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Organizing principles for vegetation dynamics


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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Fig. 1: Optimality model of CO2 and N availability effects in FACE experiments.
Fig. 2: CO2 uptake parameters predicted by an optimality principle.
Fig. 3: Modelling diverse communities based on evolutionarily stable strategies.
Fig. 4: Spatial self-organization in ecosystems.
Fig. 5: Vegetation distributions predicted by the principle of MaxEnt.
Fig. 6: Framework for the use of organizing principles in vegetation modelling.


  1. 1.

    Prentice, I. C. & Cowling, S. A. in Encyclopedia of Biodiversity 2nd edn (Ed. Levin, S. A.) 670–689 (Academic Press, 2013).

  2. 2.

    Fisher, J. B., Huntzinger, D. N., Schwalm, C. R. & Sitch, S. Modeling the terrestrial biosphere. Annu. Rev. Env. Resour. 39, 91–123 (2014).

    Google Scholar 

  3. 3.

    Prentice, I. C., Liang, X., Medlyn, B. E. & Wang, Y. P. Reliable, robust and realistic: the three R’s of next-generation land-surface modelling. Atmos. Chem. Phys. 15, 5987–6005 (2015).

    CAS  Google Scholar 

  4. 4.

    Whitley, R. et al. Challenges and opportunities in land surface modelling of savanna ecosystems. Biogeosciences 14, 4711–4732 (2017).

    Google Scholar 

  5. 5.

    Pugh, T. A. M. et al. A large committed long-term sink of carbon due to vegetation dynamics. Earths Future 6, 1413–1432 (2018).

    Google Scholar 

  6. 6.

    Huang, Y., Gerber, S., Huang, T. & Lichstein, J. W. Evaluating the drought response of CMIP5 models using global gross primary productivity, leaf area, precipitation, and soil moisture data. Global Biogeochem. Cy. 30, 1827–1846 (2016).

    CAS  Google Scholar 

  7. 7.

    Walker, A. P. et al. Predicting long-term carbon sequestration in response to CO2 enrichment: how and why do current ecosystem models differ? Global Biogeochem. Cy. 29, 476–495 (2015).

    CAS  Google Scholar 

  8. 8.

    Thurner, M. et al. Evaluation of climate‐related carbon turnover processes in global vegetation models for boreal and temperate forests. Glob. Change Biol. 23, 3076–3091 (2017).

    Google Scholar 

  9. 9.

    Xia, J., Yuan, W., Wang, Y.-P. & Zhang, Q. Adaptive carbon allocation by plants enhances the terrestrial carbon sink. Sci. Rep. 7, 3341 (2017).

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Montané, F. et al. Evaluating the effect of alternative carbon allocation schemes in a land surface model (CLM4.5) on carbon fluxes, pools, and turnover in temperate forests. Geosci. Model Dev. 10, 3499–3517 (2017).

    Google Scholar 

  11. 11.

    Zaehle, S. et al. Evaluation of 11 terrestrial carbon–nitrogen cycle models against observations from two temperate Free-Air CO2 Enrichment studies. New Phytol. 202, 803–822 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Sulman, B. N. et al. Diverse mycorrhizal associations enhance terrestrial C storage in a global model. Global Biogeochem. Cy. 33, 501–523 (2019).

    CAS  Google Scholar 

  13. 13.

    Fyllas, N. et al. Analysing Amazonian forest productivity using a new individual and trait-based model (TFS v. 1). Geosci. Model Dev. 7, 1251–1269 (2014).

  14. 14.

    Sakschewski, B. et al. Resilience of Amazon forests emerges from plant trait diversity. Nat. Clim. Change 6, 1032–1036 (2016).

    Google Scholar 

  15. 15.

    Gaillard, C. et al. African shrub distribution emerges via a trade-off between height and sapwood conductivity. J. Biogeogr. 45, 2815–2826 (2018).

    Google Scholar 

  16. 16.

    Langan, L., Higgins, S. I. & Scheiter, S. Climate-biomes, pedo-biomes or pyro-biomes: which world view explains the tropical forest–savanna boundary in South America? J. Biogeogr. 44, 2319–2330 (2017).

    Google Scholar 

  17. 17.

