Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Tropical tree mortality has increased with rising atmospheric water stress


Evidence exists that tree mortality is accelerating in some regions of the tropics1,2, with profound consequences for the future of the tropical carbon sink and the global anthropogenic carbon budget left to limit peak global warming below 2 °C. However, the mechanisms that may be driving such mortality changes and whether particular species are especially vulnerable remain unclear3,4,5,6,7,8. Here we analyse a 49-year record of tree dynamics from 24 old-growth forest plots encompassing a broad climatic gradient across the Australian moist tropics and find that annual tree mortality risk has, on average, doubled across all plots and species over the last 35 years, indicating a potential halving in life expectancy and carbon residence time. Associated losses in biomass were not offset by gains from growth and recruitment. Plots in less moist local climates presented higher average mortality risk, but local mean climate did not predict the pace of temporal increase in mortality risk. Species varied in the trajectories of their mortality risk, with the highest average risk found nearer to the upper end of the atmospheric vapour pressure deficit niches of species. A long-term increase in vapour pressure deficit was evident across the region, suggesting that thresholds involving atmospheric water stress, driven by global warming, may be a primary cause of increasing tree mortality in moist tropical forests.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Temporal increase of modelled annual mortality risk, annualized observed percentage of tree death and atmospheric VPD across the 24 studied plots.
Fig. 2: Temporal increase of tree mortality risk and influence of the mean climate of plots, the position of species on their climatic niche and functional traits on mortality risk.
Fig. 3: Plot-level and species-level temporal changes of annual mortality risk (1984–2019) and change of plot basal area between consecutive censuses.
Fig. 4: Spatial effect of the proximity of species from the upper limit of their VPD niche on the average mortality risk (model M5).

Data availability

The raw demographic data that supported the findings are available in Bradford et al.58, in the CSIRO Data Access Portal ( The survival dataset used for the 81 studied species was archived on the platform of the Terrestrial Ecosystem Research Network infrastructure (, which is enabled by the Australian Government’s National Collaborative Research Infrastructure Strategy. The climate data used in the models are openly available under the same DOI, and trait data will be made available on reasonable request.

Code availability

A detailed and commented R code supporting the findings of this study is available in Supplementary Methods 2 as well as on the same Terrestrial Ecosystem Research Network repository as the data (


  1. Brienen, R. J. W. et al. Long-term decline of the Amazon carbon sink. Nature 519, 344–348 (2015).

    Article  ADS  CAS  PubMed  Google Scholar 

  2. Hubau, W. et al. Asynchronous carbon sink saturation in African and Amazonian tropical forests. Nature 579, 80–87 (2020).

    Article  ADS  CAS  PubMed  Google Scholar 

  3. Zuleta, D., Duque, A., Cardenas, D., Muller-Landau, H. C. & Davies, S. J. Drought-induced mortality patterns and rapid biomass recovery in a terra firme forest in the Colombian Amazon. Ecology 98, 2538–2546 (2017).

    Article  PubMed  Google Scholar 

  4. Phillips, O. L. et al. Drought sensitivity of the Amazon rainforest. Science 323, 1344–1347 (2009).

    Article  ADS  CAS  PubMed  Google Scholar 

  5. Powers, J. S. et al. A catastrophic tropical drought kills hydraulically vulnerable tree species. Glob. Chang. Biol. 26, 3122–3133 (2020).

    Article  ADS  PubMed  Google Scholar 

  6. Bennett, A. C. et al. Resistance of African tropical forests to an extreme climate anomaly. Proc. Natl Acad. Sci. USA 118, e2003169118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Brodribb, T. J., Powers, J., Cochard, H. & Choat, B. Hanging by a thread? Forests and drought. Science 368, 261–266 (2020).

    Article  ADS  CAS  PubMed  Google Scholar 

  8. McDowell, N. G. et al. Pervasive shifts in forest dynamics in a changing world. Science 368, (2020).

  9. Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).

    Article  ADS  CAS  PubMed  Google Scholar 

  10. Matthews, H. D. et al. An integrated approach to quantifying uncertainties in the remaining carbon budget. Commun. Earth Environ. 2, 7 (2021).

