Trees are an important carbon sink as they accumulate biomass through photosynthesis1. Identifying tree species that grow fast is therefore commonly considered to be essential for effective climate change mitigation through forest planting. Although species characteristics are key information for plantation design and forest management, field studies often fail to detect clear relationships between species functional traits and tree growth2. Here, by consolidating four independent datasets and classifying the acquisitive and conservative species based on their functional trait values, we show that acquisitive tree species, which are supposedly fast-growing species, generally grow slowly in field conditions. This discrepancy between the current paradigm and field observations is explained by the interactions with environmental conditions that influence growth. Acquisitive species require moist mild climates and fertile soils, conditions that are generally not met in the field. By contrast, conservative species, which are supposedly slow-growing species, show generally higher realized growth due to their ability to tolerate unfavourable environmental conditions. In general, conservative tree species grow more steadily than acquisitive tree species in non-tropical forests. We recommend planting acquisitive tree species in areas where they can realize their fast-growing potential. In other regions, where environmental stress is higher, conservative tree species have a larger potential to fix carbon in their biomass.

AGACAL Centro de Investigación Forestal de Lourizán Pontevedra Spain

Centre for Forest Research Université du Québec à Montréal Montreal Quebec Canada

CNR IBE Consiglio Nazionale delle Ricerche Istituto per la BioEconomia Sassari Italy

Department of Agricultural Food and Forest Sciences University of Palermo Palermo Italy

Department of Biological Sciences Royal Holloway University of London Egham UK

Department of Earth and Environmental Sciences KU Leuven Leuven Belgium

Department of Forest Protection and Wildlife Management Mendel University in Brno Brno Czech Republic

Department of Geosciences and Natural Resource Management University of Copenhagen Frederiksberg Denmark

Department of Organismic and Evolutionary Biology Harvard University Cambridge MA USA

Department of Plant Production and Forest Resources University of Valladolid Palencia Spain

DROTRH Ponta Delgada Portugal

Earth and Life Institute UCLouvain Université Catholique de Louvain Louvain la Neuve Belgium

Forest and Nature Lab Department of Environment Ghent University Melle Gontrode Belgium

Forest Research Alice Holt Lodge Farnham UK

Forest Research Centre School of Agriculture University of Lisbon Lisbon Portugal

Forest Research Institute Hellenic Agricultural Organization Dimitra Thessaloniki Greece

Forest Research Northern Research Station Roslin UK

GAN NIK Pamplona Spain

Geobotany Faculty of Biology University of Freiburg Freiburg Germany

German Centre for Integrative Biodiversity Research Halle Jena Leipzig Leipzig Germany

Granja Modelo HAZI Arkaute Spain

Helmholtz Centre for Environmental Research UFZ Halle Germany

INRAE BEF Nancy France

INRAE Bordeaux Sciences Agro UMR 1391 ISPA Villenave d'Ornon France

INRAE UEFP Cestas France

INRAE UEVT Antibes Juan les Pins France

INRAE University of Bordeaux BIOGECO Cestas France

Institut Européen de la Forêt Cultivée Cestas France

Institut pour le Développement Forestier Paris France

Institute of Biology Leipzig University Leipzig Germany

Institute of Forest Ecology Department of Ecosystem Management Climate and Biodiversity BOKU University Vienna Austria

Institute of Forest Science CSIC Madrid Spain

Latvia University of Life Sciences and Technologies Jelgava Latvia

Leuven Plant Institute KU Leuven Leuven Belgium

Natural Resources Institute Finland Helsinki Finland

NEIKER Basque Institute for Agricultural Research and Development Department of Forest Sciences Bizkaia Spain

ONF UMR 0588 BioForA Orléans France

Ontario Ministry of Natural Resources and Forestry Sault Ste Marie Ontario Canada

Research Centre AgroFoodNature HOGENT University of Applied Sciences and Arts Ghent Belgium