    Thornley, J. H. M. Modelling shoot:root relations: the only way forward? Ann. Bot. 81, 165–171 (1998).

    Google Scholar 

  18. 18.

    Chen, J. L. & Reynolds, J. F. A coordination model of whole-plant carbon allocation in relation to water stress. Ann. Bot. 80, 45–55 (1997).

    CAS  Google Scholar 

  19. 19.

    Bloom, A. J. Plant economics. Trends Ecol. Evol. 1, 98–100 (1986).

    CAS  PubMed  Google Scholar 

  20. 20.

    Franklin, O. Optimal nitrogen allocation controls tree responses to elevated CO2. New Phytol. 174, 811–822 (2007).

    CAS  PubMed  Google Scholar 

  21. 21.

    Franklin, O. et al. Forest fine-root production and nitrogen use under elevated CO2: contrasting responses in evergreen and deciduous trees explained by a common principle. Glob. Change Biol. 15, 132–144 (2009).

    Google Scholar 

  22. 22.

    Schymanski, S. J., Roderick, M. L. & Sivapalan, M. Using an optimality model to understand medium and long-term responses of vegetation water use to elevated atmospheric CO2 concentrations. AoB PLANTS 7, plv060 (2015).

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Wang, H. et al. Towards a universal model for carbon dioxide uptake by plants. Nat. Plants 3, 734–741 (2017).

    CAS  PubMed  Google Scholar 

  24. 24.

    Bloomfield, K. J. et al. The validity of optimal leaf traits modelled on environmental conditions. New Phytol. 221, 1409–1423 (2019).

    CAS  PubMed  Google Scholar 

  25. 25.

    Xu, X., Medvigy, D., Powers, J. S., Becknell, J. M. & Guan, K. Diversity in plant hydraulic traits explains seasonal and inter-annual variations of vegetation dynamics in seasonally dry tropical forests. New Phytol. 212, 80–95 (2016).

    PubMed  Google Scholar 

  26. 26.

    Eller, C. B. et al. Modelling tropical forest responses to drought and El Niño with a stomatal optimization model based on xylem hydraulics. Philos. T. R. Soc. Lon. B 373, 20170315 (2018).

    Google Scholar 

  27. 27.

    Kennedy, D. et al. Implementing plant hydraulics in the community land model, version 5. J. Adv. Model. Earth Sy. 11, 485–513 (2019).

    Google Scholar 

  28. 28.

    De Kauwe, M. G. et al. A test of an optimal stomatal conductance scheme within the CABLE land surface model. Geosci. Model Dev. 8, 431–452 (2015).

    Google Scholar 

  29. 29.

    Franks, P. J. et al. Comparing optimal and empirical stomatal conductance models for application in Earth system models. Glob. Change Biol. 24, 5708–5723 (2018).

    Google Scholar 

  30. 30.

    Xu, C. et al. Toward a mechanistic modeling of nitrogen limitation on vegetation dynamics. PLoS ONE 7, e37914 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Weng, E. et al. Scaling from individual trees to forests in an Earth system modeling framework using a mathematically tractable model of height-structured competition. Biogeosciences 12, 2655–2694 (2015).

    Google Scholar 

  32. 32.

    Fisher, R. A. et al. Taking off the training wheels: the properties of a dynamic vegetation model without climate envelopes, CLM4.5(ED). Geosci. Model Dev. 8, 3593–3619 (2015).

    Google Scholar 

  33. 33.

    Medlyn, B. E. et al. Reconciling the optimal and empirical approaches to modelling stomatal conductance. Glob. Change Biol. 17, 2134–2144 (2011).

    Google Scholar 

  34. 34.

    Manzoni, S., Vico, G., Palmroth, S., Porporato, A. & Katul, G. Optimization of stomatal conductance for maximum carbon gain under dynamic soil moisture. Adv. Water Resour. 62, 90–105 (2013).

    CAS  Google Scholar 

  35. 35.

    Dewar, R. et al. New insights into the covariation of stomatal, mesophyll and hydraulic conductances from optimization models incorporating nonstomatal limitations to photosynthesis. New Phytol. 217, 571–585 (2018).

    CAS  PubMed  Google Scholar 

  36. 36.

