Abstract
Tropical secondary forests grow back naturally after the original forest has been cleared, while degraded forests comprise regrowth within forested land that has experienced partial structural and functional loss. Both represent important carbon sinks. However, natural forest expansion into originally unforested land also occurs, and despite covering 6% more area than secondary forests in the moist tropics, its carbon sink remains unquantified. Here we quantify the above-ground carbon sink and analyse its drivers across natural forest expansion, secondary forest and degraded forest by combining satellite-derived tropical moist forest changes with spaceborne LiDAR-derived biomass. The above-ground carbon accumulation of natural forest expansion is comparable to that of secondary forests, particularly in the Americas, but shows greater sensitivity to climatic and environmental variations. Natural forest expansion sequesters 5.4% more above-ground carbon (795 ± 132 TgC) than secondary forests (754 ± 105 TgC). It offsets an additional 2.4 ± 0.6% of the carbon emissions from deforestation and degradation of old-growth forests, while regrowth in secondary and degraded forests offsets 2.3 ± 0.5% and 13.6 ± 2.1%, respectively. Our results highlight natural forest expansion as an overlooked pan-tropical carbon sink with great mitigation potential, if invested in sustainably, alongside the protection of old-growth and regenerating forests.
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Data availability
All original datasets used in this research are publicly available. The JRC TMF dataset1 (v.2024) is available at https://forobs.jrc.ec.europa.eu/TMF/data. NASA’s GEDI AGBD and relative height footprints are taken from GEDI04_A70 (https://doi.org/10.3334/ORNLDAAC/2056) and GEDI02_A27 (https://doi.org/10.5067/GEDI/GEDI02_A.002) products, respectively. Natural land mask26 is extracted from Land & Carbon Lab SBTN Natural Lands Map v.1.1 available at https://landcarbonlab.org/data/natural-lands-map/. The Forest Data Partnership tree crop probability54 products (that is, palm, cocoa and rubber version of model 2024a) are available at https://www.wri.org/initiatives/forest-data-partnership. TerraClimate monthly maximum temperature (TMAX)62 is available at https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_TERRACLIMATE. CHIRPS annual precipitation (PREC)63 is available at https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_DAILY. The height above the nearest drainage system (HAND)64 is available at https://gee-community-catalog.org/projects/hand/. OpenLandMap Soil Organic Carbon (SOC)65,66 is available at https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_ORGANIC-CARBON_USDA-6A1C_M_v02. The FAO Global Ecological Zones are available at https://data.apps.fao.org/catalog/iso/2fb209d0-fd34-4e5e-a3d8-a13c241eb61b. The global drivers of forest disturbances68 are available at https://zenodo.org/records/15224684. Basemap data of boundaries shown in map-based figures is available at https://public.opendatasoft.com/pages/home/. All final data used to produce the main figures of this study are available in a public repository (https://doi.org/10.5281/zenodo.19330022)71. Source data are provided with this paper.
Code availability
All code used to produce the main figures of this study are available in a public repository (https://doi.org/10.5281/zenodo.19330022)71.
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Acknowledgements
We thank A. Holcomb (University of Cambridge) for her assistance in filtering GEDI biomass footprints for regenerating forests, and Y. Zhang (Institute of Botany, CAS) for his guidance on woody encroachment. This work was supported in part by the National Natural Science Foundation of China (42271400 Y.Z. and 32401372 X.W.), the Joint Funds of the National Natural Science Foundation of China (U24A20587 X.L.), the Joint Funds of the Hubei Provincial National Natural Science Foundation of China (2025AFD167 X.W.), the Hubei Provincial Natural Science Foundation of China for Distinguished Young Scholars (2022CFA045 Y.Z.), the Key Research Program of Frontier Sciences, Chinese Academy of Sciences (ZDBS-LY-DQC034 Y.Z.), the Young Top-notch Talent Cultivation Program of Hubei Province, and the joint Postdoctoral Program of China Scholarship Council and Lancaster University during 2021–2023.
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Y.Z., V.H.A.H. and C.B. conceived the idea and designed the methodology. Y.Z. V.H.A.H., C.B., X.W. and X.L. carried out the main data analysis in this research and Y.Z. wrote the code with support from all authors. V.H.A.H. provided guidance on the above-ground carbon accumulation modelling of forest regrowth. C.B. provided guidance on the sensitivity analysis of JRC TMF datasets and GEDI biomass footprints. Y.Z. wrote the initial draft of the paper. Y.Z., V.H.A.H., C.B., X.W., X.L., Y.D. and P.M.A. participated in the discussion and editing of the paper and gave final approval for publication.
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Extended data
Extended Data Fig. 1 Regenerating forest types.
A conceptual illustration of the regeneration processes of forest expansion, secondary forest and degraded forest. Credit: icons, Freepik.com.
Extended Data Fig. 2 Modelled AGC accumulation in the lower and upper 50% limits of PREC zones.
AGC accumulation curves for the lower and upper 50% PREC zones are shown for natural forest expansion, secondary forests and degraded forests across the tropical Americas (first row; n = 148,625, 101,711 and 597,741), Africa (second row; n = 45,304, 42,470 and 118,940) and Asia (third row; n = 37,819, 37,522 and 283,150), in which data are presented as median values with 95% confidence intervals. Points refer to the median AGC for each recovery age and undisturbed old-growth forests (UF), and lines denote the nonlinear fitted AGC within the lower and upper 50% PREC zones, respectively. Shading of the fitted lines indicates the 95% confidence interval for the nonlinear prediction, error bars of UF AGC represent the 95% confidence interval from the Monte Carlo simulations, and dashed line represents the AGC of UF. Inset maps created with ArcGIS 10.8 with basemap data from the World Food Programme (https://public.opendatasoft.com/pages/home/).
