Data from: Adapting environmental management to uncertain but inevitable change

Implementation of adaptation actions to protect biodiversity is limited by uncertainty about the future. One reason for this is the fear of making the wrong decisions caused by the myriad future scenarios presented to decision-makers. We propose an adaptive management (AM) method for optimally managing a population under uncertain and changing habitat conditions. Our approach incorporates multiple future scenarios and continually learns the best management strategy from observations, even as conditions change. We demonstrate the performance of our AM approach by applying it to the spatial management of migratory shorebird habitats on the East Asian–Australasian flyway, predicted to be severely impacted by future sea-level rise. By accounting for non-stationary dynamics, our solution protects 25 000 more birds per year than the current best stationary approach. Our approach can be applied to many ecological systems that require efficient adaptation strategies for an uncertain future.

保护生物多样性的适应行动推进往往受制于未来的不确定性。究其核心缘由之一，是决策者面对纷繁复杂的各类未来情景时，极易因担忧做出错误决策而踌躇不前。为此，我们提出一种自适应管理（Adaptive Management, AM）方法，用于在不确定且动态变化的栖息地条件下对物种种群实施优化管理。该方法整合了多种未来情景，即便栖息地条件发生变化，仍可通过持续观测学习获取最优管理策略。我们将此AM方法应用于东亚-澳大利西亚迁飞区的迁徙鸻鹬类栖息地空间管理场景以验证其性能——该区域被预测将因未来海平面上升遭受严重冲击。通过考量非平稳动态变化，我们的方案每年可比当前最优的静态管理策略多保护25000只鸟类。本方法可推广应用于众多亟需针对不确定未来制定高效适应策略的生态系统。