    Article  ADS  Google Scholar 

  11. Girardin, C. A. J. et al. Nature-based solutions can help cool the planet—if we act now. Nature 593, 191–194 (2021).

    Article  ADS  CAS  PubMed  Google Scholar 

  12. Friedlingstein, P. et al. Earth Syst. Sci. Data 14, 1917–2005 (2022)

    Google Scholar 

  13. Choat, B. et al. Triggers of tree mortality under drought. Nature 558, 531–539 (2018).

    Article  ADS  CAS  PubMed  Google Scholar 

  14. Rowland, L. et al. Death from drought in tropical forests is triggered by hydraulics not carbon starvation. Nature 528, 119–122 (2015).

    Article  ADS  CAS  PubMed  Google Scholar 

  15. Lloyd, J. & Farquhar, G. D. Effects of rising temperatures and [CO2] on the physiology of tropical forest trees. Phil. Trans. R. Soc. B 363, 1811–1817 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. O’Sullivan, O. S. et al. Thermal limits of leaf metabolism across biomes. Glob. Chang. Biol. 23, 209–223 (2017).

    Article  ADS  PubMed  Google Scholar 

  17. Grossiord, C. et al. Plant responses to rising vapor pressure deficit. New Phytol. 226, 1550–1566 (2020).

    Article  PubMed  Google Scholar 

  18. Rifai, S. W., Li, S. & Malhi, Y. Coupling of El Niño events and long-term warming leads to pervasive climate extremes in the terrestrial tropics. Environ. Res. Lett. 14, 105002 (2019).

    Article  ADS  CAS  Google Scholar 

  19. Rifai, S. W. et al. ENSO drives interannual variation of forest woody growth across the tropics. Phil. Trans. R. Soc. B 373, 20170410 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Smith, M. N. et al. Empirical evidence for resilience of tropical forest photosynthesis in a warmer world. Nat. Plants 6, 1225–1230 (2020).

    Article  CAS  PubMed  Google Scholar 

  21. Malhi, Y. et al. Exploring the likelihood and mechanism of a climate-change-induced dieback of the Amazon rainforest. Proc. Natl Acad. Sci. USA 106, 20610–20615 (2009).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  22. McDowell, N., Allen, C. D. & Anderson‐Teixeira, K. Drivers and mechanisms of tree mortality in moist tropical forests. New Phytol. 219, 851–869 (2018).

    Article  PubMed  Google Scholar 

  23. McDowell, N. et al. Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought? New Phytol. 178, 719–739 (2008).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  25. Esquivel-Muelbert, A. et al. Tree mode of death and mortality risk factors across Amazon forests. Nat. Commun. 11, 5515 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  26. Anderegg, W. R. L., Anderegg, L. D. L., Kerr, K. L. & Trugman, A. T. Widespread drought-induced tree mortality at dry range edges indicates that climate stress exceeds species’ compensating mechanisms. Glob. Chang. Biol. 25, 3793–3802 (2019).

    Article  ADS  PubMed  Google Scholar 

  27. Aguirre-Gutiérrez, J. et al. Drier tropical forests are susceptible to functional changes in response to a long-term drought. Ecol. Lett. 22, 855–865 (2019).

    Article  PubMed  Google Scholar 

  28. Aguirre-Gutiérrez, J. et al. Long-term droughts may drive drier tropical forests towards increased functional, taxonomic and phylogenetic homogeneity. Nat. Comm. 11, 3346 (2020).

    Article  ADS  CAS  Google Scholar 

  29. Meir, P., Mencuccini, M. & Dewar, R. C. Drought-related tree mortality: addressing the gaps in understanding and prediction. New Phytol. 207, 28–33 (2015).

    Article  PubMed  Google Scholar 

  30. Sullivan, M. J. P. et al. Long-term thermal sensitivity of Earth’s tropical forests. Science 368, 869–874 (2020).

    Article  ADS  CAS  PubMed  Google Scholar 

  31. Yuan, W. et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 5, eaax1396 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  32. McMahon, S. M., Arellano, G. & Davies, S. J. The importance and challenges of detecting changes in forest mortality rates. Ecosphere 10, e02615 (2019).

    Article  Google Scholar 

  33. Trugman, A. T. et al. Tree carbon allocation explains forest drought-kill and recovery patterns. Ecol. Lett. 21, 1552–1560 (2018).

    Article  CAS  PubMed  Google Scholar 

  34. Trugman, A. T., Anderegg, L. D. L., Anderegg, W. R. L., Das, A. J. & Stephenson, N. L. Why is tree drought mortality so hard to predict? Trends Ecol. Evol. 36, 520–532 (2021).

