Abstract
Tree longevity is thought to increase in growth-limiting, adverse environments, but a quantitative assessment of drivers of global variation in tree longevity is lacking. We assemble a global database of maximum longevity for 739 tree species and analyse associations between longevity and climate, soil, and species’ functional traits. Our results show two primary pathways towards long lifespans. The first is slow growth in resource-limited environments, consistent with the “adversity begets longevity” paradigm. The second pathway is through relief from abiotic constraints in productive environments. Despite notable exceptions, long-lived gymnosperms tend to follow the first path through slow growth in cold environments, whereas long-lived angiosperms tend to follow the second (“productivity”) path reaching maximum longevity generally in humid environments. For angiosperms, we identify two mechanisms for increased longevity under humid conditions. First, higher water availability increases species’ maximum tree height which is associated with greater longevities. Secondly, greater water availability increases stand density and inter-tree competition, limiting growth which may increase tree lifespan. The documented differences between gymnosperm and angiosperm longevity are likely rooted in intrinsic differences in hydraulic architecture that provide fitness advantages for gymnosperms under high abiotic stress, and for angiosperms under increased productivity or competition.
Data availability
Data on species’ maximum longevity, traits, and climate that support the findings of this study are available from https://doi.org/10.6084/m9.figshare.29876984. Original raw tree ring data from the ITRDB can be downloaded from https://www.ncei.noaa.gov/products/paleoclimatology/tree-ring, and tropical tree ring data compilations from https://figshare.com/articles/dataset/Locoselli_et_al_2020_Global_tree-ring_analysis_reveals_rapid_decrease_in_tropical_tree_longevity_with_temperature_PNAS/13119842?file=25178405. Individual longevity records from following oldlists http://www.rmtrr.org/oldlist.htm, https://www.ldeo.columbia.edu/~adk/oldlisteast/, http://www.nativetreesociety.org/dendro/ents_maximum_ages.htm, https://www.oldgrowth.ca/oldtrees/. Tree height data can be downloaded from https://zenodo.org/record/6637599, and maximum height measurements were obtained from https://www.conifers.org and https://Monumentaltrees.com. Wood density data can be obtained from https://zenodo.org/records/13322441, and from https://doi.org/10.18167/DVN1/KRVF0E. Conduit density from https://doi.org/10.5061/dryad.1138, and conduit density, P50 and HSM from https://doi.org/10.5061/dryad.1138, and from https://doi.org/10.1126/sciadv.aav1332. Leaf traits from https://www.nature.com/articles/nature02403#Sec15, and seedmass data from https://www.try-db.org/TryWeb/dp.php, database request No 30569. Mean climate and soil data for a species were obtained from the TreeGOER database https://zenodo.org/records/10008994, and gridded climate and elevation data from https://www.worldclim.org/data/worldclim21.html, growing season length and site level Net Primary Productivity (NPP) from https://chelsa-climate.org/. Species occurrence data from https://doi.org/10.15468/dl.77gcvq.
Code availability
Code to reproduce the Figs. 1–3 and Supplementary Figs. 3–6, 8, 9 and statistics are available from https://doi.org/10.6084/m9.figshare.29876984.
References
Piovesan, G. & Biondi, F. On tree longevity. N. Phytologist 231, 1318–1337 (2021).
Körner, C. A matter of tree longevity. Science 355, 130–131 (2017).
Reich, P. B. The world-wide ‘fast–slow’plant economics spectrum: a traits manifesto. J. Ecol. 102, 275–301 (2014).
Salguero-Gómez, R. Applications of the fast–slow continuum and reproductive strategy framework of plant life histories. N. Phytologist 213, 1618–1624 (2017).
Bialic-Murphy, L. et al. The pace of life for forest trees. Science 386, 92–98 (2024).
Morris, W. F. et al. Longevity can buffer plant and animal populations against changing climatic variability. Ecology 89, 19–25 (2008).
Chondol, T. et al. Habitat preferences and functional traits drive longevity in Himalayan high-mountain plants. Oikos 2023, e010073 (2023).
Friend, A. D. et al. Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2. Proc. Natl. Acad. Sci. USA 111, 3280–3285 (2014).
Galbraith, D. et al. Residence times of woody biomass in tropical forests. Plant Ecol. Diversity 6, 139–157 (2013).
