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High aboveground carbon stock of African tropical montane forests


Tropical forests store 40–50 per cent of terrestrial vegetation carbon1. However, spatial variations in aboveground live tree biomass carbon (AGC) stocks remain poorly understood, in particular in tropical montane forests2. Owing to climatic and soil changes with increasing elevation3, AGC stocks are lower in tropical montane forests compared with lowland forests2. Here we assemble and analyse a dataset of structurally intact old-growth forests (AfriMont) spanning 44 montane sites in 12 African countries. We find that montane sites in the AfriMont plot network have a mean AGC stock of 149.4 megagrams of carbon per hectare (95% confidence interval 137.1–164.2), which is comparable to lowland forests in the African Tropical Rainforest Observation Network4 and about 70 per cent and 32 per cent higher than averages from plot networks in montane2,5,6 and lowland7 forests in the Neotropics, respectively. Notably, our results are two-thirds higher than the Intergovernmental Panel on Climate Change default values for these forests in Africa8. We find that the low stem density and high abundance of large trees of African lowland forests4 is mirrored in the montane forests sampled. This carbon store is endangered: we estimate that 0.8 million hectares of old-growth African montane forest have been lost since 2000. We provide country-specific montane forest AGC stock estimates modelled from our plot network to help to guide forest conservation and reforestation interventions. Our findings highlight the need for conserving these biodiverse9,10 and carbon-rich ecosystems.

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Fig. 1: Pantropical variation in AGC stocks sampled by plot networks in montane (≥800 m a.s.l.) and lowland (<800 m a.s.l.) tropical forests.
Fig. 2: Proportion of plot-level AGC stock and stems accounted for by each size class in montane and lowland forests in Africa.
Fig. 3: Variables as a function of elevation.
Fig. 4: Old-growth evergreen humid forests in lowland and montane tropical Africa.

Data availability

Source data to generate figures and tables are available from

Code availability

The R code to generate figures and tables is available from


  1. 1.

    Erb, K. et al. Unexpectedly large impact of forest management and grazing on global vegetation biomass. Nature 553, 73–76 (2018).

    CAS  PubMed  Article  ADS  Google Scholar 

  2. 2.

    Spracklen, D. V. & Righelato, R. Tropical montane forests are a larger than expected global carbon store. Biogeosciences 11, 2741–2754 (2014).

    CAS  Article  ADS  Google Scholar 

  3. 3.

    Fahey, T. J., Sherman, R. E. & Tanne, E. V. J. Tropical montane cloud forest: environmental drivers of vegetation structure and ecosystem function. J. Trop. Ecol. 32, 355–367 (2016).

    Article  Google Scholar 

  4. 4.

    Lewis, S. L. et al. Above-ground biomass and structure of 260 African tropical forests. Phil. Trans. R. Soc. Lond. B 368, 20120295 (2013).

    Article  Google Scholar 

  5. 5.

    Vilanova, E. et al. Environmental drivers of forest structure and stem turnover across Venezuelan tropical forests. PLoS ONE 13, e0198489 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  6. 6.

    Álvarez-Dávila, E. et al. Forest biomass density across large climate gradients in northern South America is related to water availability but not with temperature. PLoS ONE 12, e0171072 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  7. 7.

    Sullivan, M. J. P. et al. Long-term thermal sensitivity of Earth’s tropical forests. Science 368, 869–874 (2020).

    CAS  PubMed  Article  ADS  Google Scholar 

  8. 8.

    Domke, G. et al. in 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories Vol. 4 (eds Calvo Buendia, E. et al.) Ch. 4, 48 (IPCC, 2019).

  9. 9.

    African Mountains Atlas (UNEP, 2014).

  10. 10.

    Rahbek, C. et al. Humboldt’s enigma: what causes global patterns of mountain biodiversity? Science 365, 1108–1113 (2019).

    CAS  PubMed  Article  ADS  Google Scholar 

  11. 11.

    Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).