    Article  PubMed  Google Scholar 

  35. Phillips, O. L. et al. Drought–mortality relationships for tropical forests. New Phytol. 187, 631–646 (2010).

    Article  PubMed  Google Scholar 

  36. Aleixo, I. et al. Amazonian rainforest tree mortality driven by climate and functional traits. Nat. Clim. Change 9, 384–388 (2019).

    Article  ADS  Google Scholar 

  37. Lingenfelder, M. & Newbery, D. M. On the detection of dynamic responses in a drought-perturbed tropical rainforest in Borneo. Plant Ecol. 201, 267–290 (2009).

    Article  Google Scholar 

  38. McDowell, N. G. et al. The interdependence of mechanisms underlying climate-driven vegetation mortality. Trends Ecol. Evol. 26, 523–532 (2011).

    Article  PubMed  Google Scholar 

  39. Zuleta, D. et al. Individual tree damage dominates mortality risk factors across six tropical forests. New Phytol. 233, 705–721 (2022).

    Article  PubMed  Google Scholar 

  40. Fontes, C. G. et al. Dry and hot: the hydraulic consequences of a climate change-type drought for Amazonian trees. Phil. Trans. R. Soc. B 373, 20180209 (2018).

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

    Article  PubMed  Google Scholar 

  42. Peters, J. M. R. et al. Living on the edge: a continental-scale assessment of forest vulnerability to drought. Glob. Chang. Biol. 27, 3620–3641 (2021).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  44. Espírito-Santo, F. D. B. et al. Size and frequency of natural forest disturbances and the Amazon forest carbon balance. Nat. Commun. 5, 3434 (2014).

    Article  ADS  PubMed  CAS  Google Scholar 

  45. Chambers, J. Q. et al. The steady-state mosaic of disturbance and succession across an old-growth Central Amazon forest landscape. Proc. Natl Acad. Sci. USA 110, 3949–3954 (2013).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  46. Rifai, S. W. et al. Landscape-scale consequences of differential tree mortality from catastrophic wind disturbance in the Amazon. Ecol. Appl. 26, 2225–2237 (2016).

    Article  PubMed  Google Scholar 

  47. López, J., Way, D. A. & Sadok, W. Systemic effects of rising atmospheric vapor pressure deficit on plant physiology and productivity. Glob. Chang. Biol. 27, 1704–1720 (2021).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  48. Brando, P. M. et al. Abrupt increases in Amazonian tree mortality due to drought\textendashfire interactions. Proc. Natl Acad. Sci. USA 111, 6347–6352 (2014).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  49. Phillips, O. L. et al. Pattern and process in Amazon tree turnover, 1976–2001. Phil. Trans. R. Soc. Lond. B 359, 381–407 (2004).

    Article  CAS  Google Scholar 

  50. Harris, R. M. B. et al. Biological responses to the press and pulse of climate trends and extreme events. Nat. Clim. Change 8, 579–587 (2018).

    Article  ADS  Google Scholar 

  51. Andrus, R. A., Chai, R. K., Harvey, B. J., Rodman, K. C. & Veblen, T. T. Increasing rates of subalpine tree mortality linked to warmer and drier summers. J. Ecol. 109, 2203–2218 (2021).

    Article  Google Scholar 

  52. Murphy, H. T., Bradford, M. G., Dalongeville, A., Ford, A. J. & Metcalfe, D. J. No evidence for long-term increases in biomass and stem density in the tropical rain forests of Australia. J. Ecol. 101, 1589–1597 (2013).

    Article  Google Scholar 

  53. Bennett, A. C., McDowell, N. G., Allen, C. D. & Anderson-Teixeira, K. J. Larger trees suffer most during drought in forests worldwide. Nat. Plants 1, 15139 (2015).

    Article  PubMed  Google Scholar 

  54. Chitra-Tarak, R. et al. Hydraulically-vulnerable trees survive on deep-water access during droughts in a tropical forest. New Phytol. 231, 1798–1813 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Anderegg, W. R. L. et al. Meta-analysis reveals that hydraulic traits explain cross-species patterns of drought-induced tree mortality across the globe. Proc. Natl Acad. Sci. USA 113, 5024–5029 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  56. Taylor, T. C., Smith, M. N., Slot, M. & Feeley, K. J. The capacity to emit isoprene differentiates the photosynthetic temperature responses of tropical plant species. Plant Cell Environ. 42, 2448–2457 (2019).

