Abstract
Structurally intact tropical forests sequestered about half of the global terrestrial carbon uptake over the 1990s and early 2000s, removing about 15 per cent of anthropogenic carbon dioxide emissions1,2,3. Climate-driven vegetation models typically predict that this tropical forest ‘carbon sink’ will continue for decades4,5. Here we assess trends in the carbon sink using 244 structurally intact African tropical forests spanning 11 countries, compare them with 321 published plots from Amazonia and investigate the underlying drivers of the trends. The carbon sink in live aboveground biomass in intact African tropical forests has been stable for the three decades to 2015, at 0.66 tonnes of carbon per hectare per year (95 per cent confidence interval 0.53–0.79), in contrast to the long-term decline in Amazonian forests6. Therefore the carbon sink responses of Earth’s two largest expanses of tropical forest have diverged. The difference is largely driven by carbon losses from tree mortality, with no detectable multi-decadal trend in Africa and a long-term increase in Amazonia. Both continents show increasing tree growth, consistent with the expected net effect of rising atmospheric carbon dioxide and air temperature7,8,9. Despite the past stability of the African carbon sink, our most intensively monitored plots suggest a post-2010 increase in carbon losses, delayed compared to Amazonia, indicating asynchronous carbon sink saturation on the two continents. A statistical model including carbon dioxide, temperature, drought and forest dynamics accounts for the observed trends and indicates a long-term future decline in the African sink, whereas the Amazonian sink continues to weaken rapidly. Overall, the uptake of carbon into Earth’s intact tropical forests peaked in the 1990s. Given that the global terrestrial carbon sink is increasing in size, independent observations indicating greater recent carbon uptake into the Northern Hemisphere landmass10 reinforce our conclusion that the intact tropical forest carbon sink has already peaked. This saturation and ongoing decline of the tropical forest carbon sink has consequences for policies intended to stabilize Earth’s climate.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
Source data to generate figures and tables are available from https://doi.org/10.5521/Forestplots.net/2019_1.
Code availability
R code to generate figures and tables is available from: https://doi.org/10.5521/Forestplots.net/2019_1.
References
Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).
Sitch, S. et al. Recent trends and drivers of regional sources and sinks of carbon dioxide. Biogeosciences 12, 653–679 (2015).
Gaubert, B. et al. Global atmospheric CO2 inverse models converging on neutral tropical land exchange, but disagreeing on fossil fuel and atmospheric growth rate. Biogeosciences 16, 117–134 (2019).
Huntingford, C. et al. Simulated resilience of tropical rainforests to CO2-induced climate change. Nat. Geosci. 6, 268–273 (2013).
Mercado, L. M. et al. Large sensitivity in land carbon storage due to geographical and temporal variation in the thermal response of photosynthetic capacity. New Phytol. 218, 1462–1477 (2018).
Brienen, R. J. W. et al. Long-term decline of the Amazon carbon sink. Nature 519, 344–348 (2015).
Piao, S. et al. Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends. Glob. Change Biol. 19, 2117–2132 (2013).
Schimel, D., Stephens, B. B. & Fisher, J. B. Effect of increasing CO2 on the terrestrial carbon cycle. Proc. Natl Acad. Sci. USA 112, 436–441 (2015).
Anderegg, W. R. L. et al. Tropical nighttime warming as a dominant driver of variability in the terrestrial carbon sink. Proc. Natl Acad. Sci. USA 112, 15591–15596 (2015).
Ciais, P. et al. Five decades of northern land carbon uptake revealed by the interhemispheric CO2 gradient. Nature 568, 221–225 (2019).
Lewis, S. L., Edwards, D. P. & Galbraith, D. Increasing human dominance of tropical forests. Science 349, 827–832 (2015).
Pugh, T. A. M. et al. Role of forest regrowth in global carbon sink dynamics. Proc. Natl Acad. Sci. USA 116, 4382–4387 (2019).
Lewis, S. L. et al. Increasing carbon storage in intact African tropical forests. Nature 457, 1003–1006 (2009).
Phillips, O. L. et al. Drought sensitivity of the Amazon rainforest. Science 323, 1344–1347 (2009).
Qie, L. et al. Long-term carbon sink in Borneo’s forests halted by drought and vulnerable to edge effects. Nat. Commun. 8, 1966 (2017).
Gatti, L. V. et al. Drought sensitivity of Amazonian carbon balance revealed by atmospheric measurements. Nature 506, 76–80 (2014).
Nemani, R. R. et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300, 1560–1563 (2003).
Keenan, T. F. et al. Recent pause in the growth rate of atmospheric CO2 due to enhanced terrestrial carbon uptake. Nat. Commun. 7, 13428 (2016).
Booth, B. B. B. et al. High sensitivity of future global warming to land carbon cycle processes. Environ. Res. Lett. 7, 024002 (2012).
Lombardozzi, D. L., Bonan, G. B., Smith, N. G., Dukes, J. S. & Fisher, R. A. Temperature acclimation of photosynthesis and respiration: a key uncertainty in the carbon cycle-climate feedback. Geophys. Res. Lett. 42, 8624–8631 (2015).
Le Quéré, C. et al. Global carbon budget 2018. Earth Syst. Sci. Data 10, 2141–2194 (2018).
Lewis, S. L., Brando, P. M., Phillips, O. L., van der Heijden, G. M. F. & Nepstad, D. The 2010 Amazon drought. Science 331, 554 (2011).