Smithsonian Environmental Research Center Edgewater MD USA

SRAAC Azores Regional Ministry for Environment and Climate Change Angra do Heroísmo Azores Portugal

Sustainable Forest Management Research Institute University of Valladolid Palencia Spain

Swedish University of Agricultural Sciences Umeå Sweden

Zobrazit více v PubMed

Canadell, J. G. & Schulze, E. D. Global potential of biospheric carbon management for climate mitigation. Nat. Commun. 5, 5282 (2014). PubMed DOI

Paine, C. E. T. et al. Globally, functional traits are weak predictors of juvenile tree growth, and we do not know why. J. Ecol. 103, 978–989 (2015). DOI

IPCC Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Pörtner, H.-O. et al. (eds)) (2022).

Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008). PubMed DOI

Tagesson, T. et al. Recent divergence in the contributions of tropical and boreal forests to the terrestrial carbon sink. Nat. Ecol. Evol. 4, 202–209 (2020). PubMed DOI

Seidl, R., Schelhaas, M.-J., Rammer, W. & Verkerk, P. J. Increasing forest disturbances in Europe and their impact on carbon storage. Nat. Clim. Change 4, 806–810 (2014). DOI

Alkama, R. et al. Vegetation-based climate mitigation in a warmer and greener world. Nat. Commun. 13, 606 (2022). PubMed DOI PMC

Lambers, H. & Poorter, H. Inherent variation in growth rate between higher plants: a search for physiological causes and ecological consequences. Adv. Ecol. Res. 23, 187–261 (1992). DOI

Grime, J. et al. Integrated screening validates primary axes of specialisation in plants. Oikos 79, 259–281 (1997). DOI

Herms, D. A. & Mattson, W. J. The dilemma of plants: to grow or defend. Q. Rev. Biol. 67, 283–335 (1992). DOI

Laughlin, D. C. et al. Intraspecific trait variation can weaken interspecific trait correlations when assessing the whole-plant economic spectrum. Ecol. Evol. 7, 8936–8949 (2017). PubMed DOI PMC

Bongers, F. J. et al. Growth-trait relationships in subtropical forest are stronger at higher diversity. J. Ecol. 108, 256–266 (2020). DOI

Wright, S. J. et al. Functional traits and the growth–mortality trade-off in tropical trees. Ecology 91, 3664–3674 (2010). PubMed DOI

Herault, B. et al. Functional traits shape ontogenetic growth trajectories of rain forest tree species. J. Ecol. 99, 1431–1440 (2011). DOI

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

Gibert, A., Gray, E. F., Westoby, M., Wright, I. J. & Falster, D. S. On the link between functional traits and growth rate: meta-analysis shows effects change with plant size, as predicted. J. Ecol. 104, 1488–1503 (2016). DOI

Weemstra, M., Zambrano, J., Allen, D. & Umaña, M. N. Tree growth increases through opposing above-ground and below-ground resource strategies. J. Ecol. 109, 3502–3512 (2021). DOI

Augusto, L., Achat, D. L., Jonard, M., Vidal, D. & Ringeval, B. Soil parent material - a major driver of plant nutrient limitations in terrestrial ecosystems. Glob. Change Biol. 23, 3808–3824 (2017). DOI

Nemani, R. R. et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300, 1560–1563 (2003). PubMed DOI

Fisher, J. B., Badgley, G. & Blyth, E. Global nutrient limitation in terrestrial vegetation. Glob. Biogeochem. Cycles 26, GB3007 (2012). DOI

Jonard, M. et al. Tree mineral nutrition is deteriorating in Europe. Glob. Change Biol. 21, 418–430 (2015). DOI

Aerts, R. & Chapin, F. S. The mineral nutrition of wild plants revisited: a re-evaluation of processes and patterns. Adv. Ecol. Res. 30, 1–67 (2000).

