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.

翻译：生态学家越来越需要在不确定性下为管理决策提供信息，然而大多数分析流程止步于统计推断。这种脱节限制了生态建模的实际影响，尤其是在野生动物管理等高利害情境中——此类决策必须平衡生态、经济和社会目标。贝叶斯决策理论提供了弥补这一差距的连贯框架。它通过效用函数将不确定性从后验分布传递至替代行动后果的量化过程。尽管该理论基础扎实，但在生态学中尚未得到充分利用。本文提出了一套使用标准贝叶斯工具实现贝叶斯决策理论的实践工作流程，并通过两个案例研究说明该方法。首先，法国狼群管理案例中，决策是在种群动态不确定性条件下选择可移除的狼数量。其次，荷兰入侵麝鼠管理案例中，决策涉及在空间上分配固定的控制投入。两个案例均基于后验模拟计算期望效用，明确纳入不确定性和权衡关系。结果表明最优决策是竞争性目标之间的折中方案：狼群案例中，最优猎杀量平衡了移除收益与种群风险；麝鼠案例中，最优投入随种群削减重要性的增加而提高，且各省份的分配呈现不均衡特征。这些案例表明，贝叶斯决策理论可作为标准推断的直接延伸加以实现。通过明确权衡关系，该方法增强了管理的透明度、可复现性及实用性。更广泛而言，它为生态建模与决策制定的整合提供了灵活基础。