Feldpausch, T. R. et al. Amazon forest response to repeated droughts. Glob. Biogeochem. Cycles 30, 964–982 (2016).
McDowell, N. et al. Drivers and mechanisms of tree mortality in moist tropical forests. New Phytol. 219, 851–869 (2018).
Aleixo, I. et al. Amazonian rainforest tree mortality driven by climate and functional traits. Nat. Clim. Chang. 9, 384–388 (2019).
Lewis, S. L. et al. Concerted changes in tropical forest structure and dynamics: evidence from 50 South American long-term plots. Phil. Trans. R. Soc. Lond. B 359, 421–436 (2004).
Lewis, S. L. et al. Above-ground biomass and structure of 260 African tropical forests. Phil. Trans. R. Soc. Lond. B 368, 20120295 (2013).
Quesada, C. A. et al. Basin-wide variations in Amazon forest structure and function are mediated by both soils and climate. Biogeosciences 9, 2203–2246 (2012).
Malhi, Y. et al. The above-ground coarse wood productivity of 104 neotropical forest plots. Glob. Change Biol. 10, 563–591 (2004).
Galbraith, D. et al. Residence times of woody biomass in tropical forests. Plant Ecol. Divers. 6, 139–157 (2013).
Reich, P. B. et al. Boreal and temperate trees show strong acclimation of respiration to warming. Nature 531, 633–636 (2016).
ter Steege, H. et al. Continental-scale patterns of canopy tree composition and function across Amazonia. Nature 443, 444–447 (2006).
Bauters, M. et al. High fire-derived nitrogen deposition on central African forests. Proc. Natl Acad. Sci. USA 115, 549–554 (2018).
Parmentier, I. et al. The odd man out? Might climate explain the lower tree alpha-diversity of African rain forests relative to Amazonian rain forests? J. Ecol. 95, 1058–1071 (2007).
Slik, J. W. F. et al. Phylogenetic classification of the world’s tropical forests. Proc. Natl Acad. Sci. USA 115, 1837–1842 (2018).
Phillips, O. L. et al. Increasing dominance of large lianas in Amazonian forests. Nature 418, 770–774 (2002).
Schnitzer, S. A. & Bongers, F. Increasing liana abundance and biomass in tropical forests: emerging patterns and putative mechanisms. Ecol. Lett. 14, 397–406 (2011).
Meinshausen, M. et al. The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim. Change 109, 213–241 (2011).
Terrer, C. et al. Nitrogen and phosphorus constrain the CO2 fertilization of global plant biomass. Nat. Clim. Chang. 9, 684–689 (2019).
Fleischer, K. et al. Amazon forest response to CO2 fertilization dependent on plant phosphorus acquisition. Nat. Geosci. 12, 736–741 (2019).
Jiang, Y. et al. Widespread increase of boreal summer dry season length over the Congo rainforest. Nat. Clim. Chang. 9, 617–622 (2019).
Gloor, M. et al. Recent Amazon climate as background for possible ongoing and future changes of Amazon humid forests. Glob. Biogeochem. Cycles 29, 1384–1399 (2015).
Kolby Smith, W. et al. Large divergence of satellite and Earth system model estimates of global terrestrial CO2 fertilization. Nat. Clim. Chang. 6, 306–310 (2016).
Chen, C. et al. China and India lead in greening of the world through land-use management. Nature Sustain. 2, 122–129 (2019).
Chambers, J. Q., Higuchi, N., Schimel, J. P., Ferreira, L. V. & Melack, J. M. Decomposition and carbon cycling of dead trees in tropical forests of the central Amazon. Oecologia 122, 380–388 (2000).
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).
Pearson, T. R. H., Brown, S., Murray, L. & Sidman, G. Greenhouse gas emissions from tropical forest degradation: an underestimated source. Carbon Balance Manag. 12, 3 (2017).
Schwartz, N. B., Uriarte, M., DeFries, R., Gutierrez-Velez, V. H. & Pinedo-Vasquez, M. A. Land-use dynamics influence estimates of carbon sequestration potential in tropical second-growth forest. Environ. Res. Lett. 12, 074023 (2017).
Lewis, S. L., Wheeler, C. E., Mitchard, E. T. A. & Koch, A. Regenerate natural forests to store carbon. Nature 568, 25–28 (2019).
Yu, K. et al. Pervasive decreases in living vegetation carbon turnover time across forest climate zones. Proc. Natl Acad. Sci. USA 116, 24662–24667 (2019).
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).
Lopez-Gonzalez, G., Lewis, S. L., Burkitt, M. & Phillips, O. L. ForestPlots.net: a web application and research tool to manage and analyse tropical forest plot data. J. Veg. Sci. 22, 610–613 (2011).
Lopez-Gonzalez, G., Lewis, S. L., Burkitt, M., Baker, T. R. & Phillips, O. L. ForestPlots.net Database http://www.forestplots.net (2009).
Sheil, D. & Bitariho, R. Bwindi Impenetrable Forest TEAM Site https://www.wildlifeinsights.org/team-network, TEAM-DataPackage-20151201235855_1254 (2009).
Kenfack, D. Korup National Park TEAM Site https://www.wildlifeinsights.org/team-network, TEAM-DataPackage-20151201235855_1254 (2011).