    CAS  PubMed  Article  ADS  Google Scholar 

  12. 12.

    Booth, B. B. B. et al. High sensitivity of future global warming to land carbon cycle processes. Environ. Res. Lett. 7, 024002 (2012).

    Article  ADS  CAS  Google Scholar 

  13. 13.

    Hubau, W. et al. Asynchronous carbon sink saturation in African and Amazonian tropical forests. Nature 579, 80–87 (2020).

    CAS  Article  ADS  Google Scholar 

  14. 14.

    Feldpausch, T. R. et al. Tree height integrated into pantropical forest biomass estimates. Biogeosciences 9, 3381–3403 (2012).

    Article  ADS  Google Scholar 

  15. 15.

    Bastin, J.-F. et al. Pan‐tropical prediction of forest structure from the largest trees. Glob. Ecol. Biogeogr. 27, 1366–1383 (2018).

    Article  Google Scholar 

  16. 16.

    CCI BIOMASS Product User Guide Year 1 Version 1.0, (Aberystwyth University and GAMMA Remote Sensing, 2019).

  17. 17.

    Lefsky, M. A., Keller, M., Pang, Y., de Camargo, P. & Hunter, M. O. Revised method for forest canopy height estimation from the Geoscience Laser Altimeter System waveforms. J. Appl. Remote Sens. 1, 013537 (2007).

    Article  Google Scholar 

  18. 18.

    Willcock, S. et al. Quantifying and understanding carbon storage and sequestration within the Eastern Arc Mountains of Tanzania, a tropical biodiversity hotspot. Carbon Balance Manag. 9, 2 (2014); correction 12, 20 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  19. 19.

    Bussmann, R. W. Vegetation zonation and nomenclature of African Mountains—an overview. Lyonia 11, 41–66 (2006).

    Google Scholar 

  20. 20.

    Hamilton, A. Vegetation, climate and soil, altitudinal relationships on the East Usambara Mountains of Tanzania. J. East Afr. Nat. Hist. 87, 85–89 (1998).

    Article  Google Scholar 

  21. 21.

    Phillips, O., Baker, T., Brienen, R. & Feldpausch, T. RAINFOR Field Manual for Plot Establishment and Remeasurement, (Univ. Leeds, 2016)

  22. 22.

    Jarvis, A. & Mulligan, M. The climate of cloud forests. Hydrol. Process. 25, 327–343 (2011).

    Article  ADS  Google Scholar 

  23. 23.

    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).

    Article  Google Scholar 

  24. 24.

    McInerny, G. J. & Purves, D. W. Fine‐scale environmental variation in species distribution modelling: regression dilution, latent variables and neighbourly advice. Methods Ecol. Evol. 2, 248–257 (2011).

    Article  Google Scholar 

  25. 25.

    Poulsen, J. R. et al. Ecological consequences of forest elephant declines for Afrotropical forests. Conserv. Biol. 32, 559–567 (2018).

    PubMed  Article  Google Scholar 

  26. 26.

    Berzaghi, F. et al. Carbon stocks in central African forests enhanced by elephant disturbance. Nat. Geosci. 12, 725–729 (2019); correction 12, 1050 (2019).

    CAS  Article  ADS  Google Scholar 

  27. 27.

    Enquist, B. J. et al. The megabiota are disproportionately important for biosphere functioning. Nat. Commun. 11, 699 (2020).

    CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

  28. 28.

    Lin, T.-C., Hogan, J. A. & Chang, C. T. Tropical cyclone ecology: a scale-link perspective. Trends Ecol. Evol. 35, 594–604 (2020).

    PubMed  Article  Google Scholar 

  29. 29.

    Favalli, M. et al. Lava flow hazard and risk at Mt. Cameroon volcano. Bull. Volcanol. 74, 423–439 (2012).

    Article  ADS  Google Scholar 

  30. 30.

    Stanley, T. & Kirschbaum, D. B. A heuristic approach to global landslide susceptibility mapping. Nat. Hazards 87, 145–164 (2017).

