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Pervasive increase in tree mortality across the Australian continent

Nature Plants volume 12, pages 62–73 (2026)Cite this article

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Abstract

Widespread climate-driven increases in background tree mortality rates have the potential to reduce the carbon storage of terrestrial ecosystems, challenging their effectiveness as natural buffers against atmospheric CO2 enrichment with major consequences for the global carbon budget. However, the global extent of trends in tree mortality and their drivers remains poorly quantified. The Australian continent experiences one of the most variable climates on Earth and is host to a diverse range of forest biomes that have evolved high resistance to disturbance, providing a valuable test case for the pervasiveness of tree mortality trends. Here we compile an 83-year tree dynamics database (1941–2023) from >2,700 forest plots across Australia covering tropical savanna and rainforest and warm and cool temperate forests, to explore spatiotemporal patterns of tree mortality and the associated drivers. Over the past eight decades, we found a consistent trend of increasing tree mortality across the four forest biomes. This temporal trend persisted after accounting for stand structure and was exacerbated in forests with low moisture index or a high competition index. Species with traits associated with high growth rate—low wood density, high specific leaf area and short maximum height—exhibited higher average mortality, but the rate of mortality increase was comparable across different functional groups. Increasing mortality was not associated with increasing growth, given that stand basal area increments either declined or remained unchanged over time, but it was associated with increasing temperature over time. Our findings suggest that ongoing climate change has driven pervasive shifts in forest dynamics beyond natural recovery in a range of forest biomes with high resilience to disturbance, threatening the enduring capacity of forests to sequester carbon under current and future climate scenarios.

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Fig. 1: Spatial coverage and temporal trajectories of tree mortality across four major forest biomes in Australia.
Fig. 2: Drivers of tree mortality across four major forest biomes in Australia.
Fig. 3: Functional traits mediate species-level mortality rates and their temporal trends across four major forest biomes in Australia.
Fig. 4: Size-dependent tree mortality across four major forest biomes in Australia.
Fig. 5: Temporal trends in stand basal area increment and their dependence on stand structural attributes across four major forest biomes in Australia.

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Data availability

The tree-by-tree observations are publicly available via Figshare at https://doi.org/10.6084/m9.figshare.28407893 (ref. 98). However, some datasets have been anonymized (for example, geographic locations removed) or excluded, as required by dataset custodians (Supplementary Table 1). The records of tropical cyclone events occurring in QPRP-CSIRO plots can be accessed via https://data.csiro.au/collection/csiro:6638v3. The Bushfire Boundaries dataset is available from the Digital Atlas of Australia (Historical Bushfire Boundaries|Historical Bushfire Boundaries|Digital Atlas of Australia). For climate, the SPEI dataset is available at https://spei.csic.es/spei_database_2_10. The moisture index dataset is available at CGIAR CSI Global Aridity and PET Database. TerraClimate dataset is available at https://doi.org/10.1038/sdata.2017.191. The AusTraits database is available at https://austraits.org/.

Code availability

The codes used for this study are available via Figshare at https://doi.org/10.6084/m9.figshare.28407893 (ref. 98).

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Acknowledgements

We thank all collaborators, including those not listed as coauthors, for supporting this work and for their contributions to data collection and management. We thank the Terrestrial Ecosystem Research Network (Daintree Rainforest, Cow Bay and Robson Creek Supersites) and individual scientists, including Lucas Cernusak and Susan Laurance (James Cook University, QLD), for making their measurements openly accessible. The legacy and contribution of past Queensland Government Forestry Departments and staff in data collection, collation and maintenance of QLD Native Forest Permanent Plot data since 1941 is gratefully acknowledged. We thank H. Murphy (CSIRO) for assistance with the QPRP-CSIRO dataset. We acknowledge that the QPRP-CSIRO data is the long-term work of CSIRO staff. We encourage prospective investigators to inform the principal investigators of their intent to use these data in publications. We acknowledge the Department of Energy, Environment and Climate Action, Victoria, for contributing the Victorian Forest Monitoring Program data. We acknowledge the former VicForests for contributing the Victorian Permanent Growth Plot dataset. For Western Australian data, we thank the Forest Management Branch, Department of Biodiversity, Conservation and Attractions and predecessors, especially the work of L. McCaw, M. Rayner and R. Breidahl. R.L. and J.X. were supported by National Key R&D Program of China (grant no. 2022YFF0802104), National Natural Science Foundation of China (grant no. 32325033) and Shanghai Pilot Program for Basic Research (grant no. TQ20220102). The Forest Industries Climate Change Research Fund grant from the Department of Agriculture, Fisheries and Forestry (project no. B0018298 DAFF) to D. Bowman supported the initial collation of much of the data used in this study. R.T. was funded by an Australian Research Council Discovery Project (grant no. DP220103711). B.E.M. and L.J.W. were supported by an Australian Research Council Laureate Fellowship (grant no. FL190100003) awarded to B.E.M.

