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3.1 Introduction and Purpose

The purpose has been to develop and build a Management Strategy Evaluation (MSE) framework that allows the performance of alternative management strategies to be evaluated prior to implementation and their robustness to be tested across a range of plausible hypotheses about the dynamics of the stocks, fisheries and fleets. The developed framework allows simulating and evaluating, respectively, the biological, social and economical consequences of a range of proposed fishery management options and objectives within different management regimes that can compare and predict the outcomes of alternative scenarios before potentially being implemented.

The MSE framework comprises an operating model which simulates the system to be managed, and a management procedure that models any management measures to be applied, which include models or algorithms used to process the data sampled from the system to be managed (as represented by the operating model). The management evaluation framework(s) (MEF) developed is able to simulate different management options and theie ability to monitor and control the system in order to achieve wished management objectives. This has been done using results from scientifically based tests of hypotheses and analyses performed in EFIMAS WP3-WP4 (in a cyclic feed-back process between project work packages), with relevant descriptive biological stock based and economical fishery and fleet based models addressing main fisheries advisory and management problems. Consequently, this can be used to evaluate results and output generated from a broad range of software packages (descriptive fisheries/fleets/stock assessment models as well as other evaluation and analysis tools), analyses, and existing databases being used for production of advice to management bodies, and can be applied to important EU fisheries.

The simulation framework is based on stochastic simulation techniques and can take into account of a range of uncertainties (parametric as well as structural uncertainty) and allows a variety of sensitivity analysis and risk assessments to be conducted. The success of the MSE approach depends on the extent to which the true range of uncertainty can be identified and represented in operating models. Several authors (e.g. Rosenberg and Restrepo 1994, Francis and Shotton 1997, Kell et al. 2007, Ulrich et al. 2007 and Mahevas and Pelletier 2004) have attempted to identify and categorize the uncertainties that can hinder attempts to manage fisheries (and other natural resources) successfully. These uncertainties include the following:

  • process error – natural variation in dynamic processes such as recruitment, somatic growth, natural mortality, and the selectivity of the fishery;
  • observation error – related to collecting data from a system (e.g. age sampling, catches, surveys);
  • estimation error – related to estimating parameters, both in the operating model, and, if a model-based management procedure is used, in the assessment model within the management procedure that leads to the perception of current resource status;
  • model error – related to uncertainty about model structure (e.g. causal assumptions of the models), both in the operating model and in the management procedure; and
  • implementation error – because management actions are never implemented perfectly and may result in realised catches that differ from those intended.

It includes simulated data collection using existing databases and calculated variance in data, perform assessment of the system (with use of output from currently applied descriptive models and analysis tools, alternative existing models/tools, or modified existing (alternative) models/tools for fisheries/stock evaluation), and provide advice according to harvest control rules, management options and objectives. Simulations are mainly performed using an integrated suite of software facilities with implementation of a common language (main basis being R/FLR) and interface, i.e. a common simulation frame, which can handle output and results from a variety of descriptive models and analysis tools for analyzing different management scenarios, options and objectives.

Managing fisheries in a virtual environment provides more reliable scientific advice to stakeholders: In the same way that a pilot might fly in a simulator before flying for real, the simulation tools evaluates the robustness of alternative strategies and virtual regimes to give a more holistic management advice in broader context before implementation. This provides managers and stakeholders a better idea of the consequence of a given management strategy or intervention before opting for a particular management approach.

Francis, R.I.C.C. and R. Shotton (1997). ‘Risk’ in fisheries management: a review. Can. J. Fish. Aquat. Sci. 54: 1699–1715.

Kell, L.T., Mosqueira, I., Grosjean, P., et al. (2007) FLR: an open-source framework for the evaluation and development of management strategies. ICES Journal of Marine Science 64:640-646

Mahevas, S., and Pelletier, D. 2004. Isis-Fish, a generic and spatially explicit simulation tool for evaluating impact of management measures on fisheries dynamics. Ecological Modelling, 171: 65-84

Rosenberg, A.A. and Restrepo, V.R., (1994) Uncertainty and risk evaluation in stock assessment advice for U.S. marine fisheries. CAN. J. FISH. AQUAT. SCI. Vol. 51 (12), pp. 2715-2720.

Ulrich, C., Andersen, B. S., Sparre, P. J., Nielsen, J. R. 2007. TEMAS: fleet-based bioeconomic simulation software to evaluate management strategies accounting for fleet behaviour. ICES J. Mar. Sci. 64: 647-651

Specific Tasks and Description of Work under EFIMAS Work Package 3:

Specific Tasks and Description of Work under WP3

J. Rasmus Nielsen 2008/09/08 12:24

 
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