Africa is forecasted to experience large and rapid climate change1 and population growth2 during the twenty-first century, which threatens the world’s second largest rainforest. Protecting and sustainably managing these African forests requires an increased understanding of their compositional heterogeneity, the environmental drivers of forest composition and their vulnerability to ongoing changes. Here, using a very large dataset of 6 million trees in more than 180,000 field plots, we jointly model the distribution in abundance of the most dominant tree taxa in central Africa, and produce continuous maps of the floristic and functional composition of central African forests. Our results show that the uncertainty in taxon-specific distributions averages out at the community level, and reveal highly deterministic assemblages. We uncover contrasting floristic and functional compositions across climates, soil types and anthropogenic gradients, with functional convergence among types of forest that are floristically dissimilar. Combining these spatial predictions with scenarios of climatic and anthropogenic global change suggests a high vulnerability of the northern and southern forest margins, the Atlantic forests and most forests in the Democratic Republic of the Congo, where both climate and anthropogenic threats are expected to increase sharply by 2085. These results constitute key quantitative benchmarks for scientists and policymakers to shape transnational conservation and management strategies that aim to provide a sustainable future for central African forests.
This is a preview of subscription content, access via your institution
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
Subscribe to Journal
Get full journal access for 1 year
only $3.90 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
All maps and data used for this article are accessible online in a public repository at https://doi.org/10.18167/DVN1/UCNCA7. Raw floristic data are, however, archived in a private data repository, owing to the highly sensitive nature of commercial inventory data, and access may be granted for research purposes using the form provided in the public repository.
R scripts are available at https://github.com/MaximeRM/ScriptNature.
Diffenbaugh, N. S. & Giorgi, F. Climate change hotspots in the CMIP5 global climate model ensemble. Clim. Change 114, 813–822 (2012).
United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects: The 2017 Revision, Key Findings and Advance Tables. Working Paper No. ESA/P/WP/248 (2017).
Malhi, Y., Adu-Bredu, S., Asare, R. A., Lewis, S. L. & Mayaux, P. African rainforests: past, present and future. Phil. Trans. R. Soc. B 368, 20120312 (2013).
James, R., Washington, R. & Rowell, D. P. Implications of global warming for the climate of African rainforests. Phil. Trans. R. Soc. B 368, 20120298 (2013).
Abernethy, K., Maisels, F. & White, L. J. Environmental issues in Central Africa. Annu. Rev. Environ. Resour. 41, 1–33 (2016).
Hubau, W. et al. Asynchronous carbon sink saturation in African and Amazonian tropical forests. Nature 579, 80–87 (2020).
De Wasseige, C., Tadoum, M., Atyi, E. & Doumenge, C. The Forests of the Congo Basin: Forests and Climate Change (Weyrich, 2015).
Stévart, T. et al. A third of the tropical African flora is potentially threatened with extinction. Sci. Adv. 5, eaax9444 (2019).
Parmentier, I. et al. The odd man out? Might climate explain the lower tree α-diversity of African rain forests relative to Amazonian rain forests? J. Ecol. 95, 1058–1071 (2007).
Réjou-Méchain, M. et al. Regional variation in tropical forest tree species composition in the Central African Republic: an assessment based on inventories by forest companies. J. Trop. Ecol. 24, 663–674 (2008).
Réjou-Méchain, M. et al. Tropical tree assembly depends on the interactions between successional and soil filtering processes. Glob. Ecol. Biogeogr. 23, 1440–1449 (2014).
Fayolle, A. et al. Geological substrates shape tree species and trait distributions in African moist forests. PLoS One 7, e42381 (2012).
Fayolle, A. et al. Patterns of tree species composition across tropical African forests. J. Biogeogr. 41, 2320–2331 (2014).
Droissart, V. et al. Beyond trees: biogeographical regionalization of tropical Africa. J. Biogeogr. 45, 1153–1167 (2018).
Sosef, M. S. et al. Exploring the floristic diversity of tropical Africa. BMC Biol. 15, 15 (2017).
Parmentier, I. et al. Predicting alpha diversity of African rain forests: models based on climate and satellite-derived data do not perform better than a purely spatial model. J. Biogeogr. 38, 1164–1176 (2011).
Bry, X., Trottier, C., Verron, T. & Mortier, F. Supervised component generalized linear regression using a PLS-extension of the fisher scoring algorithm. J. Multivariate Anal. 119, 47–60 (2013).
ter Steege, H. et al. Continental-scale patterns of canopy tree composition and function across Amazonia. Nature 443, 444–447 (2006).