    Article  CAS  PubMed  Google Scholar 

  57. Arellano, G., Zuleta, D. & Davies, S. J. Tree death and damage: a standardized protocol for frequent surveys in tropical forests. J. Veg. Sci. 32, e12981 (2021).

    Article  Google Scholar 

  58. Bradford, M. G., Murphy, H. T., Ford, A. J., Hogan, D. L. & Metcalfe, D. J. Long-term stem inventory data from tropical rain forest plots in Australia. Ecology 95, 2362 (2014).

    Article  Google Scholar 

  59. Johnson, D. J. et al. Climate sensitive size-dependent survival in tropical trees. Nat. Ecol. Evol. 2, 1436–1442 (2018).

    Article  PubMed  Google Scholar 

  60. Needham, J., Merow, C., Chang-Yang, C.-H., Caswell, H. & McMahon, S. M. Inferring forest fate from demographic data: from vital rates to population dynamic models. Proc. Biol. Sci. 285, 20172050 (2018).

    PubMed  PubMed Central  Google Scholar 

  61. Lewis, S. L. et al. Tropical forest tree mortality, recruitment and turnover rates: calculation, interpretation and comparison when census intervals vary. J. Ecol. 92, 929–944 (2004).

    Article  Google Scholar 

  62. Reeves, J., Chen, J., Wang, X. L., Lund, R. & Lu, Q. Q. A review and comparison of changepoint detection techniques for climate data. J. Appl. Meteorol. Climatol. 46, 900–915 (2007).

    Article  ADS  Google Scholar 

  63. Clark, J. S., Bell, D. M., Kwit, M. C. & Zhu, K. Competition-interaction landscapes for the joint response of forests to climate change. Glob. Chang. Biol. 20, 1979–1991 (2014).

    Article  ADS  PubMed  Google Scholar 

  64. Oliva, J., Stenlid, J. & Martínez-Vilalta, J. The effect of fungal pathogens on the water and carbon economy of trees: implications for drought-induced mortality. New Phytol. 203, 1028–1035 (2014).

    Article  CAS  PubMed  Google Scholar 

  65. Franklin, J. F., Shugart, H. H. & Harmon, M. E. Tree death as an ecological process. Bioscience 37, 550–556 (1987).

    Article  Google Scholar 

  66. Yanoviak, S. P. et al. Lightning is a major cause of large tree mortality in a lowland neotropical forest. New Phytol. 225, 1936–1944 (2020).

    Article  PubMed  Google Scholar 

  67. Preisler, Y., Tatarinov, F., Grünzweig, J. M. & Yakir, D. Seeking the ‘point of no return’ in the sequence of events leading to mortality of mature trees. Plant Cell Environ. 44, 1315–1328 (2020).

    Article  PubMed  CAS  Google Scholar 

  68. Aragão, L. E. O. C. et al. Spatial patterns and fire response of recent Amazonian droughts. Geophys. Res. Lett. 34, L07701 (2007).

    Article  ADS  Google Scholar 

  69. Malhi, Y. et al. The linkages between photosynthesis, productivity, growth and biomass in lowland Amazonian forests. Glob. Chang. Biol. 21, 2283–2295 (2015).

    Article  ADS  PubMed  Google Scholar 

  70. Hutchinson, M. F., Xu, T., Kesteven, J. L., Marang, I. J. & Evans, B. J.ANUClimate v2.0, NCI Australia. (2021).

  71. Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Carscadden, K. A. et al. Niche breadth: causes and consequences for ecology, evolution, and conservation. Q. Rev. Biol. 95, 179–214 (2020).

    Article  Google Scholar 

  73. Swenson, N. G. et al. A reframing of trait–demographic rate analyses for ecology and evolutionary biology. Int. J. Plant Sci. 181, 33–43 (2020).

    Article  Google Scholar 

  74. Morueta-Holme, N. et al. Habitat area and climate stability determine geographical variation in plant species range sizes. Ecol. Lett. 16, 1446–1454 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Brum, M. et al. Hydrological niche segregation defines forest structure and drought tolerance strategies in a seasonal Amazon forest. J. Ecol. 107, 318–333 (2019).

    Article  Google Scholar 

  76. Chitra-Tarak, R. et al. The roots of the drought: hydrology and water uptake strategies mediate forest-wide demographic response to precipitation. J. Ecol. 106, 1495–1507 (2018).

