International Institute for Applied Systems Analysis, Laxenburg, Austria.
Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden.
Nat Plants. 2020 May;6(5):444-453. doi: 10.1038/s41477-020-0655-x. Epub 2020 May 11.
Plants and vegetation play a critical-but largely unpredictable-role in global environmental changes due to the multitude of contributing processes at widely different spatial and temporal scales. In this Perspective, we explore approaches to master this complexity and improve our ability to predict vegetation dynamics by explicitly taking account of principles that constrain plant and ecosystem behaviour: natural selection, self-organization and entropy maximization. These ideas are increasingly being used in vegetation models, but we argue that their full potential has yet to be realized. We demonstrate the power of natural selection-based optimality principles to predict photosynthetic and carbon allocation responses to multiple environmental drivers, as well as how individual plasticity leads to the predictable self-organization of forest canopies. We show how models of natural selection acting on a few key traits can generate realistic plant communities and how entropy maximization can identify the most probable outcomes of community dynamics in space- and time-varying environments. Finally, we present a roadmap indicating how these principles could be combined in a new generation of models with stronger theoretical foundations and an improved capacity to predict complex vegetation responses to environmental change.
由于在广泛不同的空间和时间尺度上存在众多的贡献过程，植物和植被在全球环境变化中起着至关重要但在很大程度上不可预测的作用。在本观点中，我们探讨了掌握这种复杂性并通过明确考虑约束植物和生态系统行为的原则来提高我们预测植被动态的能力的方法：自然选择、自组织和熵最大化。这些思想越来越多地被用于植被模型中，但我们认为它们的全部潜力尚未得到实现。我们展示了基于自然选择的最优性原则在预测光合作用和碳分配对多种环境驱动因素的响应方面的强大功能，以及个体可塑性如何导致森林冠层的可预测自组织。我们展示了如何通过对少数关键特征进行自然选择的模型生成现实的植物群落，以及熵最大化如何识别在空间和时变环境中群落动态的最可能结果。最后，我们提出了一个路线图，指出如何将这些原则结合到新一代具有更强理论基础和提高预测复杂植被对环境变化响应能力的模型中。