Rovero, F., Marshall, A. & Martin, E. Udzungwa TEAM Site https://www.wildlifeinsights.org/team-network, TEAM-DataPackage-20151130235007_5069 (2009).
Hockemba, M. B. N. Nouabalé Ndoki TEAM Site https://www.wildlifeinsights.org/team-network, TEAM-DataPackage-20151201235855_1254 (2010).
Anderson-Teixeira, K. J. et al. CTFS-ForestGEO: a worldwide network monitoring forests in an era of global change. Glob. Change Biol. 21, 528–549 (2015).
Gourlet-Fleury, S. et al. Tropical forest recovery from logging: a 24 year silvicultural experiment from Central Africa. Phil. Trans. R. Soc. Lond. B 368, 20120302 (2013).
Claeys, F. et al. Climate change would lead to a sharp acceleration of Central African forests dynamics by the end of the century. Environ. Res. Lett. 14, 044002 (2019).
Chave, J. et al. Improved allometric models to estimate the aboveground biomass of tropical trees. Glob. Change Biol. 20, 3177–3190 (2014).
R Development Core Team R: A Language and Environment for Statistical Computing http://www.R-project.org/ (2015).
Lopez-Gonzalez, G., Sullivan, M. & Baker, T. BiomasaFP. R package version 0.2.1 http://www.forestplots.net/en/resources/analysis (2017).
Phillips, O., Baker, T., Brienen, R. & Feldpausch, T. RAINFOR field manual for plot establishment and remeasurement. http://www.rainfor.org/upload/ManualsEnglish/RAINFOR_field_manual_version_2016.pdf (Univ. Leeds, 2016).
Talbot, J. et al. Methods to estimate aboveground wood productivity from long-term forest inventory plots. For. Ecol. Manage. 320, 30–38 (2014).
Sullivan, M. J. P. et al. Field methods for sampling tree height for tropical forest biomass estimation. Methods Ecol. Evol. 9, 1179–1189 (2018).
Feldpausch, T. R. et al. Tree height integrated into pantropical forest biomass estimates. Biogeosciences 9, 3381–3403 (2012).
Chave, J. et al. Towards a worldwide wood economics spectrum. Ecol. Lett. 12, 351–366 (2009).
Zanne, A. E. et al. Towards a Worldwide Wood Economics Spectrum https://doi.org/10.5061/dryad.234 (Dryad Digital Repository, 2009).
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).
Kohyama, T. S., Kohyama, T. I., Sheil, D. & Rees, M. Definition and estimation of vital rates from repeated censuses: choices, comparisons and bias corrections focusing on trees. Methods Ecol. Evol. 9, 809–821 (2018).
Bates, D., Maechler, M., Bolker, B. & Walker, S. lme4: linear mixed-effects models using Eigen and S4. R package version 1.0-4 http://www.inside-r.org/packages/lme4/versions/1-0-4 (2013).
Fox, J. Applied Regression Analysis and Generalized Linear Models 2nd edn (Sage Publishing, 2008).
Chave, J. et al. Assessing evidence for a pervasive alteration in tropical tree communities. PLoS Biol. 6, 0455–0462 (2008).
Yuen, J. Q., Ziegler, A. D., Webb, E. L. & Ryan, C. M. Uncertainty in below-ground carbon biomass for major land covers in Southeast Asia. For. Ecol. Manage. 310, 915–926 (2013).
Aragão, L. E. O. C. et al. Spatial patterns and fire response of recent Amazonian droughts. Geophys. Res. Lett. 34, L07701 (2007).
Aragão, L. E. O. C. et al. Environmental change and the carbon balance of Amazonian forests. Biol. Rev. Camb. Phil. Soc. 89, 913–931 (2014).
Tans, P. & Keeling, R. Trends in Atmospheric Carbon Dioxide for Mauna Loa, Hawaii http://www.esrl.noaa.gov/gmd/ccgg/trends/ (ESRL, 2016).
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).
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Ramirez-Villegas, J. & Jarvis, A. Downscaling Global Circulation Model Outputs: The Delta Method. Decision and Policy Analysis Working Paper No. 1 https://cgspace.cgiar.org/handle/10568/90731 (International Center for Tropical Agriculture (CIAT), 2010).
Schneider, U. et al. GPCC Full Data Reanalysis Version 6.0 at 0.5°: Monthly Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historic Data https://opendata.dwd.de/climate_environment/GPCC/html/fulldata_v6_doi_download.html (Global Precipitation Climatology Centre (GPCC) at Deutscher Wetterdienst, 2011).
Sun, Q. et al. Review of global precipitation data sets: data sources, estimation, and intercomparisons. Rev. Geophys. 56, 79–107 (2017).
Huffman, G. J. et al. The TRMM Multisatellite Precipitation Analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeorol. 8, 38–55 (2007).
Kume, T. et al. Ten-year evapotranspiration estimates in a Bornean tropical rainforest. Agric. For. Meteorol. 151, 1183–1192 (2011).
Zelazowski, P., Malhi, Y., Huntingford, C., Sitch, S. & Fisher, J. B. Changes in the potential distribution of humid tropical forests on a warmer planet. Phil. Trans. R. Soc. A 369, 137–160 (2011).
James, R., Washington, R. & Rowell, D. P. Implications of global warming for the climate of African rainforests. Phil. Trans. R. Soc. Lond. B 368, 20120298 (2013).