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Authors and Affiliations

  1. Hawkesbury Institute for the Environment, Western Sydney University, Sydney, New South Wales, Australia

    Ruiling Lu, Laura J. Williams, Hannah Carle & Belinda E. Medlyn

  2. Research Center for Global Change and Ecological Forecasting, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, China

    Ruiling Lu & Jianyang Xia

  3. School of Agriculture, Food, and Ecosystem Sciences, The University of Melbourne, Melbourne, Victoria, Australia

    Raphael Trouvé & Patrick J. Baker

  4. Research Institute for the Environment and Livelihoods, Charles Darwin University, Darwin, Northern Territory, Australia

    Brett P. Murphy

  5. CSIRO Environment, Canberra, Australian Capital Territory, Australia

    David I. Forrester

  6. Department of Environment and Genetics, School of Agriculture, Biomedicine and Environment, La Trobe University, Melbourne, Victoria, Australia

    Peter T. Green

  7. Centre for Tropical Environmental and Sustainability Science, James Cook University, Cairns, Queensland, Australia

    Michael J. Liddell

  8. Sustainable Timber Tasmania, Hobart, Tasmania, Australia

    Crispen Marunda

  9. Forestry Corporation of New South Wales, Sydney, New South Wales, Australia

    David Mannes

  10. Biodiversity and Conservation Science, Department of Biodiversity, Conservation and Attractions, Perth, Western Australia, Australia

    Richard Mazanec & Katinka X. Ruthrof

  11. Queensland Herbarium and Biodiversity Science, Department of Environment, Tourism, Science and Innovation, Brisbane, Queensland, Australia

    Michael R. Ngugi & Victor J. Neldner

  12. School of Natural Sciences, University of Tasmania, Hobart, Tasmania, Australia

    Lynda Prior & Shaun Suitor

  13. School of Environmental and Conservation Sciences, Murdoch University, Murdoch, Western Australia, Australia

    Katinka X. Ruthrof

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Contributions

B.E.M. initially conceptualized the study. B.P.M., H.C., P.T.G., M.J.L., C.M., D.M., R.M., M.R.N., V.J.N., K.R. and S.S. contributed data and assisted in their interpretation. L.J.W. and B.E.M. collated the datasets with assistance from L.P., P.J.B., D.I.F. and R.T. R.L. harmonized the datasets; led the data analysis with assistance from R.T., L.J.W. and B.E.M; and wrote the first draft of the manuscript. All authors contributed to manuscript revisions.

Corresponding authors

Correspondence to Ruiling Lu or Belinda E. Medlyn.

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Nature Plants thanks Roel Brienen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Temporal pattern of tree mortality rate across four major biomes in Australia.

Temporal trajectories of annual tree mortality rate across four major forest biomes: a, tropical savanna; b, tropical rainforest; c, warm temperate forest; and d, cool temperate forest. Solid lines show mean annual mortality rate, and shaded bands indicate 95% confidence intervals based on 1,000 bootstrap resamples per year.

Extended Data Fig. 2 Temporal variation in plot number, mean plot area, and census interval across four major biomes in Australia.

Changes over time in the number of plots, mean plot area, and census interval for four Australian biomes. Colors denote tropical savannas (orange), tropical rainforests (green), warm temperate forests (purple), and cool temperate forests (blue).

