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The International Council for the Exploration of the Sea (ICES) has provided scientific stock advice based on reference points to manage fisheries in the North Atlantic Ocean and adjacent seas for decades. ICES advice integrates the precautionary approach with the objective of achieving maximum sustainable yield. Here, we examine ICES reference point evolution over the last 25 yr and provide a comprehensive empirical review of current ICES reference points for data-rich stocks (Category 1; 79 stocks). The consistency of reference point estimation with the ICES guidelines is evaluated. We demonstrate: (1) how the framework has evolved over time in an intergovernmental setting, (2) that multiple precautionary components and sources of stochasticity are included, (3) that the relationship and historical context of stock size and recruitment are crucial for non-proxy reference points, (4) that reference points are reviewed frequently, taking into account fluctuations and multiple sources of variability, (5) that there are occasional inconsistencies with the guidelines, and (6) that more comprehensive and clearer documentation is needed. Simplifying the stock-recruit typology and developing quantitative criteria would assist with this critically important classification. We recommend a well-documented, transparent, and reproducible framework, and periodic syntheses comparing applications across all stocks. [ABSTRACT FROM AUTHOR]

Target and limit reference points are fundamental management components used to define sustainable harvest strategies. Maximum Sustainable Yield (MSY) and the precautionary principle underpin many reference points. Non-proxy reference points based on MSY in age-based single-species assessments depend on the stock–recruitment (SR) relationship, which can display complex variability. Current reference points ignore persistent dynamic change by assuming that the SR relationship is stationary and with constant recruitment parameters over selected time periods. We highlight Peterman's productivity method (PPM), which is capable of tracking temporal dynamics of recruitment productivity via time-varying SR parameters. We show how temporal variability in SR parameters affects fishing mortality and biomass MSY-based reference points. Implementation of PPM allows for integrated dynamic ecosystem influences in tactical management while avoiding overwrought and sometimes ephemeral mechanistic hypotheses tested on small and variable SR datasets. While some of these arguments have been made in individual papers, in our opinion the method has not yet garnered the attention that is due to it. [ABSTRACT FROM AUTHOR]

Many management bodies require applying the precautionary approach when managing marine fisheries resources to achieve sustainability and avoid exceeding limits. For data-limited stocks, however, defining and achieving management objectives can be difficult. Management procedures can be optimized towards specific management objectives with genetic algorithms. We explored the feasibility of including an objective that limited the risk of a stock falling below various limit reference points in the optimization routine for an empirical data-limited control rule that uses a biomass index, mean catch length, and includes constraints (the "rfb-rule"). This was tested through management strategy evaluation on several fish stocks representing various life-history traits. We show that risk objectives could be met, but more restrictive risk limits can lead to a potential loss of yield. Outcomes were sensitive to simulation conditions such as observation uncertainty, which can be highly uncertain in data-limited situations. The rfb-rule outperforms the method currently applied by ICES, particularly when risk limitation objectives are considered. We conclude that the application of explicit precautionary levels is useful to avoid overfishing. However, we caution against the indiscriminate use of arbitrary risk limits without scientific evaluation to analyse their impact on stock yields and sustainability. [ABSTRACT FROM AUTHOR]

The adoption of the Precautionary Approach requires providing advice that is robust to uncertainty. Therefore, when conducting stock assessment alternative, model structures and data sets are commonly considered. The primary diagnostics used to compare models are to examine residuals patterns to check goodness-of-fit and to conduct retrospective analysis to check the stability of estimates. However, residual patterns can be removed by adding more parameters than justified by the data, and retrospective patterns removed by ignoring the data. Therefore, neither alone can be used for validation, which requires assessing whether it is plausible that a system identical to the model generated the data. Therefore, we use hindcasting to estimate prediction skill, a measure of the accuracy of a predicted value unknown by the model relative to its observed value, to explore model misspecification and data conflicts. We compare alternative model structures based on integrated statistical and Bayesian state-space biomass dynamic models using, as an example, Indian Ocean yellowfin tuna. Validation is not a binary process (i.e. pass or fail) but a continuum; therefore, we discuss the use of prediction skill to identify alternative hypotheses, weight ensemble models and agree on reference sets of operating models when conducting Management Strategy Evaluation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]