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Kaikoura, New Zealand

Butterworth D.S.,University of Cape Town | Bentley N.,Trophia Ltd | De Oliveira J.A.A.,Center for Environment | Donovan G.P.,International Whaling Commission | And 7 more authors.
ICES Journal of Marine Science

Rochet and Rice, while recognizing management strategy evaluation (MSE) as an important step forward in fisheries management, level a number of criticisms at its implementation. Some of their points are sound, such as the need for care in representing uncertainties and for thorough documentation of the process. However, others evidence important misunderstandings. Although the difficulties in estimating tail probabilities and risks, as discussed by Rochet and Rice, are well known, their arguments that Efron's nonparametric bootstrap re-sampling method underestimates the probabilities of low values are flawed. In any case, though, the focus of MSEs is primarily on comparing performance and robustness across alternative management procedures (MPs), rather than on estimating absolute levels of risk. Qualitative methods can augment MSE, but their limitations also need to be recognized. Intelligence certainly needs to play a role in fisheries management, but not at the level of tinkering in the provision of annual advice, which Rochet and Rice apparently advocate, inter alia because this runs the risk of advice following noise rather than signal. Instead, intelligence should come into play in the exercise of oversight through the process of multiannual reviews of MSE and associated MPs. A number of examples are given of the process of interaction with stakeholders which should characterize MSE. © 2010 UK and Australian Crown Copyright. Source

Bentley N.,Trophia Ltd | Kendrick T.H.,Trophia Ltd | Starr P.J.,61A Rhine Street | Breen P.A.,12 Birkhall Grove
ICES Journal of Marine Science

Standardization of catch per unit effort using generalized linear models (GLMs) is a common procedure that attempts to remove the confounding effects of variables other than abundance. Simple plots and metrics are described to assist understanding the standardization effects of explanatory variables included in GLMs, illustrated with an example based on New Zealand trevally (Caranx lutescens) data. © 2011 International Council for the Exploration of the Sea. Published by Oxford University Press. All rights reserved. Source

Bentley N.,Trophia Ltd
ICES Journal of Marine Science

The increasingly sophisticated methods developed for stock assessment are not always suited to data-poor fisheries. Data-poor fisheries are often low in value, so the researcher time available for their assessment is also small. The dual constraints of reduced data and reduced time make stock assessments for low-value stocks particularly challenging. Prior probability distributions are useful for transferring knowledge from data-rich to data-poor fisheries. When data are limited, it is important to make the most of what few data is available. However, fully understanding potential biases in data are just as important in the data-poor context as it is in data-rich fisheries. A key aspect of stock assessment is peer review. Providing a comprehensive, yet concise, set of diagnostics is crucial to a stock assessment where time is limited. Against the standards by which data-rich stock assessments are judged, stock assessments for data-poor stocks are likely to be found deficient. A key challenge is to maintain a balance between the opposing risks of inappropriate management "action" due to assessment inaccuracy, and inappropriate management "inaction" due to assessment uncertainty. © 2014 © International Council for the Exploration of the Sea 2014. All rights reserved. Source

Bentley N.,Trophia Ltd | Langley A.D.,Trophia Ltd
Canadian Journal of Fisheries and Aquatic Sciences

We describe a sequential estimation approach designed to be used as part of a fisheries management procedure; it is computationally efficient and able to be applied to varying types, and extents, of data. The estimator maintains a pool of stock trajectories, each having a unique combination of model parameters (e.g., stock-recruitment steepness) sampled from prior probability distributions. Each year, for each trajectory, the values of variables (e.g., current biomass) are updated and tested against specified constraints. Constraints further determine the feasibility of the trajectories by defining likelihood functions for model variables, or combinations of variables, in particular years. Trajectories that fail to meet one or more of the constraints are discarded from the pool and replaced by new trajectories. Each year, stochastic forward projections of the trajectories in the pool are used to determine an optimal catch level. The flexibility and accuracy of the estimator is evaluated using the fishery for snapper, Pagrus auratus, off northern New Zealand as a case study. The sequential nature of the algorithm suggests alternative methods of presentation for understanding and explaining the fisheries estimation process. We provide recommendations for both the evaluation and operation of management procedures that employ the estimator. Source

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