Slik, J. W. et al. Soils on exposed Sunda shelf shaped biogeographic patterns in the equatorial forests of Southeast Asia. Proc. Natl Acad. Sci. USA 108, 12343–12347 (2011).
Philippon, N. et al. The light-deficient climates of western Central African evergreen forests. Environ. Res. Lett. 14, 034007 (2019).
Sullivan, M. J. P. et al. Long-term thermal sensitivity of Earth’s tropical forests. Science 368, 869–874 (2020).
Beale, C. M., Lennon, J. J. & Gimona, A. Opening the climate envelope reveals no macroscale associations with climate in European birds. Proc. Natl Acad. Sci. USA 105, 14908–14912 (2008).
Deblauwe, V. et al. Remotely sensed temperature and precipitation data improve species distribution modelling in the tropics. Glob. Ecol. Biogeogr. 25, 443–454 (2016).
Maguire, K. C. et al. Controlled comparison of species- and community-level models across novel climates and communities. Proc. R. Soc. B 283, 20152817 (2016).
Morin-Rivat, J. et al. Present-day central African forest is a legacy of the 19th century human history. eLife 6, e20343 (2017).
Ricklefs, R. E. Intrinsic dynamics of the regional community. Ecol. Lett. 18, 497–503 (2015).
Violle, C. et al. Let the concept of trait be functional! Oikos 116, 882–892 (2007).
Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).
Rüger, N. et al. Demographic trade-offs predict tropical forest dynamics. Science 368, 165–168 (2020).
Ouédraogo, D.-Y. et al. The determinants of tropical forest deciduousness: disentangling the effects of rainfall and geology in central Africa. J. Ecol. 104, 924–935 (2016).
Shipley, B. From Plant Traits to Vegetation Structure: Chance and Selection in the Assembly of Ecological Communities (Cambridge University Press, 2010).
Feeley, K. J. & Silman, M. R. Biotic attrition from tropical forests correcting for truncated temperature niches. Glob. Change Biol. 16, 1830–1836 (2010).
Parry, M. et al. Climate Change 2007 – Impacts, Adaptation, and Vulnerability: Contribution of Working Group II to the Fourth Assessment Report of the IPCC (Cambridge University Press, 2007).
Foden, W. B. et al. Identifying the world’s most climate change vulnerable species: a systematic trait-based assessment of all birds, amphibians and corals. PLoS One 8, e65427 (2013).
Lachenaud, O., Stévart, T., Ikabanga, D., Ndjabounda, E. C. N. & Walters, G. The littoral forests of the Libreville area (Gabon) and their importance for conservation: description of a new endemic species (Rubiaceae). Plant Ecol. Evol. 146, 68–74 (2013).
Aguirre-Gutiérrez, J. et al. Drier tropical forests are susceptible to functional changes in response to a long-term drought. Ecol. Lett. 22, 855–865 (2019).
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).
McDowell, N. G. et al. Pervasive shifts in forest dynamics in a changing world. Science 368, eaaz9463 (2020).
Zhou, L. et al. Widespread decline of Congo rainforest greenness in the past decade. Nature 509, 86–90 (2014).
Purvis, A. Phylogenetic approaches to the study of extinction. Annu. Rev. Ecol. Evol. Syst. 39, 301–319 (2008).
Yachi, S. & Loreau, M. Biodiversity and ecosystem productivity in a fluctuating environment: the insurance hypothesis. Proc. Natl Acad. Sci. USA 96, 1463–1468 (1999).
Neves, D. M. et al. Evolutionary diversity in tropical tree communities peaks at intermediate precipitation. Sci. Rep. 10, 1188 (2020).
Letcher, S. G. Phylogenetic structure of angiosperm communities during tropical forest succession. Proc. R. Soc. B 277, 97–104 (2010).
Letouzey, R. Notice de la carte phytogéographique du Cameroun au 1:500000 (Institut de la Carte Internationale de la végétation Toulouse-France et Institut de la recherche agronomique (Herbier National) Yaoundé-Cameroun, 1985).
Boulvert, Y. Carte phytogéographique de la République Centrafricaine (feuille oust–feuille est) à 1 000 000 (Editions de l’ORSTOM, 1986).
