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The conservation and management of wild populations and ecosystems almost always involves making decisions in the face of uncertainty and risk. The application of science to the ecological decision-making process was something that the late Professor Daniel Goodman thought deeply about. In this paper we outline the three main principles that Dr. Goodman espoused for good practice when conducting analyses for ecological decision-making: 1) the results should be conditioned on all relevant data and information, 2) there must be a full characterization of all uncertainty, and it should be fully propagated into the result, and 3) doing so in the correct way will result in the calculation of an accurate probability distribution (conditioned on our understanding of the state of nature) that should be used directly for ecological decision-making. Dr. Goodman believed that in the context of threatened and endangered species management Population Viability Analysis (PVA), Bayesian statistics, and structured decision-making are the most logical tools to achieve the three principles. To illustrate the application of the principles and tools in a real management setting, we discuss a Bayesian PVA that Dr. Goodman produced for the endangered Steller sea lion. We conclude by discussing the practical and philosophical impediments that may limit the full realization of the three principles and we offer some suggested solutions.
This is a preprint submission to PeerJ Preprints. It derived from talks presented by the authors at the Daniel Goodman Memorial Symposium, held in Bozeman, MT on March 20th and 21st, 2014. The symposium was held in honor of the late Dr. Daniel Goodman, professor of biology and ecology at Montana State University, and focused on the theme of "Decision-making under uncertainty: Risk assessment and the best available science" in the context of applied ecology and environmental science.