Skip to main content

Thank you for visiting nature.com. 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.

  • Article
  • Published:

Climate-induced tree-mortality pulses are obscured by broad-scale and long-term greening

Nature Ecology & Evolution (2024)Cite this article

Subjects

Abstract

Vegetation greening has been suggested to be a dominant trend over recent decades, but severe pulses of tree mortality in forests after droughts and heatwaves have also been extensively reported. These observations raise the question of to what extent the observed severe pulses of tree mortality induced by climate could affect overall vegetation greenness across spatial grains and temporal extents. To address this issue, here we analyse three satellite-based datasets of detrended growing-season normalized difference vegetation index (NDVIGS) with spatial resolutions ranging from 30 m to 8 km for 1,303 field-documented sites experiencing severe drought- or heat-induced tree-mortality events around the globe. We find that severe tree-mortality events have distinctive but localized imprints on vegetation greenness over annual timescales, which are obscured by broad-scale and long-term greening. Specifically, although anomalies in NDVIGS (ΔNDVI) are negative during tree-mortality years, this reduction diminishes at coarser spatial resolutions (that is, 250 m and 8 km). Notably, tree-mortality-induced reductions in NDVIGS (|ΔNDVI|) at 30-m resolution are negatively related to native plant species richness and forest height, whereas topographic heterogeneity is the major factor affecting ΔNDVI differences across various spatial grain sizes. Over time periods of a decade or longer, greening consistently dominates all spatial resolutions. The findings underscore the fundamental importance of spatio-temporal scales for cohesively understanding the effects of climate change on forest productivity and tree mortality under both gradual and abrupt changes.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Changes in NDVIGS from multiscale sensors corresponding to locations of tree-mortality sites.
Fig. 2: Four categories of tree-mortality sites based on ΔNDVI values across different sensors.
Fig. 3: Dependence of ΔNDVI from multiscale sensors on potential drivers.
Fig. 4: Percentage of Landsat pixels with tree mortality within 270-m (P270 m) and 8-km (P8 km) combined grids and its drivers.

Similar content being viewed by others

Data availability

The tree-mortality sites can be found at https://www.iufro.org/science/task-forces/tree-mortality-patterns (https://doi.org/10.6084/m9.figshare.24847698) (ref. 79). The Google Earth sub-metre high-resolution satellite images can be found at https://doi.org/10.6084/m9.figshare.23243915 (ref. 80). The climate, vegetation and soil data can be found at https://doi.org/10.6084/m9.figshare.24847788 (ref. 81). The combined grids of 270 m and about 8 km can be found at https://doi.org/10.6084/m9.figshare.24850734 (ref. 82). The Landsat NDVI (EVI), MODIS NDVI (EVI), DEM (including elevation, slope and aspect) and TerraClimate database (including precipitation and PDSI product) were calculated on GEE, which is available at https://code.earthengine.google.com/. The GIMMS NDVI data can be obtained from https://poles.tpdc.ac.cn/en/data/9775f2b4-7370-4e5e-a537-3482c9a83d88/. The land cover data were downloaded from http://maps.elie.ucl.ac.be/CCI/viewer/. The world continental boundaries were obtained from https://hub.arcgis.com/datasets/esri::world-continents/about. The sub-metre high-resolution satellite images were downloaded from Google Earth, which is available at https://earth.google.com/. The SPEI data are available from https://digital.csic.es/handle/10261/202305. The precipitation data can be retrieved from https://data.ceda.ac.uk/badc/cru/data/cru_ts. The available water-storage capacity, soil clay and soil sand data were downloaded from https://daac.ornl.gov/SOILS/guides/HWSD.html. The canopy height can be obtained from https://webmap.ornl.gov/ogc/dataset.jsp?dg_id=10023_1. The global maximum rooting depth was derived from https://wci.earth2observe.eu/thredds/catalog/usc/root-depth/catalog.html. The tree density was derived from https://elischolar.library.yale.edu/yale_fes_data/1/. The native plant species richness was downloaded from https://anthroecology.org/.

Code availability

Java, MATLAB, Python and R codes for the analysis of these data can be obtained from https://github.com/YCY-github-YCY/Tree.

References

  1. IPCC. Climate Change 2022: Impacts, Adaptation and Vulnerability (eds Pörtner, H.-O. et al.) (Cambridge Univ, Press, 2022).