Chapin, F. S., Autumn, K. & Pugnaire, F. Evolution of suites of traits in response to environmental-stress. Am. Nat. 142, S78–S92 (1993). DOI

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

Song, C. et al. Differential tree demography mediated by water stress and functional traits in a moist tropical forest. Funct. Ecol. 37, 2927–2939 (2023). DOI

Chave, J. et al. Towards a worldwide wood economics spectrum. Ecol. Lett. 12, 351–366 (2009). PubMed DOI

Poorter, H., Lambers, H. & Evans, J. R. Trait correlation networks: a whole-plant perspective on the recently criticized leaf economic spectrum. N. Phytol. 201, 378–382 (2014). DOI

Hunter, I. & Schuck, A. Increasing forest growth in Europe—possible causes and implications for sustainable forest management. Plant Biosyst. 136, 133–141 (2002). DOI

Hoffmann, N., Heinrichs, S., Schall, P. & Vor, T. Climatic factors controlling stem growth of alien tree species at a mesic forest site: a multispecies approach. Eur. J. For. Res. 139, 915–934 (2020). DOI

Van Sundert, K. et al. Towards comparable assessment of the soil nutrient status across scales—review and development of nutrient metrics. Glob. Change Biol. 26, 392–409 (2020). DOI

Makoto, K., Kitagawa, R. & Blume-Werry, G. How do leaf functional traits and age influence the maximum rooting depth of trees? Eur. J. For. Res. 142, 1197–1206 (2023). DOI

Koehler, K., Center, A. & Cavender-Bares, J. Evidence for a freezing tolerance-growth rate trade-off in the live oaks (Quercus series Virentes) across the tropical-temperate divide. N. Phytol. 193, 730–744 (2012). DOI

Rueda, M., Godoy, O. & Hawkins, B. A. Trait syndromes among North American trees are evolutionarily conserved and show adaptive value over broad geographic scales. Ecography 41, 450–550 (2018). DOI

Pierce, S. et al. A global method for calculating plant CSR ecological strategies applied across biomes world-wide. Funct. Ecol. 31, 444–457 (2017). DOI

Mirabel, A. et al. A whole-plant functional scheme predicting the early growth of tropical tree species: evidence from 15 tree species in Central Africa. Trees Struct. Funct. 33, 491–505 (2019). DOI

Baez, S. & Homeier, J. Functional traits determine tree growth and ecosystem productivity of a tropical montane forest: insights from a long-term nutrient manipulation experiment. Glob. Change Biol. 24, 399–409 (2018). DOI

Salgado-Luarte, C. & Gianoli, E. Shade tolerance and herbivory are associated with RGR of tree species via different functional traits. Plant Biol. 19, 413–419 (2017). PubMed DOI

Bauman, D. et al. Tropical tree growth sensitivity to climate is driven by species intrinsic growth rate and leaf traits. Glob. Change Biol. 28, 1414–1432 (2022). DOI

Serra-Maluquer, X. et al. Wood density and hydraulic traits influence species’ growth response to drought across biomes. Glob. Change Biol. 28, 3871–3882 (2022). DOI

Salguero-Gomez, R. et al. Fast-slow continuum and reproductive strategies structure plant life-history variation worldwide. Proc. Natl Acad. Sci. USA 113, 230–235 (2016). PubMed DOI

Francis, E. J. et al. Quantifying the role of wood density in explaining interspecific variation in growth of tropical trees. Glob. Ecol. Biogeogr. 26, 1078–1087 (2017). DOI

Rodríguez-Alarcón, S., González-M, R., Carmona, C. P. & Tordoni, E. Trait-growth relationships in Colombian tropical dry forests: incorporating intraspecific variation and trait interactions. J. Veg. Sci. 35, e13233 (2024). DOI

Huston, M. A. Precipitation, soils, NPP, and biodiversity: resurrection of Albrecht’s curve. Ecol. Monogr. 82, 277–296 (2012). DOI

Townsend, A. R., Cleveland, C. C., Asner, G. P. & Bustamante, M. M. C. Controls over foliar N:P ratios in tropical rain forests. Ecology 88, 107–118 (2007). PubMed DOI

Qin, Y. et al. Interactions between leaf traits and environmental factors help explain the growth of evergreen and deciduous species in a subtropical forest. For. Ecol. Manage. 560, 121854 (2024). DOI

Prado-Junior, J. A. et al. Conservative species drive biomass productivity in tropical dry forests. J. Ecol. 104, 817–827 (2016).

