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Unveiling African rainforest composition and vulnerability to global change

Nature volume 593, pages 90–94 (2021)Cite this article

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Abstract

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.

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Fig. 1: Floristic composition of central African forests.
Fig. 2: Predicted functional composition of central African forests.
Fig. 3: Main forest types across central Africa and their functional composition.
Fig. 4: Predicted vulnerability of central African tree communities to global changes.

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Data availability

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.

Code availability

R scripts are available at https://github.com/MaximeRM/ScriptNature.

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Acknowledgements

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).

Author information

Authors and Affiliations

  1. AMAP, Univ. Montpellier, IRD, CNRS, CIRAD, INRAE, Montpellier, France

    Maxime Réjou-Méchain, Jean-François Bastin, Nicolas Barbier, Gilles Dauby, Pierre Ploton & Raphaël Pélissier

  2. CIRAD, Forêts et Sociétés, Montpellier, France

    Frédéric Mortier, Jean-François Bastin, Guillaume Cornu, Fabrice Bénédet, Charles Doumenge, Claude Garcia & Sylvie Gourlet-Fleury

  3. Forêts et Sociétés, Univ. Montpellier, CIRAD, Montpellier, France

    Frédéric Mortier, Jean-François Bastin, Guillaume Cornu, Fabrice Bénédet, Charles Doumenge, Claude Garcia & Sylvie Gourlet-Fleury

  4. TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium

    Jean-François Bastin, Jean-Louis Doucet & Adeline Fayolle

  5. FRM Ingénierie, Mauguio, France

    Nicolas Bayol

  6. IMAG, CNRS, Univ. Montpellier, Montpellier, France

    Xavier Bry & Catherine Trottier

  7. Center for Tropical Research, Institute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, CA, USA

    Vincent Deblauwe

  8. International Institute of Tropical Agriculture, Yaoundé, Cameroon

    Vincent Deblauwe

  9. Forest Management and Development Team, Department of Environmental System Science, Institute of Integrative Biology, ETH Zürich, Zürich, Switzerland

    Claude Garcia

  10. Faculté des Sciences Agronomiques, Département de Gestion des Ressources Naturelles, Université de Kinshasa, Kinshasa, Democratic Republic of the Congo

    Jean-Paul Kibambe Lubamba

  11. Wildlife Conservation Society, Kinshasa, Democratic Republic of the Congo

    Jean-Paul Kibambe Lubamba

  12. Faculté des Sciences et Techniques, Université Marien Ngouabi, Brazzaville, Congo

    Jean-Joël Loumeto

  13. Institut de Recherche en Écologie Tropicale (IRET/CENAREST), Libreville, Gabon

    Alfred Ngomanda

  14. Plant Systematic and Ecology Laboratory, Higher Teacher’s Training College, University of Yaoundé I, Yaoundé, Cameroon

    Bonaventure Sonké

  15. AMIS, Univ. Paul Valéry Montpellier 3, Montpellier, France

    Catherine Trottier

  16. GEODE UMR 5602, CNRS, Université Jean-Jaurès, Toulouse, France

    Ruppert Vimal

  17. Laboratoire de Biodiversité Végétale et Fongique, Faculté des Sciences, Université de Bangui, Bangui, Central African Republic

    Olga Yongo

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  1. Maxime Réjou-Méchain
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  2. Frédéric Mortier
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  8. Xavier Bry
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Contributions

Conceptualization: M.R.-M., F.M., R.P. and S.G.-F.; data curation: G.C. and F.B.; formal analysis: M.R.-M. and F.M.; project administration: C.G.; writing (original draft): M.R.-M., F.M., R.P. and S.G.-F.; writing (review and editing): all authors.

Corresponding author

Correspondence to Maxime Réjou-Méchain.

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Competing interests

The authors declare no competing interests.

Additional information

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

Extended Data Fig. 1 Study area and sampling plots.

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.

Extended Data Fig. 2 Taxon CA planes 1–2 and 1–3 with labels for the 12 most representative taxa on each axis.

a, Planes 1–2. b, Planes 1–3. Colour code corresponds to that reported in Fig. 1. The first eigenvalues are reported in b, highlighting in black the first three axes. Taxon codes and scores of the 193 taxa are provided in Supplementary Table 2.

Extended Data Fig. 3 Individual predicted floristic gradients illustrated by the three first axes of the correspondence analysis performed on predicted taxon abundances.

a–c, CA axis 1 (a), CA axis 2 (b) and CA axis 3 (c). A composite map of these three axes is given in Fig. 1 and the corresponding taxon CA planes are provided in Extended Data Fig. 2.

Extended Data Fig. 4 Plans 1–2, 1–3 and 2–3 of the SCGLR CCs.

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.

Extended Data Fig. 5 Spatial cross-validation results of the predictions of functional assemblages.

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).

Extended Data Fig. 8 Current and projected anthropogenic pressure over central Africa.

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.

Extended Data Table 1 Characteristics of the floristic groups
Full size table
Extended Data Table 2 Climatic predictors
Full size table

Supplementary information

Supplementary Information

This file contains Supplementary Figs 1-9 and Supplementary Tables 1-2.

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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

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