    Schymanski, S. J., Sivapalan, M., Roderick, M., Hutley, L. B. & Beringer, J. An optimality‐based model of the dynamic feedbacks between natural vegetation and the water balance. Water Resour. Res. 45, W01412 (2009).

    Google Scholar 

  37. 37.

    Guswa, A. J. Effect of plant uptake strategy on the water−optimal root depth. Water Resour. Res. 46, W09601 (2010).

    Google Scholar 

  38. 38.

    Yang, Y., Donohue, R. J. & McVicar, T. R. Global estimation of effective plant rooting depth: implications for hydrological modeling. Water Resour. Res. 52, 8260–8276 (2016).

    Google Scholar 

  39. 39.

    Franklin, O. et al. Modeling carbon allocation in trees: a search for principles. Tree Physiol. 32, 648–666 (2012).

    CAS  PubMed  Google Scholar 

  40. 40.

    King, D. A. The adaptive significance of tree height. Am. Nat. 135, 809–828 (1990).

    Google Scholar 

  41. 41.

    Farrior, C. E., Rodriguez-Iturbe, I., Dybzinski, R., Levin, S. A. & Pacala, S. W. Decreased water limitation under elevated CO2 amplifies potential for forest carbon sinks. Proc. Natl Acad. Sci. USA 112, 7213–7218 (2015).

    CAS  PubMed  Google Scholar 

  42. 42.

    Franklin, O., Palmroth, S. & Näsholm, T. How eco-evolutionary principles can guide tree breeding and tree biotechnology for enhanced productivity. Tree Physiol. 34, 1149–1166 (2014).

    CAS  PubMed  Google Scholar 

  43. 43.

    Hikosaka, K. & Anten, N. P. R. An evolutionary game of leaf dynamics and its consequences for canopy structure. Funct. Ecol. 26, 1024–1032 (2012).

    Google Scholar 

  44. 44.

    Valentine, H. T. & Mäkelä, A. Modeling forest stand dynamics from optimal balances of carbon and nitrogen. New Phytol. 194, 961–971 (2012).

    CAS  PubMed  Google Scholar 

  45. 45.

    Farrior, C. E. et al. Resource limitation in a competitive context determines complex plant responses to experimental resource additions. Ecology 94, 2505–2517 (2013).

    PubMed  Google Scholar 

  46. 46.

    Franklin, O., Näsholm, T., Högberg, P. & Högberg, M. N. Forests trapped in nitrogen limitation – an ecological market perspective on ectomycorrhizal symbiosis. New Phytol. 203, 657–666 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Wolf, A., Anderegg, W. R. L. & Pacala, S. W. Optimal stomatal behavior with competition for water and risk of hydraulic impairment. Proc. Natl Acad. Sci. USA 113, E7222–E7230 (2016).

    CAS  PubMed  Google Scholar 

  48. 48.

    Yang, J., Cao, M. & Swenson, N. G. Why functional traits do not predict tree demographic rates. Trends Ecol. Evol. 33, 326–336 (2018).

    PubMed  Google Scholar 

  49. 49.

    Dong, N. et al. Leaf nitrogen from first principles: field evidence for adaptive variation with climate. Biogeosciences 14, 481–495 (2017).

    CAS  Google Scholar 

  50. 50.

    Meng, T.-T. et al. Responses of leaf traits to climatic gradients: adaptive variation versus compositional shifts. Biogeosciences 12, 5339–5352 (2015).

    Google Scholar 

  51. 51.

    Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).

    PubMed  Google Scholar 

  52. 52.

    Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).

    CAS  PubMed  Google Scholar 

  53. 53.

    Reich, P. B. The world-wide ‘fast-slow’ plant economics spectrum: a traits manifesto. J. Ecol. 102, 275–301 (2014).

    Google Scholar 

  54. 54.

    McMurtrie, R. E. & Dewar, R. C. Leaf-trait variation explained by the hypothesis that plants maximize their canopy carbon export over the lifespan of leaves. Tree Physiol. 31, 1007–1023 (2011).

    CAS  PubMed  Google Scholar 

  55. 55.