Fyllas, N. M., Quesada, C. A. & Lloyd, J. Deriving plant functional types for Amazonian forests for use in vegetation dynamics models. Perspect. Plant Ecol. Evol. Syst. 14, 97–110 (2012).
Mitchard, E. T. A. et al. Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites. Glob. Ecol. Biogeogr. 23, 935–946 (2014).
Visconti, P., Pressey, R. L., Bode, M. & Segan, D. B. Habitat vulnerability in conservation planning—when it matters and how much. Conserv. Lett. 3, 404–414 (2010).
Putz, F. E. et al. Sustaining conservation values in selectively logged tropical forests: the attained and the attainable. Conserv. Lett. 5, 296–303 (2012).
Gourlet-Fleury, S. et al. Tropical forest recovery from logging: a 24 year silvicultural experiment from Central Africa. Phil. Trans. R. Soc. B 368, 20120302 (2013).
Clark, C. J., Poulsen, J. R., Malonga, R. & Elkan, P. W. Jr. Logging concessions can extend the conservation estate for Central African tropical forests. Conserv. Biol. 23, 1281–1293 (2009).
Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).
Réjou-Méchain, M. et al. Detecting large-scale diversity patterns in tropical trees: can we trust commercial forest inventories? For. Ecol. Manage. 261, 187–194 (2011).
African Plant Database v.3.4.0 (Conservatoire et Jardin Botaniques de la Ville de Genève and South African National Biodiversity Institute, Pretoria, accessed 10 February 2017).
The Angiosperm Phylogeny Group. An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG III. Bot. J. Linn. Soc. 161, 105–121 (2009).
Dauby, G. et al. RAINBIO: a mega-database of tropical African vascular plants distributions. PhytoKeys 74, 1–18 (2016).
Chave, J. et al. Towards a worldwide wood economics spectrum. Ecol. Lett. 12, 351–366 (2009).
Zanne, A. E. et al. Data from: towards a worldwide wood economic spectrum. Dryad https://doi.org/10.5061/dryad.234 (2009).
Gourlet-Fleury, S. et al. Environmental filtering of dense‐wooded species controls above‐ground biomass stored in African moist forests. J. Ecol. 99, 981–990 (2011).
Westoby, M. & Wright, I. J. Land-plant ecology on the basis of functional traits. Trends Ecol. Evol. 21, 261–268 (2006).
Chave, J. et al. Improved allometric models to estimate the aboveground biomass of tropical trees. Glob. Change Biol. 20, 3177–3190 (2014).
Bénédet, F. et al. CoForTraits, African plant traits information database v.1.0, https://doi.org/10.18167/DVN1/Y2BIZK (2013).
Davies, T. J. et al. Phylogenetic conservatism in plant phenology. J. Ecol. 101, 1520–1530 (2013).
Cramer, W. et al. Global response of terrestrial ecosystem structure and function to CO2 and climate change: Results from six dynamic global vegetation models. Glob. Change Biol. 7, 357–373 (2001).
Menzel, A. Phenology: its importance to the global change community. Clim. Change 54, 379 (2002).
Borchert, R., Rivera, G. & Hagnauer, W. Modification of vegetative phenology in a tropical semi-deciduous forest by abnormal drought and rain. Biotropica 34, 27–39 (2002).
Kraft, N. J. B., Valencia, R. & Ackerly, D. D. Functional traits and niche-based tree community assembly in an Amazonian forest. Science 322, 580–582 (2008).
Schamp, B. S. & Aarssen, L. W. The assembly of forest communities according to maximum species height along resource and disturbance gradients. Oikos 118, 564–572 (2009).
New, M., Lister, D., Hulme, M. & Makin, I. A high-resolution data set of surface climate over global land areas. Clim. Res. 21, 1–25 (2002).
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surface for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).
Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci. Data 2, 150066 (2015).
Nachtergaele, F. et al. The harmonized world soil database. In Proc. 19th World Congress of Soil Science, Soil Solutions for a Changing World (eds Gilkes, R. & Prakongkep, N.) 34–37 (International Union of Soil Sciences, 2010).
Woolmer, G. et al. Rescaling the human footprint: a tool for conservation planning at an ecoregional scale. Landsc. Urban Plan. 87, 42–53 (2008).
Venter, O. et al. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat. Commun. 7, 12558 (2016).
Geldmann, J., Joppa, L. N. & Burgess, N. D. Mapping change in human pressure globally on land and within protected areas. Conserv. Biol. 28, 1604–1616 (2014).