Felipe-Lucia, M. R. et al. Multiple forest attributes underpin the supply of multiple ecosystem services. Nat. Commun. 9, 4839 (2018). PubMed DOI PMC

Warner, E. et al. Young mixed planted forests store more carbon than monocultures—a meta-analysis. Front. For. Glob. Change 6, 1226514 (2023). DOI

Baeten, L. et al. Identifying the tree species compositions that maximize ecosystem functioning in European forests. J. Appl. Ecol. 56, 733–744 (2019). DOI

Yang, H. et al. Global increase in biomass carbon stock dominated by growth of northern young forests over past decade. Nat. Geosci. 16, 886–892 (2023). DOI

Schwinning, S., Lortie, C. J., Esque, T. C. & DeFalco, L. A. What common-garden experiments tell us about climate responses in plants. J. Ecol. 110, 986–996 (2022). DOI

Correia, A. H. et al. Early survival and growth plasticity of 33 species planted in 38 arboreta across the European Atlantic area. Forests 9, 630 (2018). DOI

Manohan, B. et al. Use of functional traits to distinguish successional guilds of tree species for restoring forest ecosystems. Forests 14, 1075 (2023). DOI

Paquette, A. et al. A million and more trees for science. Nat. Ecol. Evol. 2, 763–766 (2018). PubMed DOI

Verheyen, K. et al. Contributions of a global network of tree diversity experiments to sustainable forest plantations. Ambio 45, 29–41 (2016). PubMed DOI

Augusto, L. & Boča, A. Tree functional traits, forest biomass, and tree species diversity interact with site properties to drive forest soil carbon. Nat. Commun. 13, 1097 (2022). PubMed DOI PMC

Falster, D. S., Duursma, R. A. & FitzJohn, R. G. How functional traits influence plant growth and shade tolerance across the life cycle. Proc. Natl Acad. Sci. USA 115, E6789–E6798 (2018). PubMed DOI PMC

Oktavia, D., Park, J. W. & Jin, G. Life stages and habitat types alter the relationships of tree growth with leaf traits and soils in an old-growth temperate forest. Flora 293, 152104 (2022). DOI

Chen, G., Hobbie, S. E., Reich, P. B., Yang, Y. & Robinson, D. Allometry of fine roots in forest ecosystems. Ecol. Lett. 22, 322–331 (2019). PubMed DOI

Enquist, B. J., Brown, J. H. & West, G. B. Allometric scaling of plant energetics and population density. Nature 395, 163–165 (1999). DOI

Mokany, K., Raison, R. J. & Prokushkin, A. S. Critical analysis of root: shoot ratios in terrestrial biomes. Glob. Change Biol. 12, 84–96 (2006). DOI

Ma, H. et al. The global distribution and environmental drivers of aboveground versus belowground plant biomass. Nat. Ecol. Evol. 5, 1110–1122 (2021). PubMed DOI

Niklas, K. J. & Spatz, H.-C. Growth and hydraulic (not mechanical) constraints govern the scaling of tree height and mass. Proc. Natl Acad. Sci. USA 101, 15661–15663 (2004). PubMed DOI PMC

Chiba, Y. Architectural analysis of relationship between biomass and basal area based on pipe model theory. Ecol. Modell. 108, 219–225 (1998). DOI

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

Kunstler, G. et al. Plant functional traits have globally consistent effects on competition. Nature 529, 204–209 (2016). PubMed DOI

Wright, I. J. et al. Assessing the generality of global leaf trait relationships. N. Phytol. 166, 485–496 (2005). DOI

