Performance of Probabilistic Harvest Control Rules for Lake Erie Walleye

Uncertainty is increasingly being integrated into fishery management decision-making.  The uncertainty in stock biomass or fishing mortality rates can be incorporated into annual catch quotas via the adoption of probabilistic harvest control rules.  Probabilistic control rules set harvest quotas such that the probability of exceeding the limit reference point in fishing mortality (or projected stock biomass) does not exceed some threshold probability P*.  Such policies are increasingly being used for management of marine stocks.  We know of no examples of the application of probabilistic control rules to freshwater fisheries.  We evaluated the performance of these control rules for the Lake Erie walleye fishery, a bi-national fishery with commercial and recreational stakeholders.  The performance of probabilistic control rules were assessed using closed loop simulation models known as management strategy evaluation.  The technical aspects of the modeling were conducted in the context of a stakeholder-inclusive process for evaluating alternative management strategies.  Our finding and experience will have implications for the management of other freshwater and marine fisheries.