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Decisions in ecological risk management for chemical substances must be made based on incomplete information due to uncertainties. To protect the ecosystems from the adverse effect of chemicals, a precautionary approach is often taken. The precautionary approach, which is based on conservative assumptions about the risks of chemical substances, can be applied selecting management models and data. This approach can lead to an adequate margin of safety for ecosystems by reducing exposure to harmful substances, either by reducing the use of target chemicals or putting in place strict water quality criteria. However, the reduction of chemical use or effluent concentrations typically entails a financial burden. The cost effectiveness of the precautionary approach may be small. Hence, we need to develop a formulaic methodology in chemical risk management that can sufficiently protect ecosystems in a cost-effective way, even when we do not have sufficient information for chemical management. Information-gap decision theory can provide the formulaic methodology. Information-gap decision theory determines which action is the most robust to uncertainty by guaranteeing an acceptable outcome under the largest degree of uncertainty without requiring information about the extent of parameter uncertainty at the outset. In this paper, we illustrate the application of information-gap decision theory to derive a framework for setting effluent limits of pollutants for point sources under uncertainty. Our application incorporates a cost for reduction in pollutant emission and a cost to wildlife species affected by the pollutant. Our framework enables us to settle upon actions to deal with severe uncertainty in ecological risk management of chemicals., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Assessing the vulnerability and adaptive capacity of species, communities, and ecosystems is essential for successful conservation. Climate change, however, induces extreme uncertainty in various pathways of assessments, which hampers robust decision-making for conservation. Here, we developed a framework that allows us to quantify the level of acceptable uncertainty as a metric of ecosystem robustness, considering the uncertainty due to climate change. Under the framework, utilizing a key concept from info-gap decision theory, vulnerability is measured as the inverse of maximum acceptable uncertainty to fulfill the minimum required goal for conservation. We applied the framework to 42 natural forest ecosystems and assessed their acceptable uncertainties in terms of maintenance of species richness and forest functional type. Based on best-guess estimate of future temperature in various GCM models and RCP scenarios, and assuming that tree species survival is primarily determined by mean annual temperature, we performed simulations with increasing deviation from the best-guess temperature. Our simulations indicated that the acceptable uncertainty varied greatly among the forest plots, presumably reflecting the distribution of ecological traits and niches among species within the communities. Our framework provides acceptable uncertainty as an operational metric of ecosystem robustness under uncertainty, while incorporating both system properties and socioeconomic conditions. We argue that our framework can enhance social consensus building and decision-making in the face of the extreme uncertainty induced by global climate change. [ABSTRACT FROM AUTHOR]