Gomarasca, U. et al. Leaf-level coordination principles propagate to the ecosystem scale. Nat. Commun. 14, 3948 (2023). PubMed DOI PMC

Poorter, H., Remkes, C. & Lambers, H. Carbon and nitrogen economy of 24 wild species differing in relative growth rate. Plant Physiol. 94, 621–627 (1990). PubMed DOI PMC

Reich, P. B., Tjoelker, M., Walters, M., Vanderklein, D. & Buschena, C. Close association of RGR, leaf and root morphology, seed mass and shade tolerance in seedlings of nine boreal tree species grown in high and low light. Funct. Ecol. 12, 327–338 (1998). DOI

Doraisami, M. et al. A global database of woody tissue carbon concentrations. Sci. Data 9, 284 (2022). DOI PMC

Garnier, E., Shipley, B., Roumet, C. & Laurent, G. A standardized protocol for the determination of specific leaf area and leaf dry matter content. Funct. Ecol. 15, 688–695 (2001). DOI

Perez-Harguindeguy, N. et al. New handbook for standardised measurement of plant functional traits worldwide. Aust. J. Bot. 61, 167–234 (2013). DOI

Bruelheide, H. et al. Global trait-environment relationships of plant communities. Nat. Ecol. Evol. 2, 1906–1917 (2018). PubMed DOI

Caminha-Paiva, D., Negreiros, D., Barbosa, M. & Fernandes, G. W. Functional trait coordination in the ancient and nutrient-impoverished campo rupestre: soil properties drive stem, leaf and architectural traits. Biol. J. Linn. Soc. 133, 531–545 (2021). DOI

Eviner, V. T. & Chapin, F. S. III Functional matrix: a conceptual framework for predicting multiple plant effects on ecosystem processes. Ann. Rev. Ecol. Evol. Syst. 34, 455–485 (2003). DOI

Flores-Moreno, H. et al. Robustness of trait connections across environmental gradients and growth forms. Glob. Ecol. Biogeogr. 28, 1806–1826 (2019). DOI

Osnas, J. L. D., Lichstein, J. W., Reich, P. B. & Pacala, S. W. Global leaf trait relationships: mass, area, and the leaf economics spectrum. Science 340, 741–744 (2013). PubMed DOI

Reich, P. B. et al. The evolution of plant functional variation: traits, spectra, and strategies. Int. J. Plant Sci. 164, S143–S164 (2003). DOI

de la Riva, E. G. et al. Root traits across environmental gradients in Mediterranean woody communities: are they aligned along the root economics spectrum? Plant Soil 424, 35–48 (2018). DOI

Vet, R. et al. A global assessment of precipitation chemistry and deposition of sulfur, nitrogen, sea salt, base cations, organic acids, acidity and pH, and phosphorus. Atmos. Environ. 93, 3–100 (2014). DOI

Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017). PubMed DOI PMC

Shangguan, W., Dai, Y., Duan, Q., Liu, B. & Yuan, H. A global soil data set for earth system modeling. J. Adv. Model. Earth Syst. 6, 249–263 (2014). DOI

Lu, J. et al. Remarkable effects of microbial factors on soil phosphorus bioavailability: a country-scale study. Glob. Change Biol. 28, 4459–4471 (2022). DOI

Toloşi, L. & Lengauer, T. Classification with correlated features: unreliability of feature ranking and solutions. Bioinformatics 27, 1986–1994 (2011). PubMed DOI

Brienen, R. J. et al. Forest carbon sink neutralized by pervasive growth-lifespan trade-offs. Nat. Commun. 11, 4241 (2020). PubMed DOI PMC

Charru, M., Seynave, I., Hervé, J. C., Bertrand, R. & Bontemps, J. D. Recent growth changes in Western European forests are driven by climate warming and structured across tree species climatic habitats. Ann. For. Sci. 74, 33 (2017). DOI

