The globally important carbon sink of intact, old-growth tropical humid forests is declining because of climate change, deforestation and degradation from fire and logging1,2,3. Recovering tropical secondary and degraded forests now cover about 10% of the tropical forest area4, but how much carbon they accumulate remains uncertain. Here we quantify the aboveground carbon (AGC) sink of recovering forests across three main continuous tropical humid regions: the Amazon, Borneo and Central Africa5,6. On the basis of satellite data products4,7, our analysis encompasses the heterogeneous spatial and temporal patterns of growth in degraded and secondary forests, influenced by key environmental and anthropogenic drivers. In the first 20 years of recovery, regrowth rates in Borneo were up to 45% and 58% higher than in Central Africa and the Amazon, respectively. This is due to variables such as temperature, water deficit and disturbance regimes. We find that regrowing degraded and secondary forests accumulated 107 Tg C year−1 (90–130 Tg C year−1) between 1984 and 2018, counterbalancing 26% (21–34%) of carbon emissions from humid tropical forest loss during the same period. Protecting old-growth forests is therefore a priority. Furthermore, we estimate that conserving recovering degraded and secondary forests can have a feasible future carbon sink potential of 53 Tg C year−1 (44–62 Tg C year−1) across the main tropical regions studied.
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All the original datasets used in this research are publicly available from their sources: JRC-TMF dataset4 (https://forobs.jrc.ec.europa.eu/TMF/download/); ESA-CCI AGB/AGC map7 (https://catalogue.ceda.ac.uk/uuid/84403d09cef3485883158f4df2989b0c); Descal et al. (2021) oil palm map53 (https://developers.google.com/earth-engine/datasets/catalog/BIOPAMA_GlobalOilPalm_v1#description); TerraClimate Maximum Temperature59 (https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_TERRACLIMATE); MCWD data can be produced by combining monthly rainfall dataset from Funk et al.61 (https://edcintl.cr.usgs.gov/downloads/sciweb1/shared/fews/web/global/monthly/chirps/final/downloads/monthly/) with code from Silva Junior and Campanharo (2019)60; HAND data69 (https://code.earthengine.google.com/ed75ecef7fcf94897b74ac56bfbb3f43); Xu et al. Peatland dataset67 (https://archive.researchdata.leeds.ac.uk/251/); MapBiomas dataset70 (https://amazonia.mapbiomas.org/) and the code to extract secondary forest area and age58; logging concession areas71 (https://data.globalforestwatch.org/documents/managed-forest-concessions-downloadable/explore). Both the Tmax and HAND indices were pre-processed in GEE. Country boundaries shown in map-based figures (http://thematicmapping.org/downloads/world_borders.php)72. All final data produced in this study are available in a public repository (https://zenodo.org/record/7515854#.Y8kVQEFxeUk)73. Source data are provided with this paper.
All code used to produce the main figures of the paper are available in a public repository (https://zenodo.org/record/7515854#.Y8kVQEFxeUk)73. Source data are provided with this paper.
Qie, L. et al. Author Correction: Long-term carbon sink in Borneo’s forests halted by drought and vulnerable to edge effects. Nat. Commun. 9, 342 (2018).
Hubau, W. et al. Asynchronous carbon sink saturation in African and Amazonian tropical forests. Nature 579, 80–87 (2020).
Gatti, L. V. et al. Amazonia as a carbon source linked to deforestation and climate change. Nature 595, 388–393 (2021).
Vancutsem, C. et al. Long-term (1990–2019) monitoring of forest cover changes in the humid tropics. Sci. Adv. 7, eabe1603 (2021).
Chazdon, R. L. et al. Carbon sequestration potential of second-growth forest regeneration in the Latin American tropics. Sci. Adv. 2, e1501639 (2016).
Poorter, L. et al. Biomass resilience of Neotropical secondary forests. Nature 530, 211–214 (2016).
Santoro, M. & Cartus, O. ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017 and 2018, v2. Centre for Environmental Data Analysis. https://doi.org/10.5285/84403d09cef3485883158f4df2989b0c (2021).
COP26, UN Climate Change Conference UK 2021. Glasgow Leaders’ Declaration on Forests and Land Use. https://ukcop26.org/glasgow-leaders-declaration-on-forests-and-land-use/ (2021).
Seddon, N. Harnessing the potential of nature-based solutions for mitigating and adapting to climate change. Science 376, 1410–1416 (2022).
Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).
Harris, N. L. et al. Global maps of twenty-first century forest carbon fluxes. Nat. Clim. Change 11, 234–240 (2021).
