Management strategy evaluation (MSE) involves using simulation to compare the relative effectiveness for achieving management objectives of different combinations of data collection schemes, methods of analysis and subsequent processes leading to management actions. MSE can be used to identify a 'best' management strategy among a set of candidate strategies, or to determine how well an existing strategy performs. The ability of MSE to facilitate fisheries management achieving its aims depends on how well uncertainty is represented, and how effectively the results of simulations are summarized and presented to the decision-makers. Key challenges for effective use of MSE therefore include characterizing objectives and uncertainty, assigning plausibility ranks to the trials considered, and working with decisionmakers to interpret and implement the results of the MSE. This paper explores how MSEs are conducted and characterizes current 'best practice' guidelines, while also indicating whether and how these best practices were applied to two casestudies: the Bering-Chukchi-Beaufort Seas bowhead whales (Balaena mysticetus; Balaenidae) and the northern subpopulation of Pacific sardine (Sardinops sagax caerulea; Clupeidae).

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When sexually mature, Antarctic (true) blue whales are substantially longer than pygmy blue whales. To estimate the proportions of these two subspecies in various regions, Bayesian mixture models were fitted to catch length frequencies of sexually mature females. The extent of rounding to 5‐ft intervals was also estimated. Antarctic blue whales dominated (99.2%) pelagic catches south of 52°S, whereas pygmy blue whales dominated (99.9%) north of 52°S and in 35°–180°E. South of 60°S, only 0.7% (95% credibility interval 0.5%–1.0%) were pygmy blue whales, lower than the 7% upper bound currently assumed. Shore‐based catches from SW Africa and those before 1937 from South Georgia and the South Shetlands were estimated to contain 90%–92% Antarctic blue whales. Actual proportions were probably higher, but these data show evidence of rounding (up to 19% of records), poor length‐estimation methods, and other problems. The mean length of sexually mature female Chilean blue whales (77.1 ft, 23.5 m) was intermediate between pygmy (68.9 ft, 21.0 m) and Antarctic blue whales (83.4–86.3 ft, 25.4–26.6 m). A good fit to these data was obtained only by assuming that the Chilean whales are a separate subspecies or distinctive population. This finding is also consistent with their discrete distribution, and genetic and call type differences, compared to Antarctic and pygmy blue whales.

The ability of management strategies to achieve the fishery management goals are impacted by environmental variation and, therefore, also by global climate change. Management strategies can be modified to use environmental data using the “dynamic B0” concept, and changing the set of years used to define biomass reference points. Two approaches have been developed to apply management strategy evaluation to evaluate the impact of environmental variation on the performance of management strategies. The “mechanistic approach” estimates the relationship between the environment and elements of the population dynamics of the fished species and makes predictions for population trends using the outputs from global climate models. In contrast, the “empirical approach” examines possible broad scenarios without explicitly identifying mechanisms. Many reviewed studies have found that modifying management strategies to include environmental factors does not improve the ability to achieve management goals much, if at all, and only if the manner in which these factors drive the system is well known. As such, until the skill of stock projection models improves, it seems more appropriate to consider the implications of plausible broad forecasts related to how biological parameters may change in the future as a way to assess the robustness of management strategies, rather than attempting specific predictions per se.

Butterworth, D. S. 2007. Why a management procedure approach? Some positives and negatives. – ICES Journal of Marine Science, 64: 613–617. The origin of the management procedure (MP) approach (sometimes termed management strategy evaluation), with its simulation testing of feedback-control algorithms as a necessary and structured basis for dealing with the inevitable uncertainties associated with fisheries assessments, is briefly reviewed. Also discussed are the advantages that overcome some of the difficulties of the “traditional” approach of coupling an annual “best” assessment to some harvest control rule, such as a failure to consider longer-term trade-offs properly. The MP approach does, however, also have disadvantages, such as the length of time typically required for its development and an argued inflexibility after implementation. Solutions that have been developed to overcome some of these difficulties are discussed.

Rademeyer, R. A., Plagányi, É. E., and Butterworth, D. S. 2007. Tips and tricks in designing management procedures. – ICES Journal of Marine Science, 64: 618–625. Management procedures (MPs) are becoming widely used in fisheries management, but guidelines to assist in their construction, evaluation, and implementation are few. We provide simple guidelines by drawing on experience from developing and applying MPs in southern Africa and internationally. Suggestions are provided on how to choose between candidate MPs and on key trade-offs in selecting between data-based (empirical) and model-based formulations. Assistance is also provided in dealing with different sources of uncertainty, such as deciding which operating models should be included in a reference set used for primary simulation testing and tuning (in contrast to robustness or sensitivity tests), and on how weights for the associated alternative hypotheses are most practically assigned. Finally, some guidelines are given for presenting the results effectively, which is one of the key challenges of a successful implementation process.

The effects of future consumption by the Cape fur seal on catches and catch rates of the Cape hakes. 4. Modelling the biological interaction between Cape fur seals Arctocephaluspusilluspusillus and the Cape hakes Merluccius