Liu, J. et al. Age and spatial distribution of the world’s oldest trees. Conserv. Biol. 36, e13907 (2022).
Locosselli, G. M. et al. Global tree-ring analysis reveals rapid decrease in tropical tree longevity with temperature. Proc. Natl. Acad. Sci. 117, 33358–33364 (2020).
Schulman, E. Longevity under Adversity in Conifers. Science 119, 396–399 (1954).
Stahle, D. et al. Longevity, climate sensitivity, and conservation status of wetland trees at Black River, North Carolina. Environ. Res. Commun. 1, 041002 (2019).
Patrut, A. et al. Radiocarbon dating of a very large African baobab. Tree Physiol. 27, 1569–1574 (2007).
Condit, R., Hubbel, S. P. & Foster, R. B. Identifying fast-growing native trees from the neotropics using data from a large, permanent census plot. Ecol. Manag. 62, 123–143 (1993).
Kurokawa, H., Yoshida, T., Nakamura, T., Lai, J. & Nakashizuka, T. The age of tropical rain-forest canopy species, Borneo ironwood (Eusideroxylon zwageri), determined by 14 C dating. J. Trop. Ecol. 19, 1–7 (2003).
Vieira, S. et al. Slow growth rates of Amazonian trees: Consequences for carbon cycling. Proc. Natl. Acad. Sci. USA 102, 18502–18507 (2005).
Stearns, S. C. Trade-offs in life-history evolution. Funct. Ecol. 3, 259–268 (1989).
Stephenson, N. L. et al. Causes and implications of the correlation between forest productivity and tree mortality rates. Ecol. Monogr. 81, 527–555 (2011).
Brienen, R. J. et al. Forest carbon sink neutralized by pervasive growth-lifespan trade-offs. Nat. Commun. 11, 4241 (2020).
Rüger, N. et al. Beyond the fast–slow continuum: demographic dimensions structuring a tropical tree community. Ecol. Lett. 21, 1075–1084 (2018).
Adler, P. B. et al. Functional traits explain variation in plant life history strategies. Proc. Natl. Acad. Sci. 111, 740–745 (2014).
Wright, S. J. et al. Functional traits and the growth-mortality trade-off in tropical trees. Ecology 91, 3664–3674 (2011).
Poorter, L. et al. Are functional traits good predictors of demographic rates? Evidence from five Neotropical forests. Ecology 89, 1908–1920 (2008).
Loehle, C. Tree Life-History Strategies – the Role of Defenses. Can. J. For. Res. 18, 209–222 (1988).
Lu, R. et al. The U-shaped pattern of size-dependent mortality and its correlated factors in a subtropical monsoon evergreen forest. J. Ecol. 109, 2421–2433 (2021).
Xu, C. & Liu, H. Hydraulic adaptability promotes tree life spans under climate dryness. Glob. Ecol. Biogeogr. 31, 51–61 (2022).
Liu, L. et al. Tropical tall forests are more sensitive and vulnerable to drought than short forests. Glob. Chang Biol. 28, 1583–1595 (2022).
Bond, W. The tortoise and the hare: ecology of angiosperm dominance and gymnosperm persistence. Biol. J. Linn. Soc. 36, 227–249 (1989).
Lusk, C. H., Wright, I. & Reich, P. B. Photosynthetic differences contribute to competitive advantage of evergreen angiosperm trees over evergreen conifers in productive habitats. N. Phytologist 160, 329–336 (2003).
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).
Johnson, D. M., McCulloh, K. A., Woodruff, D. R. & Meinzer, F. C. Hydraulic safety margins and embolism reversal in stems and leaves: why are conifers and angiosperms so different?. Plant Sci. 195, 48–53 (2012).
Sperry, J. S., Hacke, U. G. & Pittermann, J. Size and function in conifer tracheids and angiosperm vessels. Am. J. Bot. 93, 1490–1500 (2006).
Gao, J. et al. Climate-driven patterns of global tree longevity. Commun. Earth Environ. 6, 610 (2025).
Willis, K. J. & McElwain, J. C. The Evolution of Plants (Oxford University Press, USA, 2014).
Ma, H. et al. The global biogeography of tree leaf form and habit. Nat. Plants, 9, 1795–1809 (2023).
Joswig, J. S. et al. Climatic and soil factors explain the two-dimensional spectrum of global plant trait variation. Nat. Ecol. Evolution 6, 36–50 (2022).
