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From tree species to forest services: ethnic differences in Lomami, Democratic Republic of the Congo. Ethnicity is well–known to affect plant species’ utilization, but how ethnicity affects the identification and importance ranking of forest ecosystem services has been less documented, particularly in the Congo Basin. This research investigates how six different ethnic groups (farmers of Bantu origin and Mbote hunter–gatherers) use and value tree species and forest ecosystem services in Lomami National Park, in central Democratic Republic of the Congo (DRC). Data were collected through 24 focus–group discussions with village elders, four for each ethnic group studied. Considerable variation in preferred tree species was observed: of the 89 morphospecies cited in total only two were cited by all ethnic groups for the same usage. Ethnicity also affected the identification and importance ranking of forest ecosystem services. Mbote hunters–gatherers prioritized bushmeat, honey, and identity, while farmer groups prioritized bushmeat, fish, and microclimate regulation. We discuss the implications of the findings for forest management in the Buffer Zone of the national park.

Our ecological understanding of tropical montane forests in Africa is still limited, particularly in the Albertine Rift. Because of a greater role of environmental filtering at higher elevations, tree species' richness and aboveground biomass (AGB) is expected to decrease with increasing elevation. However, broader scale patterns are complex and different patterns have also been observed in different mountains. We established eight permanent 1-ha plots in Itombwe Mountains and sampled all trees >= 10 cm diameter following standard RAINFOR protocols to investigate tree species' richness, forest structure, and estimated AGB. We also collected and analyzed soil samples to establish how these affected AGB. We identified over 120 tree species, 12 of which were of international conservation concern according to IUCN criteria. Species richness was significantly negatively correlated with elevation. AGB ranged from 268 Mg/ha (submontane) to 396 Mg/ha (middle montane forests), and it was significantly positively correlated with elevation, soil organic matter, and soil organic C and N. We compare Itombwe with other montane forests in the Albertine Rift, to demonstrate their high floristic diversity, high abundance of large stems, and high AGB, insights which are crucial for the future management of these unique forests. Abstract in French is available with online material.

For most of the Democratic Republic of the Congo quantitative data on bushmeat exploitation are scarce. We conducted focus group discussions on preferred species for household consumption and income generation in 24 villages around Lomami National Park, created in 2016. We also carried out a bushmeat market survey in Kindu, a major town in the study area, to estimate annual sales volumes and retail values. Villagers reported household consumption of 22 mammal species, with the most important being the African brush-tailed porcupine, Peters's duiker, bay duiker and red river hog. The latter three were also the most important for income generation. A greater number of smaller species were consumed at the household level, compared with those traded. A total of 17 mammal and one reptile species were traded in Kindu. Those traded in greater numbers were the African brush-tailed porcupine, blue and bay duiker, red river hog, red-tailed monkey and the sitatunga. We estimated > 40,000 carcasses were traded in Kindu annually, with a retail value of USD 725,000. Several species of conservation concern, such as the bonobo, were mentioned or observed. Few rodents and numerous large animals were traded in Kindu, suggesting resources have not yet been depleted. However, both villagers and urban vendors perceived a decline of many species and reported an increase in the use of firearms and the number of foreign hunters in the area. Among other interventions, we discuss how local communities could be encouraged to help preserve wildlife in the Park's buffer zone.