United Nations Framework Convention on Climate Change (UNFCCC). Global Stocktake. https://unfccc.int/topics/global-stocktake (2015).
Requena Suarez, D. et al. Estimating aboveground net biomass change for tropical and subtropical forests: refinement of IPCC default rates using forest plot data. Glob. Change Biol. 25, 3609–3624 (2019).
Cook-Patton, S. C. et al. Mapping carbon accumulation potential from global natural forest regrowth. Nature 585, 545–550 (2020).
Heinrich, V. H. A. et al. Large carbon sink potential of secondary forests in the Brazilian Amazon to mitigate climate change. Nat. Commun. 12, 1785 (2021).
Philipson, C. D. et al. Active restoration accelerates the carbon recovery of human-modified tropical forests. Science 369, 838–841 (2020).
Rappaport, D. I. et al. Quantifying long-term changes in carbon stocks and forest structure from Amazon forest degradation. Environ. Res. Lett. 13, 065013 (2018).
Hayward, R. M. et al. Three decades of post-logging tree community recovery in naturally regenerating and actively restored dipterocarp forest in Borneo. For. Ecol. Manag. 488, 119036 (2021).
Putz, F. E. et al. Intact forest in selective logging landscapes in the tropics. Front. For. Glob. Change 2, 30 (2019).
Silva, C. V. J. et al. Estimating the multi-decadal carbon deficit of burned Amazonian forests. Environ. Res. Lett. 15, 114023 (2020).
Poorter, L. et al. Wet and dry tropical forests show opposite successional pathways in wood density but converge over time. Nat. Ecol. Evol. 3, 928–934 (2019).
Dubayah, R. et al. The Global Ecosystem Dynamics Investigation: high-resolution laser ranging of the Earth’s forests and topography. Sci. Remote Sens. 1, 100002 (2020).
Sullivan, M. J. P. et al. Long-term thermal sensitivity of earth’s tropical forests. Science 368, 869–874 (2020).
Poorter, L. et al. Multidimensional tropical forest recovery. Science 374, 1370–1376 (2021).
Rozendaal, D. et al. Aboveground forest biomass varies across continents, ecological zones and successional stages: refined IPCC default values for tropical and subtropical forests. Environ. Res. Lett. 17, 014047 (2022).
Griscom, B., Ellis, P. & Putz, F. E. Carbon emissions performance of commercial logging in East Kalimantan, Indonesia. Glob. Change Biol. 20, 923–937 (2014).
Putz, F. E. et al. Sustaining conservation values in selectively logged tropical forests: the attained and the attainable. Conserv. Lett. 5, 296–303 (2012).
Avitabile, V. et al. An integrated pan-tropical biomass map using multiple reference datasets. Glob. Change Biol. 22, 1406–1420 (2016).
Lloyd, J. & Farquhar, G. D. Effects of rising temperatures and [CO2] on the physiology of tropical forest trees. Philos. Trans. R. Soc. B Biol. Sci. 363, 1811–1817 (2008).
Bennett, A. C. et al. Resistance of African tropical forests to an extreme climate anomaly. Proc. Natl Acad. Sci. 118, e2003169118 (2021).
Ross, C. W. et al. Woody-biomass projections and drivers of change in sub-Saharan Africa. Nat. Clim. Change 11, 449–455 (2021).
Esquivel-Muelbert, A. et al. A spatial and temporal risk assessment of the impacts of El Niño on the tropical forest carbon cycle: theoretical framework, scenarios, and implications. Atmosphere 10, 588 (2019).
Zhou, L. et al. Widespread decline of Congo rainforest greenness in the past decade. Nature 508, 86–90 (2014).
Saatchi, S. et al. Detecting vulnerability of humid tropical forests to multiple stressors. One Earth 4, 988–1003 (2021).
Brando, P. M. et al. Abrupt increases in Amazonian tree mortality due to drought-fire interactions. Proc. Natl Acad. Sci. USA 111, 6347–6352 (2014).
Ferraz, A. et al. Carbon storage potential in degraded forests of Kalimantan, Indonesia. Environ. Res. Lett. 13, 095001 (2018).
Jucker, T. et al. Topography shapes the structure, composition and function of tropical forest landscapes. Ecol. Lett. 21, 989–1000 (2018).
Blackham, G. V., Webb, E. L. & Corlett, R. T. Natural regeneration in a degraded tropical peatland, Central Kalimantan, Indonesia: implications for forest restoration. For. Ecol. Manag. 324, 8–15 (2014).