    Maire, V. et al. Disentangling coordination among functional traits using an individual-centred model: impact on plant performance at intra- and inter-specific levels. PLoS ONE 8, e77372 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    McNickle, G. G., Gonzalez-Meler, M. A., Lynch, D. J., Baltzer, J. L. & Brown, J. S. The world’s biomes and primary production as a triple tragedy of the commons foraging game played among plants. P. Roy. Soc. Lond. B-Biol. Sci. 283, 20161993 (2016).

    Google Scholar 

  57. 57.

    Marks, C. O. The causes of variation in tree seedling traits: the roles of environmental selection versus chance. Evolution 61, 455–469 (2007).

    PubMed  Google Scholar 

  58. 58.

    van Bodegom, P. M., Douma, J. C. & Verheijen, L. M. A fully traits-based approach to modeling global vegetation distribution. Proc. Natl Acad. Sci. USA 111, 13733–13738 (2014).

    PubMed  Google Scholar 

  59. 59.

    Laughlin, D. C. & Messier, J. Fitness of multidimensional phenotypes in dynamic adaptive landscapes. Trends Ecol. Evol. 30, 487–496 (2015).

    PubMed  Google Scholar 

  60. 60.

    Clark, J. S. Why species tell more about traits than traits about species: predictive analysis. Ecology 97, 1979–1993 (2016).

    PubMed  Google Scholar 

  61. 61.

    Achat, D. L., Augusto, L., Gallet-Budynek, A. & Loustau, D. Future challenges in coupled C-N-P cycle models for terrestrial ecosystems under global change: a review. Biogeochemistry 131, 173–202 (2016).

    CAS  Google Scholar 

  62. 62.

    Tilman, D. et al. The influence of functional diversity and composition on ecosystem processes. Science 277, 1300–1302 (1997).

    CAS  Google Scholar 

  63. 63.

    de Almeida Castanho, A. D. et al. Changing Amazon biomass and the role of atmospheric CO2 concentration, climate, and land use. Global Biogeochem. Cy. 30, 18–39 (2016).

    Google Scholar 

  64. 64.

    Kleidon, A., Fraedrich, K. & Low, C. Multiple steady-states in the terrestrial atmosphere-biosphere system: a result of a discrete vegetation classification? Biogeosciences 4, 707–714 (2007).

    Google Scholar 

  65. 65.

    Lavorel, S. et al. in Terrestrial Ecosystems in a Changing World (eds Canadell, J. G. et al.) 149–164 (Springer, 2007).

  66. 66.

    Follows, M. J., Dutkiewicz, S., Grant, S. & Chisholm, S. W. Emergent biogeography of microbial communities in a model ocean. Science 315, 1843–1846 (2007).

    CAS  PubMed  Google Scholar 

  67. 67.

    Scheiter, S., Langan, L. & Higgins, S. I. Next-generation dynamic global vegetation models: learning from community ecology. New Phytol. 198, 957–969 (2013).

    PubMed  Google Scholar 

  68. 68.

    Falster, D. S., Brännström, Å., Westoby, M. & Dieckmann, U. Multitrait successional forest dynamics enable diverse competitive coexistence. Proc. Natl Acad. Sci. USA 114, E2719–E2728 (2017).

    CAS  PubMed  Google Scholar 

  69. 69.

    Pavlick, R., Drewry, D. T., Bohn, K., Reu, B. & Kleidon, A. The jena diversity-dynamic global vegetation model (JeDi-DGVM): a diverse approach to representing terrestrial biogeography and biogeochemistry based on plant functional trade-offs. Biogeosciences 10, 4137–4177 (2013).

    Google Scholar 

  70. 70.

    Hofbauer, J. & Sigmund, K. The Theory of Evolution and Dynamical Systems: Mathematical Aspects of Selection (Cambridge Univ. Press, 1988).

  71. 71.

    Franks, S. J., Sim, S. & Weis, A. E. Rapid evolution of flowering time by an annual plant in response to a climate fluctuation. Proc. Natl Acad. Sci. USA 104, 1278–1282 (2007).

    CAS  PubMed  Google Scholar 

  72. 72.

    Jump, A. S. & Peñuelas, J. Running to stand still: adaptation and the response of plants to rapid climate change. Ecol. Lett. 8, 1010–1020 (2005).

    Google Scholar 

  73. 73.

