Beyond Biological Performance Measures in Management Strategy Evaluation: Bringing in Economics and the Effects of Trawling on the Benthos

摘要

The performance of management strategies for a prawn fishery in northern Australia is evaluated using the management strategy evaluation (MSE) approach. The operating model on which the analyses are based includes population dynamics models for four prawn species and five stocks of each species, an effort allocation model and a benthic impacts model. Management is implemented through controls on the fishing effort that targets the two main target species (Penaeus semisulcatus and Penaeus esculentus) and the technical interactions between the two species are also taken into account. The total effort set by management is distributed to regions and grid cells in each region through effort allocation models. The performance measures used in this study cover conservation of the target species, economic returns and the impact of fishing on benthic communities. Two classes of management strategy are evaluated. One class seeks to move stocks towards the target spawning stock size which is a pre-specified fraction of the spawning stock size at which Maximum Sustainable Yield (MSY) is achieved using a threshold control rule, while the other class selects time-trajectories of future effort to maximize discounted profit. Management strategies that control effort levels to maximize the total profit over the long-term outperform those which aim to move the spawning stock size toward SMSY in terms of most performance measures. For example, even when the target stock size for the MSY-based management strategy is selected to be the same as that which maximizes profits, selecting effort to maximize profits leads to lower variability in catches and profits. This study illustrates how broader ecosystem considerations can be included in MSE analyses without the need for the development and implementation of full ecosystem models and hence provides a “middle road” between single-species MSEs and full ecosystem MSEs.