Jung, M. et al. Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature 467, 951–954 (2010).
Jung, M. et al. Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. J. Geophys. Res. 116, https://doi.org/10.1029/2010JG001566 (2011).
Lloyd, J. & Farquhar, G. D. The CO2 dependence of photosynthesis, plant growth responses to elevated atmospheric CO2 concentrations and their interaction with soil nutrient status. I. General principles and forest ecosystems. Funct. Ecol. 10, 4–32 (1996).
Aspinwall, M. J. et al. Convergent acclimation of leaf photosynthesis and respiration to prevailing ambient temperatures under current and warmer climates in Eucalyptus tereticornis. New Phytol. 212, 354–367 (2016).
Bonal, D., Burban, B., Stahl, C., Wagner, F. & Hérault, B. The response of tropical rainforests to drought—lessons from recent research and future prospects. Ann. For. Sci. 73, 27–44 (2016).
Quesada, C. A. et al. Variations in chemical and physical properties of Amazon forest soils in relation to their genesis. Biogeosciences 7, 1515–1541 (2010).
Baker, T. R., Swaine, M. D. & Burslem, D. F. R. P. Variation in tropical forest growth rates: combined effects of functional group composition and resource availability. Perspect. Plant Ecol. Evol. Syst. 6, 21–36 (2003).
Pinheiro, J. C. & Bates, D. M. Mixed-Effects Models in S and S-PLUS 1st edn 528 (Springer, 2000).
Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S 4th edn 498 (Springer, 2002).
Olejnik, S., Mills, J. & Keselman, H. Using Wherry’s adjusted R2 and Mallow’s Cp for model selection from all possible regressions. J. Exp. Educ. 68, 365–380 (2000).
Whittingham, M. J., Stephens, P. A., Bradbury, R. B. & Freckleton, R. P. Why do we still use stepwise modelling in ecology and behaviour? J. Anim. Ecol. 75, 1182–1189 (2006).
Bartoń, K. MuMIn: Multi-Model Inference. Tools for performing model selection and model averaging. R package version 1.43.6 (2019).
Gelman, A. & Hill, J. Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge Univ. Press, 2007).
Mayaux, P., De Grandi, G. & Malingreau, J.-P. Central African forest cover revisited: a multisatellite analysis. Remote Sens. Environ. 71, 183–196 (2000).
Mayaux, P. et al. The Land Cover Map for Africa in the Year 2000 GLC2000 database, https://forobs.jrc.ec.europa.eu/products/glc2000/products.php (European Commission Joint Research Centre, 2003).
Acknowledgements
This paper is a product of the African Tropical Rainforest Observatory Network (AfriTRON), curated at ForestPlots.net. AfriTRON has been supported by numerous people and grants since its inception. We sincerely thank the people of the many villages and local communities who welcomed our field teams and without whose support this work would not have been possible: Sierra Leone (villages: Barrie, Gaura, Koya, Makpele, Malema, Nomo, Tunkia; teams in protected areas: the Gola Rainforest National Park), Liberia (villages: Garley town, River Gbeh, Glaro Freetown), Ghana (villages: Nkwanta, Asenanyo, Bonsa, Agona, Boekrom, Dadieso, Enchi, Dabiasem, Mangowase, Draw, Fure, Esuboni, Okumaninin, Kade, Asamankese, Tinte Bepo, Tonton), Nigeria (Oban village), Gabon (villages: Ekobakoba, Mikongo, Babilone, Makokou, Tchimbele, Mondah, Ivindo, Ebe, Ekouk, Oveng, Sette Cama; teams in protected areas: Ivindo National Park, Lope National Park, Waka National Park; teams in concessions: Ipassa station, Kingele station, Leke/Moyabi Rougier Forestry Concession), Cameroon (villages: Campo, Nazareth, Lomié, Djomédjo, Alat-Makay, Somalomo, Deng Deng, Eyumojok, Mbakaou, Myere, Nguti, Bejange, Kekpane, Basho, Mendhi, Matene, Mboh, Takamanda, Obonyi, Ngoïla; teams in protected areas: Ejagham forest reserve), Democratic Republic of Congo (villages: Yoko, Yangambi, Epulu, Monkoto), Republic of Congo (villages: Bomassa, Ekolongouma, Bolembe, Makao, Mbeli, Kabo, Niangui, Ngubu, Goualaki, Essimbi). We thank the field assistants whose expertise and enthusiasm is indispensable to successful fieldwork, including: M. E. Abang, U. P. Achui, F. Addai, E. J. Agbachon, J. Agnaka, A. J. Akaza, G. Alaman, G. Alaman, A. E. Alexander, K. Allen, M. Amalphi, D. Amandus, J. Andju, L. A. Limbanga, S. Asamoah, T. M. Ashu, M. Ashu, J. Asse, B. Augustine, H. Badjoko, M. Balimu, J. Baviogui-Baviogui, S. Benteh, A. Bertrand, A. Bettus, A. Bias, A. Bikoula, A. Bimba, P. Bissiemou, M. Boateng, E. Bonyenga, M. B. Ekaya, G. Bouka, J. Boussengui, D. B. Ngomo, C. Chalange, S. Chenikan, J. Dabo, E. Dadize, T. Degraft, J. Dibakou, J.-T. Dikangadissi, P. Dimbonda, E. Dimoto, C. Ditougou, D. Dorbor, M. Dorbor, V. Droissart, K. Duah, E. Ebe, O. J. Eji, E. B. Ekamam, J.