  2. McDowell, N. G. et al. Pervasive shifts in forest dynamics in a changing world. Science 368, z9463 (2020).

    Article  Google Scholar 

  3. Allen, C. D. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manag. 259, 660–684 (2010).

    Article  Google Scholar 

  4. Allen, C. D., Breshears, D. D. & McDowell, N. G. On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere 6, 1–55 (2015).

    Article  Google Scholar 

  5. Hartmann, H. et al. Research frontiers for improving our understanding of drought-induced tree and forest mortality. N. Phytol. 218, 15–28 (2018).

    Article  Google Scholar 

  6. Williams, A. P. et al. Temperature as a potent driver of regional forest drought stress and tree mortality. Nat. Clim. Change 3, 292–297 (2013).

    Article  ADS  Google Scholar 

  7. Hammond, W. M. et al. Global field observations of tree die-off reveal hotter-drought fingerprint for Earth’s forests. Nat. Commun. 13, 1761 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  8. Anderegg, W. R. et al. Pervasive drought legacies in forest ecosystems and their implications for carbon cycle models. Science 349, 528–532 (2015).

    Article  ADS  CAS  PubMed  Google Scholar 

  9. Doughty, C. E. et al. Drought impact on forest carbon dynamics and fluxes in Amazonia. Nature 519, 78–82 (2015).

    Article  ADS  CAS  PubMed  Google Scholar 

  10. Gatti, L. V. et al. Drought sensitivity of Amazonian carbon balance revealed by atmospheric measurements. Nature 506, 76–80 (2014).

    Article  ADS  CAS  PubMed  Google Scholar 

  11. Lewis, S. L. et al. The 2010 Amazon drought. Science 331, 554 (2011).

    Article  ADS  CAS  PubMed  Google Scholar 

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

    Article  ADS  CAS  PubMed  Google Scholar 

  13. Peñuelas, J. et al. Shifting from a fertilization-dominated to a warming-dominated period. Nat. Ecol. Evol. 1, 1438–1445 (2017).

    Article  PubMed  Google Scholar 

  14. Chen, C. et al. China and India lead in greening of the world through land-use management. Nat. Sustain. 2, 122–129 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Myneni, R. B. et al. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386, 698–702 (1997).

    Article  ADS  CAS  Google Scholar 

  16. Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).

    Article  ADS  Google Scholar 

  17. Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. Change 6, 791–795 (2016).

    Article  ADS  CAS  Google Scholar 

  18. Friedlingstein, P. et al. Global carbon budget 2021. Earth Syst. Sci. Data 14, 1917–2005 (2022).

    Article  ADS  Google Scholar 

  19. Qiao, X. et al. Latitudinal patterns of forest ecosystem stability across spatial scales as affected by biodiversity and environmental heterogeneity. Glob. Chang. Biol. 29, 2242–2255 (2023).

    Article  CAS  PubMed  Google Scholar 

  20. Qiao, X. et al. Spatial asynchrony matters more than alpha stability in stabilizing ecosystem productivity in a large temperate forest region. Glob. Ecol. Biogeogr. 31, 1133–1146 (2022).

    Article  Google Scholar 

  21. Peng, X. et al. Northern Hemisphere greening in association with warming permafrost. J. Geophys. Res. Biogeosci. 125, e2019JG005086 (2020).

    Article  ADS  Google Scholar 

  22. Piao, S. et al. Evidence for a weakening relationship between interannual temperature variability and northern vegetation activity. Nat. Commun. 5, 5018 (2014).

    Article  ADS  CAS  PubMed  Google Scholar 

  23. Wu, X. et al. Higher temperature variability reduces temperature sensitivity of vegetation growth in Northern Hemisphere. Geophys. Res. Lett. 44, 6173–6181 (2017).

    Article  ADS  Google Scholar 

  24. Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. In Proc. 31st International Conference on Neural Information Processing Systems (eds von Luxburg, U. et al.) 4768–4777 (2017).

  25. Lundberg, S. M. et al. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2, 56–67 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Grossiord, C. Having the right neighbors: how tree species diversity modulates drought impacts on forests. N. Phytol. 228, 42–49 (2020).

    Article  Google Scholar 

  27. O’Brien, M. J., Reynolds, G., Ong, R. & Hector, A. Resistance of tropical seedlings to drought is mediated by neighbourhood diversity. Nat. Ecol. Evol. 1, 1643–1648 (2017).

    Article  PubMed  Google Scholar 

  28. Giardina, F. et al. Tall Amazonian forests are less sensitive to precipitation variability. Nat. Geosci. 11, 405–409 (2018).

