Bayesian decision theory for wildlife management under uncertainty: from inference to action
Ecologists are increasingly expected to inform management decisions under uncertainty, yet most analytical workflows stop at statistical inference. Bayesian decision theory provides a coherent framework to bridge this gap by propagating posterior uncertainty to evaluate alternative actions through utility functions, but remains underused in ecology. Here, we present a practical workflow for implementing Bayesian decision theory using standard Bayesian tools, illustrated with two case studies: wolf management in France, where the decision is the number of wolves to remove under uncertain population dynamics, and invasive muskrat management in the Netherlands, where control effort is allocated across space. In both cases, expected utility integrates posterior uncertainty and management trade-offs. Optimal decisions emerge as compromises between competing objectives. For wolves, optimal harvest balances removal benefits and population risk. For muskrats, optimal effort increases with the importance of population reduction and is unevenly allocated across provinces. Bayesian decision theory provides a formal interface between scientists, who characterize ecological systems and uncertainty, and decision-makers, who define objectives, values and trade-offs. By making these trade-offs explicit, it enhances transparency and relevance for management. It also provides a common framework for bringing together Bayesian statistics, decision analysis and risk analysis, strengthening the link between ecological inference and action.