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Small persistent humid forest clearings drive tropical forest biomass losses

Nature volume 649, pages 375–380 (2026)Cite this article

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Abstract

Tropical forests store about half of the global forest aboveground carbon (AGC)1, yet extensive areas are affected by disturbances, such as deforestation from agricultural expansion2,3 and degradation from fires4, selective logging5, and edge effects6,7. Over time, disturbed forests can recover, gradually restoring carbon stocks and ecological functions8. However, how recovery rates vary with disturbance size, type and location remains poorly quantified. Here we use a bookkeeping approach with spatially explicit vegetation recovery curves to quantify AGC dynamics in disturbed tropical forests during 1990–2020. We find that disturbed tropical dry forests remained carbon neutral, whereas disturbed tropical humid forests experienced a net AGC loss of 15.6 ± 3.7 PgC, primarily driven by small but persistent deforestation clearings. Despite affecting only about 5% of the disturbed area, these small-size (less than 2 ha) deforestation events accounted for about 56% of carbon losses, owing to persistent land-use conversion without forest regrowth. By contrast, large fire-induced carbon losses were offset by the long-term post-fire recovery. Over time, deforestation expanded into humid forests with higher carbon stock density, intensifying AGC losses per unit area. These findings highlight the disproportionate impact of small clearings on tropical carbon losses, suggesting the need to curb land-use changes and protect young and recovering forests.

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Fig. 1: Cumulative AGC changes for tropical forests disturbed during 1990–2020.
Fig. 2: Temporal dynamics of forest C changes after disturbances in tropical forests during 1990–2020.
Fig. 3: Net AGC balance changes for disturbed tropical forests associated with different disturbance patch sizes.
Fig. 4: Temporal changes of gross forest AGC loss per unit of disturbed forest area caused by different types of disturbance from 1990 to 2020.

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Data availability

The ESA-CCI Biomass dataset is available at https://climate.esa.int/en/odp/#/project/biomass. The burned-area dataset can be accessed from https://vapd.gitlab.io/post/gabam/. The TMF data are available at https://forobs.jrc.ec.europa.eu/TMF. The tree cover loss from GFW can be accessed from https://www.globalforestwatch.org/. The spatially explicit recovery curves derived in this study are available on Zenodo via https://zenodo.org/records/15869647 (ref. 76). Source data are provided with this paper.

Code availability

The scripts used to generate all the results are MATLAB (2021B) and Python 11.1. The code used in this study is available on Zenodo via https://zenodo.org/records/15869647 (ref. 76).

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Acknowledgements

W.L. acknowledges the support from the Yunnan Provincial Science and Technology Project at Southwest United Graduate School (202302AO370001). P.C., Y.X. and W.L. acknowledge the support from the CALIPSO (Carbon Loss in Plant Soils and Oceans) project funded through the generosity of Schmidt Science. P.C. and Y.X. thank the support from the European Space Agency Climate Change Initiative (ESA-CCI) RECCAP2-CS project (ESA ESRIN/4000144908/24/I-LR). J.C. is supported by projects ANR-10-LABX-0041 and ANR-21-CE32-0009. S.P.K.B. acknowledges support from the ESA-CCI cross-essential climate variables project XFires (ESA grant 4000145351). This work was also supported by the ESA-CCI Biomass project (ESA ESRIN/4000123662/18/I-NB), the French National Research Agency (ANR) under the French–German AI4Forest project (ANR-22-FAI1-0002-01), and the One Forest Vision initiative funded by the French Ministry of Higher Education and Research and the French Ministry for Europe and Foreign Affairs.

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Authors and Affiliations

  1. Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France

    Yidi Xu, Philippe Ciais, François Ritter, Agnès Pellissier-Tanon, Yu Feng, Chuanlong Zhou, Simon P. K. Bowring, Ibrahim Fayad & Yang Su

  2. Gamma Remote Sensing, Gümligen, Switzerland

    Maurizio Santoro

  3. European Commission, Joint Research Centre (JRC), Ispra, Italy

    Clément Bourgoin

  4. Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark

    François Ritter

  5. Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, China

    Yu Feng

  6. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

    Guojin He

  7. GFZ Helmholtz Centre for Geosciences, Potsdam, Germany

    Viola Heinrich & Simon Besnard

  8. School of Geographical Sciences, University of Bristol, Bristol, UK

    Viola Heinrich

  9. CIFOR-ICRAF, Nairobi, Kenya

    Nathaniel Robinson

  10. The Nature Conservancy, Natural Climate Solutions Team, Arlington, VA, USA

    Susan C. Cook-Patton

  11. Centre de Recherche sur la Biodiversité et l’Environnement (CRBE), Université de Toulouse, IRD, Toulouse INP, Toulouse, France

    Jérôme Chave

  12. National Institute for Space Research (INPE), São José Dos Campos, Brazil

    Luiz E. O. C. Aragao & Jean P. Ometto

  13. Faculty of Environment, Science and and Economy, University of Exeter, Exeter, UK

    Luiz E. O. C. Aragao

  14. Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Department of Earth System Science, Tsinghua University, Beijing, China