    Article  Google Scholar 

  31. 31.

    Lovett, J. C. Elevational and latitudinal changes in tree associations and diversity in the Eastern Arc mountains of Tanzania. J. Trop. Ecol. 12, 629–650 (1996).

    Article  Google Scholar 

  32. 32.

    Hemp, A. et al. Africa’s highest mountain harbours Africa’s tallest trees. Biodivers. Conserv. 26, 103–113 (2017).

    Article  Google Scholar 

  33. 33.

    Culmsee, H., Leuschner, C., Moser, G. & Pitopang, R. Forest aboveground biomass along an elevational transect in Sulawesi, Indonesia, and the role of Fagaceae in tropical montane rain forests. J. Biogeogr. 37, 960–974 (2010).

    Article  Google Scholar 

  34. 34.

    Enright, N. J. & Ogden, J. in Ecology of the Southern Conifers (eds Enright, N. J. & Hill, R. S.) 271–287 (Melbourne Univ. Press, 1995).

  35. 35.

    Neale, D. B. & Wheeler, N. C. in The Conifers: Genomes, Variation and Evolution (eds Neale, D. B. & Wheeler, N. C.) 1–21 (Springer, 2019).

  36. 36.

    Mill, R. R. Towards a biogeography of the Podocarpaceae. Acta Hortic. 615, 137–147 (2003).

    Article  Google Scholar 

  37. 37.

    Helmer, E. H. et al. Neotropical cloud forests and paramo to contract and dry from declines in cloud immersion and frost. PLoS ONE 14, e0213155 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).

    CAS  Article  ADS  Google Scholar 

  39. 39.

    Turubanova, S., Potapov, P., Tyukavina, A. & Hansen, M. Ongoing primary forest loss in Brazil, Democratic Republic of the Congo, and Indonesia. Environ. Res. Lett. 13, 074028 (2018).

    Article  ADS  Google Scholar 

  40. 40.

    Pellikka, P. K. E., Lötjönen, M., Siljander, M. & Lens, L. Airborne remote sensing of spatiotemporal change (1955–2004) in indigenous and exotic forest cover in the Taita Hills, Kenya. Int. J. Appl. Earth Obs. Geoinf. 11, 221–232 (2009).

    Article  ADS  Google Scholar 

  41. 41.

    Zeng, Z. et al. Deforestation-induced warming over tropical mountain regions regulated by elevation. Nat. Geosci. 14, 23–29 (2021).

    CAS  Article  ADS  Google Scholar 

  42. 42.

    Spira, C., Kirkby, A., Kujirakwinja, D. & Plumptre, A. J. The socio-economics of artisanal mining and bushmeat hunting around protected areas: Kahuzi–Biega National Park and Itombwe nature reserve, eastern Democratic Republic of Congo. Oryx 53, 136–144 (2019).

    Article  Google Scholar 

  43. 43.

    Bebber, D. P. & Butt, N. Tropical protected areas reduced deforestation carbon emissions by one third from 2000–2012. Sci. Rep. 7, 14005 (2017); 8, 14845 (2018).

    PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

  44. 44.

    Tegha, K. C. & Sendze, Y. G. Soil organic carbon stocks in Mt Cameroon National Park under different land uses. J. Ecol. Nat. Environ. 8, 20–30 (2016).

    CAS  Google Scholar 

  45. 45.

    Munishi, P. K. T. & Shear, T. H. Carbon storage in afromontane rain forests of the eastern arc mountains of Tanzania: their net contribution to atmospheric carbon. J. Trop. For. Sci. 16, 78–98 (2004).

    Google Scholar 

  46. 46.

    Wheeler, C. E. et al. Carbon sequestration and biodiversity following 18 years of active tropical forest restoration. For. Ecol. Manage. 373, 44–55 (2016).

    Article  Google Scholar 

  47. 47.

