Worldwide, the majorities of fish stocks are data-limited and lack fully quantitative stock assessments. Within ICES, such data-limited stocks are currently managed by setting total allowable catch without the use of target reference points. To ensure that such advice is precautionary, we used management strategy evaluation to evaluate an empirical rule that bases catch advice on recent catches, information from a biomass survey index, catch length frequencies, and MSY reference point proxies. Twenty-nine fish stocks were simulated covering a wide range of life histories. The performance of the rule varied substantially between stocks, and the risk of breaching limit reference points was inversely correlated to the von Bertalanffy growth parameter k. Stocks with k>0.32 year−1 had a high probability of stock collapse. A time series cluster analysis revealed four types of dynamics, i.e. groups with similar terminal spawning stock biomass (collapsed, BMSY, 2BMSY, 3BMSY). It was shown that a single generic catch rule cannot be applied across all life histories, and management should instead be linked to life-history traits, and in particular, the nature of the time series of stock metrics. The lessons learnt can help future work to shape scientific research into data-limited fisheries management and to ensure that fisheries are MSY compliant and precautionary.
Anarhichas lupus is a boreo-Arctic species with biological characteristics often associated with vulnerability to overexploitation. Although not commercially targeted in the North Sea, A. lupus is a bycatch species in mixed demersal fisheries. Here we provide an overview of the status of A. lupus in the North Sea, as observed from commercial landings and fishery-independent trawl survey data. A. lupus was once common across much of the central and northern North Sea but, since the 1980s, have declined in abundance, demographic characteristics (reduced size) and geographical range, with the shallower and more southerly parts of its range most impacted. A. lupus is still relatively frequent in the northern North Sea, where fishing intensity, though decreasing, is high. Bycatch through fishing remains a potential threat and, considering the likely impacts of predicted climate change on cold-water species, risks of further regional depletion and/or range contraction remain. Whether or not A. lupus is able to re-establish viable populations in former habitat in UK coastal waters is unknown. Given the lack of data, the precautionary principle would suggest that manageable pressures be minimized where the species and its habitat are at risk of further impacts, and more regular assessments of population status be undertaken.
Many data-limited fish stocks worldwide require management advice. Simple empirical management procedures have been used to manage data-limited fisheries but do not necessarily ensure compliance with maximum sustainable yield objectives and precautionary principles. Genetic algorithms are efficient optimization procedures for which the objectives are formalized as a fitness function. This optimization can be included when testing management procedures in a management strategy evaluation. This study explored the application of a genetic algorithm to an empirical catch rule and found that this approach could substantially improve the performance of the catch rule. The optimized parameterization and the magnitude of the improvement were dependent on the specific stock, stock status, and definition of the fitness function. The genetic algorithm proved to be an efficient and automated method for tuning the catch rule and removed the need for manual intervention during the optimization process. Therefore, we conclude that the approach could also be applied to other management procedures, case-specific tuning, and even data-rich stocks. Finally, we recommend the phasing out of the current generic ICES “2 over 3” advice rule in favour of case-specific catch rules of the form tested here, although we caution that neither works well for fast-growing stocks.
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