Mountain environments experience more rapid changes in temperature than lower elevations. However, little is known about the climatic changes already observed in African mountains, or the adaptation strategies used by hunter-gatherer communities. Semi-structured interviews were administered to 100 Twa hunter-gatherers living around Mt Kahuzi in eastern Democratic Republic of the Congo (DR Congo). We also organized 10 focus-group discussions with Tembo farmers living in the same area and we gathered historical data from Kamembe meteorological station. Twa respondents perceived reduced rainfall and fog, and increased temperatures. They also reported several impacts including reduced crop yields and abundance of forest products (caterpillars, mushrooms, honey). Tembo perceptions of climatic changes and impacts agreed with the Twa. Meteorological data available shows reduced rainfall and increased temperatures - but there are no records on fog. Despite being aware of climatic changes and impacts, Twa are not using any adaptation strategy, while Tembo farmers are using some (as they own land for farming or animal rearing, and are more business minded). For the Twa, their socioeconomic condition create high sensitivity to climate change and constrain adaptive capacity. For the Twa, we recommend the use of 'science with society' (SWS) participatory approach

The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025° × 0.025°) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from ~1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers. The team collaboration and manuscript development are supported by the web-based team science platform: science-i.org, with the project number 202205GFB2. We thank the following initiatives, agencies, teams and individuals for data collection and other technical support: the Global Forest Biodiversity Initiative (GFBI) for establishing the data standards and collaborative framework; United States Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program; University of Alaska Fairbanks; the SODEFOR, Ivory Coast; University Félix Houphouët-Boigny (UFHB, Ivory Coast); the Queensland Herbarium and past Queensland Government Forestry and Natural Resource Management departments and staff for data collection for over seven decades; and the National Forestry Commission of Mexico (CONAFOR). We thank M. Baker (Carbon Tanzania), together with a team of field assistants (Valentine and Lawrence); all persons who made the Third Spanish Forest Inventory possible, especially the main coordinator, J. A. Villanueva (IFN3); the French National Forest Inventory (NFI campaigns (raw data 2005 and following annual surveys, were downloaded by GFBI at https://inventaire-forestier.ign.fr/spip.php?rubrique159; site accessed on 1 January 2015)); the Italian Forest Inventory (NFI campaigns raw data 2005 and following surveys were downloaded by GFBI at https://inventarioforestale.org/; site accessed on 27 April 2019); Swiss National Forest Inventory, Swiss Federal Institute for Forest, Snow and Landscape Research WSL and Federal Office for the Environment FOEN, Switzerland; the Swedish NFI, Department of Forest Resource Management, Swedish University of Agricultural Sciences SLU; the National Research Foundation (NRF) of South Africa (89967 and 109244) and the South African Research Chair Initiative; the Danish National Forestry, Department of Geosciences and Natural Resource Management, UCPH; Coordination for the Improvement of Higher Education Personnel of Brazil (CAPES, grant number 88881.064976/2014-01); R. Ávila and S. van Tuylen, Instituto Nacional de Bosques (INAB), Guatemala, for facilitating Guatemalan data; the National Focal Center for Forest condition monitoring of Serbia (NFC), Institute of Forestry, Belgrade, Serbia; the Thünen Institute of Forest Ecosystems (Germany) for providing National Forest Inventory data; the FAO and the United Nations High Commissioner for Refugees (UNHCR) for undertaking the SAFE (Safe Access to Fuel and Energy) and CBIT-Forest projects; and the Amazon Forest