What is Management Strategy Evaluation?

Management Strategy Evaluation

At the core of the openMSE framework is an integrated management strategy evaluation (MSE) function. Management strategy evaluation is a computer simulation approach for testing prospective management options over a wide range of possible realities for the fishery and the population. Ideally, management options can be identified that are robust and perform well over all credible scenarios for the fishery.

It is extremely difficult, perhaps impossible, to conduct large-scale experiments to evaluate directly the trade-offs associated with fisheries management. Even among well-studied fisheries, considerable uncertainty often exists regarding stock status and the dynamics of the fishery, and it can be difficult to attribute particular outcomes to distinct management actions. The mathematical description of fish population dynamics and the interaction with different exploitation patterns, first developed by Beverton and Holt (1957), together with the advent of powerful and affordable computers, has allowed the development of the MSE approach (Butterworth, 2007; Punt et al. 2014).

Management strategy evaluation was originally developed by the International Whaling Commission as a tool to evaluate the various trade-offs involved the management of marine mammals, and to guide the decision-making process for selecting an appropriate management strategy. Since its development in the mid-1970s, MSE has become widely used in fisheries science and is routinely applied to evaluate the trade-offs in alternative management strategies of many of the world’s fisheries.

An MSE is usually comprised of four key components:

  1. an operating model that is used to simulate the stock and fleet dynamics;
  2. a set of alternative management procedures (interchangeably referred to as harvest strategies, or management strategies) that use the simulated fishery data from the operating model to generate a management recommendation (e.g., a total allowable catch (TAC) or effort control). In some cases this can be a stock assessment model that estimates the status of the (simulated) stock and linked to a harvest control rule to generate management advice from the output of the assessment. In other cases, the management procedure can be simple empirical rules that prescribe management recommendations directly from the observed fishery data (e.g., an index targeting method). The key feature of a management procedure is that it is reproducible and objective: that is, the same data will always result in the same management recommendation;
  3. an observation model that is used to generate the simulated observed data that would typically be used in management (i.e., with realistic imprecision and bias);
  4. an implementation model that describes how the management advice generated by the management procedures is implemented back into the simulated fishery.

The management recommendations by each management procedure are then fed-back into the operating model and projected forward one-time step. The process of simulating the population dynamics of the fishery along with the management process that feeds back and impacts the simulated fish population is known as closed-loop simulation.

A benefit of closed-loop simulation is that it allows the direct comparison and evaluation of alternative management strategies against perfect knowledge of the simulated system; something that is impossible in the real world (Walters and Martell, 2004). With the aid of computer simulation, it is possible to run many hundreds of simulation runs for each management procedure being evaluated - each representing a different possible simulated future of what could happen to the fishery under various management strategies - and to take into account the uncertainty in knowledge of the stock and fishery (i.e., errors in observation), as well as the uncertainty in future environmental and ecological conditions that are likely to affect the stock dynamics.

Through these simulations, MSE reveals the relative impacts of specified management approaches to their fishery decades into the future and enables managers to choose the approach that best achieves their management objectives, as articulated through a set of well-defined performance metrics.

How does Management Strategy Evaluation Differ from Stock Assessment?

Stock assessments are intended to provide one-off management advice, such as a catch limit (e.g. 20,000 tonnes), based on historical data. However, a stock assessment on its own provides no knowledge of the expected performance of the assessment, harvest control rule, or management system in general.

In an assessment setting there is no way to know whether a simpler assessment using other data might provide more robust performance (e.g. less overfishing, more yield) over a time horizon that managers are considering (e.g. the next 30 years). Management strategy evaluation tests a range of management approaches (of which an assessment linked to a harvest control rule is one such approach) and offers a scientific basis for selecting a management approach. MSE does not provide a catch-limit in tonnes, it identifies a modus operandi that will provide the desired management performance (it is analogous to selecting a suitable airplane via flight simulation testing rather than actually flying a plane to a specific destination).

The advantage of MSE over stock assessment is that it is possible to consider a much wider range of uncertainty in stock dynamics, fleet dynamics, and data collection, which often better represents the state of knowledge (particularly for data-limited stocks). No matter how much uncertainty is factored into the MSE, a single management approach may be selected that can provide management advice.

MSE was specifically introduced in controversial fishery settings where it was not possible to decide the ‘best’ representation of the state of nature. In the end, MSE was used to circumvent this problem by including all possible states of nature, often revealing that the disputes were in fact inconsequential all along.