A framework for assessing which sampling programmes provide the best trade-off between accuracy and cost of data in stock assessments

Abstract

Fisheries surveys are required to assess the status of fish populations but are rarely evaluated to determine which data provide most information for least cost. We develop such a method focused on Pacific herring (Clupea pallasii) in Prince William Sound, Alaska. This population collapsed in 1992-93 and an intensive monitoring programme has been developed to assess why herring have not yet recovered, including the development of a Bayesian stock assessment model. We conducted a Monte-Carlo simulation study that calculated the deterioration in assessment performance when each survey was excluded, which allowed us to assess the trade-off between cost and improvement in model performance from including each survey data. For $10,000 a year the disease survey reduces bias and imprecision in current biomass by 34% on average, increases model reliability by 22%, and decreases by 31% the probability of a false management conclusion related to regulating the fishery. For $350,000 a year the diver survey reduces bias and imprecision by 12%, increases model reliability by 6%, and decreases the probability of a false management conclusion by 23%. The framework presented here can be used in other fisheries to weigh the costs and benefits of alternative sampling programmes in estimating current biomass.