Harvey, J. E. et al. Tree growth influenced by warming winter climate and summer moisture availability in northern temperate forests. Glob. Change Biol. 26, 2505–2518 (2020). DOI

Ols, C., Hervé, J.-C. & Bontemps, J.-D. Recent growth trends of conifers across Western Europe are controlled by thermal and water constraints and favored by forest heterogeneity. Sci. Total Environ. 742, 140453 (2020). PubMed DOI

Lloyd, J. & Taylor, J. A. On the temperature-dependence of soil respiration. Funct. Ecol. 8, 315–323 (1994). DOI

Adair, E. C. et al. Simple three-pool model accurately describes patterns of long-term litter decomposition in diverse climates. Glob. Change Biol. 14, 2636–2660 (2008). DOI

Chen, S. et al. National estimation of soil organic carbon storage potential for arable soils: a data-driven approach coupled with carbon-landscape zones. Sci. Total Environ. 666, 355–367 (2019). PubMed DOI

Kottek, M., Grieser, J., Beck, C., Rudolf, B. & Rubel, F. World map of the Koppen-Geiger climate classification updated. Meteorol. Z. 15, 259–263 (2006). DOI

Chini, L. et al. LUH2-GCB2019: Land-Use Harmonization 2 Update For The Global Carbon Budget, 850-2019 (ORNL DAAC, 2021).

Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015). DOI

Hebbali, A. Olsrr: tools for building OLS regression models (2020); cran.r-project.org/package=olsrr .

Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).

Díaz-Uriarte, R. & Alvarez de Andrés, S. Gene selection and classification of microarray data using random forest. BMC Bioinform. 7, 3 (2006). DOI

Shao, Z., Zhang, L. & Wang, L. Stacked sparse autoencoder modeling using the synergy of airborne LiDAR and satellite optical and SAR data to map forest above-ground biomass. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10, 5569–5582 (2017). DOI

Trefflich, I., Dietrich, S., Braune, A., Abraham, K. & Weikert, C. Short-and branched-chain fatty acids as fecal markers for microbiota activity in vegans and omnivores. Nutrients 13, 1808 (2021). PubMed DOI PMC

Babst, F. et al. Site- and species-specific responses of forest growth to climate across the European continent. Glob. Ecol. Biogeogr. 22, 706–717 (2013). DOI

Poorter, L. et al. Biodiversity and climate determine the functioning of Neotropical forests. Glob. Ecol. Biogeogr. 26, 1423–1434 (2017). DOI

Soong, J. L. et al. Soil properties explain tree growth and mortality, but not biomass, across phosphorus-depleted tropical forests. Sci. Rep. 10, 2302 (2020). PubMed DOI PMC

van der Sande, M. T. et al. Soil fertility and species traits, but not diversity, drive productivity and biomass stocks in a Guyanese tropical rainforest. Funct. Ecol. 32, 461–474 (2018). DOI

Noy-Meir, I., Walker, D. & Williams, W. Data transformations in ecological ordination: II. On the meaning of data standardization. J. Ecol. 63, 779–800 (1975).

Razali, N. M., Wah, Y. B. & others. Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests. J. Stat. Model. Anal. 2, 21–33 (2011).

Hedges, L. V., Gurevitch, J. & Curtis, P. S. The meta-analysis of response ratios in experimental ecology. Ecology 80, 1150–1156 (1999). DOI

Malyjurek, Z., de Beer, D., Joubert, E. & Walczak, B. Working with log-ratios. Anal. Chim. Acta 1059, 16–27 (2019). PubMed DOI

Voelkl, B., Würbel, H., Krzywinski, M. & Altman, N. The standardization fallacy. Nat. Methods 18, 5–7 (2021). PubMed DOI

Reich, P. B., Walters, M. B. & Ellsworth, D. S. From tropics to tundra: global convergence in plant functioning. Proc. Natl Acad. Sci. USA 94, 13730–13734 (1997). PubMed DOI PMC