Nikonovas, T., Spessa, A., Doerr, S. H., Clay, G. D. & Mezbahuddin, S. Near-complete loss of fire-resistant primary tropical forest cover in Sumatra and Kalimantan. Commun. Earth Environ. 1, 65 (2020).
Riutta, T. et al. Major and persistent shifts in below-ground carbon dynamics and soil respiration following logging in tropical forests. Glob. Change Biol. 27, 2225–2240 (2021).
Noon, M. L. et al. Mapping the irrecoverable carbon in Earth’s ecosystems. Nat. Sustain. 5, 37–46 (2022).
Rosan, T. M. et al. Fragmentation-driven divergent trends in burned area in Amazonia and Cerrado. Front. For. Glob. Change 5, 801408 (2022).
Poulsen, J. R. et al. Old growth Afrotropical forests critical for maintaining forest carbon. Glob. Ecol. Biogeogr. 29, 1785–1798 (2020).
Haenssgen, M. J. et al. Implementation of the COP26 declaration to halt forest loss must safeguard and include Indigenous people. Nat. Ecol. Evol. 6, 235–236 (2022).
Maxwell, S. L. et al. Degradation and forgone removals increase the carbon impact of intact forest loss by 626%. Sci. Adv. 5, eaax2546 (2019).
Reynolds, G., Payne, J., Sinun, W., Mosigil, G. & Walsh, R. P. D. Changes in forest land use and management in Sabah, Malaysian Borneo, 1990–2010, with a focus on the Danum Valley region. Philos. Trans. R. Soc. B Biol. Sci. 366, 3168–3176 (2011).
Boul Lefeuvre, N. et al. The value of logged tropical forests: a study of ecosystem services in Sabah, Borneo. Environ. Sci. Policy 128, 56–67 (2022).
Lennox, G. D. et al. Second rate or a second chance? Assessing biomass and biodiversity recovery in regenerating Amazonian forests. Glob. Change Biol. 24, 5680–5694 (2018).
Vieira, I. C. G., Gardner, T., Ferreira, J., Lees, A. C. & Barlow, J. Challenges of governing second-growth forests: a case study from the Brazilian Amazonian state of Pará. Forests 5, 1737–1752 (2014).
Roe, S. et al. Land-based measures to mitigate climate change: potential and feasibility by country. Glob. Change Biol. 27, 6025–6058 (2021).
Martin, A. R., Doraisami, M. & Thomas, S. C. Global patterns in wood carbon concentration across the world’s trees and forests. Nat. Geosci. 11, 915–920 (2018).
ESRI. ArcGIS Pro Desktop (2.6.0) (2020).
Descals, A. et al. High-resolution global map of smallholder and industrial closed-canopy oil palm plantations. Earth Syst. Sci. Data 13, 1211–1231 (2021).
R Core Team. R: A Language and Environment for Statistical Computing. http://www.R-project.org/ (R Foundation for Statistical Computing, 2008).
Richards, F. J. A flexible growth function for empirical use. J. Exp. Bot. 10, 290–301 (1959).
Smith, C. C. et al. Secondary forests offset less than 10% of deforestation-mediated carbon emissions in the Brazilian Amazon. Glob. Change Biol. 26, 7006–7020 (2020).
Nunes, S., Oliveira, L.Jr, Siqueira, J., Morton, D. C. & Souza, C. M. Unmasking secondary vegetation dynamics in the Brazilian Amazon. Environ. Res. Lett. 15, 034057 (2020).
Silva Junior, C. H. L. et al. Benchmark maps of 33 years of secondary forest age for Brazil. Sci. Data 7, 269 (2020).
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, 170191 (2018).
Silva Junior, C. H. L, & Campanharo, W. A. Maximum Cumulative Water Deficit - MCWD: a R language script (v1.1.0). https://doi.org/10.5281/zenodo.2652629 (2019).
Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci. Data 2, 150066 (2015).
Phillips, O. L. et al. Drought sensitivity of the Amazon rainforest. Science 323, 1344–1347 (2009).
Nobre, A. D. et al. Height Above the Nearest Drainage – a hydrologically relevant new terrain model. J. Hydrol. 404, 13–29 (2011).
Almeida, C. A. et al. High spatial resolution land use and land cover mapping of the Brazilian legal Amazon in 2008 using Landsat-5/TM and MODIS data. Acta Amazon 46, 291–302 (2016).
Ploton, P. et al. Spatial validation reveals poor predictive performance of large-scale ecological mapping models. Nat. Commun. 11, 4540 (2020).
