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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.

Stage-based assessment models are a type of fisheries stock assessment model that offer an alternative middle ground between aggregate and compositional models. We compare the capabilities of two theoretically different stage-based assessment approaches: an implementation of a biomass-based delay-difference model first described in a theoretical paper by Schnute in 1987, and an implementation of the well-known numbers-based two-stage model Catch-Survey Analysis (CSA). Models were tested within a simulation framework as well as on the real stock of white-bellied anglerfish ( Lophius piscatorius) in the Celtic Seas and Northern Bay of Biscay. For the simulated stocks, estimates from the biomass-based two-stage models were close to the true values in certain scenarios, but were sensitive to selectivity assumptions and configuration of growth within the model. CSA was more robust to selectivity assumptions, performing well in all simulated stock scenarios. Overall, results indicated that CSA was a robust stock assessment model but with relatively low precision, whereas the Schnute model was precise but required growth and mean fish weight data unaffected by selectivity.

For many environmental indicators, the sustainable status can change because of changes in either the monitored state or the policy goal. Fisheries provide an intensively monitored setting to investigate the relative impacts of such change. Key fisheries sustainability indicators comprise the ratio between fishing pressure or biomass and their respective reference levels. We developed a retrospective database of population status, reference point changes and reported reasons for changes for all data-rich stocks in the ICES region. We derived methods to distinguish the impacts of either source of change (monitored state or policy goal) on sustainable status. We found that reference points changed frequently (64% of populations had reference point changes) with varying magnitudes. Contrary to expectation, reference point changes were often not compensated by changes in the state thus significantly impacting inferred sustainability status and dependent scientific advice. Across a range of life histories and assessments, changes in reference points dominate retrospective revisions in status over the full time series. Overall, status before and after the change of reference point had no significant directional differences that would suggest reference point change effecting movement towards or away from sustainability. Although multiple factors have contributed to reference point changes, our results show that the reference point definition and the technical basis for estimation were the most important reasons for change. Recognizing that reference points are not constant in time but rather form reference series is paramount to quantifying present and historical sustainability. Properly documenting, justifying and quantifying the impacts of such change is an ongoing challenge.

Analysis of length frequency distributions from surveys is one well-known method for obtaining growth parameter estimates where direct age estimates are not available. We present a likelihood-based procedure that uses mixture models and the expectation–maximization algorithm to estimate growth parameters from length frequency data (LFEM). A basic LFEM model estimates a single set of growth parameters that produce one set of component means and standard deviations that best fits length frequency distributions over all years and surveys. The hierarchical extension incorporates bivariate random effects into the model. A hierarchical framework enables inter-annual or inter-cohort variation in some of the growth parameters to be modelled, thereby accommodating some of the natural variation that occurs in fish growth. Testing on two fish species, haddock (Melanogrammus aeglefinus) and white-bellied anglerfish (Lophius piscatorius), we were able to obtain reasonable estimates of growth parameters, as well as successfully model growth variability. Estimated growth parameters showed some sensitivity to the starting values and occasionally failed to converge on biologically realistic values. This was dealt with through model selection and was partly addressed by the addition of the hierarchical extension.