Gao, S. et al. An earlier start of the thermal growing season enhances tree growth in cold humid areas but not in dry areas. Nat. Ecol. evolution 6, 397–404 (2022).
Reich, P. B. & Oleksyn, J. Global patterns of plant leaf N and P in relation to temperature and latitude. Proc. Natl. Acad. Sci. 101, 11001–11006 (2004).
Rossi, S., Deslauriers, A., Anfodillo, T. & Carraro, V. Evidence of threshold temperatures for xylogenesis in conifers at high altitudes. Oecologia 152, 1–12 (2007).
Björklund, J., Fonti, M. V., Fonti, P., Van den Bulcke, J. & von Arx, G. Cell wall dimensions reign supreme: cell wall composition is irrelevant for the temperature signal of latewood density/blue intensity in Scots pine. Dendrochronologia 65, 125785 (2021).
Berry, J. & Bjorkman, O. Photosynthetic response and adaptation to temperature in higher-plants. Annu. Rev. Plant Physiol. Plant Mol. Biol. 31, 491–543 (1980).
Reich, P. B., Walters, M. & Ellsworth, D. Leaf life-span in relation to leaf, plant, and stand characteristics among diverse ecosystems. Ecol. Monogr. 62, 365–392 (1992).
Reich, P. B., Rich, R. L., Lu, X., Wang, Y.-P. & Oleksyn, J. Biogeographic variation in evergreen conifer needle longevity and impacts on boreal forest carbon cycle projections. Proc. Natl. Acad. Sci. 111, 13703–13708 (2014).
Issartel, J. & Coiffard, C. Extreme longevity in trees: live slow, die old?. Oecologia 165, 1–5 (2011).
Sillett, S. C. et al. Comparative development of the four tallest conifer species. Ecol. Manag. 480, 118688 (2021).
Franceschi, V. R., Krokene, P., Christiansen, E. & Krekling, T. Anatomical and chemical defenses of conifer bark against bark beetles and other pests. N. Phytol. 167, 353–376 (2005).
Sillett, S. C. et al. How do tree structure and old age affect growth potential of California redwoods?. Ecol. Monogr. 85, 181–212 (2015).
Scheffer, M. et al. A global climate niche for giant trees. Glob. Change Biol. 24, 2875–2883 (2018).
Moles, A. T. et al. Global patterns in plant height. J. Ecol. 97, 923–932 (2009).
Liu, H. et al. Hydraulic traits are coordinated with maximum plant height at the global scale. Sci. Adv. 5, eaav1332 (2019).
Klein, T., Randin, C. & Körner, C. Water availability predicts forest canopy height at the global scale. Ecol. Lett. 18, 1311–1320 (2015).
Madrigal-González, J. et al. Global patterns of tree density are contingent upon local determinants in the world’s natural forests. Commun. Biol. 6, 47 (2023).
Crowther, T. et al. Mapping tree density at a global scale. Nature 525, 201–205 (2015).
Brienen, R. et al. Paired analysis of tree ring width and carbon isotopes indicates when controls on tropical tree growth change from light to water limitations. Tree Physiol. 42, 1131–1148 (2022).
Canham, C. D. Suppression and release during canopy recruitment in Acer saccharum. Bull. Torre. Bot. Club 112, 145 (1985).
Brienen, R. J. W. & Zuidema, P. A. Lifetime growth patterns and ages of Bolivian rain forest trees obtained by tree ring analysis. J. Ecol. 94, 481–493 (2006).
Di Filippo, A. et al. The longevity of broadleaf deciduous trees in Northern Hemisphere temperate forests: insights from tree-ring series. Front. Ecol. Evol. 3, 46 (2015).
Pavlin, J. et al. Disturbance history is a key driver of tree life span in temperate primary forests. J. Vegetation Sci. 32, e13069 (2021).
Hubau, W. et al. The persistence of carbon in the African forest understory. Nat. Plants 5, 133–140 (2019).
Lusk, C. H. & Reich, P. B. Relationships of leaf dark respiration with light environment and tissue nitrogen content in juveniles of 11 cold-temperate tree species. Oecologia 123, 318–329 (2000).