-R. Ekomindong, E. J. Enow, H. Entombo, E. M. Ernest, C. Esola, J. Essouma, A. Gabriel, N. Genesis, B. Gideon, A. Godwin, E. Grear, D. J. Grear, M. Ismael, M. Iwango, M. Iyafo, N. Kamdem, B. Kibinda, A. Kidimbu, E. Kimumbu, J. Kintsieri, C. K. Opepa, A. Kitegile, T. Komo, P. Koué, A. Kouanga, J. J. Koumikaka, I. Liengola, E. Litonga, L. Louvouando, O. Luis, N. M. Mady, F. Mahoula, A. Mahundu, C. A. Mandebet, P. Maurice, K. Y. Mayossa, R. M. Nkogue, I. D. Mbe, C. Mbina, H. Mbona, A. Mboni, A. Mbouni, P. Menzo, M. Menge, A. Michael, A. Mindoumou, J. Minpsa, J. P. Mondjo, E. Mounoumoulossi, S. Mpouam, T. Msigala, J. Msirikale, S. Mtoka, R. Mwakisoma, D. Ndong-Nguema, G. Ndoyame, G. Ngongbo, F. Ngowa, D. Nguema, L. Nguye, R. Niangadouma, Y. Nkrumah, S. Nshimba, M. N. Mboumba, F. N. Obiang, L. Obi, R. Obi, E. L. Odjong, F. Okon, F. Olivieira, A. L. Owemicho, L. Oyeni-Amoni, A. Platini, P. Ploton, S. Quausah, E. Ramazani, B. S. Jean, L. Sagang, R. Salter, A. Seki, D. Shirima, M. Simo, I. Singono, A. E. Tabi, T. G. Tako, N. G. Tambe, T. Tcho, A. Teah, V. Tehtoe, B. J. Telephas, M. L. Tonda, A. Tresor, H. Umenendo, R. Votere, C. K. Weah, S. Weah, B. Wursten, E. Yalley, D. Zebaze, L. Cerbonney, E. Dubiez, H. Moinecourt, F. Lanckriet, S. Samai, M. Swaray, P. Lamboi, M. Sullay, D. Bannah, I. Kanneh, M. Kannah, A. Kemokai, J. Kenneh and M. Lukulay. For logistical and administrative support, we are indebted to international, national and local institutions: the Forestry Department of the Government of Sierra Leone, the Conservation Society of Sierra Leone, the Royal Society for the Protection of Birds (RSPB, UK), The Gola Rainforest National Park (Sierra Leone), the Forestry Development Authority of the Government of Liberia (FDA), the University of Liberia, the Forestry Commission of Ghana (FC), the Forestry Research Institute of Ghana (FORIG), University of Ibadan (Nigeria), the University of Abeokuta (Nigeria), the Ministère des Eaux, Forêts, Chasse et Pêche (MEFCP, Central African Republic), the Institut Centrafricain de Recherche Agronomique (ICRA, Central African Republic), The Service de Coopération et d’Actions Culturelles (SCAC/MAE, Central African Republic), The University of Bangui (Central African Republic), the Société Centrafricaine de Déroulage (SCAD, Central African Republic), the University of Yaounde I (Cameroon), the National Herbarium of Yaounde (Cameroon), the University of Buea (Cameroon), Bioversity International (Cameroon), the Ministry of Forests, Seas, Environment and Climate (Gabon), the Agence Nationale des Parcs Nationaux de Gabon (ANPN), Institut de Recherche en Écologie Tropicale du Gabon, Rougier-Gabon, the Marien Ngouabi University of Brazzaville (Republic of Congo), the Ministère des Eaux et Forêts (Republic of Congo), the Ministère de la Rercherche Scientifique et de l’Innovation Technologique (Republic of Congo), the Nouabalé-Ndoki Foundation (Republic of Congo), WCS-Congo, Salonga National Park (Democratic Republic of Congo), The Centre de Formation et de Recherche en Conservation Forestière (CEFRECOF, Epulu, Democratic Republic of Congo), the Institut National pour l’Étude et la Recherche Agronomiques (INERA, Democratic Republic of Congo), the École Régionale Postuniversitaire d’Aménagement et de Gestion intégrés des Forêts et Territoires tropicaux (ERAIFT Kinshasa, Democratic Republic of Congo), WWF-Democratic Republic of Congo, WCS-Democratic Republic of Congo, the Université de Kisangani (Democratic Republic of Congo), Université Officielle de Bukavu (Democratic Republic of Congo), Université de Mbujimayi (Democratic Republic of Congo), le Ministère de l'Environnement et Développement Durable (Democratic Republic of Congo), the FORETS project in Yangambi (CIFOR, CGIAR and the European Union; Democratic Republic of Congo), the Lukuru Wildlife Research Foundation (Democratic Republic of Congo), Mbarara University of Science and Technology (MUST, Uganda), WCS-Uganda, the Uganda Forest Department, the Commission of Central African Forests (COMIFAC), the Udzungwa Ecological Monitoring Centre (Tanzania) and the Sokoine University of Agriculture (Tanzania). We thank C. Chatelain (Geneva Botanic Gardens) for access to the African Plants Database. Grants that have funded the AfriTRON network including data in this paper are: a European Research Council Advanced Grant to O.L.P. and S.L.L. (T-FORCES; 291585; Tropical Forests in the Changing Earth System), a NERC grant to O.L.P., Y.M., and S.L.L. (NER/A/S/2000/01002), a Royal Society University Research Fellowship to S.L.L., a NERC New Investigators Grant to S.L.L., a Philip Leverhulme Award to S.L.L., a European Union FP7 grant to E.G. and S.L.L. (GEOCARBON; 283080), Valuing the Arc Leverhulme Program Grant to Andrew Balmford and S.L.L., a Natural Environment Research Council (NERC) Consortium Grant to Jon Lloyd and S.L.L. (TROBIT; NE/D005590/), the Gordon and Betty Moore Foundation to L.J.T.W and S.L.L., the David and Lucile Packard Foundation to L.J.T.W. and S.L.L., the Centre for International Forestry Research to T.S. and S.L.L. (CIFOR), and Gabon’s National Parks Agency (ANPN) to S.L.L. W.H. was funded by T-FORCES and the Brain programme of the Belgian Federal Government (BR/132/A1/AFRIFORD grant to Olivier Hardy and the BR/143/A3/HERBAXYLAREDD grant to H.B.). O.L.P., S.L.L., M.J.P.S, A.E.