Organizing principles for vegetation dynamics
Organizing principles for vegetation dynamics
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DOI：
10.1038/s41477-020-0655-x
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发表时间：
2020-05-11
期刊：
影响因子：
18
通讯作者：
Prentice, I. Colin
中科院分区：
文献类型：
--
作者：
Franklin, Oskar;Harrison, Sandy P.;Prentice, I. Colin
植物和植被在全球环境变化中发挥着至关重要的作用，但在很大程度上是不可预测的，因为在广泛不同的空间和时间尺度上有大量的贡献过程。在这个视角中，我们探索了掌握这种复杂性的方法，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：自然选择、自组织和熵最大化。这些想法越来越多地用于植被模型，但我们认为它们的全部潜力尚未实现。我们展示了基于自然选择的最优性原则的力量，可以预测光合作用和碳分配对多种环境驱动因素的响应，以及个体可塑性如何导致森林冠层可预测的自组织。我们展示了自然选择模型如何作用于几个关键特征，从而产生现实的植物群落，以及熵最大化如何识别时空变化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模型中，这些模型具有更强的理论基础，并提高了预测复杂植被对环境变化响应的能力。将自然选择和其他组织原则纳入下一代植被模型可以使它们在地球系统应用和模拟气候影响方面在理论上更加可靠和有用。
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.Integrating natural selection and other organizing principles into next-generation vegetation models could render them more theoretically sound and useful for earth system applications and modelling climate impacts.