B. Introduction

Effectively managing an exploited population sustainably requires an understanding of the way a population will respond to changes in exploitation. This can be a difficult task because of the uncertainties inherent in a manager’s understanding of the exploited population’s dynamics. In recent years, management strategy evaluation (MSE) has been proposed as the gold standard for the testing of management strategies under uncertainty. MSE is accomplished by simulating populations with characteristics similar to the target population, drawing data with error from the simulated populations required to assess the stock using a given management strategy, applying an assessment method to estimate quantities used in management (e.g. the current biomass and biological reference points), and finally using a specified harvest control rule to determine the allowable catches in a given year. The calculated allowable catches are removed from the simulated population and process is repeated into the ‘future’ to mimic the feedback that occurs in the management of an actual fishery. MSE has been recently used for many fisheries around the globe (e.g. Bering Sea pollock, groundfish, and crab species, sardines, whales) in the world to compare and select management strategies. For example, 'regime-based' harvest control rules were tested for snow crab in the Bering Sea in response to an overfished declaration that may have been related to a change in the Pacific Decadal Osciliation (below).

MSE is attractive to fishery managers because it allows for quantitatively comparing the performance of a suite of management strategies given some assumed underlying dynamics. The models used to estimate quantities used in management are a simplification of the natural world, but it is often unclear how well these simplifications represent nature. MSE also allows for the comparison of management strategies over different projected ‘states of nature’. For example, population processes like growth, natural mortality, and recruitment are thought to be influenced by the environment, and climate change will likely impact these processes in the future. However, the true impact of climate change on a population is not known, so comparison over a range of potential projected states of nature is a critical aspect of any thorough management strategy. Similarly, the relative merits of implementing a size limit vs. a marine protected area vs. a quota system based on assessment output can be evaluated using MSE. Although it is increasingly used in well-managed fisheries, MSE is a time intensive process that requires fairly extensive fishery data and the ability to write code. Here we present a Generalized Management Strategy Evaluation framework (GeMS) to circumvent these two requirements by requiring only inputs of life history parameters and a time series of historical fishing effort (historical recruitment can also be provided if available) to begin the process of management strategy evaluation.