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

Ellsworth, D. S. et al. Convergence in phosphorus constraints to photosynthesis in forests around the world. Nat. Commun. 13, 5005 (2022). PubMed DOI PMC

Grime, J. P. & Hunt, R. Relative growth-rate: its range and adaptive significance in a local flora. J. Ecol. 63, 393–422 (1975). DOI

Thomas, F. M. & Vesk, P. A. Are trait-growth models transferable? Predicting multi-species growth trajectories between ecosystems using plant functional traits. PLoS ONE 12, e0176959 (2017). PubMed DOI PMC

Bujang, M. A. & Baharum, N. Sample size guideline for correlation analysis. World J. Soc. Sci. Res. 3, 37–46 (2016). DOI

Altman, N. & Krzywinski, M. Points of significance: association, correlation and causation. Nat. Methods 12, 899–900 (2015). PubMed DOI

Altman, N. & Krzywinski, M. Analyzing outliers: influential or nuisance? Nat. Methods 13, 281–283 (2016). PubMed DOI

West, P. A review of the growth behaviour of stands and trees in even-aged, monospecific forest. Ann. For. Sci. 81, 34 (2024). DOI

Mayer, D. G. & Butler, D. G. Statistical validation. Ecol. Model. 68, 21–32 (1993). DOI

Isaac, M. E. et al. Intraspecific trait variation and coordination: root and leaf economics spectra in coffee across environmental gradients. Front. Plant Sci. 8, 1196 (2017). PubMed DOI PMC

Kazakou, E. et al. Are trait-based species rankings consistent across data sets and spatial scales? J. Veg. Sci. 25, 235–247 (2014). DOI

Treurnicht, M. et al. Functional traits explain the Hutchinsonian niches of plant species. Glob. Ecol. Biogeogr. 29, 534–545 (2020). DOI

Ma, Z. et al. Evolutionary history resolves global organization of root functional traits. Nature 555, 94–97 (2018). PubMed DOI

Pietsch, K. A. et al. Global relationship of wood and leaf litter decomposability: the role of functional traits within and across plant organs. Glob. Ecol. Biogeogr. 23, 1046–1057 (2014). DOI

Joswig, J. S. et al. Climatic and soil factors explain the two-dimensional spectrum of global plant trait variation. Nat. Ecol. Evol. 6, 36–50 (2022). PubMed DOI

Fajardo, A. Insights into intraspecific wood density variation and its relationship to growth, height and elevation in a treeline species. Plant Biol. 20, 456–464 (2018). PubMed DOI

Li, T. et al. Intraspecific functional trait variability across different spatial scales: a case study of two dominant trees in Korean pine broadleaved forest. Plant Ecol. 219, 875–886 (2018). DOI

Pompa-García, M. et al. Tree-ring wood density reveals differentiated hydroclimatic interactions in species along a bioclimatic gradient. Dendrochronologia 85, 126208 (2024). DOI

Ji, M., Jin, G. & Liu, Z. Effects of ontogenetic stage and leaf age on leaf functional traits and the relationships between traits in Pinus koraiensis. J. For. Res. 32, 2459–2471 (2021). DOI

Kattge, J. et al. TRY plant trait database—enhanced coverage and open access. Glob. Change Biol. 26, 119–188 (2020). DOI

Boehnke, M. & Bruelheide, H. How do evergreen and deciduous species respond to shade? Tolerance and plasticity of subtropical tree and shrub species of South-East China. Environ. Exp. Bot. 87, 179–190 (2013). DOI

Cornelissen, J. A triangular relationship between leaf size and seed size among woody species: allometry, ontogeny, ecology and taxonomy. Oecologia 118, 248–255 (1999). PubMed DOI

Unterholzner, L., Stolz, J., van der Maaten-Theunissen, M., Liepe, K. & van der Maaten, E. Site conditions rather than provenance drive tree growth, climate sensitivity and drought responses in European beech in Germany. For. Ecol. Manage. 572, 122308 (2024). DOI