    Article  Google Scholar 

  77. Boria, R. A., Olson, L. E., Goodman, S. M. & Anderson, R. P. Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecol. Modell. 275, 73–77 (2014).

    Article  Google Scholar 

  78. Farquhar, G. D., von Caemmerer, S. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).

    Article  CAS  PubMed  Google Scholar 

  79. Duursma, R. A. Plantecophys—an R package for analysing and modelling leaf gas exchange data. PLoS ONE 10, e0143346 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. De Kauwe, M. G. et al. A test of the ‘one-point method’ for estimating maximum carboxylation capacity from field-measured, light-saturated photosynthesis. New Phytol. 210, 1130–1144 (2016).

    Article  PubMed  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  82. McElreath, R. Statistical Rethinking: A Bayesian Course with Examples in R and STAN (CRC Press, 2020).

  83. “RStan: the R interface to Stan.” R package version 2.21.2. (Stan Development Team, 2020).

  84. Bürkner, P.-C. brms: An R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).

    Article  Google Scholar 

  85. R Core Team. R: a language and environment for statistical computing. (R Foundation for Statistical Computing, 2021).

  86. Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

Download references


We thank A. Cheesman for his help with field work in Bellenden Ker, and M. Torrello Raventos for the first census of Bellender Ker. D.B. was funded by the Belgian American Educational Foundation (BAEF), the Philippe Wiener–Maurice Anspach Foundation, and the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 895799. G.D. was supported by the Philippe Wiener–Maurice Anspach Foundation. Y.M. was supported by the Frank Jackson Foundation. S.M.M. was partially funded by NASA grant 20-CARBON20-0033. J.A.-G. was funded by the UK Natural Environmental Research Council (NERC; grants NE/T011084/1 and NE/S011811/1). The trait campaign and data analysis were funded by NERC grant NE/P001092/1 to Y.M. and the European Research Council projects T-FORCES (Tropical Forests in the Changing Earth System; 291585) to O.L.P. and Y.M., and GEM-TRAIT (321131) to Y.M., under the European Union's Seventh Framework Programme (FP7/2007-2013). We acknowledge the foresight of G. Stocker in establishing the CSIRO permanent plots and the many CSIRO staff and volunteers who have helped to measure and maintain the plots since 1971; thankful to the Daintree Rainforest Observatory for providing a subsidy on accommodation and station fees, and to the Australian Supersite Network, part of the Australian Government’s Terrestrial Ecosystem Network (Daintree Rainforest, Cow Bay, and Robson Creek Supersites) for provision of data used as part of the study. Computational resources have been provided by the Consortium des Équipements de Calcul Intensif (CÉCI), funded by the Fonds de la Recherche Scientifique de Belgique (F.R.S.-FNRS) under grant no. 2.5020.11 and by the Walloon Region. This study is a product of the Global Ecosystems Monitoring (GEM) network.

Author information

Authors and Affiliations



D.B. and S.M.M. designed the study and the statistical models of tree survival. D.B. tidied and vetted the demographic and trait data and performed the analyses. D.B. led the writing with regular feedback from S.M.M., Y.M. and C.F. on intermediate stages of the analyses and manuscript. G.D. extracted species biographical data and the climatology associated with their locations to calculate species climatic niches. S.W.R. helped to generate the climatic covariates. L.A.C. and L.P.B. led the trait data collection, assisted by R.D., B.E.M., H.R.N.-R., J.R.C.P. and P.E.S.-A. and initiated and supported by Y.M. M.F.H. provided the raw climate product from ANUClimate. M.B. supplied demographic data for the 20 CSIRO plots and Robson Creek. C.F., L.A.C., S.W.R., J.A.-G. J and G.D. provided feedback on the discussion. S.G.W.L. contributed demographic data of the Daintree Observatory and commented on the manuscript. All authors commented on the manuscript and gave their approval for publication.

Corresponding author

Correspondence to David Bauman.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Maxime Cailleret, Brendan Choat and Xiangtao Xu for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Schematic illustration of the size-dependent multilevel survival model and its parameters.