    Article  ADS  CAS  Google Scholar 

  29. Stephenson, N. L. & Das, A. J. Height-related changes in forest composition explain increasing tree mortality with height during an extreme drought. Nat. Commun. 11, 3402 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  30. Wu, H., Franklin, S. B., Liu, J. & Lu, Z. Relative importance of density dependence and topography on tree mortality in a subtropical mountain forest. For. Ecol. Manag. 384, 169–179 (2017).

    Article  Google Scholar 

  31. Adams, H. R., Barnard, H. R. & Loomis, A. K. Topography alters tree growth–climate relationships in a semi-arid forested catchment. Ecosphere 5, 1–16 (2014).

    Article  Google Scholar 

  32. Ashcroft, M. B., Chisholm, L. A. & French, K. O. Climate change at the landscape scale: predicting fine-grained spatial heterogeneity in warming and potential refugia for vegetation. Glob. Chang. Biol. 15, 656–667 (2009).

    Article  ADS  Google Scholar 

  33. Keppel, G. et al. Refugia: identifying and understanding safe havens for biodiversity under climate change. Glob. Ecol. Biogeogr. 21, 393–404 (2012).

    Article  Google Scholar 

  34. McDowell, N. G. et al. Mechanisms of a coniferous woodland persistence under drought and heat. Environ. Res. Lett. 14, 45014 (2019).

    Article  CAS  Google Scholar 

  35. Serra-Diaz, J. M., Scheller, R. M., Syphard, A. D. & Franklin, J. Disturbance and climate microrefugia mediate tree range shifts during climate change. Landsc. Ecol. 30, 1039–1053 (2015).

    Article  Google Scholar 

  36. Ashcroft, M. B., Gollan, J. R., Warton, D. I. & Ramp, D. A novel approach to quantify and locate potential microrefugia using topoclimate, climate stability, and isolation from the matrix. Glob. Chang. Biol. 18, 1866–1879 (2012).

    Article  ADS  Google Scholar 

  37. Dobrowski, S. Z. A climatic basis for microrefugia: the influence of terrain on climate. Glob. Chang. Biol. 17, 1022–1035 (2011).

    Article  ADS  Google Scholar 

  38. Keppel, G. et al. The capacity of refugia for conservation planning under climate change. Front. Ecol. Environ. 13, 106–112 (2015).

    Article  Google Scholar 

  39. Schwendenmann, L. et al. Tree water uptake in a tropical plantation varying in tree diversity: interspecific differences, seasonal shifts and complementarity. Ecohydrology 8, 1–12 (2015).

    Article  Google Scholar 

  40. West, A. G. et al. Diverse functional responses to drought in a Mediterranean-type shrubland in South Africa. N. Phytol. 195, 396–407 (2012).

    Article  CAS  Google Scholar 

  41. Lebourgeois, F., Gomez, N., Pinto, P. & Mérian, P. Mixed stands reduce Abies alba tree-ring sensitivity to summer drought in the Vosges mountains, western Europe. For. Ecol. Manag. 303, 61–71 (2013).

    Article  Google Scholar 

  42. Oliveira, B. F. & Scheffers, B. R. Vertical stratification influences global patterns of biodiversity. Ecography 42, 249 (2019).

    Article  ADS  Google Scholar 

  43. Morin, X. et al. Temporal stability in forest productivity increases with tree diversity due to asynchrony in species dynamics. Ecol. Lett. 17, 1526–1535 (2014).

    Article  PubMed  Google Scholar 

  44. Ouyang, S. et al. Stability in subtropical forests: the role of tree species diversity, stand structure, environmental and socio-economic conditions. Glob. Ecol. Biogeogr. 30, 500–513 (2021).

    Article  Google Scholar 

  45. Schnabel, F. et al. Species richness stabilizes productivity via asynchrony and drought-tolerance diversity in a large-scale tree biodiversity experiment. Sci. Adv. 7, k1643 (2021).

    Article  ADS  Google Scholar 

  46. Kemppinen, J. et al. Consistent trait–environment relationships within and across tundra plant communities. Nat. Ecol. Evol. 5, 458–467 (2021).

    Article  PubMed  Google Scholar 

  47. Sitch, S. et al. Evaluation of the terrestrial carbon cycle, future plant geography and climate-carbon cycle feedbacks using five dynamic global vegetation models (DGVMs). Glob. Chang. Biol. 14, 2015–2039 (2008).

    Article  ADS  Google Scholar 

  48. Khoury, S. & Coomes, D. A. Resilience of Spanish forests to recent droughts and climate change. Glob. Chang. Biol. 26, 7079–7098 (2020).

