Using Bayesian decision analysis to help achieve a precautionary approach to managing newly developing fisheries

Abstract

Bayesian decision analysis offers a useful framework for helping to achieve a precautionary approach to managing developing fisheries. With few data on the resource, data and experience from ecologically similar fish populations (or prior information) can also be used to quantitatively evaluate alternative procedures for managing the resource and to provide management advice. We applied Bayesian decision analysis to a hypothetical developing fishery in which a logistic model was fitted to catch per unit effort data. We evaluated the trade-offs in yield and the risk of depletion of catch and effort control rules. Effort control rules yielded average catches nearly 40% larger for levels of risk similar to those given by catch control rules. Management procedures designed to reduce the risks of implementing high harvest rates and to promote resource recovery at low stock sizes reduced the risks of overexploitation and caused only very small reductions in average catch. However, -50 and +100% biases in prior probability distributions for some parameters and the use of overly precise priors (CV