Exploring a relative harvest rate strategy for moderately data-limited fisheries management

Abstract

Abstract Moderately data-limited fisheries can be managed with simple empirical management procedures without analytical stock assessments. Often, control rules adjust advised catches by the trend of an abundance index. We explored an alternative approach where a relative harvest rate, defined by the catch relative to a biomass index, is used and the target level derived from analysing historical catch length data. This harvest rate rule was tested generically with management strategy evaluation. A genetic algorithm was deployed as an optimisation procedure to tune the parameters of the control rule to meet maximum sustainable yield and precautionary management objectives. Results indicated that this method could outperform trend-based strategies, particularly when optimised, achieving higher long-term yields while remaining precautionary. However, optimum harvest rate levels can be narrow and challenging to find because they depend on historical exploitation and life history characteristics. Misspecification of target levels can have a detrimental impact on management. Nevertheless, harvest rates appear to be a suitable management option for moderately data-limited resources, and their application has modest data requirements. Harvest rate strategies are especially suitable for stocks for which case-specific analyses can be conducted.