Inventory Network (RAINFOR), the African Tropical Rainforest Observation Network (AfriTRON) and the ForestPlots.net initiative for their contributions from Amazonian and African forests. The Natural Forest plot data collected between January 2009 and March 2014 by the LUCAS programme for the New Zealand Ministry for the Environment are provided by the New Zealand National Vegetation Survey Databank https://nvs.landcareresearch.co.nz/. We thank the International Boreal Forest Research Association (IBFRA); the Forestry Corporation of New South Wales, Australia; the National Forest Directory of the Ministry of Environment and Sustainable Development of the Argentine Republic (MAyDS) for the plot data of the Second National Forest Inventory (INBN2); the National Forestry Authority and Ministry of Water and Environment of Uganda for their National Biomass Survey (NBS) dataset; and the Sabah Biodiversity Council and the staff from Sabah Forest Research Centre. All TEAM data are provided by the Tropical Ecology Assessment and Monitoring (TEAM) Network, a collaboration between Conservation International, the Missouri Botanical Garden, the Smithsonian Institution and the Wildlife Conservation Society, and partially funded by these institutions, the Gordon and Betty Moore Foundation and other donors, with thanks to all current and previous TEAM site manager and other collaborators that helped collect data. We thank the people of the Redidoti, Pierrekondre and Cassipora village who were instrumental in assisting with the collection of data and sharing local knowledge of their forest and the dedicated members of the field crew of Kabo 2012 census. We are also thankful to FAPESC, SFB, FAO and IMA/SC for supporting the IFFSC. This research was supported in part through computational resources provided by Information Technology at Purdue, West Lafayette, Indiana.This work is supported in part by the NASA grant number 12000401 ‘Multi-sensor biodiversity framework developed from bioacoustic and space based sensor platforms’ (J. Liang, B.P.); the USDA National Institute of Food and Agriculture McIntire Stennis projects 1017711 (J. Liang) and 1016676 (M.Z.); the US National Science Foundation Biological Integration Institutes grant NSF‐DBI‐2021898 (P.B.R.); the funding by H2020 VERIFY (contract 776810) and H2020 Resonate (contract 101000574) (G.-J.N.); the TEAM project in Uganda supported by the Moore foundation and Buffett Foundation through Conservation International (CI) and Wildlife Conservation Society (WCS); the Danish Council for Independent Research | Natural Sciences (TREECHANGE, grant 6108- 00078B) and VILLUM FONDEN grant number 16549 (J.-C.S.); the Natural Environment Research Council of the UK (NERC) project NE/T011084/1 awarded to J.A.-G. and NE/S011811/1; ERC Advanced Grant 291585 (‘T-FORCES’) and a Royal Society-Wolfson Research Merit Award (O.L.P.); RAINFOR plots supported by the Gordon and Betty Moore Foundation and the UK Natural Environment Research Council, notably NERC Consortium Grants ‘AMAZONICA’ (NE/F005806/1), ‘TROBIT’ (NE/D005590/1) and ‘BIO-RED’ (NE/N012542/1); CIFOR’s Global Comparative Study on REDD+ funded by the Norwegian Agency for Development Cooperation, the Australian Department of Foreign Affairs and Trade, the European Union, the International Climate Initiative (IKI) of the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety and the CGIAR Research Program on Forests, Trees and Agroforestry (CRP-FTA) and donors to the CGIAR Fund; AfriTRON network plots funded by the local communities and NERC, ERC, European Union, Royal Society and Leverhume Trust; a grant from the Royal Society and the Natural Environment Research Council, UK (S.L.L.); National Science Foundation CIF21 DIBBs: EI: number 1724728 (A.C.C.); National Natural Science Foundation of China (31800374) and Shandong Provincial Natural Science Foundation (ZR2019BC083) (H.L.). UK NERC Independent Research Fellowship (grant code: NE/S01537X/1) (T.J.); a Serra-Húnter Fellowship provided by the Government of Catalonia (Spain) (S.d.-M.