    Medvigy, D., Wofsy, S. C., Munger, J. W., Hollinger, D. Y. & Moorcroft, P. R. Mechanistic scaling of ecosystem function and dynamics in space and time: Ecosystem Demography model version 2. J. Geophys. Res. Biogeosci. 114, G01002 (2009).

    Google Scholar 

  74. 74.

    Fisher, R. A. et al. Vegetation demographics in Earth System Models: a review of progress and priorities. Glob. Change Biol. 24, 35–54 (2018).

    Google Scholar 

  75. 75.

    Loreau, M. From Populations to Ecosystems: Theoretical Foundations for a new Ecological Synthesis (MPB-46) (Princeton Univ. Press, 2010).

  76. 76.

    Adler, P. B., Fajardo, A., Kleinhesselink, A. R. & Kraft, N. J. B. Trait-based tests of coexistence mechanisms. Ecol. Lett. 16, 1294–1306 (2013).

    PubMed  Google Scholar 

  77. 77.

    Clark, J. S. et al. Resolving the biodiversity paradox. Ecol. Lett. 10, 647–659 (2007).

    PubMed  Google Scholar 

  78. 78.

    Isbell, F. et al. Quantifying effects of biodiversity on ecosystem functioning across times and places. Ecol. Lett. 21, 763–778 (2018).

    PubMed  PubMed Central  Google Scholar 

  79. 79.

    Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).

    CAS  PubMed  Google Scholar 

  80. 80.

    Craven, D. et al. Multiple facets of biodiversity drive the diversity–stability relationship. Nat. Ecol. Evol. 2, 1579–1587 (2018).

    PubMed  Google Scholar 

  81. 81.

    García-Palacios, P., Gross, N., Gaitán, J. & Maestre, F. T. Climate mediates the biodiversity–ecosystem stability relationship globally. Proc. Natl Acad. Sci. USA 115, 8400–8405 (2018).

    PubMed  Google Scholar 

  82. 82.

    Weiner, J., Stoll, P., Muller-Landau, H. & Jasentuliyana, A. The effects of density, spatial pattern, and competitive symmetry on size variation in simulated plant populations. Am. Nat. 158, 438–450 (2001).

    CAS  PubMed  Google Scholar 

  83. 83.

    Moorcroft, P. R., Hurtt, G. C. & Pacala, S. W. A method for scaling vegetation dynamics: the ecosystem demography model (ED). Ecol. Monogr. 71, 557–586 (2001).

    Google Scholar 

  84. 84.

    Strigul, N., Pristinski, D., Purves, D., Dushoff, J. & Pacala, S. Scaling from trees to forests: tractable macroscopic equations for forest dynamics. Ecol. Monogr. 78, 523–545 (2008).

    Google Scholar 

  85. 85.

    Purves, D. W., Lichstein, J. W., Strigul, N. & Pacala, S. W. Predicting and understanding forest dynamics using a simple tractable model. Proc. Natl Acad. Sci. USA 105, 17018–17022 (2008).

    CAS  PubMed  Google Scholar 

  86. 86.

    Dybzinski, R., Farrior, C., Wolf, A., Reich, P. B. & Pacala, S. W. Evolutionarily stable strategy carbon allocation to foliage, wood, and fine roots in trees competing for light and nitrogen: an analytically tractable, individual-based model and quantitative comparisons to data. Am. Nat. 177, 153–166 (2011).

    PubMed  Google Scholar 

  87. 87.

    Farrior, C., Bohlman, S., Hubbell, S. & Pacala, S. W. Dominance of the suppressed: power-law size structure in tropical forests. Science 351, 155–157 (2016).

    CAS  PubMed  Google Scholar 

  88. 88.

    Favier, C., Chave, J., Fabing, A., Schwartz, D. & Dubois, M. A. Modelling forest–savanna mosaic dynamics in man-influenced environments: effects of fire, climate and soil heterogeneity. Ecol. Model. 171, 85–102 (2004).

    Google Scholar 

  89. 89.

    Meron, E. Pattern-formation approach to modelling spatially extended ecosystems. Ecol. Model. 234, 70–82 (2012).

    Google Scholar 

  90. 90.

    Rietkerk, M., Dekker, S. C., de Ruiter, P. C. & van de Koppel, J. Self-organized patchiness and catastrophic shifts in ecosystems. Science 305, 1926–1929 (2004).