-M., A.L., G.L.-G., G.P. and L.Q. were supported by T-FORCES. Eight plots (codes ANK, IVI, LPG, MNG) included in AfriTRON are also part of the Global Ecosystem Monitoring network (GEM). Additional African data were included from the consortium MEFCP-ICRA-CIRAD (Centre de Coopération Internationale en Recherche Agronomique pour le Développement), the Tropical Ecology Assessment and Monitoring Network (TEAM), and the Forest Global Earth Observatory Network (ForestGEO; formerly the Center for Tropical Forest Science, CTFS). The TEAM network is a collaboration between Conservation International, the Missouri Botanical Garden, the Smithsonian Institution and the Wildlife Conservation Society, and funded by the Gordon and Betty Moore Foundation and other donors. The ForestGEO Network is a collaboration between the Smithsonian Institution, other federal agencies of the United States, the Wildlife Conservation Society (WCS) and the World Wide Fund for Nature (WWF), and funded by the US National Science Foundation and other donors. The paper was made possible by the RAINFOR network in Amazonia, with multiple funding agencies and hundreds of investigators working in Amazonia, acknowledged in ref. 6, providing comprehensive published data and code and assisting in the onward analysis of their data; see ref. 6. Data from AfriTRON and RAINFOR are stored and curated by ForestPlots.net, a long-term cyber-infrastructure initiative hosted at the University of Leeds that unites permanent plot records and their contributing scientists from the world’s tropical forests. The development of ForestPlots.net and curation of most data analysed here was funded by many sources, including grants to O.L.P. (principally from ERC AdG 291585 ‘T-FORCES’, NERC NE/B503384/1 and the Gordon and Betty Moore Foundation 1656 ‘RAINFOR’), T.R.B. (the University of Leeds contribution to ‘AMAZALERT’, NERC (NE/I028122/1) with T. Pennington, the Gordon and Betty Moore Foundation (‘MonANPeru’) and a NERC Impact Accelerator grant for the initial development of the BiomasaFP R package), E.G. (‘GEOCARBON’ and NE/F005806/1 ‘AMAZONICA’) and S.L.L. (Royal Society University Research Fellowship, NERC New Investigators Award, NERC NE/P008755/1). We acknowledge the contributions of the ForestPlots.net developers (M. Burkitt, G. Lopez-Gonzalez) and the steering committee (T.R.B., A.L., S.L.L., O.L.P., L.Q., E. N. H. Coronado and B. S. Marimon) for advice on database development and management.
Author information
Authors and Affiliations
Contributions
S.L.L. conceived and managed the AfriTRON forest plot recensus programme, O.L.P., T.C.H.S., L.J.T.W. and Y.M. contributed to its development. W.H., S.L.L., O.L.P., B.S. and M.J.P.S. developed the study. W.H., S.L.L., O.L.P., K.A.-B., H.B., A.C.-S., C.E.N.E., S.F., D.S., B.S., T.C.H.S., S.C.T., K.A.A., S.A.-B., C.A.A., T.R.B., L.F.B., F. Baya, S.K.B., F. Benedet, R.B., Y.E.B., P. Boeckx, P. Boundja, T.B., E.C., G.B.C., C.J.C., M.C., J.A.C., D.C., A.K.D., G.C.D., T.d.H., M.D.K., J.-L.D., T.R.F., A.F., E.G.F., M.G., C.G., S.G.-F., J.S.H., A.C.H., D.J.H., T.B.H., M.B.N.H., A.H., S.A.I., K.J.J., T.J., E.K.Y., E.K., D.K., M.E.L., J.A.L., J.L., J.C.L., J.-R.M., Y.M., A.R.M., J.M., E.H.M., F.M.M., V.P.M., V.M., E.T.A.M., S.M., J.M.M., P.K.T.M., N.N.B., L.O., F.E., K.S.-H.P., A.D.P., J.R.P., L.Q., J.R., F.R., M.D.S., H.T., J. Talbot, J. Taplin, D.M.T., D.W.T., B.T., J.T.M., D.T., P.M.U., G.v.d.H., H.V., J.V., L.J.T.W., S.W., H.W., J.T.W. and L.Z. contributed data (with larger field contributions by S.L.L., W.H., A.C.-S., B.S., H.T., A.K.D., C.E.N.E., J.M.M., K.A.-B. and S.F.). O.L.P., T.R.B., S.L.L. and G.L.-G. conceived and managed forestplots.net; O.L.P., T.R.B., S.L.L., E.G., G.L.-G., G.C.P., A.L., R.J.W.B., T.R.F. and M.J.P.S. developed it. W.H., M.J.P.S., S.L.L., O.L.P., R.J.W.B., A.L., G.L.-G., A.E.-M., A.K., E.G., T.R.B., A.C.B. and G.C.P. contributed analysis tools. W.H. and S.L.L. analysed the data (with important contributions from M.J.P.S.). S.L.L. and W.H. wrote the paper. All co-authors read and approved the manuscript (with important insights provided by O.L.P., S.F., R.J.W.B., E.G., H.B., D.S., M.J.P.S., S.G.-F., P.B., H.V. and S.C.T).