    Avitabile, V., Baccini, A., Friedl, M. A. & Schmullius, C. Capabilities and limitations of Landsat and land cover data for aboveground woody biomass estimation of Uganda. Remote Sens. Environ. 117, 366–380 (2012).

    Article  ADS  Google Scholar 

  48. 48.

    Aneseyee, B. A., Soromessa, T. & Belliethathan, S. Carbon sock of Gambella National Park: implication for climate change mitigation. Int. J. Adv. Life Sci. 35, 41–56 (2015).

    Google Scholar 

  49. 49.

    Lisboa, S. N. et al. Biomass allometric equation and expansion factor for a mountain moist evergreen forest in Mozambique. Carbon Balance Manag. 13, 23 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    Lopez-Gonzalez, G., Lewis, S. L., Burkitt, M. & Phillips, O. L. a web application and research tool to manage and analyse tropical forest plot data. J. Veg. Sci. 22, 610–613 (2011).

    Article  Google Scholar 

  51. 51.

    Lopez-Gonzalez, G., Lewis, S. L., Burkitt, M., Baker, T. R. & Phillips, O. L. Database, (ForestPlots, 2009).

  52. 52.

    Cavanaugh, K. et al. Carbon storage in tropical forests correlates with taxonomic diversity and functional dominance on a global scale. Glob. Ecol. Biogeogr. 23, 563–573 (2014).

    Article  Google Scholar 

  53. 53.

    Lewis, S. L. et al. 2009 Increasing carbon storage in intact African tropical forests. Nature 457, 1003–1006 (2009).

    CAS  PubMed  Article  ADS  Google Scholar 

  54. 54.

    Chave, J. et al. Improved allometric models to estimate the aboveground biomass of tropical trees. Glob. Change Biol. 20, 3177–3190 (2014).

    Article  ADS  Google Scholar 

  55. 55.

    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).

    CAS  Article  ADS  Google Scholar 

  56. 56.

    Chave, J. et al. Towards a worldwide wood economics spectrum. Ecol. Lett. 12, 351–366 (2009).

    PubMed  Article  Google Scholar 

  57. 57.

    Zanne, A. E. et al. Towards a worldwide wood economics spectrum. Dryad (2009).

  58. 58.

    Clark, D. A. et al. Net primary production in tropical forests: an evaluation and synthesis of existing field data. Ecol. Appl. 11, 371–384 (2001).

    Article  Google Scholar 

  59. 59.

    Paul, T. S. H., Kimberley, M. O. & Beets, P. N. Thinking outside the square: evidence that plot shape and layout in forest inventories can bias estimates of stand metrics. Methods Ecol. Evol. 10, 381–388 (2019).

    Article  Google Scholar 

  60. 60.

    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).

    Article  Google Scholar 

  61. 61.

    Wilson, A. M. & Jetz, W. Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions. PLoS Biol. 14, e1002415 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  62. 62.

    Albrecht, R., Goodman, S., Buechler, D., Blakeslee R. & Christian, H. LIS 0.1 Degree Very High Resolution Gridded Lightning Climatology Data Collection, (NASA Global Hydrology Resource Center, 2016).

  63. 63.

    Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  64. 64.

    Harris, N. L. et al. Global maps of twenty-first century forest carbon fluxes. Nat. Clim. Chang. 11, 234–240 (2021).

    Article  ADS  Google Scholar 

  65. 65.

    Santoro, M. & Cartus, O. ESA Biomass Climate Change Initiative (Biomass_cci): Global Datasets of Forest Above-ground Biomass for the year 2017, v1 (Centre for Environmental Data Analysis, 2019).

  66. 66.

    Saatchi, S. et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl Acad. Sci. USA 108, 9899–9904 (2011).

    CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

  67. 67.

    Avitabile, V. et al. An integrated pan-tropical biomass map using multiple reference datasets. Glob. Change Biol. 22, 1406–1420 (2016).

    Article  ADS  Google Scholar 

  68. 68.

    Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team nlme: linear and nonlinear mixed effects models. R package version 3.1-151 (2020).

