Managing the trifecta of disease, climate, and contaminants: Searching for robust choices under multiple sources of uncertainty

Abstract/Summary

Amphibian populations are exposed to multiple stressors, with potential for synergistic effects. These synergies can increase uncertainty in our ability to characterize the effects of each stressor and to understand the degree to which their effects interact to impact population processes. This uncertainty challenges our ability to identify appropriate management alternatives. Finding solutions that are robust to these uncertainties can improve management when knowledge is absent or equivocal and identify critical knowledge gaps. Bayesian Belief Networks (BBNs) are probabilistic graphical models that explicitly account for various sources of uncertainty and are used increasingly by environmental practitioners because of their broad applicability to ecological risk assessments. BBNs allow the user to: 1) generate a conceptual model to link actions to outcomes, 2) use a variety of source data (empirical or expert opinion), 3) explore robust management strategies under uncertainty, 4) use sensitivity analysis to identify opportunities for developing new management actions, and 5) guide the design of data collection for monitoring to improve management decisions. BBNs contribute considerably to environmental research and management because they are transparent and treat uncertainty explicitly. Because of the high level of uncertainty in stressor response, we developed a BBN to conceptually evaluate the effects of potential management actions on amphibian populations exposed to disease, environmental contaminants, and increasingly frequent and severe droughts

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