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 Map showing the locations of the 244 plots included in this study.
Dark green represents all lowland closed-canopy forests, submontane forests and forest-agriculture mosaics; light green shows swamp forests and mangroves, blue circles represent plot clusters, referred to by three-letter codes (see Supplementary Table 1 for the full list of plots). Clusters <50 km apart are shown as one point for display only, with the circle size corresponding to sampling effort in terms of hectares monitored. Land cover data are from The Land Cover Map for Africa in the Year 2000 (GLC2000 database)101,102. This map was created using the R statistical platform, version 3.2.1 (ref. 62), which is under the GNU Public License.
Extended Data Fig. 2 Long-term aboveground carbon dynamics of 244 African structurally intact old-growth tropical forest inventory plots.
Points in the scatterplots indicate the mid-census interval date, with horizontal bars connecting the start and end date for each census interval for net aboveground biomass carbon change (a), carbon gains (from woody production from tree growth and newly recruited stems) (b), and carbon losses (from tree mortality) (c). Examples of time series for three individual plots are shown in purple, yellow and green. Associated histograms show the distribution of the plot-level net aboveground biomass carbon (with a three-parameter Weibull probability density distribution fitted in blue, showing that the carbon sink is significantly larger than zero; one-tailed t-test: P < 0.001) (d), carbon gains (e) and carbon losses (f).
Extended Data Fig. 3 AIC from correlations between the carbon gain in tropical forest inventory plots and changes in atmospheric CO2, temperature (MAT) or drought (MCWD), each calculated over ever-longer prior intervals.
Panels show the AIC from linear mixed effects models of carbon gains from 565 African and Amazonian plots and corresponding changes in atmospheric CO2 (CO2-change) (a), MAT (MAT-change) (b), and drought (MCWD-change) (c). For CO2 the AIC minimum was observed when predicting the carbon gain from the change in CO2 calculated over a 56-year-long prior interval length. We use this length of time to calculate our CO2-change parameter. Such a value is expected because forest stands will respond most strongly to CO2 when most individuals have grown under the new rapidly changing condition, which should be at its maximum at a time approximately equivalent to the CRT of a forest stand30,90 (mean of 62 years in this pooled African and Amazonian dataset). For MAT the AIC minimum was 5 years, which we use as the prior interval to calculate our MAT-change parameter. This length is consistent with experiments showing temperature acclimation of leaf- and plant-level photosynthetic and respiration processes over approximately half-decadal timescales31,91. For MCWD the AIC minimum is not obvious, while the slope of the correlation, shown in d, shows no overall trend and oscillates between positive or negative values, meaning there is no relationship between carbon gains and the change in MCWD over intervals longer than 1 year; therefore MCWD-change is not included in our models. This result suggests that once a drought ends, its impact on tree growth fades rapidly, as seen in other studies14,92. Furthermore, in the moist tropics wet-season rainfall is expected to recharge soil water, and hence lagged impacts of droughts are not expected.
Extended Data Fig. 4 Potential forest dynamics-related drivers of carbon gains and losses in structurally intact old-growth African and Amazonian tropical forest inventory plots.
The aboveground carbon gains, from woody production (a, b), and aboveground carbon losses, from tree mortality (c, d), are plotted against the CRT, and wood density for African (blue) and Amazonian (brown) inventory plots. Linear mixed effects models were performed with census intervals (n = 1,566) nested within plots (n = 565) to avoid pseudo-replication, using an empirically derived weighting based on interval length and plot area (see Methods). Significant regression lines from the linear mixed effects models for the complete dataset are shown as a solid line; non-significant regressions are shown as a dashed line. Each dot represents a time-weighted mean plot-level value; the shading of the dot represents total monitoring length, with empty circles corresponding to plots monitored for ≤5 years and solid circles for plots monitored for >20 years. Carbon loss data are presented untransformed for comparison with carbon gains; linear mixed effects models on transformed data to fit normality assumptions do not change the significance of the results. Note that CRT is calculated differently for the carbon gains and losses models (see Methods).
Extended Data Fig. 5 Trends in predictor variables used to estimate long-term trends in aboveground carbon gains, carbon losses and the resulting net carbon sink in African and Amazonian structurally intact old-growth tropical forest inventory plot networks.