    Lei Zhu & Wei Li

  15. Département d’Informatique, École Normale Supérieure – PSL, Paris, France

    Yang Su

  16. ISPA, UMR 1391, INRAE Nouvelle-Aquitaine, Bordeaux Sciences Agro (BSA), Université de Bordeaux, Bordeaux Villenave d’Ornon, France

    Jean-Pierre Wigneron

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  1. Yidi Xu
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  2. Philippe Ciais
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Contributions

Y.X., P.C. and W.L. designed the research. Y.X. collected and processed the data and constructed the figures and tables with contributions from P.C., F.R., A.P.-T., Y.F., C.Z., S.B., V.H., S.C.C.-P., C.B., Y.S. and W.L. M.S., C.B., G.H., J.P.O and J.-P.W. contributed to data acquisition. M.S., C.B., N.R., J.C., L.E.O.C.A., S.P.K.B., I.F. and L.Z. contributed to the interpretation of the data and results. Y.X. drafted the paper, and all authors contributed to revising and improving the text.

Corresponding authors

Correspondence to Philippe Ciais or Wei Li.

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Extended data figures and tables

Extended Data Fig. 1 Workflow of the new bookkeeping model with spatially-explicit recovery curves.

(1) Categorization of tropical forests: Differentiation of dry and humid tropical forests. (2) Identification of disturbances: Separation of various types of disturbances, including fire-driven degradation, other degradation, deforestation, and the regrowth from newly established forests and regrowth from deforestation by combining different disturbance datasets; (3) Reconstruction of forest recovery: Derivation of forest biomass recovery curves for each 1° grid by combining the biomass and disturbance datasets; (4) Carbon budget calculation: Estimation of above-ground carbon (AGC) losses and subsequent gains for each disturbance event from 1990 to 2020. Icons adapted from Pixabay (https:/pixabay.com) under a Creative Commons licence CC0.

Extended Data Fig. 2 Cumulative AGC changes in disturbed and undisturbed tropical forests during 1990–2020.

Net AGC balance is calculated as the sum of gross AGC gains from disturbed and undisturbed forests and gross AGC losses from the disturbed forests. Black dots represent the net AGC changes (sum of all gains and losses) and the error bars represent the standard errors across each tropical region.

Extended Data Fig. 3 Temporal dynamics of the forest AGC changes after disturbances in disturbed tropical humid and dry forests during 1990–2020.

a) Tropical forests in Africa, b) America, and c) Asia. Black lines and shaded areas represent the annual net C balance by summing up all the C flux components and the corresponding standard errors. The bars represent the annual disturbed area associated with different types of disturbances. Due to the limited availability of Landsat data, there is a 5-year lacuna of burned areas in the 1990s. Icons adapted from Pixabay (https:/pixabay.com) under a Creative Commons licence CC0.

Extended Data Fig. 4 Area proportion of tropical forest disturbance types by patch size.

Bars represent the cumulative contribution of different disturbance patch sizes aggregated over the period from 1990 to 2020.

Extended Data Fig. 5 Gross and net C changes contributed by disturbances from different size classes during 1990–2020 across tropical regions.

Tropical a) America, b) Africa, and c) Asia. Black dots and error bars represent the net C balance and the corresponding standard errors for each disturbance patch size class. The C losses, gains, and area changes are separated into FRF and LCC categories. FRF: disturbances occurring within the remaining forest, such as fire-driven degradation and other degradation. LCC: disturbances associated with forest land cover change (permanent or temporary), such as deforestation or afforestation. Positive values indicate a C gains, and negative values indicate a C losses.

Extended Data Fig. 6 Trend in forest fire frequency during 1990–2020 based on Kendall’s tau.

Kendall’s correlation tau is calculated based on the annual forest burned area during 1990–2020 across each 1° grid cell from16. tau values close to +1 suggest a strong increase in fire frequency, while tau values close to −1 indicate a strong downward trend in fire frequency. Only grid cells with statistically significant trends (p-value ≤ 0.05) are shown. Administrative boundaries adapted from data © European Union, 1995–2025.

Extended Data Fig. 7 Temporal changes of gross forest AGC loss per unit of disturbed forest area caused by different types of disturbance from 1990 to 2020.

Tropical forests in a) America, b) Africa, and c) Asia. Solid lines indicate statistically significant trends (linear regression, slope t-test, p < 0.01), while dashed lines represent non-significant trends.

Extended Data Table 1 Definition and data source of disturbance types described in this study
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Xu, Y., Ciais, P., Santoro, M. et al. Small persistent humid forest clearings drive tropical forest biomass losses. Nature 649, 375–380 (2026). https://doi.org/10.1038/s41586-025-09870-7

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  • DOI: https://doi.org/10.1038/s41586-025-09870-7

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