Abstract
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.
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Data availability
The raw demographic data that supported the findings are available in Bradford et al.58, in the CSIRO Data Access Portal (https://doi.org/10.4225/08/59475c67be7a4). The survival dataset used for the 81 studied species was archived on the platform of the Terrestrial Ecosystem Research Network infrastructure (https://doi.org/10.25901/rxtc-th28), 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 (https://doi.org/10.25901/rxtc-th28).
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Acknowledgements
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.
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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.
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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. a–d, 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 https://ecoregions2017.appspot.com/).
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.
Supplementary Methods 2
Detailed commented R code underlying the analyses.
Supplementary Tables
This file contains Supplementary Tables 1–8
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Bauman, D., Fortunel, C., Delhaye, G. et al. Tropical tree mortality has increased with rising atmospheric water stress. Nature 608, 528–533 (2022). https://doi.org/10.1038/s41586-022-04737-7
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DOI: https://doi.org/10.1038/s41586-022-04737-7
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