Extended Data Fig. 3 Plot-level tree mortality trends across four major forest biomes in Australia.

Annual change in mortality rate for individual plots in a, tropical savanna; b, tropical rainforest; c, warm temperate forest; and d, cool temperate forest. Analyses include only plots with ≥3 censuses (sample sizes: 158 of 198, 24 of 25, 895 of 1,168, and 822 of 1,333, respectively). Random-slope models were fitted to estimate within-plot changes through time, and the annual change in mortality rate was approximated as exp(β) − 1, where β is the year coefficient (see Supplementary Methods 1). Plots with β values outside the range -0.5 to 2 were excluded to avoid extreme fits.

Extended Data Fig. 4 Temporal trends in annual maximum temperature across four major forest biomes in Australia.

Points show the annual mean maximum temperature averaged across plots for a, tropical savanna; b, tropical rainforest; c, warm temperate forest; and d, cool temperate forest. Solid lines show the temporal trends predicted by the linear mixed-effects model (Model 7).

Extended Data Fig. 5 Temporal trends in annual mean vapor pressure deficit (VPD) across four major forest biomes in Australia.

Points show the annual mean vapor pressure deficit (VPD) averaged across plots for a, tropical savanna; b, tropical rainforest; c, warm temperate forest; and d, cool temperate forest. Solid lines show the temporal trends estimated using a linear mixed-effects model (Model 7).

Extended Data Fig. 6 Temporal trends in drought severity across four major forest biomes in Australia.

Points show the annual minimum Standardized Precipitation Evapotranspiration Index (SPEI) averaged across plots for a, tropical savanna; b, tropical rainforest; c, warm temperate forest; and d, cool temperate forest. Solid lines show the temporal trends predicted by the linear mixed-effects model (Model 7). Although SPEI is a relative measure of climatic water balance rather than direct vegetation water loss, sustained negative trends indicate intensifying drought stress and an increasing frequency of extreme drought events.

Extended Data Fig. 7 Trait effects on average mortality rate and temporal change across four major forest biomes in Australia.

Effects of species functional traits on average mortality rate and their temporal changes across biomes. Points show estimated hazard ratios with 95% confidence intervals for maximum tree height (Hmax), specific leaf area (SLA) and wood density (WD). Traits with positive effects on mortality are shown in red, and those with negative effects in blue. Species numbers were 135 in tropical savannas, 522 in tropical rainforests, 282 in warm temperate forests, and 126 in cool temperate forests.

Extended Data Fig. 8 Principal component analysis (PCA) of species trait distribution.

PCA of species-level mean functional traits. Arrows denote trait loadings, and points represent species mean positions in trait space. Contour lines indicate kernel density of species occurrence, with red showing higher density and yellow lower density.

Extended Data Fig. 9 Predicted mortality patterns across key functional trait gradients.

Modelled temporal mortality patterns across gradients of a, maximum tree height (Hmax); b, specific leaf area (SLA); and c, wood density (WD) for all biomes combined. Shaded bands indicate 95% confidence intervals for fixed effects.

Extended Data Fig. 10 Temporal trends in tree growth rate and determinants across four major forest biomes in Australia.

Temporal patterns of tree growth rate across four major biomes and their dependence on tree- and stand-level characteristics. a, Fixed effects of stand basal area (BA), diameter at breast height (lnDBH), and year on annual tree growth rate. Red and blue triangles denote significant positive and negative effects, respectively. b, Modelled annual tree growth rates over time. Solid lines show significant temporal trends with shaded 95% confidence intervals; dashed lines denote non-significant trends. Trees with DBH > 80 cm (2% of total) were excluded due to nonlinear growth responses not captured by the linear model (see Supplementary Fig. 10).

Supplementary information

Supplementary Information

Supplementary Figs. 1–11, Discussion and Tables 1–4.

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Lu, R., Williams, L.J., Trouvé, R. et al. Pervasive increase in tree mortality across the Australian continent. Nat. Plants 12, 62–73 (2026). https://doi.org/10.1038/s41477-025-02188-2

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