Bayesian decision theory for wildlife management under uncertainty: from inference to action

TL;DR

This paper demonstrates how Bayesian decision theory can be practically applied in ecological management to explicitly account for uncertainty and optimize decisions, illustrated through case studies on wolf and muskrat management.

Contribution

It provides a practical workflow for implementing Bayesian decision theory in ecology, bridging the gap between statistical inference and decision-making under uncertainty.

Findings

Optimal decisions balance ecological, economic, and social objectives.

Expected utility can be computed from posterior simulations explicitly accounting for uncertainty.

Bayesian decision theory enhances transparency and reproducibility in ecological management.

Abstract

Ecologists are increasingly expected to inform management decisions under uncertainty, yet most analytical workflows stop at statistical inference. This disconnect limits the practical impact of ecological modelling, particularly in high-stakes contexts such as wildlife management, where decisions must balance ecological, economic and social objectives. Bayesian decision theory provides a coherent framework to bridge this gap. It propagates uncertainty from posterior distributions to quantify the consequences of alternative actions through utility functions. Despite its strong theoretical foundations, it remains underused in ecology. Here, we present a practical workflow for implementing Bayesian decision theory using standard Bayesian tools. We illustrate the approach with two case studies. First, wolf management in France, where the decision consists of selecting the number of wolves that can be removed under uncertainty about population dynamics. Second, invasive muskrat management in the Netherlands, where the decision involves allocating a fixed control effort across space. In both cases, expected utility is computed from posterior simulations, explicitly accounting for uncertainty and trade-offs. Results show that optimal decisions emerge as a compromise between competing objectives. In the wolf case, optimal harvest balances removal benefits and population risk. In the muskrat case, optimal effort increases with the importance of population reduction and is unevenly allocated across provinces. These examples show that Bayesian decision theory can be implemented as a direct extension of standard inference. By making trade-offs explicit, it enhances transparency, reproducibility, and relevance for management. More broadly, it provides a flexible basis for integrating ecological modelling with decision-making.