Haining, R. P. in International Encyclopedia of the Social & Behavioral Sciences (eds. Smelser, N. J. & Baltes, P. B.) 14822–14827 (Pergamon, 2001).
Xu, J., Morris, P. J., Liu, J. & Holden, J. PEATMAP: refining estimates of global peatland distribution based on a meta-analysis. Catena 160, 134–140 (2018).
Xu, J., Morris, P. J., Liu, J. & Holden, J. PEATMAP: refining estimates of global peatland distribution based on a meta-analysis. Research Data Leeds Repository https://archive.researchdata.leeds.ac.uk/251/ (2017).
Donchyts, G., Winsemius, H., Schellekens, J., Erickson, T. & Gao, H. Global 30m height above the nearest drainage. Geophys. Res. Abstr. 18, EGU2016-17445-3 (2016).
Souza, C. M.Jr et al. Reconstructing three decades of land use and land cover changes in Brazilian biomes with Landsat archive and Earth Engine. Remote Sens. 12, 2735 (2020).
Global Forest Watch. Managed Forest Concessions. https://www.globalforestwatch.org/ (2020).
ThematicMapping. http://thematicmapping.org/downloads/world_borders.php (2009).
Heinrich, V. H. A. et al. Data and code from paper: The carbon sink of secondary and degraded humid tropical forests. https://zenodo.org/record/7515854#.Y8kVQEFxeUk (2022).
We thank A. Esquivel-Muelbert and E. Mitchard for their valuable input during the preparation of this manuscript. We thank M. Brasika, A. Jumail, H. R. Yen, C. Cheng, L. Mercado, J. Echeverría and M. Heinrich for translating the summary paragraph (translations available in the Supplementary Information). V.H.A.H. was supported by a NERC GW4+ Doctoral Training Partnership studentship from the Natural Environment Research Council (NE/L002434/1). R.D. was supported by São Paulo Research Foundation (FAPESP) grant 2019/21662-8. T.J. was supported by a UK NERC Independent Research Fellowship (NE/S01537X/1). V.H.A.H., T.M.R., D.F. and S.S. were supported by the RECCAP2 project, which is part of the ESA Climate Change Initiative (contract no. 4000123002/18/I-NB) and the H2020 European Institute of Innovation and Technology (4C; grant no. 821003). H.L.G.C. was supported by São Paulo Research Foundation (FAPESP) grant nos. 2018/14423-4 and 2020/02656-4. C.V. was supported by the Directorate General for Climate Action of the European Commission through the ForMonPol (Forest Monitoring for Policies) Administrative Arrangement. C.H.L.S.-J. was supported by The University of Manchester through the ‘Forest fragmentation mapping of Amazon and its vulnerable margin Aamzon-Cerrado transition forests' project. This work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (NASA). We thank the School of Geographical Sciences, University of Bristol for their extra support. This research was financed in part by the Natural Environment Research Council (NE/L002434/1). For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) public copyright license to any Author Accepted Manuscript (AAM) version arising from this submission.
The authors declare no competing interests.
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Extended data figures and tables
Extended Data Fig. 1 The AGC after 20 years in recovering forests relative to old-growth-forest values across different studies compared with this study.
Values are expressed as the percentage AGC recovered relative to old-growth-forest values across the three study regions, the Amazon, Borneo and Central Africa, in recovering degraded and secondary forests. For previous studies that capture a different region to those used in this study, the specific region has been indicated alongside the study name in brackets. For example, W. Africa refers to West Africa in Poorter et al. and N’Guessan et al. This region is not in Central Africa but represents the closest region that could be found containing such information. Uncertainty types are either the 95% confidence interval or the minimum and maximum values presented by the studies for the respective regions. More information on the previous studies and the associated values is given in the source data for this figure and the supplementary material.
Extended Data Fig. 2 Correlation coefficients of different variables driving tropical AGC (in Mg C ha−1).
Values shown are the average standardized (to be in the range from −1 to 1) coefficients from several general linear model runs based on spatial data that was sampled by means of stratified random sample accounting for spatial autocorrelation of the variables. The number of model runs to determine the average was based on the number of samples in each run such that the total sample size was 100,000. Bars denote the average standard deviation. Each coloured circle/triangle represents the respective standardized coefficient in degraded/secondary forests in the three regions (colours). Smaller shapes within the large, coloured shapes represent whether the result was statistically significant, for which black denotes P < 0.05, white denotes P < 0.1 and no colour denotes P ≥ 0.1. The variables are YSLD equivalent to age for secondary forests, average annual maximum temperature and distance from nearest undisturbed (old-growth) TMF. The effects of MCWD are positive because the MCWD values are negative and so have an opposite effect: less negative values indicate less water deficit, which is associated with generally higher AGC and thus a positive coefficient.
