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.
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Source data to generate figures and tables are available from https://doi.org/10.5521/Forestplots.net/2019_1.
R code to generate figures and tables is available from: https://doi.org/10.5521/Forestplots.net/2019_1.
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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.
The authors declare no competing interests.
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 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.
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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
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