Linard, C., Gilbert, M., Snow, R. W., Noor, A. M. & Tatem, A. J. Population distribution, settlement patterns and accessibility across Africa in 2010. PLoS One 7, e31743 (2012).
Lloyd, C. T. et al. Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets. Big Earth Data 3, 108–139 (2019).
Boulesteix, A.-L. & Strimmer, K. Partial least squares: a versatile tool for the analysis of high-dimensional genomic data. Brief. Bioinform. 8, 32–44 (2007).
Carrascal, L. M., Galván, I. & Gordo, O. Partial least squares regression as an alternative to current regression methods used in ecology. Oikos 118, 681–690 (2009).
Tenenhaus, M. La Régression PLS: Théorie et Pratique (Editions Technip, 1998).
Sabatier, R., Lebreton, J. D. & Chessel, D. in Multiway Data Analysis (eds Coppi, R. & Bolasco, S.) 341–352 (1989).
Ter Braak, C. J. F. in Theory and Models In Vegetation Science (eds Prentice, I. C. & van der Maarel, E.) 69–77 (Springer, 1987).
Bry, X. & Verron, T. THEME: THEmatic model exploration through multiple co-structure maximization. J. Chemometr. 29, 637–647 (2015).
Cornu, G., Mortier, F., Trottier, C. & Bry, X. SCGLR: supervised component generalized linear regression. R version 3.0 https://cran.r-project.org/web/packages/SCGLR/index.html (2016).
Ward, J. H. Jr. Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58, 236–244 (1963).
Ploton, P. et al. Spatial validation reveals poor predictive performance of large-scale ecological mapping models. Nat. Commun. 11, 4540 (2020).
Scrucca, L., Fop, M., Murphy, T. B. & Raftery, A. E. mclust 5: clustering, classification and density estimation using gGussian finite mixture models. R J. 8, 289–317 (2016).
Dormann, C. F. et al. Methods to account for spatial autocorrelation in the analysis of species distributional data: A review. Ecography 30, 609–628 (2007).
Renard, D. et al. RGeostats: the geostatistical package v.11.0. 1 http://rgeostats.free.fr/ (MINES ParisTech, 2014).
Platts, P. J., Omeny, P. A. & Marchant, R. AFRICLIM: high-resolution climate projections for ecological applications in Africa. Afr. J. Ecol. 53, 103–108 (2015).
Janssens, S. B. et al. A large-scale species level dated angiosperm phylogeny for evolutionary and ecological analyses. Biodivers. Data J. 8, e39677 (2020).
Abouheif, E. A method for testing the assumption of phylogenetic independence in comparative data. Evol. Ecol. Res. 1, 895–909 (1999).
Chao, A., Chiu, C.-H. & Jost, L. Phylogenetic diversity measures based on Hill numbers. Phil. Trans. R. Soc. B 365, 3599–3609 (2010).
R Core Team. R: a language and environment for statistical computing. (R Foundation for Statistical Computing, 2017).
Chessel, D., Dufour, A. B. & Thioulouse, J. The ade4 package – I: one-table methods. R News 4, 5–10 (2004).
Lafarge, T. & Pateiro-Lopez, B. alphashape3d: implementation of the 3D alpha-shape for the reconstruction of 3D sets from a point cloud. R version 1.3.1 https://cran.r-project.org/web/packages/alphashape3d/index.html (2017).
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).
Hijmans, R. J. raster: geographic data analysis and modelling. R version 3.4-5 https://cran.r-project.org/web/packages/raster/index.html (2017).
Marcon, E. & Hérault, B. entropart: An R package to measure and partition diversity. J. Stat. Softw. 67, 1–26 (2015).
Dufrêne, M. & Legendre, P. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol. Monogr. 67, 345–366 (1997).
