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The Workshop on guidelines and methods for the evaluation of rebuilding plans (WKREBUILD) chaired by Vanessa Trijoulet (Denmark) and Martin Pastoors (Netherlands) met from 24 to 28 February 2020. The workshop attracted 27 participants from the US, Canada, Europe and FAO. When stocks are estimated to be below Blim1 and there is no perceived possibility of rebuilding above Blim within the time-frame of a short-term forecast, ICES has regularly recommended zero catch in combination with the development of a rebuilding plan. A review was carried out on the international experience on the development, evaluation and implementation of rebuilding plans for fisheries management in the Northeast Atlantic and in other fora around the world. In the Northeast Atlantic, rebuilding plans have been implemented in the past (e.g. the cod recovery plans of the early 2000s) but ICES has played a limited role in evaluating the performance of such recovery plans and does not have the tools or criteria to evaluate such plans. Recently, when a rebuilding plan for herring in 6.a 7.bc was submitted to ICES for evaluation, ICES refrained from providing such an evaluation. In the US and Canadian approaches, the legal framework determines the triggering and required elements of rebuilding plans. Such a legal imperative does not exist in the Northeast Atlantic. Nevertheless, the US and Canadian experiences provided useful elements that could be included in establishing ICES approach to rebuilding plans. Several case studies were presented on potential tools for the evaluation of rebuilding plans. Particular attention was given to evaluating options for harvest control rule options of such a plan. The tools focused mostly on short to medium term explorations of the probability of achieving a rebuilding of stocks. Because rebuilding plan evaluations need to be ready and available at short-notice when required, it was concluded that relatively standardized tools (i.e. packages or compiled code) to carry out such evaluations would be preferable over custommade evaluation tools. In addition, certain modelling considerations were highlighted as important such as realistic assumptions of productivity, uncertainty, bias in assessments and implementation error and the possibility of estimating the probability of achieving a rebuilding of stocks.Criteria for the acceptability of rebuilding plans will require an agreed Limit Reference Point (LRP) for initiating a rebuilding plan, definition of targets for fishing mortality or stock biomass, time-frames and the acceptable probabilities whether the rebuilding targets have been achieved. All of these should take into account realistic levels of uncertainty and being consistent with international best (scientific) practices. Although it was recognized that Blim would be the most likely candidate LRP triggering a rebuilding plan, the current approach in ICES for the determination of Blim was questioned during the workshop because it requires a more or less subjective classification of the stock-recruitment pairs into different types. In other regions, the LRP is often set as a certain proportion of the SSB at maximum sustainable yield (BMSY), e.g. 40% BMSY. If changes in productivity have been experienced in recent years at these are taken into account when estimating MSY reference points, the proportion of BMSY approach would likely lead to greater changes in the estimated value of LRP than the current ICES procedures used to estimate Blim, which rely on stock-recruitment pairs or definition of the lowest observed biomass (Bloss). This could have a large impact on the rebuilding target for stocks that experience changes in productivity regimes. Some concerns were raised regarding the often small distance between Blim and MSY Btrigger2 reference points for ICES stocks in comparison to the distance between trigger and limit in other jurisdictions. MSY Btrigger could therefore represent a late trigger to start decreasing fishing mortality when SSB is decreasing. The workshop recommended a future workshop on the revision of the procedure to estimate reference points within the ICES framework.An estimate of the minimum time (TMIN) by which rebuilding may be expected to be achieved, could be calculated by assuming zero catch and should be used as baseline for comparison with other rebuilding scenarios. The maximum time for rebuilding in the US and New Zealand is set to TMAX = 2 * TMIN or to TMIN plus one generation time1 (average length of time between when an individual is born and the birth of its offspring NRC (2014)). While the workshop did not arrive at an overall agreement on a default value for TMAX, it was suggested that TMAX = 2 * TMIN could be explored as a potential bounding on the rebuilding period, even though this should be subject to scientific analysis of potential effects on the stock in question. The workshop generated a guidance table summarizing the best practices for evaluation of rebuilding plans against the potential criteria of acceptability. The guidance table includes elements such as estimation of reference points, time-frames for rebuilding, rebuilding targets, handling uncertainties and bias, probability of achieving rebuilding targets and visualizing results. The workshop recommended that a follow-up workshop (WKREBUILD2) be organized for testing the guidelines with actual test cases, with the aim of defining more specific criteria and guidelines, i.e. learning by doing. Some of the elements that were discussed in the workshop but that have not (yet) entered the guidelines for evaluation of rebuilding plans are socio-economic trade-offs (e.g. between fast and slow rebuilding), mixed fisheries aspects (e.g. unavoidable bycatch due to mixed fisheries) and elements in rebuilding plans other than the HCR part (e.g. monitoring to improve the knowledge base). Most of the discussion at WKREBUILD was centred on stocks with analytical assessments (Category 1+2). Identifying when a data limited stock is in need of rebuilding (or has rebuilt) and how to evaluate rebuilding plan options for such stocks would likely require a separate process.

The purpose of the meeting was to bring up to date the methodologies and technical specifications that should be incorporated in Management Strategy Evaluation (MSE) work in ICES. The workshop was tasked with reviewing recent methodological and practical MSE work conducted in ICES and around the world, as well as the guidelines provided by the 2013 ICES Workshop on Guidelines for Management Strategy Evaluations (WKGMSE). The Terms of Reference indicated that the revision should include all aspects involved in MSE, while paying specific attention to several issues that had been identified through ICES practice. The Terms of Reference also requested WKGMSE 2 to consider how best to disseminate the guidelines to experts within the ICES community and the need for training courses. The workshop addressed all its Terms of Reference. The main results of the workshop are the revised MSE guidelines, as well as recommendations in relation to the ICES criterion for defining a management strategy as precautionary and in relation to the evaluation and advice on rebuilding strategies.