Survival probability per year (theta) for a given species is defined by either one of two similar logistic functions, depending on a DBH threshold, only changing in the sign of r (r1 > 0, r2 < 0), yielding two S-shaped logistic function related through their common plateau K. This size-dependent model of survivorship was fitted separately for each of the 81 studied species. p1 and p2 are the inflection points of the two curves, with p2 characterising the species DBH at 50% survival per year. r1 and r2 are rates of survival change per year in the corresponding portions of the survival curve. While K was initially the background survival rate of the species during most of the tree live59,60, our model decomposes K into a species-specific average survival rate per year across all the sites and years (K_μ), a site-specific deviation from the average (K_P), a year-specific deviation from the average (K_T), and an error term (not represented, here). Because nearly all species occurred in multiple plots, and because different plots were recensused on different years, every two to five years mostly, the overlap of census years across plots allowed us to infer a latent survival probability per year, between 1971 and 2019 (K_lat). K_lat, the latent logit-survival probability per year, generated separately for each species through this survival model, was used as the response variable of all subsequent analyses (M1 to M6, see Supplementary Table 4) to study how species’ mortality risk (i.e. 1 - survivorship, or 1 - K) changed through the years, whether this change varied among species and sites, and how climate, species’ climatic niche, and species functional traits related to mortality risk changes through time. Examples of the above theoretical curve from some of the studied species are presented in Extended Data Fig. 2.

Extended Data Fig. 2 Illustration of the interspecific variability of survival probability as a function of tree diameter.

The figures represent species-specific survival probability predictions between 10 and 160 cm DBH for four of the 80 species of the study, across forest plots and years, based on the species-specific Bayesian multilevel models of survival (eq. 1; Extended Data Fig. 1). The red line is the posterior survival probability per year median, and the dark and light grey shaded areas are the 50%- and 90% posterior credibility intervals, respectively. Vertical dashed lines correspond to the observed maximum DBH for the corresponding species (Acacia celsa Tindale, Acronychia laevis J.G.Forest & G.Forst, Cardwellia sublimis F.Muell., and Flindersia bourjotiana F.Muell.).

Extended Data Fig. 3 Effects of local climate on average mortality risk and mortality risk temporal change across plots (models M3).

Model M3 tests the effect of ‘year’, and either Tmax and MCWD, or VPD and MCWD on mortality risk (see es. 3; Tmax and VPD tested in separate models to limit collinearity, see Supplementary Methods 1). Climate variables influence the model grand intercept (average mortality risk across plots, α0) and grand ‘year’ slope (mortality risk increase over time, β0), through coefficients α1-2 and β1-2, respectively. Average mortality risk is higher in plots presenting on higher long-term mean monthly Tmax and VPD during the warmest/driest quarter (95%-HPDI of α1 encompass positive values only). Mean climate does not clearly predict how steep the temporal increase in mortality risk is among the plots (95%-HPDI encompass zero).

Extended Data Fig. 4 Sensitivity analyses of the mortality risk increase over time: Species-level and plot-level slopes of change of mortality risk per year (1984-2019) without influence of cyclones.

a, c, Plot-level (a) and species-level (c) slopes of mortality risk change per year (βk and βj, respectively; eqs. 3 and 4), obtained with models M3 and M4 on the basis of the subset of survival data obtained from the first approach to remove the effect of cyclones: Any census interval in a plot damaged by a cyclone was removed from the initial dataset, and the Bayesian size-dependent survival model (eq. 1) was run for the species that presented >400 observations in the reduced dataset. Models M3 and M4 were then run on the resulting latent survival probabilities K_lat (see eqs. 1, 3, 4) to test for temporal changes in mortality risk across plots and species, with plot-level and species-level slopes. b, d, The second approach to remove the influence of cyclones consisted of only keeping the plots that remained totally unaffected by cyclones in the last 49 years (see Supplementary Methods 1), then selecting the species that had >400 observations in the remaining plots, generating the latent survival probability per year (K_lat; eq. 1), and running models M3 and M4 as for the first approach. ad, Species and plots whose 95%-highest posterior density interval (HPDI) encompassed zero are half-transparent. Blue and red circles indicate clear mortality increase and decrease through time, respectively (intervals not encompassing zero). The black vertical solid and dashed lines are the median and 95%-HPDI of the grand ‘year’ slope (i.e. generalisable effect across plots (a, b) and species (c, d)). The two approaches to test the robustness of the mortality risk temporal change without the main influence of cyclones (removal of intervals, or removal of plots) respectively had 69 and 15 species, and 23 and five plots The percentage and number of plots and species whose mortality risk increased between 1984 and 2019 for approach 1 and 2 respectively were 96% (22) and 100% (5) of the plots, and 81% (56) and 92% (11) of species.