    Article  ADS  PubMed  Google Scholar 

  49. Forzieri, G. et al. Emerging signals of declining forest resilience under climate change. Nature 608, 534–539 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Rogers, B. M. et al. Detecting early warning signals of tree mortality in boreal North America using multiscale satellite data. Glob. Chang. Biol. 24, 2284–2304 (2018).

    Article  ADS  PubMed  Google Scholar 

  51. Pinzon, J. E. & Tucker, C. J. A non-stationary 1981–2012 AVHRR NDVI3g time series. Remote Sens. 6, 6929–6960 (2014).

    Article  ADS  Google Scholar 

  52. Holben, B. N. Characteristics of maximum-value composite images from temporal AVHRR data. Int. J. Remote Sens. 7, 1417–1434 (1986).

    Article  Google Scholar 

  53. Masek, J. G. et al. A Landsat surface reflectance dataset for North America, 1990-2000. IEEE Geosci. Remote S. 3, 68–72 (2006).

    Article  Google Scholar 

  54. Vermote, E & Saleous, N. LEDAPS Surface Reflectance Product Description (University of Maryland, 2007).

  55. Vermote, E., Justice, C., Claverie, M. & Franch, B. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sens. Environ. 185, 46–56 (2016).

    Article  ADS  Google Scholar 

  56. Foga, S. et al. Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sens. Environ. 194, 379–390 (2017).

    Article  ADS  Google Scholar 

  57. Roy, D. P. et al. Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote Sens. Environ. 185, 57–70 (2016).

    Article  ADS  Google Scholar 

  58. Amani, M. et al. Google Earth Engine cloud computing platform for remote sensing big data applications: a comprehensive review. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 5326–5350 (2020).

    Article  ADS  Google Scholar 

  59. Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).

    Article  ADS  Google Scholar 

  60. Tamiminia, H. et al. Google Earth Engine for geo-big data applications: a meta-analysis and systematic review. ISPRS J. Photogramm. 164, 152–170 (2020).

    Article  Google Scholar 

  61. 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, 1–12 (2018).

    Article  Google Scholar 

  62. Vicente-Serrano, S. M. et al. A global 0.5 gridded dataset (1901–2006) of a multi-scalar drought index considering the joint effects of precipitation and temperature. J. Hydrometeorol. 11, 1033–1043 (2010).

    Article  ADS  Google Scholar 

  63. Tong, X. et al. Increased vegetation growth and carbon stock in China karst via ecological engineering. Nat. Sustain. 1, 44–50 (2018).

    Article  Google Scholar 

  64. Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations-the CRU TS3. 10 Dataset. Int. J. Climatol. 34, 623–642 (2014).

    Article  Google Scholar 

  65. Wieder, W. R., Boehnert, J., Bonan, G. B. & Langseth, M. Regridded Harmonized World Soil Database v1.2 https://doi.org/10.3334/ORNLDAAC/1247 (Oak Ridge National Laboratory Distributed Active Archive Center, 2014).

  66. Simard, M., Pinto, N., Fisher, J. B. & Baccini, A. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. Biogeosci. 116, 4021 (2011).

    Article  Google Scholar 

  67. Crowther, T. W. et al. Mapping tree density at a global scale. Nature 525, 201–205 (2015).

    Article  ADS  CAS  PubMed  Google Scholar 

  68. Ellis, E. C., Antill, E. C. & Kreft, H. All is not loss: plant biodiversity in the Anthropocene. PLoS One 7, e30535 (2012).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  69. Anderegg, W. R. et al. Hydraulic diversity of forests regulates ecosystem resilience during drought. Nature 561, 538–541 (2018).

    Article  ADS  CAS  PubMed  Google Scholar 

  70. Hamed, K. H. & Rao, A. R. A modified Mann–Kendall trend test for autocorrelated data. J. Hydrol. 204, 182–196 (1998).

    Article  ADS  Google Scholar 

  71. Yang, H. et al. The detection and attribution of extreme reductions in vegetation growth across the global land surface. Glob. Chang. Biol. 29, 2351–2362 (2023).

    Article  CAS  PubMed  Google Scholar 

  72. Batunacun, W. R., Lakes, T. & Nendel, C. Using Shapley additive explanations to interpret extreme gradient boosting predictions of grassland degradation in Xilingol, China. Geosci. Model Dev. 14, 1493–1510 (2021).