Reich, P. B., Uhl, C., Walters, M. B., Prugh, L. & Ellsworth, D. S. Leaf demography and phenology in Amazonian rain forest: a census of 40 000 leaves of 23 tree species. Ecol. Monogr. 74, 3–23 (2004).
Sanchez-Martinez, P. et al. Increased hydraulic risk in assemblages of woody plant species predicts spatial patterns of drought-induced mortality. Nat. Ecol. Evol. 7, 1620–1632 (2023).
Choat, B. et al. Global convergence in the vulnerability of forests to drought. Nature 491, 752 (2012).
Tavares, J. V. et al. Basin-wide variation in tree hydraulic safety margins predicts the carbon balance of Amazon forests. Nature 617, 111–117 (2023).
McCulloh, K. et al. Moving water well: comparing hydraulic efficiency in twigs and trunks of coniferous, ring-porous, and diffuse-porous saplings from temperate and tropical forests. N. Phytol. 186, 439–450 (2010).
Brodribb, T. J., Feild, T. S. & Jordan, G. J. Leaf maximum photosynthetic rate and venation are linked by hydraulics. Plant Physiol. 144, 1890–1898 (2007).
Carlquist, S. J. Ecological Strategies of Xylem Evolution (Univ of California Press, 1975).
Brodribb, T. & Hill, R. The importance of xylem constraints in the distribution of conifer species. N. Phytologist 143, 365–372 (1999).
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).
Morris, H., Brodersen, C., Schwarze, F. W. & Jansen, S. The parenchyma of secondary xylem and its critical role in tree defense against fungal decay in relation to the CODIT model. Front. Plant Sci. 7, 1665 (2016).
Weedon, J. T. et al. Global meta-analysis of wood decomposition rates: a role for trait variation among tree species?. Ecol. Lett. 12, 45–56 (2009).
Herms, D. A. & Mattson, W. J. The dilemma of plants: to grow or defend. Q. Rev. Biol. 67, 283–335 (1992).
Chave, J. et al. Towards a worldwide wood economics spectrum. Ecol. Lett. 12, 351–366 (2009).
Hacke, U. G., Sperry, J. S., Pockman, W. T., Davis, S. D. & McCulloh, K. A. Trends in wood density and structure are linked to prevention of xylem implosion by negative pressure. Oecologia 126, 457–461 (2001).
Esquivel-Muelbert, A. et al. Tree mode of death and mortality risk factors across Amazon forests. Nat. Commun. 11, 5515 (2020).
Maynard, D. S. et al. Global relationships in tree functional traits. Nat. Commun. 13, 3185 (2022).
Diaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).
Canham, C. D., Papaik, M. J. & Latty, E. F. Interspecific variation in susceptibility to windthrow as a function of tree size and storm severity for northern temperate tree species. Can. J. For. Res. 31, 1–10 (2001).
Rich, R. L., Frelich, L. E. & Reich, P. B. Wind-throw mortality in the southern boreal forest: Effects of species, diameter and stand age. J. Ecol. 95, 1261–1273 (2007).
Lee, C. A., Voelker, S., Holdo, R. M. & Muzika, R.-M. Tree architecture as a predictor of growth and mortality after an episode of red oak decline in the Ozark Highlands of Missouri, USA. Can. J. For. Res. 44, 1005–1012 (2014).
Gora, E. M. et al. A mechanistic and empirically supported lightning risk model for forest trees. J. Ecol. 108, 1956–1966 (2020).
Ishii, H. & Ford, E. D. Persistence of Pseudotsuga menziesii (Douglas-fir) in temperate coniferous forests of the Pacific Northwest Coast, USA. Folia Geobotanica 37, 63–69 (2002).
Gora, E. M. & Esquivel-Muelbert, A. Implications of size-dependent tree mortality for tropical forest carbon dynamics. Nat. Plants 7, 384–391 (2021).
Bigler, C. Trade-offs between growth rate, tree size and lifespan of mountain pine (Pinus montana) in the Swiss National Park. PloS One 11, e0150402 (2016).
Yang, J., Cao, M. & Swenson, N. G. Why functional traits do not predict tree demographic rates. Trends Ecol. Evol. 33, 326–336 (2018).
Hecking, M. J., Zukswert, J. M., Drake, J. E., Dovciak, M. & Burton, J. I. Montane temperate-boreal forests retain the leaf economic spectrum despite intraspecific variability. Front. For. Glob. Change 4, 754063 (2022).