Extended Data Fig. 5 Illustration of functional trait relationships among the 40 species with trait data, used in the M6 models of mortality risk over time.

The figures represent the functional space occupied by the 40 tree species on the first four ordination axes of a correlation PCA, together describing 71.4% of the trait values (see Supplementary Table 3 for trait abbreviations).

Extended Data Fig. 6 Effects of species functional traits on average mortality risk and mortality risk change through time.

a, b, Trait mediation of species-level intercept and ‘year’ slopes (α1 and β1, respectively, see eqs. 6 in Supplementary Methods 1). a, Positive and negative slopes indicate traits increasing and decreasing average mortality risk, respectively. b, Positive slopes correspond to traits that accentuate the rate of mortality risk change per year, while negative slopes correspond to attenuations of this rate. Circles and intervals are median and 95%-highest posterior density intervals (HPDI) for slope coefficients α1 (trait effect on average mortality risk) and β1 (trait mediation of ‘year’ effect on mortality risk) (see eq. 6).

Extended Data Fig. 7 Change of stand-level basal area per hectare (BA) over time.

Change of BA per forest plot between 1971 and 2019 (a). Each circle is a plot BA on the year beginning a census period. The smooth curve and shaded area are the mean and 95% confidence interval of a generalised additive model (GAM), showing a decrease in plot BA across plots, and within most plots. The circle colour is proportional to the BA value (yellow to blue for low to high BA).

Extended Data Fig. 8 Living and dead tree DBH distributions for the time period 1984-2019.

The histogram bins are 1 cm-wide. We explored the relation between increased mortality risk and tree DBH visually and did not detect any clear size association. A Pearson’s Chi-squared test with simulated p-value confirmed that no clear link could be detected between the proportions of alive and dead individuals in DBH bins of 1 or 5 cm (Chi-squaredbin_5cm = 0.0054, p-value = 1).

Extended Data Fig. 9 Changes of log-transformed absolute diameter growth rate (AGR) over time across and within species, for the 81 studied species.

The main figure shows the species-level coefficients of AGR change per year, between 1971 and 2019 (species-specific ‘year’ slope; β2j in eqs. 7, Supplementary Methods 1). Circles and horizontal bars are posterior median and 95% highest posterior density intervals (HPDI). Plain black circles and bars are species whose posterior 95%-HPDI did not encompass zero; semi-transparent circles and bars are species whose HPDI encompassed zero (no clear AGR increase or decrease). The red dashed line marks zero, that is, the separation between coefficient values indicating an increase of AGR over time (positive values) and a decrease of AGR over time (negative values). The vertical plain and dashed blue lines are the median and 95%-HPDI of the grand year slope (see inset). The inset shows the posterior probability distribution of the coefficient of AGR change per year across all species (i.e. grand ‘year’ slope mean; β2,0 in eqs. 7, Supplementary Methods 1). Overall, AGR did not clearly increase or decrease across species (unlike mortality risk; Fig. 3). AGR increased for 14 species and decreased for 19 species, while remaining mostly unchanged (95%-HPDI encompassing zero) for 48 species (semi-transparent circles and horizontal bars).

Extended Data Fig. 10 Comparison of the climatic space occupied by the 24 plots of Australian tropical moist forests of the study with the total climatic space of tropical moist forests worldwide.

The climatic spaces were obtained from 30-year climate averages (1981-2010) extracted from TerraClimate71, combined with the spatial locations of the grid cells belonging to the biomes “Tropical and subtropical moist broadleaf forests” (including tropical montane forests)86 (see

Extended Data Fig. 11 Overall temporal trends in climate across the environmental gradient.

Three Bayesian B-spline models with varying plot intercept and four basis functions were used to model monthly VPD, Tmax and MCWD over time across all 24 plots (see Methods). Both temperature and VPD showed a strong increase across all plots. MCWD did not show any clear directional trend across plots (more details in ref. 24.

Supplementary information

Supplementary Methods 1

Supplementary description and specification, including equations, of the statistical models.

Reporting Summary

Supplementary Methods 2

Detailed commented R code underlying the analyses.

Peer Review File

Supplementary Tables

This file contains Supplementary Tables 1–8

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bauman, D., Fortunel, C., Delhaye, G. et al. Tropical tree mortality has increased with rising atmospheric water stress. Nature 608, 528–533 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing