainty in the sha pe of the stock-recruitment relationship should be routinely considere d in analyses of management decisions.