(vi) Decision support

The alternative management scenarios produced in step (v) of the framework need to be prioritized. Deciding which management option to choose should be made within a framework that enables a range of scenarios, including business as usual, to be compared against each other. This step should include the estimation of ecosystem services values and involve stakeholder participation.

Choices can be made based on a range of criteria that describe each scenario including traditional economic metrics such as cost effectiveness. However, they should also be based on other criteria, such as how the estimated values of ecosystem services are likely to change following an intervention, and the degree to which benefits are equitably distributed across different groups of stakeholders (for a discussion of decision-support tools see section 15.3). Multi-Criteria Analysis can provide a framework within which decisions about the alternative scenarios can be made. This approach enables individual scenarios to be presented in a number of ways, for example, through the use of monetary and qualitative values. Cost Benefit Analysis could also be applied to provide a comparative evaluation of the different scenarios. Sophisticated Cost Benefit Analysis techniques have been developed in the context of wetlands (Luisetti et al. 2008). The modelling in step (v) can be used as a basis of decision-support systems, along with other data information, and a user-friendly graphical interface (e.g. OCEAN, ). Bayesian Belief Networks have also been applied to environmental decision-making. They are particularly useful where data are scarce and uncertainty is high. Langmead et al. (2007) used Bayesian Belief Networks to explore the impacts of habitat change, eutrophication, chemical pollution, and fishing on four European seas (the Baltic Sea, Black Sea, Mediterranean Sea, and North East Atlantic). Where data permitted, they were used to simulate the consequences of a 'business-as-usual' management scenario with four alternatives for economic and social development over the next two to three decades. The analysis of the different scenarios, however, was limited by a shortage of historic time-series data.

 
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