References
Prentice, I. C. & Cowling, S. A. in Encyclopedia of Biodiversity 2nd edn (Ed. Levin, S. A.) 670–689 (Academic Press, 2013).
Fisher, J. B., Huntzinger, D. N., Schwalm, C. R. & Sitch, S. Modeling the terrestrial biosphere. Annu. Rev. Env. Resour. 39, 91–123 (2014).
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
Whitley, R. et al. Challenges and opportunities in land surface modelling of savanna ecosystems. Biogeosciences 14, 4711–4732 (2017).
Pugh, T. A. M. et al. A large committed long-term sink of carbon due to vegetation dynamics. Earths Future 6, 1413–1432 (2018).
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
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
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).
Xia, J., Yuan, W., Wang, Y.-P. & Zhang, Q. Adaptive carbon allocation by plants enhances the terrestrial carbon sink. Sci. Rep. 7, 3341 (2017).
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).
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).
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
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).
Sakschewski, B. et al. Resilience of Amazon forests emerges from plant trait diversity. Nat. Clim. Change 6, 1032–1036 (2016).
Gaillard, C. et al. African shrub distribution emerges via a trade-off between height and sapwood conductivity. J. Biogeogr. 45, 2815–2826 (2018).
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).
Thornley, J. H. M. Modelling shoot:root relations: the only way forward? Ann. Bot. 81, 165–171 (1998).
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
Bloom, A. J. Plant economics. Trends Ecol. Evol. 1, 98–100 (1986).
CAS PubMed Google Scholar
Franklin, O. Optimal nitrogen allocation controls tree responses to elevated CO2. New Phytol. 174, 811–822 (2007).
CAS PubMed Google Scholar
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).
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).
Wang, H. et al. Towards a universal model for carbon dioxide uptake by plants. Nat. Plants 3, 734–741 (2017).
CAS PubMed Google Scholar
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
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).
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).
Kennedy, D. et al. Implementing plant hydraulics in the community land model, version 5. J. Adv. Model. Earth Sy. 11, 485–513 (2019).
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).
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).
Xu, C. et al. Toward a mechanistic modeling of nitrogen limitation on vegetation dynamics. PLoS ONE 7, e37914 (2012).
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).
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).
Medlyn, B. E. et al. Reconciling the optimal and empirical approaches to modelling stomatal conductance. Glob. Change Biol. 17, 2134–2144 (2011).
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
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
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).
Guswa, A. J. Effect of plant uptake strategy on the water−optimal root depth. Water Resour. Res. 46, W09601 (2010).
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).
Franklin, O. et al. Modeling carbon allocation in trees: a search for principles. Tree Physiol. 32, 648–666 (2012).
CAS PubMed Google Scholar
King, D. A. The adaptive significance of tree height. Am. Nat. 135, 809–828 (1990).
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
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
Hikosaka, K. & Anten, N. P. R. An evolutionary game of leaf dynamics and its consequences for canopy structure. Funct. Ecol. 26, 1024–1032 (2012).
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
Farrior, C. E. et al. Resource limitation in a competitive context determines complex plant responses to experimental resource additions. Ecology 94, 2505–2517 (2013).
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).
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
Yang, J., Cao, M. & Swenson, N. G. Why functional traits do not predict tree demographic rates. Trends Ecol. Evol. 33, 326–336 (2018).
Dong, N. et al. Leaf nitrogen from first principles: field evidence for adaptive variation with climate. Biogeosciences 14, 481–495 (2017).
CAS Google Scholar
Meng, T.-T. et al. Responses of leaf traits to climatic gradients: adaptive variation versus compositional shifts. Biogeosciences 12, 5339–5352 (2015).
Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).
Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).
CAS PubMed Google Scholar
Reich, P. B. The world-wide ‘fast-slow’ plant economics spectrum: a traits manifesto. J. Ecol. 102, 275–301 (2014).
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
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).
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).
Marks, C. O. The causes of variation in tree seedling traits: the roles of environmental selection versus chance. Evolution 61, 455–469 (2007).
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).
Laughlin, D. C. & Messier, J. Fitness of multidimensional phenotypes in dynamic adaptive landscapes. Trends Ecol. Evol. 30, 487–496 (2015).
Clark, J. S. Why species tell more about traits than traits about species: predictive analysis. Ecology 97, 1979–1993 (2016).
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
Tilman, D. et al. The influence of functional diversity and composition on ecosystem processes. Science 277, 1300–1302 (1997).
CAS Google Scholar
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).
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).
Lavorel, S. et al. in Terrestrial Ecosystems in a Changing World (eds Canadell, J. G. et al.) 149–164 (Springer, 2007).
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
Scheiter, S., Langan, L. & Higgins, S. I. Next-generation dynamic global vegetation models: learning from community ecology. New Phytol. 198, 957–969 (2013).
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
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).
Hofbauer, J. & Sigmund, K. The Theory of Evolution and Dynamical Systems: Mathematical Aspects of Selection (Cambridge Univ. Press, 1988).
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
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).
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).
Fisher, R. A. et al. Vegetation demographics in Earth System Models: a review of progress and priorities. Glob. Change Biol. 24, 35–54 (2018).
Loreau, M. From Populations to Ecosystems: Theoretical Foundations for a new Ecological Synthesis (MPB-46) (Princeton Univ. Press, 2010).
Adler, P. B., Fajardo, A., Kleinhesselink, A. R. & Kraft, N. J. B. Trait-based tests of coexistence mechanisms. Ecol. Lett. 16, 1294–1306 (2013).
Clark, J. S. et al. Resolving the biodiversity paradox. Ecol. Lett. 10, 647–659 (2007).
Isbell, F. et al. Quantifying effects of biodiversity on ecosystem functioning across times and places. Ecol. Lett. 21, 763–778 (2018).
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).
CAS PubMed Google Scholar
Craven, D. et al. Multiple facets of biodiversity drive the diversity–stability relationship. Nat. Ecol. Evol. 2, 1579–1587 (2018).
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).
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
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).
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).
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
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).
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
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).
Meron, E. Pattern-formation approach to modelling spatially extended ecosystems. Ecol. Model. 234, 70–82 (2012).
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
Meron, E. Pattern formation – a missing link in the study of ecosystem response to environmental changes. Math Biosci. 271, 1–18 (2016).
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
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).
Jaynes, E. T. Probability Theory: the Logic of Science (Cambridge Univ. Press, 2003).
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).
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
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
Caldararu, S., Purves, D. W. & Palmer, P. I. Phenology as a strategy for carbon optimality: a global model. Biogeosciences 11, 763–778 (2014).
Farrior, C. E. Theory predicts plants grow roots to compete with only their closest neighbours. P. Roy. Soc. B-Biol. Sci. 286, 20191129 (2019).
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).
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).
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
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).
