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The carbon sink of secondary and degraded humid tropical forests

Nature volume 615, pages 436–442 (2023)Cite this article

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Abstract

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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Fig. 1: Modelled AGC accumulation with YSLD in different tropical regions.
Fig. 2: Modelled AGC accumulation in different Tmax zones in different tropical regions.
Fig. 3: The modelled 2018 carbon stock in recovering forests (degraded and secondary forests) in the three main tropical forest regions.
Fig. 4: The 2018 carbon stock and maximum technical 2030 carbon sink potential across recovering forests in the three main tropical forest regions.

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Data availability

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.

Code availability

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.

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Acknowledgements

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.

Author information

Authors and Affiliations

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

    Viola H. A. Heinrich & Jo House

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

    Viola H. A. Heinrich, Thais M. Rosan, Dominic Fawcett, Stephen Sitch & Luiz E. O. C. Aragão

  3. Fincons Group, Milan, Italy

    Christelle Vancutsem

  4. Center for International Forestry Research (CIFOR), Bogor, Indonesia

    Christelle Vancutsem

  5. Earth Observation and Geoinformatics Division, National Institute for Space Research (INPE), São José dos Campos, Brazil

    Ricardo Dalagnol, Henrique L. G. Cassol & Luiz E. O. C. Aragão

  6. Institute of the Environment and Sustainability, University of California, Los Angeles (UCLA), Los Angeles, CA, USA

    Ricardo Dalagnol & Celso H. L. Silva-Junior

  7. NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

    Ricardo Dalagnol & Celso H. L. Silva-Junior

  8. Programa de Pós-graduação em Biodiversidade e Conservação, Universidade Federal do Maranhão (UFMA), São Luís, Brazil

    Celso H. L. Silva-Junior

  9. School of GeoSciences, University of Edinburgh, Edinburgh, UK

    Henrique L. G. Cassol

  10. Joint Research Centre, European Commission, Ispra, Italy

    Frédéric Achard

  11. School of Biological Sciences, University of Bristol, Bristol, UK

    Tommaso Jucker

  12. Forest Biometrics and Remote Sensing Lab (Silva Lab), School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL, USA

    Carlos A. Silva

  13. School of Earth and Environmental Sciences, Cardiff University, Cardiff, UK

    Tristram C. Hales

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  1. Viola H. A. Heinrich
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Contributions

V.H.A.H., J.H., S.S., T.C.H. and L.E.O.C.A. designed the concept and methodological process of the study. V.H.A.H. carried out the main data analysis, with support from R.D., D.F., T.M.R., C.H.L.S.-J., H.L.G.C. and T.J. C.V. provided the code for analysis and the data of the TMF dataset before the publication of the study, with guidance from F.A. C.A.S. processed the raw GEDI data for further analysis. V.H.A.H. wrote the initial draft of the manuscript. All authors (V.H.A.H., C.V., R.D., T.M.R., D.F., C.H.L.S.-J., H.L.G.C., F.A., T.J., C.A.S., J.H., S.S., T.C.H. and L.E.O.C.A) discussed results, provided comments during the preparation of the manuscript and gave their approval for publication.

Corresponding author

Correspondence to Viola H. A. Heinrich.

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Nature thanks Valerio Avitabile, John Poulsen, Pieter Zuidema and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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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.

Source data

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).

Extended Data Table 1 Carbon emissions from forest loss and removals from recovering forest and their contribution to counterbalancing forest loss emissions accumulated up to 2018 across the three regions
Full size table
Extended Data Table 2 Percentage areas of degraded forest that were deforested by 2018 and their potential carbon contribution to counterbalancing gross emissions from forest loss
Full size table

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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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