  69. 69.

    Bates, D., Maechler, M., Bolker B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

    Article  Google Scholar 

  70. 70.

    Lüdecke, D., Ben-Shachar, M., Patil, I. & Makowski, D. Parameters: extracting, computing and exploring the parameters of statistical models using R. J. Open Source Softw. 5, 2445 (2020).

    Article  ADS  Google Scholar 

  71. 71.

    Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biom. J. 50, 346–363 (2008).

    MathSciNet  PubMed  MATH  Article  Google Scholar 

  72. 72.

    Barton, K. MuMIn: multi-model inference. R package version 1.43.17 (2020)

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We thank the people of the many villages and local communities who welcomed our field teams and became our field assistants, without whose support the AfriMont dataset would not have been possible. Cameroon: villages Elak-Oku, Bokwoango, Bakingili, Muandelengoh, Enyandong, Ekangmbeng, Ngalmoa, Dikome Balue, Muyange, Matamani; assistants E. Ndive, D. Wultof, F. Keming, E. Bafon, J. Meyeih, T. K. Konsum, J. Esembe, F. Luma, F. Teke, E. E. Dagobert, E. D. Ndode, N. F. Njikang; Democratic Republic of the Congo: Bunyakiri, J. Kalume, W. Gului, D. Cirhagaga, B. Mugisho. Kenya: assistants A. M. Aide, H. Lerapo, J. Harugura, R. A. Wamuro, J. Lekatap, L. Lemooli, D. Kimuzi, B. M. Lombo, J. Broas, J. Hietanen, V. Heikinheimo, E. Schäfer. Rwanda: assistants I. Rusizana, P. Niyontegereje, J. B. Gakima, F. Ngayabahiga. Tanzania: TEAM staff and affiliates. Uganda: K. Laughlin, X. Mugumya, L. Etwodu, M. Mugisa. For logistical and administrative support, we are indebted to international, national and local institutions: SOPISDEW, Mt Cameroon National Park, Tropical Plant Exploration Group (TroPEG), Institut Congolais de Conservation de la Nature, Kahuzi-Biega National Park, Itombwe Nature Reserve, NEMA Marsabit Office, Taita Research Station, Kenya Forest Service, Rwanda Development Board, Nyungwe National Park, Conservation International, the Smithsonian Institution, Wildlife Conservation Society, Sokoine University of Agriculture, Tanzania Wildlife Research Institute, Tanzania National Parks Authority, Kilimanjaro National Park, Tanzania Commission for Science and Technology, Royal Zoological Society of Scotland, Uganda Wildlife Authority, Makerere University Biological Field Station, Uganda National Forestry Authority and Uganda National Council for Science and Technology. Field campaigns for AfriMont were funded by Marie Skłodowska-Curie Actions Intra-European Fellowships (number 328075) and Global Fellowships (number 74356), National Geographic Explorer (NGS-53344R-18), Czech Science Foundation (number 21-17125S), Rufford Small Grant Foundation (16712-B, 19476-D), Ministry of Foreign Affairs of Finland (BIODEV project), the Academy of Finland (number 318645), Swedish International Development Cooperation Agency, the Leverhulme Trust, the Strategic Research Area Biodiversity and Ecosystem Services in a Changing Climate, the German Research Foundation (DFG), Gatsby Plants, Natural Science and Engineering Research Council of Canada and International Development Research Centre of Canada. This paper is also a product of the AfriTRON network, for which we are indebted to hundreds of institutions, field assistants and local communities for establishing and maintaining the plots, including the Forestry Development Authority of the Government of Liberia, the University of Liberia, University of Ibadan (Nigeria), the University of Abeokuta (Nigeria), the University of Yaounde I (Cameroon), the National Herbarium of Yaounde (Cameroon), the University of Buea (Cameroon), Bioversity International (Cameroon), 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). The AfriTRION network has been supported by the European Research Council (291585, ‘T-FORCES’ – Tropical Forests in the Changing Earth System, Advanced Grant to O.L.P. and S.L.L.), the Gordon and Betty Moore Foundation, the David and Lucile Packard Foundation and the European Union’s Seventh Framework Programme (283080, ‘GEOCARBON’). We are grateful to A. Daniels, F. Mbayu, T. R. Feldpausch, E. Kearsley, J. Lloyd, R. Lowe, J. Mukinzi, L. Ojo, A. T. Peterson, J. Talbot and L. Zemagho for giving us access to their plot data. We also thank C. Chatelain (Geneva Botanic Gardens) for access to the African Plants Database and to H. Tang for helping to explore the use of GEDI data. Data from AfriTRON and most of AfriMont are stored and curated by, a long-term cyberinfrastructure 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 and curation of African data have been funded by many sources, including the ERC (principally from AdG 291585 ‘T-FORCES’), the UK Natural Environment Research Council (including NE/B503384/1, NE/F005806/1, NE/P008755/1, NE/N012542/1 and NE/I028122/1), the Gordon and Betty Moore Foundation (‘RAINFOR’, ‘MonANPeru’), the EU Horizon programme (especially ‘GEOCARBON’, ‘Amazalert’) and the Royal Society (University Research Fellowship to S.L.L.).