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.A.S.); a grant from the Franklinia Foundation (D.A.C.); Russian Science Foundation project number 19-77-300-12 (R.V.); the Takenaka Scholarship Foundation (A.O.A.); the German Research Foundation (DFG), grant number Am 149/16-4 (C.A.); the Romania National Council for Higher Education Funding, CNFIS, project number CNFIS-FDI-2022-0259 (O.B.); Natural Sciences and Engineering Research Council of Canada (RGPIN-2019-05109 and STPGP506284) and the Canadian Foundation for Innovation (36014) (H.Y.H.C.); the project SustES—Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797) (E.C.); Consejo de Ciencia y Tecnología del estado de Durango (2019-01-155) (J.J.C.-R.); Science and Engineering Research Board (SERB), New Delhi, Government of India (file number PDF/2015/000447)— ‘Assessing the carbon sequestration potential of different forest types in Central India in response to climate change’ (J.A.D.); Investissement d’avenir grant of the ANR (CEBA: ANR-10-LABEX-0025) (G.D.); National Foundation for Science & Technology Development of Vietnam, 106-NN.06-2013.01 (T.V.D.); Queensland government, Department of Environment and Science (T.J.E.); a Czech Science Foundation Standard grant (19-14620S) (T.M.F.); European Union Seventh Framework Program (FP7/2007– 2013) under grant agreement number 265171 (L. Finer, M. Pollastrini, F. Selvi); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (J.F.); CNPq productivity grant number 311303/2020-0 (A.L.d.G.); DFG grant HE 2719/11-1,2,3; HE 2719/14-1 (A. Hemp); European Union’s Horizon Europe research project OpenEarthMonitor grant number 101059548, CGIAR Fund INIT-32-MItigation and Transformation Initiative for GHG reductions of Agrifood systems RelaTed Emissions (MITIGATE+) (M.H.); General Directorate of the State Forests, Poland (1/07; OR-2717/3/11; OR.271.3.3.2017) and the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (A.M.J.); Czech Science Foundation 18-10781 S (S.J.); Danish of Ministry of Environment, the Danish Environmental Protection Agency, Integrated Forest Monitoring Program—NFI (V.K.J.); State of São Paulo Research Foundation/FAPESP as part of the BIOTA/FAPESP Program Project Functional Gradient-PELD/BIOTA-ECOFOR 2003/12595-7 & 2012/51872-5 (C.A.J.); Danish Council for Independent Research—social sciences—grant DFF 6109– 00296 (G.A.K.); Russian Science Foundation project 21-46-07002 for the plot data collected in the Krasnoyarsk region (V.K.); BOLFOR (D.K.K.); Department of Biotechnology, New Delhi, Government of India (grant number BT/PR7928/ NDB/52/9/2006, dated 29 September 2006) (M.L.K.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (J.N.K.); Korea Forest Service (2018113A00-1820-BB01, 2013069A00-1819-AA03, and 2020185D10- 2022-AA02) and Seoul National University Big Data Institute through the Data Science Research Project 2016 (H.S.K.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.K.); CSIR, New Delhi, government of India (grant number 38(1318)12/EMR-II, dated: 3 April 2012) (S.K.); Department of Biotechnology, New Delhi, government of India (grant number BT/ PR12899/ NDB/39/506/2015 dated 20 June 2017) (A.K.); Coordination for the Improvement of Higher Education Personnel (CAPES) #88887.463733/2019-00 (R.V.L.); National Natural Science Foundation of China (31800374) (H.L.); project of CEPF RAS ‘Methodological approaches to assessing the structural organization and functioning of forest ecosystems’ (AAAA-A18-118052590019-7) funded by the Ministry of Science and Higher Education of Russia (N.V.L.); Leverhulme Trust grant to Andrew Balmford, Simon Lewis and Jon Lovett (A.R.M.); Russian Science Foundation, project 19-77-30015 for European Russia data processing (O.M.