Pretesting the Likely Efficacy of Suggested Management Approaches to Data‐Poor Fisheries

• Published in: Marine and Coastal Fisheries
• Main Authors: Butterworth, Doug S., Johnston, Susan J., Brandão, Anabela
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2010
• Subjects: Antarc*; Antarctic; Patagonian Toothfish; Prince Edward Islands
• Online Access: http://dx.doi.org/10.1577/c08-038.1; https://afspubs.onlinelibrary.wiley.com/doi/pdf/10.1577/C08-038.1

Abstract The thrust of this paper is that decision rules for the management of data‐poor fisheries cannot be based on expert judgment alone. Such rules need to specifically link management responses to the values of the indicators available for the fishery and their trends. Prior simulation testing is needed to confirm that the application of any rules suggested is likely to achieve the objectives sought for the fishery. The management procedure (MP) approach (also called management strategy evaluation), which provides a framework for such testing, is summarized briefly. How this approach could be used to develop a decision rule for a fishery for which the only indicator available is the mean length of the catch is presented as an example. The extent to which the ability to meet management objectives could be improved if an unbiased index of relative abundance were available, and an MP based on a fitted population model applied, is illustrated. An MP developed for the fishery for Patagonian toothfish Dissotichus eleginoides off the sub‐Antarctic Prince Edward Islands is summarized. This illustrates how the MP testing framework can be used in circumstances in which the available indicators conflict, leading to considerable uncertainty about the present resource status. The information content of indicators is closely related to the extent to which they vary about trends in the underlying resource attributes (e.g., catch per unit effort and underlying abundance). The compilation of lists of the statistical properties (such as the coefficients of variation and autocorrelations) of the residuals about detrended time series of the indicators, together with their likely relationships to the underlying attribute, for fisheries worldwide is suggested. This would provide a sound basis for specifying error structure in the simulation tests advocated for both generic and case‐specific decision rules for data‐poor fisheries.