Sperry, J. S. Evolution of water transport and xylem structure. Int. J. Plant Sci. 164, S115–S127 (2003).
Baker, T. R. et al. Fast demographic traits promote high diversification rates of Amazonian trees. Ecol. Lett. 17, 527–536 (2014).
Grime, J. P. Evidence for the existence of three primary strategies in plants and its relevance to ecological and evolutionary theory. Am. Naturalist 111, 1169–1194 (1977).
Pavlin, J. et al. Pathways and drivers of canopy accession across primary temperate forests of Europe. Sci. Total Environ. 906, 167593 (2024).
Kindt, R. WorldFlora: an R package for exact and fuzzy matching of plant names against the World Flora Online taxonomic backbone data. Appl. Plant Sci. 8, e11388 (2020).
Beech, E., Rivers, M., Oldfield, S. & Smith, P. GlobalTreeSearch: the first complete global database of tree species and country distributions. J. Sustain. Forestry 36, 454–489 (2017).
Biondi, F., Meko, D. M. & Piovesan, G. Maximum tree lifespans derived from public-domain dendrochronological data. Iscience 26, 106138 (2023).
Zhao, S. et al. The International Tree-Ring Data Bank (ITRDB) revisited: data availability and global ecological representativity. J. Biogeogr. 46, 355–368 (2019).
Cazzolla Gatti, R. et al. The number of tree species on Earth. Proc. Natl. Acad. Sci. 119, e2115329119 (2022).
Chambers, J. Q., Higuchi, N. & Schimel, J. P. Ancient trees in Amazonia. Nature 39, 135–136 (1998).
Worbes, M. & Junk, W. J. How old are tropical trees? The persistence of a myth. IAWA J. 20, 255–260 (1999).
Martinez-Ramos, M. & Alvarez-Buylla, E. R. How old are tropical rain forest trees?. Trends Plant Sci. 3, 400–405 (1998).
Kindt, R. TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Glob. Change Biol. 29, 6303–6318 (2023).
Gbif.org. GBIF occurrence download https://doi.org/10.15468/dl.77gcvq (2021).
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Willmott, C. J. & Feddema, J. J. A more rational climatic moisture index. Professional Geographer 44, 84–88 (1992).
Poggio, L. et al. SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty. Soil 7, 217–240 (2021).
Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 1–20 (2017).
Paulsen, J. & Körner, C. A climate-based model to predict potential treeline position around the globe. Alp. Bot. 124, 1–12 (2014).
Jucker, T. et al. Tallo: A global tree allometry and crown architecture database. Glob. Change Biol. 28, 5254–5268 (2022).
Zanne, A. E. et al. Data from: Towards a worldwide wood economics spectrum. Dryad Digital Repos. https://doi.org/10.5061/dryad.234 (2009).
Vieilledent, G. et al. New formula and conversion factor to compute basic wood density of tree species using a global wood technology database. Am. J. Bot. 105, 1653–1661 (2018).
Zanne, A. E. et al. Angiosperm wood structure: global patterns in vessel anatomy and their relation to wood density and potential conductivity. Am. J. Bot. 97, 207–215 (2009).
Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).
Kattge, J. et al. TRY plant trait database – enhanced coverage and open access. Glob. Chang Biol. 26, 119–188 (2020).
Grömping, U. Relative importance for linear regression in R: the package relaimpo. J. Stat. Softw. 17, 1–27 (2007).
Swenson, N. G. & Enquist, B. J. Ecological and evolutionary determinants of a key plant functional trait: wood density and its community-wide variation across latitude and elevation. Am. J. Bot. 94, 451–459 (2007).
Maire, V. et al. Global effects of soil and climate on leaf photosynthetic traits and rates. Glob. Ecol. Biogeogr. 24, 706–717 (2015).
Rosseel, Y. lavaan: An R package for structural equation modeling. J. Stat. Softw. 48, 1–36 (2012).
Hu, L. T. & Bentler, P. M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Equ. Model. 6, 1–55 (1999).
Lê, S., Josse, J. & Husson, F. FactoMineR: an R package for multivariate analysis. J. Stat. Softw. 25, 1–18 (2008).
R_Core_Team. R: A Language and Environment for Statistical Computing (R_Core_Team, 2023).