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (M.T.E.M.); the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (S.M.); the Secretariat for Universities and of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund (A. Morera); Queensland government, Department of Environment and Science (V.J.N.); Pinnacle Group Cameroon PLC (L.N.N.); Queensland government, Department of Environment and Science (M.R.N.); the Natural Sciences and Engineering Research Council of Canada (RGPIN-2018-05201) (A.P.); the Russian Foundation for Basic Research, project number 20-05-00540 (E.I.P.); European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement number 778322 (H.P.); Science and Engineering Research Board, New Delhi, government of India (grant number YSS/2015/000479, dated 12 January 2016) (P.S.); the Chilean Government research grants Fondecyt number 1191816 and FONDEF number ID19 10421 (C.S.-E.); the Deutsche Forschungsgemeinschaft (DFG) Priority Program 1374 Biodiversity Exploratories (P.S.); European Space Agency projects IFBN (4000114425/15/NL/FF/gp) and CCI Biomass (4000123662/18/I-NB) (D. Schepaschenko); FunDivEUROPE, European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement number 265171 (M.S.-L.); APVV 20-0168 from the Slovak Research and Development Agency (V.S.); Manchester Metropolitan University’s Environmental Science Research Centre (G.S.); the project ‘LIFE+ ForBioSensing PL Comprehensive monitoring of stand dynamics in Białowieża Forest supported with remote sensing techniques’ which is co-funded by the EU Life Plus programme (contract number LIFE13 ENV/PL/000048) and the National Fund for Environmental Protection and Water Management in Poland (contract number 485/2014/WN10/OP-NM-LF/D) (K.J.S.); Global Challenges Research Fund (QR allocation, MMU) (M.J.P.S.); Czech Science Foundation project 21-27454S (M.S.); the Russian Foundation for Basic Research, project number 20-05-00540 (N. Tchebakova); Botanical Research Fund, Coalbourn Trust, Bentham Moxon Trust, Emily Holmes scholarship (L.A.T.); the programmes of the current scientific research of the Botanical Garden of the Ural Branch of Russian Academy of Sciences (V.A.U.); FCT—Portuguese Foundation for Science and Technology—Project UIDB/04033/2020. Inventário Florestal Nacional—ICNF (H. Viana); Grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (C.W.); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (B.W.); ATTO project (grant number MCTI-FINEP 1759/10 and BMBF 01LB1001A, 01LK1602F) (F.W.); ReVaTene/ PReSeD-CI 2 is funded by the Education and Research Ministry of Côte d’Ivoire, as part of the Debt Reduction-Development Contracts (C2Ds) managed by IRD (I.C.Z.-B.); the National Research Foundation of South Africa (NRF, grant 89967) (C.H.). The Tropical Plant Exploration Group 70 1 ha plots in Continental Cameroon Mountains are supported by Rufford Small Grant Foundation, UK and 4 ha in Sierra Leone are supported by the Global Challenge Research Fund through Manchester Metropolitan University, UK; the National Geographic Explorer Grant, NGS-53344R-18 (A.C.-S.); University of KwaZulu-Natal Research Office grant (M.J.L.); Universidad Nacional Autónoma de México, Dirección General de Asuntos de Personal Académico, Grant PAPIIT IN-217620 (J.A.M.). Czech Science Foundation project 21-24186M (R.T., S. Delabye). Czech Science Foundation project 20-05840Y, the Czech Ministry of Education, Youth and Sports (LTAUSA19137) and the long-term research development project of the Czech Academy of Sciences no. RVO 67985939 (J.A.). The American Society of Primatologists, the Duke University Graduate School, the L.S.B. Leakey Foundation, the National Science Foundation (grant number 0452995) and the Wenner-Gren Foundation for Anthropological Research (grant number 7330) (M.B.). Research grants from Conselho Nacional de Desenvolvimento Científico e Tecnologico (CNPq, Brazil) (309764/2019; 311303/2020) (A.C.V., A.L.G.). The Project of Sanya Yazhou Bay Science and Technology City (grant number CKJ-JYRC-2022-83) (H.-F.W.). The Ugandan NBS was supported with funds from the Forest Carbon Partnership Facility (FCPF), the Austrian Development Agency (ADC) and FAO. FAO’s UN-REDD Program, together with the project on ‘Native Forests and Community’ Loan BIRF number 8493-AR UNDP ARG/15/004 and the National Program for the Protection of Native Forests under UNDP funded Argentina’s INBN2.

