Print PageResource managers must make timely decisions despite multiple sources of uncertainty. These sources include model and parametric uncertainty and more extreme sorts of uncertainty, such as that caused by climate change. Modeling approaches have been developed to help managers make optimal conservation planning decisions under uncertainty at a landscape scale. However, these approaches depend on clearly specifying the correct decision problem. As part of the Southeast Regional Assessment Project, we are developing a decision-making approach to help regional stakeholders make conservation planning decisions for multiple resources (i.e. biological, cultural) throughout the southeastern U.S. We are using Structured Decision Making (SDM) to elicit stakeholder objectives and facilitate possible management strategies for meeting those objectives. We will then predict effects of management actions using model predictions of resource dynamics into the future under model-averaged climate change scenarios. We have developed a heuristic optimization algorithm for determining the best places and sequences for delivering those actions. This model accounts for probabilistic uncertainty about whether certain resources will remain extant over a given planning period. We will analyze the sensitivity of our optimization to unstructurable uncertainty (i.e. Knightian uncertainty) using an information-gap decision analysis.
Results/Conclusions
The results of our preliminary SDM exercises show that stakeholders in the southeast have very diverse conservation objectives. These objectives will inevitably compete for scarce personnel and financial resources. The actions proposed by the stakeholders also vary considerably in terms of effectiveness. Our modeling exercise, thus far, quantitatively demonstrated the consequences of trading off one action for another when cost and effectiveness varies. Generally, our results showed that the spatial and temporal sequence of decisions changes under various levels of uncertainty. This analysis also showed that there was an inevitable trade-off between tolerance to uncertainty (i.e. robustness) and what managers could expect in terms of meeting conservation targets. When developing climate adaptation strategies, stakeholders need to explicitly specify their tolerance to uncertainty because it drives how these decisions are made. Thus, our methodology provides a rigorous and objective method for assessing the consequences of this tolerance and the trade-offs among multiple objectives across the landscape.