This manual represents a review of the potential sources and methods to be applied when providing prior information to Bayesian stock assessments and marine risk analysis. The manual is compiled as a product of the EC Framework 7 ECOKNOWS project (www.ecoknows.eu). The manual begins by introducing the basic concepts of Bayesian inference and the role of prior information in the inference. Bayesian analysis is a mathematical formalization of a sequential learning process in a probabilistic rationale. Prior information (also called ”prior knowledge”, ”prior belief”, or simply a ”prior”) refers to any existing relevant knowledge available before the analysis of the newest observations (data) and the information included in them. Prior information is input to a Bayesian statistical analysis in the form of a probability distribution (a prior distribution) that summarizes beliefs about the parameter concerned in terms of relative support for different values. Apart from specifying probable parameter values, prior information also defines how the data are related to the phenomenon being studied, i.e. the model structure. Prior information should reflect the different degrees of knowledge about different parameters and the interrelationships among them. Different sources of prior information are described as well as the particularities important for their successful utilization. The sources of prior information are classified into four main categories: (i) primary data, (ii) literature, (iii) online databases, and (iv) experts. This categorization is somewhat synthetic, but is useful for structuring the process of deriving a prior and for acknowledging different aspects of it. A hierarchy is proposed in which sources of prior information are ranked according to their proximity to the primary observations, so that use of raw data is preferred where possible. This hierarchy is reflected in the types of methods that might be suitable – for example, hierarchical analysis and meta-analysis approaches are powerful, but typically require larger numbers of observations than other methods. In establishing an informative prior distribution for a variable or parameter from ancillary raw data, several steps should be followed. These include the choice of the frequency distribution of observations which also determines the shape of prior distribution, the choice of the way in which a dataset is used to construct a prior, and the consideration related to whether one or several datasets are used. Explicitly modelling correlations between parameters in a hierarchical model can allow more effective use of the available information or more knowledge with the same data. Checking the literature is advised as the next approach. Stock assessment would gain much from the inclusion of prior information derived from the literature and from literature compilers such as FishBase (www.fishbase.org), especially in data-limited situations. The reader is guided through the process of obtaining priors for length–weight, growth, and mortality parameters from FishBase. Expert opinion lends itself to data-limited situations and can be used even in cases where observations are not available. Several expert elicitation tools are introduced for guiding experts through the process of expressing their beliefs and for extracting numerical priors about variables of interest, such as stock–recruitment dynamics, natural mortality, maturation, and the selectivity of fishing gears. Elicitation of parameter values is not the only task where experts play an important role; they also can describe the process to be modelled as a whole. Information sources and methods are not mutually exclusive, so some combination may be used in deriving a prior distribution. Whichever source(s) and method(s) are chosen, it is important to remember that the same data should not be used twice. If the 2 | ICES Cooperative Research Report No. 328 plan is to use the data in the analysis for which the prior distribution is needed, then the same data cannot be used in formulating the prior. The techniques studied and proposed in this manual can be further elaborated and fine-tuned. New developments in technology can potentially be explored to find novel ways of forming prior distributions from different sources of information. Future research efforts should also be targeted at the philosophy and practices of model building based on existing prior information. Stock assessments that explicitly account for model uncertainty are still rare, and improving the methodology in this direction is an important avenue for future research. More research is also needed to make Bayesian analysis of non-parametric models more accessible in practice. Since Bayesian stock assessment models (like all other assessment models) are made from existing knowledge held by human beings, prior distributions for parameters and model structures may play a key role in the processes of collectively building and reviewing those models with stakeholders. Research on the theory and practice of these processes will be needed in the future.

There is a growing need to evaluate fisheries management plans in a comprehensive interdisciplinary context involving stakeholders. In this paper we demonstrate a probabilistic management model to evaluate potential management plans for Baltic salmon fisheries. The analysis is based on several studies carried out by scientists from respective disciplines. The main part consisted of biological and ecological stock assessment with integrated economic analysis of the commercial fisheries. Recreational fisheries were evaluated separately. Finally, a sociological study was conducted aimed at understanding stakeholder perspectives and potential commitment to alternative management plans. In order to synthesize the findings from these disparate studies a Bayesian Belief Network (BBN) methodology is used. The ranking of management options can depend on the stakeholder perspective. The trade-offs can be analysed quantitatively with the BBN model by combining, according to the decision maker’s set of priorities, utility functions that represent stakeholders’ views. We show how BBN can be used to evaluate robustness of management decisions to different priorities and various sources of uncertainty. In particular, the importance of sociological studies in quantifying uncertainty about the commitment of fishermen to management plans is highlighted by modelling the link between commitment and implementation success.

This paper analyses the management of the Atlantic salmon stocks in the Baltic Sea through a coalition game in the partition function form. The signs of economic and biological over-exploitation of these salmon stocks over the last two decades indicate that cooperation among the harvesting countries, under the European Union's Common Fisheries Policy, has been superficial. Combining a two-stage game of four asymmetric players with a comprehensive bioeconomic model, we conclude that cooperation under the Relative Stability Principle is not a stable outcome. In contrast, the equilibrium of the game is non-cooperation. The paper also addresses the possibility of enhancing cooperation through more flexible fishing strategies. The results indicate that partial cooperation is stable under a specific sharing scheme. It is also shown that substantial economic benefits could have been realised by reallocating the fishing effort.