Author information




A.C.-S. conceived the study and assembled the AfriMont dataset. A.C.-S. and M.J.P.S. analysed the plot data (with contributions from S.L.L.) and wrote the manuscript. P.J.P. analysed forest extents and contributed to writing. S.L.L. conceived and managed the AfriTRON forest plot recensus programme. E.T.A.M. and V.A. helped compare plot data with remote sensing carbon maps. All co-authors read and approved the manuscript.

Corresponding author

Correspondence to Aida Cuni-Sanchez.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks Nicolas Barbier and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Sensitivity of mean AGC stock estimates to data subsampling.

AfriMont plot data were resampled at different sample sizes either at plot level (sampling with replacement) or at site level (sampling without replacement). N = 1,000 resamples for each sample size.

Extended Data Fig. 2 Effect of plot area, aggregation procedure and plot design on estimates of AGC stocks across the AfriMont plot network.

a, Relationship between AGC stocks and plot area of plots before aggregation. The red line shows the fit of a locally weighted regression model (span 0.75) relating these variables, with dashed lines showing the standard errors. b, Variation in AGC stocks using either all plots before aggregation (unaggregated), plots before aggregation but excluding those <0.2 ha (unaggregated, >0.2 ha) or the aggregated plots used in the main analyses (aggregated). c, Effects of plot design on AGC stocks (each site represents one dot). Sampling strategies include random or stratified random, plots positioned along transects, plots established within elevation bands, subjective measures such as choosing an area of forest considered representative of the wider area, and other strategies (one plot sampled per site or unclear strategy). Carbon stocks (log transformed) did not differ significantly between sites with different sampling strategies (analysis of variance F4,39 = 0.432, P = 0.785). For specific site information, see Supplementary Table 5.

Extended Data Fig. 3 Robustness of differences in tropical montane forest AGC stocks among continents based on plot networks to differences in elevation.

a, Elevations of montane forests plots sampled in each continent. Violin plots show the distribution of data, with boxplots showing the median and interquartile range of elevation in each continent. b, Effect of removing submontane plots (800–1,000 m a.s.l.) and high elevation plots (>2,200 m a.s.l., approximately the upper quartile of elevations for the African montane plot dataset) on AGC stocks in montane forests sampled by plot networks in each continent. Mean AGC stocks and 95% CIs are shown as estimated by models using all data, excluding plots 800–1,000 m and restricting plots to 1,000–2,200 m. Means for all plots differ from the analysis in Fig. 1 as literature plots without elevation data (plots in Colombia) were excluded from this analysis. Point symbols are proportional to the square-root plot area. N = 324 plots.

Extended Data Fig. 4 Relationship between AGC stocks and elevation for tropical montane forests in each continent based on plot networks.