Extended Data Fig. 3 Modelled AGC accumulation with MCWD in different tropical regions.
AGC as a function of YSLD is shown in the Amazon (a,b), Borneo (c,d) and Central Africa (e,f) for degraded forests (left column) and secondary forests (right column). Points denote the median AGC value calculated for each YSLD, fitted lines are based on a nonlinear model (see Methods). Values in the legend denote the absolute lower 25% (yellow), middle 50% (light green) and upper 25% (dark green) of the MCWD range, which have units −mm year−1. Shading denotes the 95% confidence interval of the nonlinear model. Crosses denote the median AGC of old-growth (OG) forests in the respective regions within the respective ranges of the variable. Each subplot contains a not-to-scale map of the region showing where the ranges for the MCWD bins can be found geographically.
Extended Data Fig. 4 Modelled AGC accumulation with HAND in different tropical regions.
AGC as a function of YSLD is shown in the Amazon (a,b), Borneo (c,d) and Central Africa (e,f) for degraded forests (left column) and secondary forests (right column). Points denote the median AGC value calculated for each YSLD, fitted lines are based on a nonlinear model (see Methods). Values in the legend denote the absolute lower 25% (green), middle 50% (yellow) and upper 25% (grey) of the HAND range, which has units metres (m). Shading denotes the 95% confidence interval of the nonlinear model. Crosses denote the median AGC of old-growth (OG) forests in the respective regions within the respective ranges of the variable. Each subplot contains a not-to-scale map of the region showing where the ranges for the HAND bins can be found geographically.
Extended Data Fig. 5 Modelled AGC accumulation with distance from nearest old-growth forest in different tropical regions.
AGC as a function of YSLD is shown in the Amazon (a,b), Borneo (c,d) and Central Africa (e,f) for degraded forests (left column) and secondary forests (right column). Points denote the median AGC value calculated for each YSLD, fitted lines are based on a nonlinear model (see Methods). Values in the legend denote the distances <120 m (lime green), 120–1,000 m (green) and >1,000 m (dark green), representing the distance from the nearest old-growth forest. Shading denotes the 95% confidence interval of the nonlinear model. Crosses denote the median AGC of old-growth (OG) forests in the respective regions within the respective ranges of the variable. In this case, only a single value of old-growth-forest AGC is shown. Each subplot contains a not-to-scale map of the region showing where the ranges for the distance bins can be found geographically.
Extended Data Fig. 6 The modelled 2018 carbon stock in degraded forests in the three main tropical forest regions.
The carbon stock shows the total carbon that has accumulated since the last disturbance event using the region-wide regrowth models developed in this study for the Amazon (a), Borneo (b) and Central Africa (c). Values of the carbon stock (Tg C) are aggregated to 0.1° grid squares and show the sum of degraded forests. Regions of peatland have been highlighted (see Methods) and are denoted by the hatching. Annotated values denote the AGC stock and associated 95% confidence interval as estimated in this study using the Monte Carlo simulations per country, expressed using the ISO3 code for each country. Map created using ESRI’s ArcGIS Pro (2.6.0).
Extended Data Fig. 7 The modelled 2018 carbon stock in secondary forests in the three main tropical forest regions.
The carbon stock shows the total carbon that has accumulated since the last disturbance event using the region-wide regrowth models developed in this study for the Amazon (a), Borneo (b) and Central Africa (c). Values of the carbon stock (Tg C) are aggregated to 0.1° grid squares and show the sum of secondary forests. Regions of peatland have been highlighted (see Methods) and are denoted by the hatching. Annotated values denote the AGC stock and associated 95% confidence interval as estimated in this study using the Monte Carlo simulations per country, expressed using the ISO3 code for each country. Map created using ESRI’s ArcGIS Pro (2.6.0).
This file contains translations of the summary paragraph; Supplementary Note 1: Evaluation of datasets used; Supplementary Note 2: Comparison of results with previous studies; Supplementary Discussion 1: Exploring variability and uncertainty in our analysis; Supplementary Figures; Supplementary Tables and Supplementary References.
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Heinrich, V.H.A., Vancutsem, C., Dalagnol, R. et al. The carbon sink of secondary and degraded humid tropical forests. Nature 615, 436–442 (2023). https://doi.org/10.1038/s41586-022-05679-w
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