[en] 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 emissions1–3. 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 temperature7–9. 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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Bibliography
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 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, 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).
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