Ovenden, T. S., Jinks, R. L., Mason, W. L., Kerr, G. & Reynolds, C. A comparison of the early growth and survival of lesser-known tree species for climate change adaptation in Britain. For. Ecol. Manage. 572, 122340 (2024). DOI

Albert, C. H., Grassein, F., Schurr, F. M., Vieilledent, G. & Violle, C. When and how should intraspecific variability be considered in trait-based plant ecology? Perspect. Plant Ecol. Evol. Syst. 13, 217–225 (2011). DOI

Wooliver, R. C. et al. Phylogeny is a powerful tool for predicting plant biomass responses to nitrogen enrichment. Ecology 98, 2120–2132 (2017). PubMed DOI

Zanne, A. E. et al. Three keys to the radiation of angiosperms into freezing environments. Nature 506, 89–92 (2014). PubMed DOI

Lu, Y., Ran, J.-H., Guo, D.-M., Yang, Z.-Y. & Wang, X.-Q. Phylogeny and divergence times of gymnosperms inferred from single-copy nuclear genes. PLoS ONE 9, e107679 (2014). PubMed DOI PMC

Magallón, S., Gómez-Acevedo, S., Sánchez-Reyes, L. L. & Hernández-Hernández, T. A metacalibrated time-tree documents the early rise of flowering plant phylogenetic diversity. N. Phytol. 207, 437–453 (2015). DOI

Saladin, B. et al. Fossils matter: improved estimates of divergence times in Pinus reveal older diversification. BMC Evol. Biol. 17, 95 (2017). PubMed DOI PMC

Hipp, A. L. et al. Genomic landscape of the global oak phylogeny. N. Phytol. 226, 1198–1212 (2020). DOI

Jiang, L. et al. Phylogeny and biogeography of Fagus (Fagaceae) based on 28 nuclear single/low-copy loci. J. Syst. Evol. 60, 759–772 (2022). DOI

Liese, R., Alings, K. & Meier, I. C. Root branching is a leading root trait of the plant economics spectrum in temperate trees. Front. Plant Sci. 8, 315 (2017). PubMed DOI PMC

Cadotte, M. W., Davies, T. J. & Peres-Neto, P. R. Why phylogenies do not always predict ecological differences. Ecol. Monogr. 87, 535–551 (2017). DOI

Augusto, L., Davies, T. J., Delzon, S. & de Schrijver, A. The enigma of the rise of angiosperms: can we untie the knot? Ecol. Lett. 17, 1326–1338 (2014). PubMed DOI

Augusto, L. et al. Influences of evergreen gymnosperm and deciduous angiosperm tree species on the functioning of temperate and boreal forests. Biol. Rev. 90, 444–466 (2015). PubMed DOI

Bond, W. The tortoise and the hare: ecology of angiosperm dominance and gymnosperm persistence. Biol. J. Linn. Soc. 36, 227–249 (1989). DOI

Brodribb, T. J. & Feild, T. S. Leaf hydraulic evolution led a surge in leaf photosynthetic capacity during early angiosperm diversification. Ecol. Lett. 13, 175–183 (2010). PubMed DOI

Brodribb, T. J., Pittermann, J. & Coomes, D. A. Elegance versus speed: examining the competition between conifer and angiosperm trees. Int. J. Plant Sci. 173, 673–694 (2012). DOI

Martin, A. R., Doraisami, M. & Thomas, S. C. Global patterns in wood carbon concentration across the world’s trees and forests. Nat. Geosci. 11, 915–920 (2018). DOI

Reich, P. B. et al. Evidence of a general 2/3-power law of scaling leaf nitrogen to phosphorus among major plant groups and biomes. Proc. R. Soc. B 277, 877–883 (2010). PubMed DOI

Zheng, J. et al. A trait-based root acquisition-defence-decomposition framework in angiosperm tree species. Nat. Commun. 15, 5311 (2024). PubMed DOI PMC