    Article  ADS  Google Scholar 

  73. Odebiri, O., Mutanga, O., Odindi, J. & Naicker, R. Modelling soil organic carbon stock distribution across different land-uses in South Africa: a remote sensing and deep learning approach. ISPRS J. Photogramm. 188, 351–362 (2022).

    Article  Google Scholar 

  74. Silva, S. J., Keller, C. A. & Hardin, J. Using an explainable machine learning approach to characterize Earth System Model errors: application of SHAP analysis to modeling lightning flash occurrence. J. Adv. Model. Earth Syst. 14, e2021MS002881 (2022).

    Article  ADS  Google Scholar 

  75. Wang, H. et al. Exploring complex water stress–gross primary production relationships: impact of climatic drivers, main effects, and interactive effects. Glob. Chang. Biol. 28, 4110–4123 (2022).

    Article  CAS  PubMed  Google Scholar 

  76. Zhu, P., Abramoff, R., Makowski, D. & Ciais, P. Uncovering the past and future climate drivers of wheat yield shocks in Europe with machine learning. Earths Future 9, e2020EF001815 (2021).

    Article  ADS  Google Scholar 

  77. Chen, T. & Guestrin, C. XGBoost: a scalable tree boosting system. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (eds Krishnapuram, B. et al.) 785–794 (2016).

  78. Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).

    Article  Google Scholar 

  79. Yan, Y. et al. Tree-mortality sites for ‘Climate-induced tree mortality pulses are obscured by broad-scale and long-term greening’. Figshare https://doi.org/10.6084/m9.figshare.24847698 (2023).

  80. Yan, Y. et al. Google Earth sub-metre high-resolution satellite images for ‘Climate-induced tree mortality pulses are obscured by broad-scale and long-term greening’. Figshare https://doi.org/10.6084/m9.figshare.23243915 (2023).

  81. Yan, Y. et al. Climate, vegetation and soil data for ‘Climate-induced tree mortality pulses are obscured by broad-scale and long-term greening’. Figshare https://doi.org/10.6084/m9.figshare.24847788 (2023).

  82. Yan, Y. et al. Combined grids of 270 m and about 8 km for ‘Climate-induced tree mortality pulses are obscured by broad-scale and long-term greening’. Figshare https://doi.org/10.6084/m9.figshare.24850734 (2023).

Download references

Acknowledgements

This study was supported by the National Natural Science Foundation of China (grant no. 41988101 to S.P.). A.C. was supported by a US Department of Energy grant (grant no. DE-SC0022074). We thank D. Zhu, W. Lang, Y. Yan and Y. Deng for their useful suggestions for this paper, and H. Zhuang and M. Li for their help with the experiments. Any use of trade, product or firm names in this paper is for descriptive purposes only and does not imply endorsement by the US government.

Author information

Authors and Affiliations

  1. Institute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China

    Yuchao Yan, Shilong Piao, Songbai Hong & Hao Xu

  2. State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

    Shilong Piao

  3. Institute of Food and Agricultural Sciences, Agronomy Department, University of Florida, Gainesville, FL, USA

    William M. Hammond

  4. Department of Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA

    Anping Chen

  5. U.S. Geological Survey, Southwest Biological Science Center, Flagstaff, AZ, USA

    Seth M. Munson

  6. Department of Earth and Environment, Boston University, Boston, MA, USA

    Ranga B. Myneni

  7. Department of Geography and Environmental Studies, University of New Mexico, Albuquerque, NM, USA

    Craig D. Allen

Authors
  1. Yuchao Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shilong Piao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. William M. Hammond
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anping Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Songbai Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hao Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Seth M. Munson
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ranga B. Myneni
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Craig D. Allen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.P. designed the research. Y.Y. performed analysis and drafted the figures. Y.Y., S.P., S.H. and A.C. wrote the first draft of the manuscript. W.M.H. collected the tree-mortality sites. C.D.A., W.M.H., S.M.M., R.B.M. and H.X. revised the manuscript. All authors contributed to the interpretation of the results and to the text.

Corresponding authors

Correspondence to Shilong Piao or Anping Chen.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Ecology & Evolution thanks the anonymous reviewers for their contribution to the peer review of this work.

Additional information

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

Supplementary information

Supplementary Information

Supplementary Figures 1–13, Supplementary Tables 1 and 2.

Reporting Summary

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yan, Y., Piao, S., Hammond, W.M. et al. Climate-induced tree-mortality pulses are obscured by broad-scale and long-term greening. Nat Ecol Evol (2024). https://doi.org/10.1038/s41559-024-02372-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41559-024-02372-1

Search

Advanced search

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