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EAF Steps

  • Activity 3.3 - Management option evaluation and selection
  • Activity 4.3 - Review performance of the Management system

Purpose

Management strategy evaluation (MSE) involves assessing the consequences of a range of different management strategies or options to assist determine which approach will be the most appropriate to meet the operational objectives of the fishery

Overview

MSE is a modeling based approach aimed at testing the robustness of possible management arrangements (plans) by examining which sets of decision rules, which are used to adjust TACs or effort controls, perform the best in achieving the management objectives for a fishery. This simulation testing can also be used to determine how robust the management plan is likely to be to uncertainties. These analyses enable the choice of which management planning option has the most reasonable likelihood of achieving the management goals.

The MSE process involves using (1) an “operating model” to represent the ‘true’ underlying dynamics of the fishery resource and to generate simulated future data, (2) an estimation model is then used to assess the state of the stock relative to agreed target and limit reference points based on the data simulated using the operating model, and (3) one or more decision rules is used to determine what management actions should happen (e.g. change the TAC) given where the indicator generated by the estimation model is in comparison to the reference points. The latter two steps constitute the management strategy (see also Harvest Strategy and Control Rules Factsheet). The settings used in the management strategy can then be varied to attempt to best satisfy each of the often conflicting management goals and objectives. The outputs from the MSE are a set of performance measures that quantify the effectiveness of the estimation model and, more generally, a definition of what management/harvest strategy will best keep the fishery in the acceptable or target range of these levels.

EAF Tool Tips

Strengths: The strength of the approach is that instead of using a single model to find an optimal solution, multiple candidate models can be assessed. By modeling each step of the formal adaptive-management approach the consequences of alternate scenarios can be evaluated across the models. The other core strength of the process is that it is consultative in that both managers and stakeholders can have input into the candidate models and management scenarios. As the approach demands clear objectives to do the evaluations against, the method forces participants to be clear about their objectives.

The MSE approach is also aimed at identifying management plans that are robust to natural variation in the system and to uncertainty and error, both in stock assessments and implementation. The analysis usually attempts to identify control rules/strategies that perform well under a variety of potential future circumstances and with uncertainty in assessments.

Weaknesses : The MSE approach is only as good as the underlying models and assumptions it is based on including the extent to which the true range of uncertainty can be identified and represented in operating models. It has been noted (Rochet and Rice 2009) the use of complex mathematics and statistical tools risks giving users a false sense of rigor implying a degree of precision and accuracy that may be misleading, particularly for low probability outcomes. If undesirable outcomes have not been experienced enough times to know the conditions that cause them, and MSE may not bracket the range of possible outcomes and is unlikely to accurately determine the probability of their occurrence. So caution is required in their use to ensure that they adding to the robustness of decision making not just masking uncertainty.

EAF Tool Pedigree

This method was first used by the International Whaling Commission (e.g. IWC 1992) and Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR) (de la Mare 1996) and has been adopted as a standard fisheries tool in a number of countries including: South Africa (Punt and Butterworth 1995), New Zealand (Starr et al. 1997) and Australia (Punt and Smith 1999).

EAF Tool Synergy

This approach essentially works in concert with the Harvest Strategy and Control rules tool. It will also strongly interact with the selection of indicators and performance measures plus the selection of appropriate management actions.

EAF Tool Usage

Very hard

Cost

Moderate, High

Given the date requirements to conduct simulations, the capacity requirements to complete the simulations and the time requirements to complete an MSE, this is generally a relatively costly method to complete successfully.

EAF Tool Capacity

High

This approach requires a good level of access to individuals who are highly skilled at computer simulation modeling.

Background Requirements

High

MSEs can be performed qualitatively but they are typically done using quantitative (or at least semi-quantitative) simulations that contain sub-models for each of the main steps in the adaptive management cycle. At the core of these simulations is a "system state" model that represents the dynamics of the resource. These types of simulations require a high level of information to make the outputs robust enough to be used in the decision making process

Participation

Low, Moderate

The MSE modeling and simulations are completed done by one person or a small team, but the scenarios that are used in the modeling may be generated by a broader group of stakeholders. Therefore this approach can be done in a consultative manner but it doesn’t lend itself to input from large numbers of stakeholders.

Time Range

Long

A number of practitioners have noted the completion of MSEs can be very time consuming and that the harvest strategy based management plan can reduce the flexibility of managers after implementation (Holland 2010). It has been estimated to take twice as long as a standard stock assessment, but, it can then yield real savings by not having to repeat the assessments as often. The startup costs to develop an MSE based plan can be considerable and should be factored in before opting to use this approach.

Source of Information

Amar et al (2008) The Management Strategy Evaluation Approach and the Fishery for Walleye Pollock in the Gulf of Alaska Internet resource
CSIRO Webpages Internet resource
Holland, D. S. (2010), “Management Strategy Evaluation and Management Procedures: Tools for Rebuilding and Sustaining Fisheries”, OECD Food, Agriculture and Fisheries Working Papers, No. 25, OECD Publishing Internet resource

Other Relevant References

Walters, C.J., 1986. Adaptive Management of Renewable Resources. MacMillan Publishing Co., New York.
IWC 1992. Report of the Scientific Committee, Annex D. Report of the Sub-Committee on Management Procedures. Reports of the International Whaling Commission, 42: 87–136.
Punt, A. E., and Smith, A. D. M. 1999. Harvest strategy evaluation for the eastern gemfish (Rexea solandri). ICES Journal of Marine Science, 56: 860–875.
Sainsbury, K.J., Punt, A.E. and Smith, A.D.M. 2000. Design of operational management strategies for achieving fishery ecosystem objectives. ICES Journal of Marine Science, 57: 731–741
Butterworth, D.S., and Punt, A.E. 1999. Experiences in the evaluation and implementation of management procedures. ICES Journal of Marine Science, 56: 985–998.
de la Mare, W. K. 1996. Some recent developments in the management of marine living resources. In Frontiers of Population Ecology, pp. 599–616. Ed. by R. B. Floyd, A. W. Shepherd, and P. J. De Barro. CSIRO Publishing, Melbourne, Australia.
Starr, P.J.; Breen, P.A.; Hilborn, R.; Kendrick, T.H. (1997), Evaluation of a management decision rule for a New Zealand rock lobster substock Marine and Freshwater Research 48(8): 1093-1101.
Rochet, M-J. and Rice, J. C. 2009. Simulation-based management strategy evaluation: ignorance disguised as mathematics? – ICES Journal of Marine Science, 66: 754–762.
 
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