In the tropics, mountain forests provide numerous benefits to surrounding communities. Our participatory research investigates how different ethnic groups including Twa hunter gatherers and farmers of Bantu origin use and value mountain forests in Kibira National Park, Burundi. We carried out an ecosystem service (ES) assessment through 25 focus-group discussions, including Twa (n = 10) and farmers (n = 15). The Twa identify a greater number of forest ES than farmers, and rank wild vegetables as most important forest ES. They also show strong place dependence and identity to the forest, which they call ‘home’. Farmers rank microclimate regulation as most important forest ES, and also show place dependence to the forest. Both groups identified numerous plants for provisioning services, but the Twa identified more food-provisioning plants and medicinal plants for humans. Our findings help understand why the Twa continue to enter this park daily. Our results also help suggest some livelihood strategies for the Twa which consider their strengths rather than their weaknesses. Protected areas are the most important tool we have for species’ and habitats’ conservation, but to ensure their effectiveness -and sustainability- surrounding communities’ needs and cultures should be considered.

The forests of the Albertine Rift are known for their high biodiversity and the important ecosystem services they provide to millions of inhabitants. However, their conservation and the maintenance of ecosystem service delivery is a challenge, particularly in the Democratic Republic of the Congo. Our research investigates how livelihood strategy and ethnicity affects local perceptions of forest ecosystem services. We collected data through 25 focus-group discussions in villages from distinct ethnic groups, including farmers (Tembo, Shi, and Nyindu) and hunter-gatherers (Twa). Twa identify more food-provisioning services and rank bush meat and honey as the most important. They also show stronger place attachment to the forest than the farmers, who value other ecosystem services, but all rank microclimate regulation as the most important. Our findings help assess ecosystem services trade-offs, highlight the important impacts of restricted access to forests resources for Twa, and point to the need for developing alternative livelihood strategies for these communities.

Les caractéristiques dynamiques et structurelles des clairières de la partie haute altitude (2000 à 2300 m) du Parc National de Kahuzi Biega (PNKB) ont été analysées sur une durée de 15 ans et sur base des informations fournies par des services de conservation. L’objectif étant de caractériser la diversité floristique, la structure des clairières et d’estimer la quantité de la biomasse/carbone. Les individus ligneux de Dbh≥ 5 cm étaient comptés et leur hauteur prise par un laser dans les relevés polyvalents. Les indices de diversité, de valeurs d’importance ainsi que de similarité ont permis de caractériser la végétation et par les équations allométriques, on a déterminé la biomasse. L’analyse Anova et le test de TukeyHDS ont été utilisés pour voir la variation de la diversité et la biomasse entre les clairières. Les clairières de la partie haute altitude du Parc National de Kahuzi Biega sont caractérisées par des espèces comme Neoboutonia macrocalyx, Mimulopsis arborescens et Dombeya goetzenii. La biomasse/carbone varie significativement le long de la chronoséquence en fonction des classes d’âge. Based on information from conservation services, we analyzed structural and dynamic features of clearings in Kahuzi-Biega National Park (between 2000 and 2300 asl). This analysis covered 15 years and aimed to characterize floristic structure, diversity, and estimate biomass/carbon of clearing. Trees with DBH more than 5 cm were counted and their height measured with laser within various plots. Various indexes and allometric equations were used in to characterize the vegetation and to estimate the biomass. One way Anova and TukeyHDS were also used in order to determine the trend of variation in the diversity and biomass distribution of the clearings. Species like Neoboutonia macrocalyx, Mimulopsis arborescens and Dombeya goetzenii are shown to be prominent in the the Kahuzi-Biega national Park between 2000 and 2300 asl.

Tropical forests store 40–50 per cent of terrestrial vegetation carbon 1. However, spatial variations in aboveground live tree biomass carbon (AGC) stocks remain poorly understood, in particular in tropical montane forests 2. Owing to climatic and soil changes with increasing elevation 3, AGC stocks are lower in tropical montane forests compared with lowland forests 2. 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 Network 4 and about 70 per cent and 32 per cent higher than averages from plot networks in montane 2,5,6 and lowland 7 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 Africa 8. We find that the low stem density and high abundance of large trees of African lowland forests 4 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 biodiverse 9,10 and carbon-rich ecosystems.