Extended Data Fig. 3 Modelled AGC accumulation in the lower and upper 50% limits of HAND zones.
AGC accumulation curves for the lower and upper 50% HAND zones are shown for natural forest expansion, secondary forests and degraded forests across the tropical Americas (first row; n = 148,625, 101,711 and 597,741), Africa (second row; n = 45,304, 42,470 and 118,940) and Asia (third row; n = 37,819, 37,522 and 283,150), in which data are presented as median values with 95% confidence intervals. Points refer to the median AGC for each recovery age and undisturbed old-growth forests (UF), and lines denote the nonlinear fitted AGC within the lower and upper 50% HAND zones, respectively. Shading of the fitted lines indicates the 95% confidence interval for the nonlinear prediction, error bars of UF AGC represent the 95% confidence interval from the Monte Carlo simulations, and dashed line represents the AGC of UF. Inset maps created with ArcGIS 10.8 with basemap data from the World Food Programme (https://public.opendatasoft.com/pages/home/).
Extended Data Fig. 4 Modelled AGC accumulation in the lower and upper 50% limits of SOC zones.
AGC accumulation curves for the lower and upper 50% SOC zones are shown for natural forest expansion, secondary forests and degraded forests across the tropical Americas (first row; n = 148,625, 101,711 and 597,741), Africa (second row; n = 45,304, 42,470 and 118,940) and Asia (third row; n = 37,819, 37,522 and 283,150), in which data are presented as median values with 95% confidence intervals. Points refer to the median AGC for each recovery age and undisturbed old-growth forests (UF), and lines denote the nonlinear fitted AGC within the lower and upper 50% SOC zones, respectively. Shading of the fitted lines indicates the 95% confidence interval for the nonlinear prediction, error bars of UF AGC represent the 95% confidence interval from the Monte Carlo simulations, and dashed line represents the AGC of UF. Inset maps created with ArcGIS 10.8 with basemap data from the World Food Programme (https://public.opendatasoft.com/pages/home/).
Extended Data Fig. 5 Modelled region-specific AGC accumulation across dominant ecological contexts.
Map of (a) FAO Global Ecological Zones (GEZ) in the tropical Americas, Africa and Asia. Modelled AGC accumulation curves under the dominant GEZ of tropical rainforest, tropical moist forest, tropical dry forest and tropical mountain system for natural (b) forest expansion (n = 64,615, 100,956, 32,549 and 13,601), (c) secondary forests (n = 119,029, 44,784, 3,297 and 8,995) and (d) degraded forests (n = 567,633, 118,814, 9,572 and 30,113), respectively, in which data are presented as median values with 95% confidence intervals. Points refer to the median AGC for each recovery age and undisturbed old-growth forests (UF), lines denote the nonlinear fitted AGC within each of the three tropical regions, shading denotes the 95% confidence interval of the nonlinear prediction, error bars of UF represent the 95% confidence interval from the Monte Carlo simulations, and dashed line represents the AGC of UF. Maps in a created with ArcGIS 10.8 with basemap data from the World Food Programme (https://public.opendatasoft.com/pages/home/).
Extended Data Fig. 6 Modelled region-specific AGC accumulation across dominant disturbance contexts.
Map of (a) World Resources Institute (WRI) global forest disturbance drivers during 2000-2022 for the tropical Americas, Africa and Asia. Modelled AGC accumulation curves under dominant disturbances of permanent agriculture, shifting cultivation, logging, wildfires and other natural disturbances for natural (b) forest expansion (n = 73,736, 85,836, 27,059, 19,160, 10,794 and 12,667), (c) secondary forests (n = 22,447, 60,629, 45,965, 12,539, 31,826 and 5,797) and (d) degraded forests (n = 150,814, 199,575, 139,333, 88,877, 112,740 and 37,822), respectively, in which data are presented as median values with 95% confidence intervals. Points refer to the median AGC for each recovery age and undisturbed old-growth forests (UF), lines denote the nonlinear fitted AGC within each of the three tropical regions, shading denotes the 95% confidence interval of the nonlinear prediction, error bars of UF represent the 95% confidence interval from the Monte Carlo simulations, and dashed line represents the AGC of UF. Maps in a created with ArcGIS 10.8 with basemap data from the World Food Programme (https://public.opendatasoft.com/pages/home/).
Extended Data Fig. 7 Pantropical carbon emissions from deforestation and degradation during the period from 1985 to 2022.
Carbon emission maps aggregated to 0.1° grid squares for (a) deforestation and (b) forest degradation produced using region-specific AGC accumulation models and maps of deforestation and degradation extracted from the JRC TMF product. Maps created with ArcGIS 10.8 with basemap data from the World Food Programme (https://public.opendatasoft.com/pages/home/).
Extended Data Fig. 8 Dynamics of potential carbon stock for natural forest regeneration under five scenarios.
Annual changes of preserved carbon stock from 2022 to 2030 for pantropical natural (a) forest expansion, (b) secondary forests, (c) degraded forests, and (d) all natural forest regeneration by preserving all forest recovery ages, and forest recovery ages 5+, 10+, 15+ and 20+, respectively. The AGC stock calculation was based on the region-specific regrowth models developed for climatic Region I and II in Fig. 4. Shading denotes the 95% confidence interval of the AGC accumulation models.
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Zhang, Y., Heinrich, V.H.A., Bourgoin, C. et al. Natural forest expansion is a larger carbon sink than secondary forests in moist tropics.
Nat. Geosci. (2026). https://doi.org/10.1038/s41561-026-01984-5
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DOI: https://doi.org/10.1038/s41561-026-01984-5
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