Historically, MSE has typically been used in the context of ‘data-rich’ fisheries; that is, fisheries where there are sufficient data and information to build an operating model that reflects the understanding of the fishery dynamics.
openMSE, starting with the development of the Data-Limited Methods toolkit (DLMtool) in 2015, is designed to make MSE accessible to all fisheries, regardless of the amount of available data and information.
MSE for data-limited fisheries?
The terms ‘data-limited’, ‘data-moderate’, and ‘data-rich’ are often used in fisheries, although they are rarely clearly defined. One reason for this perhaps is that fishery dynamics are extremely complex, and in even the most well studied fisheries there remains large gaps between the understanding and the real fishery dynamics.
We use the term ‘data-limited’ to indicate situations where there is insufficient data (or more correctly, information in the data) to conduct a quantitative stock assessment and estimate the status of the stock and the historical dynamics of the fishery.
Although MSE typically hasn’t been used for these sort of ‘data-limited’ fisheries, it is arguable that it is even more important to use MSE for these fisheries. If there is no ability to estimate the status of the stock, then managers and stakeholders require assurance that the prescriptive methods (e.g., a static size limit) or the data-limited approaches (e.g., a TAC set at the average historical catch) will have a sufficiently low risk of depleting the stock to undesirably low levels.
In the absence of empirical experiments, simulation modeling and MSE appear to be the most appropriate way of evaluating the risk of alternative management methods for data-limited fisheries.
In this section we demonstrate how to build an operating model for a data-limited fishery.