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.
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We thank the participants at the workshop titled ‘Next-generation vegetation modelling’, held at IIASA in March 2017: the idea for this Perspective arose from the insights and excitement engendered by the community discussion at that meeting. We also thank IIASA, both for their financial support of the workshop and for continued support thereafter. We particularly thank IIASA’s former Director and CEO, P. Kabat, for his unfailing support for the next-generation vegetation modelling initiative. O.F. acknowledges funding provided by the Knut and Alice Wallenberg foundation. S.P.H. acknowledges the support from the European Research Council (ERC)-funded project titled ‘Global Change 2.0: Unlocking the past for a clearer future’ (GC2.0; grant no. 694481). This research is a contribution to the AXA Chair Programme in Biosphere and Climate Impacts and the Imperial College initiative on Grand Challenges in Ecosystems and the Environment (ICP). I.C.P. is supported by the ERC under the European Union’s Horizon 2020 research and innovation programme (REALM; grant no: 787203). We also thank the Labex OTMed (grant no. ANR-11-LABX-0061) funded by the French Government Investissements d’Avenir program of the French National Research Agency (ANR) through the A*MIDEX project (grant no. ANR-11-IDEX-0001-02). S.Z. was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (QUINCY; grant no. 647204). J. Bertram supplied Fig. 5. S.J.S. is supported by the Luxembourg National Research Fund (FNR) ATTRACT programme (A16/SR/11254288). M.L. was supported by the TULIP Laboratory of Excellence (ANR-10-LABX-41). P.C. and J.P. were supported by the ERC under the European Union’s Horizon 2020 research and innovation programme (IMBALANCE-P; grant no. ERC-SyG-2013-610028). P.C. acknowledges support from the CLAND institute of convergence of ANR in France (16-CONV-0003). I.J.W. was supported by the Australian Research Council (DP170103410). B.D.S. was funded by the Swiss National Science Foundation (grant no. PCEFP2_181115). S.M. is supported by the Swedish Research Councils VR (2016-04146) and Formas (2016-00998).
The authors declare that they have no competing interests.
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Franklin, O., Harrison, S.P., Dewar, R. et al. Organizing principles for vegetation dynamics. Nat. Plants 6, 444–453 (2020). https://doi.org/10.1038/s41477-020-0655-x
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