The decision on how to manage a forest under climate change is subject to deep and dynamic uncertainties. The classic approach to analyze this decision adopts a predefined strategy, tests its robustness to uncertainties, but neglects their dynamic nature (i.e., that decision-makers can learn and adjust the strategy). Accounting for learning through dynamic adaptive strategies (DAS) can drastically improve expected performance and robustness to deep uncertainties. The benefits of considering DAS hinge on identifying critical uncertainties and translating them to detectable signposts to signal when to change course. This study advances the DAS approach to forest management as a novel application domain by showcasing methods to identify potential signposts for adaptation on a case study of a classic European beech management strategy in South-West Germany. We analyze the strategy’s robustness to uncertainties about model forcings and parameters. We then identify uncertainties that critically impact its economic and ecological performance by confronting a forest growth model with a large sample of time-varying scenarios. The case study results illustrate the potential of designing DAS for forest management and provide insights on key uncertainties and potential signposts. Specifically, economic uncertainties are the main driver of the strategy’s robustness and impact the strategy’s performance more critically than climate uncertainty. Besides economic metrics, the forest stand’s past volume growth is a promising signpost metric. It mirrors the effect of both climatic and model parameter uncertainty. The regular forest inventory and planning cycle provides an ideal basis for adapting a strategy in response to these signposts.

中文翻译：

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识别气候变化下动态适应性森林管理的决策相关不确定性

如何在气候变化下管理森林的决定受制于深度和动态的不确定性。分析该决策的经典方法采用预先定义的策略，测试其对不确定性的稳健性，但忽略了它们的动态性（即决策者可以学习和调整策略）。通过动态自适应策略 (DAS) 进行学习可以显着提高预期性能和对深度不确定性的鲁棒性。考虑 DAS 的好处取决于识别关键的不确定性并将它们转换为可检测的路标，以指示何时改变路线。本研究通过展示在德国西南部经典欧洲山毛榉管理策略的案例研究中识别潜在适应路标的方法，将 DAS 森林管理方法推进为一个新的应用领域。我们分析了该策略对模型强迫和参数不确定性的稳健性。然后，我们通过面对具有大量时变情景样本的森林生长模型，确定对其经济和生态表现产生重大影响的不确定性。案例研究结果说明了为森林管理设计 DAS 的潜力，并提供有关关键不确定性和潜在路标的见解。具体而言，经济不确定性是战略稳健性的主要驱动力，比气候不确定性更重要地影响战略绩效。除了经济指标，林分过去的数量增长是一个有希望的路标指标。它反映了气候和模型参数不确定性的影响。定期的森林清查和规划周期为根据这些标志调整战略提供了理想的基础。