We thank the 105 forest companies that provided access, albeit restricted, to their inventory data for research purposes and members of the central African plot network (https://central-african-plot-network.netlify.app/), Y. Yalibanda, F. Allah-Barem, F. Baya, F. Boyemba, M. Mbasi Mbula, P. Berenger, M. Mazengue, V. Istace, I. Zombo, E. Forni, Nature+ and the CEB-Precious Woods company for giving access to the scientific inventories described in Supplementary Fig. 4, some of which were funded by the AFD and the FFEM (for example, DynAfFor and P3FAC projects). We thank J. Chave, P. Couteron, S. Lewis and M. Tadesse for their comments and discussions on previous versions, B. Sultan for useful discussions on climate projections, O. J. Hardy for advice on phylogenetical analyses, B. Locatelli for advice on vulnerability analyses, G. Vieilledent for discussions on the human-induced forest-disturbance intensity index and A. Stokes for English editing. This work was supported by the CoForTips project (ANR‐12‐EBID‐0002) funded by the ERA-NET BiodivERsA, with the national funders ANR, BELSPO and FWF, as part of the 2012 BiodivERsA call for research proposals, the GAMBAS project funded by the French National Research Agency (ANR-18-CE02-0025) and the project 3DForMod funded by the UE FACCE ERA-GAS consortium (ANR-17-EGAS-0002-01). This study is a contribution to the research program of LMI DYCOFAC (Dynamique des écosystèmes continentaux d’Afrique Centrale en contexte de changements globaux).
The authors declare no competing interests.
Peer review information Nature thanks Jonas Geldmann, Marion Pfeifer, Philip Platts and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
In green, the current distribution of tropical forests following the ESA-CCI landcover product (v.1.6), with a dark-green-to-white gradient representing anthropogenic pressure (see Methods) and non-forested areas represented in beige. The sampling grid cells (n = 1,571 10 × 10-km2 grid cells) are in black and the flooding forests, as proposed by the ESA-CCI landcover, are in blue.
a, CC1 and CC2. b, CC1 and CC3. c, CC2 and CC3. All climatic variables with a correlation of less than 0.75 with the two components (dashed circle) were excluded for the sake of clarity. For abbreviations, see Extended Data Table 2.
a–c, The observed and predicted community weighted mean trait values within the 1,571 10x10-km2 grid cells are given for wood density (a), deciduousness (b) and maximum diameter (c). The 1:1 line is displayed in red.
Extended Data Fig. 6 Projected changes under the RCP 4.5 scenario in 2085 of the climatic conditions of the 10 forest types.
Areas for which climate models predict similar climatic components (CCs) values to those currently found within forest types (in black) are illustrated with a colour gradient indicating the level of agreement amongst the 18 climate models (as a percentage; no colour indicates that none of the original 18 climate models predicted similar conditions). More specifically, we used 3D concave hull (alpha shape) models to assess where the combinations of current CCs corresponding to each forest type are predicted to be represented in 2085.
Extended Data Fig. 7 The vulnerability map under two different RCP scenarios and for two different years.
a–d, Vulnerability maps under RCP 4.5 in 2055 (a), RCP 8.5 in 2055 (b), RCP 4.5 in 2085 (c) and RCP 8.5 in 2085 (d). As can be seen, the predicted vulnerability is little affected by the IPCC scenario chosen because it expresses a relative vulnerability over the study area and, if different scenarios predict different amplitudes of climate change, spatial patterns of climate exposure remains similar (see Methods).
a, b, Current (a) and projected (b) anthropogenic pressure predicted from our index of human-induced forest-disturbance intensity.
Extended Data Fig. 9 Protected area network and areas dedicated to logging activities in central Africa.
The protected area network is shown in blue; areas dedicated to logging are shown in orange and red. Data on protected areas were obtained from the World Database on Protected Areas (https://www.iucn.org/theme/protected-areas/our-work/world-database-protected-areas, last accessed 14 August 2018), excluding marine, hunting and game-oriented areas, except for the Democratic Republic of the Congo, for which data from the World Resource Institute were used and downloaded from ArcGIS hub (https://hub.arcgis.com/datasets/1bcd463cbb6549c9a0676edb9f751f9b, last accessed 1 June 2019). Logging activity data were provided by the Observatoire des Forêts d’Afrique Centrale based on an unpublished work completed in June 2018, except for the Democratic Republic of the Congo, for which more updated data (June 2019) were provided by the AGEDUFOR national project. Areas in orange illustrate forest concessions that are known to have, or to be in the process of having, an officially validated sustainable forest management plan. Red areas illustrate forest areas that are currently dedicated to logging but that either do not have an official management plan or have an uncertain status.
About this article
Cite this article
Réjou-Méchain, M., Mortier, F., Bastin, JF. et al. Unveiling African rainforest composition and vulnerability to global change. Nature 593, 90–94 (2021). https://doi.org/10.1038/s41586-021-03483-6
Nature Africa (2021)
Nature Africa (2021)
Scientific foundations for an ecosystem goal, milestones and indicators for the post-2020 global biodiversity framework
Nature Ecology & Evolution (2021)