The dashed lines show relationships from a linear mixed-effects model of log-transformed AGC stocks as a function of elevation, continent and their interaction. Site was included as a random effect, and AGC stock–elevation relationships allowed to vary among sites. The lines show fitted slopes across sites. Neither the overall relationship between elevation and AGC stocks (slope −0.039 [95% CI = −0.127–0.057], P = 0.420) nor interactions between elevation and continent (Southeast Asia, change in slope = −0.074 [−0.294–0.149], P = 0.503; Neotropics, change in slope 0.006 [−0.132–0.149], P = 0.913) are statistically significant. N = 324 plots.

Extended Data Fig. 5 Environmental drivers of AGC stocks across the AfriMont plot network.

Coefficients are from a linear mixed-effects model with site as a random intercept. Results are following all-subsets regression and model averaging, in which variables that do not appear in well supported models are given coefficients of zero, leading to shrinkage in model coefficients. Statistically significant relationships (P < 0.05) are indicated with asterisks. TPI refers to topographic position index (positive values indicate higher than surroundings and negative values indicate lower than surroundings). T_mean, annual mean temperature; T_seasonality, temperature seasonality; Precip_total, annual precipitation; Precip_seasonality, precipitation seasonality.

Extended Data Fig. 6 Expected sampling effort if effort was distributed in proportion to the area of tropical montane forest biome in Africa.

Data are summarized at 1° resolution. The upward triangles show grid cells where AfriMont sampling effort is more than double expected effort and the downward triangles show grid cells where AfriMont sampling effort is less than half expected effort. The circles denote AfriMont sampling effort being between half and double expected effort. The extent of the tropical montane forest biome was defined as closed-canopy forests ≥800 m a.s.l. in December 2018, extracted from ref. 38 and clipped to ‘primary humid forest’ using ref. 39. This grided map differs from Fig. 4 as numerous grids have very little tropical montane forest.

Extended Data Fig. 7 Differences in the environmental conditions sampled by the AfriMont plot network and the tropical montane forest biome in Africa.

The extent of the biome was defined as closed-canopy forests ≥800 m a.s.l. in December 2018, extracted from ref. 38 and clipped to ‘primary humid forest’ using ref. 39. Environmental variables for the biome were extracted at about 1-km resolution.

Extended Data Fig. 8 Differences in AGC stocks in AfriMont plots located in montane forests with and without elephants.

a, Differences across all plots. AGC stocks are statistically significantly lower in forests with elephants (t-test, t = 3.5, d.f. = 83.5, P = 0.001). The thick line shows the median, and boxes cover the interquartile range (IQR). Values >1.5 times IQR away from the IQR are shown by points. b, Differences in countries where elephants are present in at least one of the montane sites studied. The black squares show means in each country in forests with or without elephants and the solid lines denote statistically significant differences (t-tests, P < 0.05). Elephant presence in 2019 was estimated by the co-authors (Supplementary Table 5).

Extended Data Fig. 9 Relationship between AGC stocks and Podocarpaceae.

a, Relationship between AGC stocks and Podocarpaceae basal area across plots in the AfriMont network, expressed as a percentage of total plot basal area. These variables are not significantly correlated (rs = 0.083, n = 226, P = 0.212). b, Distribution of plots with at least 20% basal area of Podocarpaceae (black points) in relation to elevation and AGC stocks. AGC stocks are not significantly related to elevation or Podocarpaceae basal area (linear mixed effects model, P = 0.152 and P = 0.132, respectively).

Extended Data Fig. 10 Within-country variation in AGC stocks based on the AfriMont plot network.

Error bars show means and 95% CIs estimated by linear mixed-effects models. Modelled means not shown for countries with fewer than five plots. Point size is proportional to plot area.

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Cuni-Sanchez, A., Sullivan, M.J.P., Platts, P.J. et al. High aboveground carbon stock of African tropical montane forests. Nature 596, 536–542 (2021).

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