A framework for assessing which sampling programmes provide the best trade-off between accuracy and cost of data in stock assessments. (3rd September 2019)

• Record Type: Journal Article
• Authors: Muradian, Melissa L; Branch, Trevor A; Punt, André E
• Editors: Kotwicki, Stan
• Is Part Of: ICES journal of marine science. Volume 76:Number 7(2019)
• Page Start: 2102
• Page End: 2113
• Publication Date: 2019-09-03
• Subjects: Bayesian methods -- Pacific herring -- simulation -- stock assessment -- value of information; Ocean -- Periodicals; Fisheries -- Periodicals; Fishes -- Periodicals; Marine biology -- Bibliography -- Periodicals; 551.4605
• DOI: 10.1093/icesjms/fsz163
• Languages: English
• ISSNs: 1054-3139

Abstract

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.