    CAS  PubMed  Google Scholar 

  91. 91.

    Meron, E. Pattern formation – a missing link in the study of ecosystem response to environmental changes. Math Biosci. 271, 1–18 (2016).

    PubMed  Google Scholar 

  92. 92.

    Gilad, E., von Hardenberg, J., Provenzale, A., Shachak, M. & Meron, E. A mathematical model of plants as ecosystem engineers. J. Theor. Biol. 244, 680–691 (2007).

    CAS  PubMed  Google Scholar 

  93. 93.

    Glenn, E., Huete, A., Nagler, P. G. & Nelson, S. Relationship between remotely-sensed vegetation indices, canopy attributes and plant physiological processes: what vegetation indices can and cannot tell us about the landscape. Sensors 8, 2136–2160 (2008).

    PubMed  Google Scholar 

  94. 94.

    Jaynes, E. T. Probability Theory: the Logic of Science (Cambridge Univ. Press, 2003).

  95. 95.

    Bertram, J. & Dewar, R. C. Statistical patterns in tropical tree cover explained by the different water demand of individual trees and grasses. Ecology 94, 2138–2144 (2013).

    PubMed  Google Scholar 

  96. 96.

    Niinemets, U., Keenan, T. F. & Hallik, L. A worldwide analysis of within-canopy variations in leaf structural, chemical and physiological traits across plant functional types. New Phytol. 205, 973–993 (2015).

    CAS  PubMed  Google Scholar 

  97. 97.

    Scheepens, J. F., Frei, E. S. & Stöcklin, J. Genotypic and environmental variation in specific leaf area in a widespread Alpine plant after transplantation to different altitudes. Oecologia 164, 141–150 (2010).

    CAS  PubMed  Google Scholar 

  98. 98.

    Caldararu, S., Purves, D. W. & Palmer, P. I. Phenology as a strategy for carbon optimality: a global model. Biogeosciences 11, 763–778 (2014).

    Google Scholar 

  99. 99.

    Farrior, C. E. Theory predicts plants grow roots to compete with only their closest neighbours. P. Roy. Soc. B-Biol. Sci. 286, 20191129 (2019).

    Google Scholar 

  100. 100.

    Chevin, L.-M., Lande, R. & Mace, G. M. Adaptation, plasticity, and extinction in a changing environment: towards a predictive theory. PLoS Biol. 8, e1000357 (2010).

    PubMed  PubMed Central  Google Scholar 

  101. 101.

    Kichenin, E., Wardle, D. A., Peltzer, D. A., Morse, C. W. & Freschet, G. T. Contrasting effects of plant inter- and intraspecific variation on community-level trait measures along an environmental gradient. Funct. Ecol. 27, 1254–1261 (2013).

    Google Scholar 

  102. 102.

    Shipley, B., Vile, D. & Garnier, É. From plant traits to plant communities: a statistical mechanistic approach to biodiversity. Science 314, 812–814 (2006).

    CAS  PubMed  Google Scholar 

  103. 103.

    Getzin, S., Wiegand, K. & Schöning, I. Assessing biodiversity in forests using very high-resolution images and unmanned aerial vehicles. Methods Ecol. Evol. 3, 397–404 (2012).

    Google Scholar 

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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).

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O.F., S.P.H., Å.B., U.D., S.P., H.W., W.C., E.R. and I.C.P. contributed to the drafting of the paper; O.F. led the writing process; and R.D., C.E.F., D.F., M.L., H.W., I.C.P., K.T.R., Å.B., E.M. and O.F. contributed display items or specific sections. O.F., S.P.H., R.D., C.E.F., Å.B., U.D., S.P., D.F., W.C., M.L., H.W., A.M., K.T.R., E.M., S.J.S., E.R., B.D.S., S.Z., S.M., M.v.O., I.J.W., P.C., P.M.v.B., J.P., F.H., C.T., N.A.S., G.M. and I.C.P. contributed to the final version of the paper.

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Correspondence to Oskar Franklin.

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Franklin, O., Harrison, S.P., Dewar, R. et al. Organizing principles for vegetation dynamics. Nat. Plants 6, 444–453 (2020).

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