Mean annual CO2-change (a), MAT (b), MAT-change (c), MCWD (d), CRT (e) and wood density (f) for African plot locations in blue, and corresponding variables for Amazon plot locations in brown (g–l). Solid lines represent observational data where >75% of the plots were monitored; long-dashed lines are plot means where <75% of plots were monitored. Dotted lines are future values estimated from linear trends from the 1 January 1983 to 31 December 2014 (Africa) or 1 January 1983 to mid-2011 (Amazon) data (slope and P value reported in each panel), see Methods for details. Upper and lower confidence intervals (shaded area) for the past are calculated by respectively adding and subtracting 2σ to the mean of each annual value. Upper and lower confidence intervals for the future (Africa: 1 January 2015 to 31 December 2039; Amazonia: mid-2011 to 31 December 2039) were estimated by adding and subtracting 2σ from the slope of the regression model.
Extended Data Fig. 6 The change in carbon losses versus CRT of long-term structurally intact old-growth forest inventory plots in Africa and Amazonia.
For plots with two census intervals, we calculated the change in carbon losses (‘∆losses’) as the carbon losses (in Mg C ha−1 yr−1) of the second interval minus the carbon losses of the first interval, divided by the difference in mid-interval dates. For plots with more than two intervals, we calculated the change in carbon losses for each pair of subsequent intervals, then calculated the plot-level mean over all pairs, weighted by the time length between mid-interval dates. This analysis includes only plots with at least two census intervals that were monitored for a total of ≥20 years (that is, roughly one-third of the mean CRT of the pooled African and Amazon dataset; n = 116). Breakpoint regression was used to assess the CRT length below which forest carbon losses begin to increase. Plots with CRT <77 years show a recent long-term increase in carbon losses; longer CRT plots do not. Blue points are African plots, brown points are Amazonian plots.
Extended Data Fig. 7 Trends in net aboveground live biomass carbon, carbon gains and carbon losses from intensively monitored structurally intact old-growth tropical forest inventory plots in Africa.
Trends are calculated for the last 15 years of the twentieth century (a–c) and the first 15 years of the twenty-first century (d–f). Plots were selected from the full dataset if their census intervals cover at least 50% of the respective time windows, that is, they are intensely monitored (n = 56 plots for 1 January 1985 to 31 December 1999, and n = 134 plots for 1 January 2000 to 31 December 2014, respectively). Solid lines show mean values, and shading corresponds to the 95% CI, as calculated in Fig. 1. Dashed lines, slopes and P values are from linear mixed effects models, as in Fig. 1. The data shows a difference compared to Fig. 1, notably the sink decline after about 2010 driven by rising carbon losses. This is because in Fig. 1 we include all available plots over the 1 January 1983 to 31 December 2014 window, which includes clusters of plots monitored only in the 2010s, often monitored for a single census interval, that had low carbon loss and high carbon sink values.
Extended Data Fig. 8 Twenty-first-century trends in aboveground biomass carbon losses from structurally intact old-growth African tropical forest inventory plots with either long or short CRT.
a, b, All plots, that is, as in Fig. 1, but split into a long-CRT group (a) and a short-CRT group (b), each containing half of the 244 plots. c, d, Plots are restricted to those spanning >50% of the time window, that is, intensely monitored plots, as in Extended Data Fig. 7, but split into a long-CRT group (c) and a short-CRT group (d), each containing half of the 134 plots. Solid lines indicate mean values, shading the 95% CI, as for Fig. 1. Dashed lines, slopes and P values are from linear mixed effects models, as for Fig. 1. Carbon losses increase at a higher rate in the short-CRT than the long-CRT group of plots, in both datasets, although this increase is not statistically significant.
Supplementary information
Supplementary Table 1
List of all plots included in this analysis, with geographic coordinates, plot size, start and end dates, and the main researchers for each plot.
Supplementary Table 2
Models to predict carbon gains and losses in African and Amazonian tropical forests (as Table 2, but averaged over all models together accounting for 95% of model weights; see Supplementary Tables 3 and 4 for lists of models).
Supplementary Table 3
List of 13 carbon gain models together accounting for 95% of model weights. The first (best ranked) model is presented in Table 2; the averaged model is presented in Supplementary Table 2.
Supplementary Table 4
List of 31 carbon loss models together accounting for 95% of model weights. The first (best ranked) model is presented in Table 2; the averaged model is presented in Supplementary Table 2.
Supplementary Table 5
Model sensitivity of the future net sink estimates to changes in slope of a single predictor variable.
Rights and permissions
About this article
Cite this article
Hubau, W., Lewis, S.L., Phillips, O.L. et al. Asynchronous carbon sink saturation in African and Amazonian tropical forests. Nature 579, 80–87 (2020). https://doi.org/10.1038/s41586-020-2035-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41586-020-2035-0
This article is cited by
-
Improving wood carbon fractions for multiscale forest carbon estimation
Carbon Balance and Management (2024)
-
Scandal in the voluntary carbon market must not impede tropical forest conservation
Nature Ecology & Evolution (2024)
-
The enduring world forest carbon sink
Nature (2024)
-
No constraint on long-term tropical land carbon-climate feedback uncertainties from interannual variability
Communications Earth & Environment (2024)
-
Evidence of thermophilization in Afromontane forests
Nature Communications (2024)
