Experience with quantitative ecosystem assessment tools in the northeast Pacific
Hollowed, AB and Aydin, KY and Essington, TE and Ianelli, JN and Megrey, BA and Punt, AE and Smith, ADM, Experience with quantitative ecosystem assessment tools in the northeast Pacific, Fish and Fisheries, 12, (2) pp. 189-208. ISSN 1467-2960 (2011) [Refereed Article]
We consider the question of which quantitative modelling tools can be used to support an ecosystem approach to management (EAM), with a focus on evaluating the implication of decisions on the biological system being managed. Managers of federal fisheries in the eastern Bering Sea, USA, have adopted an EAM. The tools used to support EAM in the eastern Bering Sea serve as a guide to what types of models could be used elsewhere. A review of the role of natural science in the implementation of EAM shows that scientific advice enters into decision-making at a variety of steps. Single-species stock assessment and projection models are the most commonly used tools employed to inform managers. Comprehensive assessments (e.g. management strategy evaluation) are emerging as a new and potentially valuable analysis technique for use in assessing trade-offs of different strategic alternatives. In the case of management in the eastern Bering Sea, end-to-end models and coupled biophysical models have been used primarily to advance scientific understanding, but have not been applied in a management context. This review highlights that implementation of an EAM in a management environment such as eastern Bering Sea requires substantial commitments to the collection and analysis of data and support for a group of analysts with interdisciplinary training in population dynamics, oceanography and ecology. This review supports the growing recognition that a diverse suite of modelling tools is needed to address tactical and strategic management issues germane to the adoption of the ecosystem approach to fisheries management.
ecosystem approach to management, end-to-end models, fisheries management, Individual-based models