Smith, S. A. & Brown, J. W. Constructing a broadly inclusive seed plant phylogeny. Am. J. Bot. 105, 302–314 (2018).
Orme, D. et al. The caper package: comparative analysis of phylogenetics and evolution in R. R. Package Version 5, 1–36 (2013).
Revell, L. J. phytools 2.0: an updated R ecosystem for phylogenetic comparative methods (and other things). PeerJ 12, e16505 (2024).
Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw. 67, 48 (2015).
Acknowledgements
We acknowledge the contributors to International Tree-Ring Data Bank for making available raw tree-ring data, and we thank staff at the Direction des Inventaires Forestiers of the Ministère des Ressources naturelles et des Forêts du Québec for sharing tree-ring and sample plot data from the forest inventory program in Quebec, Canada. We further thank Ailene Ettinger, Gregory Peterson, Janneke Hille Ris Lambers, Jeremy Little, Jill Harvey, and Jordi Axelsons for contributing original data. This study was supported by the following grants; National Environmental Research Council grants NE/S008659/1 (R.B.), NE/N012542/1 (E.G.), and NE/R005079/1 (E.G., R.S.); FAPESP grants 12/50457-4, 2019/08783-0 (G.L., G.C.) and 17/5008-3 (G.L., G.C.); Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq, grants 478503/2009 (G.L., G.C.), 311247/2021-0 (J.S.) and 441811/2020-5 (J.S.); CNPq/ FAPEAM, Fundação de Amparo à Pesquisa do Estado do Amazonas, grant number 01.02.016301.02630/2022-76 (J.S.); Czech Science Foundation research grants 24-12210 K (J.P. and M.S.) and 23-05272S (J.A., J.D., K.K., N.A., P.F., V.B.); Mobility Plus between the Czech Republic and Taiwan, NSTC-24-08 (J.A., J.D., K.K., N.A., P.F., V.B.); Czech Academy of Sciences long-term research development project No. RVO 67985939 (J.A., J.D., K.K., N.A., P.F., V.B.); Utah Agricultural Experiment Station, Utah State University, and approved as journal paper number 9803 (R.J.D.); Academy of Finland, #339788 (S.H.); European Union, NextGenerationEU, Italian Ministry of University and Research under PNRR – M4C2-I1.4 Project code: CN00000033, Title: NBFC – National Biodiversity Future Center, CUP: J83C22000860007 (G.P.); Ministry of University and Research (MUR) via the Agritech National Research Centre, European Union Next-GenerationEU PNRR M4C2-I1.4 Project Code: CN00000022 (A.D.); Departments of Excellence (Law 232/2016) Project 2023-27 “Digital, Intelligent, Green and Sustainable (D.I.Ver.So)” (A.D.); National Science Foundation, Division of Environmental Biology, award #1945910 (N.P.); Directorate for Biological Sciences, Emerging Frontiers, award #1241870 (N.P.); Redes Federales de Alto Impacto, Bosque-Clima CN32 (L.L., R.V.); MSMT INTER-EXCELLENCE, # LUAUS24258 (J.D.), Estonian Research Council, grant PSG1044 (J.A.).
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R.B., G.L., S.K., E.G., and R.W. designed the study, R.B., R.W. and S.K. downloaded and compiled functional traits and ITRDB datasets, R.B., R.W. and S.K. analysed data, G.L. and S.K. compiled the tropical longevity datasets, M.M., D.B., R.S. and P.R. provided functional traits data, R.B., G.L., S.K., S.V., C.E., G.P. and N.P. revised and improved the longevity database, R.B., G.L., S.V., J.A., N.A., L.A., M.B., V.B., B.B., P.B., G.C., J.dR., J.V.D., A.D., J.D., L.D., C.E., P.F., H.G., S.H., S.K.l., K.K., D.L., S.L., L.L., T.N., J.P., N.P., G.P., C.R., D.S., J.S., J.D.S., D.S., M.S., R.V., L.W., and C.Z. contributed original longevity data, R.B. wrote the first draft of the manuscript and all authors revised the manuscript.
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Brienen, R.J.W., Locosselli, G.M., Krottenthaler, S. et al. Contrasting pathways to tree longevity in gymnosperms and angiosperms.
Nat Commun (2025). https://doi.org/10.1038/s41467-025-67619-2
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DOI: https://doi.org/10.1038/s41467-025-67619-2
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