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Marine Ecology Progress Series

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MEPS 441:197-212 (2011)  -  DOI: https://doi.org/10.3354/meps09354

Integrated modelling tools to support risk-based decision-making in marine spatial management

V. Stelzenmüller*, T. Schulze, H. O. Fock, J. Berkenhagen

Johann Heinrich von Thünen Institute (vTI), Federal Research Institute for Rural Areas, Forestry and Fisheries, Institute of Sea Fisheries, Palmaille 9, 22767 Hamburg, Germany

ABSTRACT: The implementation of an ecosystem approach to marine spatial management requires practical tools to support risk-based decision-making. We combined a Bayesian Belief Network with a Geographical Information System (GIS) for the spatially explicit quantification of the ecological and economic risks of spatial management options. As an example we assessed the German exclusive economic zone (EEZ) in the North Sea to determine the potential effects of 2 scenarios on the vulnerability of plaice Pleuronectes platessa to fishing, fishing fleets and their revenues. In the first scenario we simulated a shift in plaice distribution due to changes in bottom temperatures to assess spatial management options. Then we imitated an expansion of offshore wind energy development with an associated reallocation of international fishing effort to assess the ecological and economic consequences. We predicted that an increase of 0.5°C in the average bottom temperature would require a significant reduction in fishing effort to maintain the current relative level of the vulnerability of plaice to fishing. The likely consequences of the second scenario were a homogenous increase in plaice catches around the areas closed for fishing, together with a decrease in the vulnerability of plaice to fishing within 17% of the study area. Our results showed the great potential of this framework to integrate the spatially explicit assessment of the economic and ecological risks of spatial management options. We conclude that this modelling framework can support the implementation of an ecosystem approach to marine spatial management, as it enables the derivation of probabilistic estimates which can be used directly in risk-based decision-making.


KEY WORDS: Bayesian Belief Network · Fishing effort · GIS · Plaice · Pleuronectes platessa · Offshore wind energy · Regression kriging


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Cite this article as: Stelzenmüller V, Schulze T, Fock HO, Berkenhagen J (2011) Integrated modelling tools to support risk-based decision-making in marine spatial management. Mar Ecol Prog Ser 441:197-212. https://doi.org/10.3354/meps09354

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