Abstract The Congo Basin forests are currently undervalued. Economic and social returns from forestry and timber processing are relatively low compared to some nations outside Africa. Furthermore, the ecosystem services provided by the forests, such as the net absorption and storage of atmospheric carbon, continue to be considered by the international community as a free commodity. We propose an economic model involving maximizing the proportion of third-level timber processing and planting new forests that can increase forest economies and job creation by an order of magnitude. We argue that only by making forests valuable to the people and nations of the Congo Basin will we be able to avoid the large-scale deforestation that has occurred in West Africa and in other tropical forest regions, where the economic and social benefits of conversion were higher than those linked to preservation and sustainable harvest. We discuss knowledge gaps that need to be filled, possible technological solutions and policy reforms, and fiscal incentives needed to implement our model across the Congo Basin.

Abstract The Congo Basin is more highly urbanized than most of the African continent. Gabon, Cameroon, Equatorial Guinea, and Congo have been majority urban populations for decades, and DRC will pass beyond 50% by 2030. CAR will also reach that mark within 20 years. The rapid growth of urban populations in the Congo Basin nations is discussed and key challenges for city-dwellers are assessed. Unplanned, rapid urbanization and the development of extensive slums with few services and weak political voice is the norm in the larger cities, increasing social inequity and causing crippling damage to human well-being and environmental health in peri-urban haloes, sometimes extending hundreds of kilometers from the urban center. Solutions to this growing challenge are evaluated and recommendations are made which, if implemented, could help to improve health, resilience, quality of life, and security in urban settings, as well as improving biodiversity outcomes. In the light of the sparse literature on the subject, the authors call for an urgent increase in scientific research on this pressing topic within the Congo Basin, which is a clear priority for the region’s majority urban population of the future.

Abstract Land-use is a key driver of forest loss and aboveground live carbon (AGC) emissions in the Congo Basin (CB) rainforest. Here we evaluate the influence of land-use disturbances on AGC stocks and fluxes by developing an AGC density map for the year 2020 and integrating it with high-resolution forest cover change data spanning 30 years (1990-2020) to quantify carbon emissions and removals. Logged forests show 8% (5%–10%) less AGC compared to old growth, while slash-and-burn and unmanaged degradations display up to 50% differences. Unmanaged areas account for 54% of the region’s AGC storage. Old growth dominates the total AGC removals (84%) with the region functioning as a net AGC sink at -37.5 ± 4.8 TgCyr 1 , driven by logging concessions (-21.3 ± 2.4 TgCyr -1 ) and protected areas (-15.7 ± 2.2 TgCyr -1 ), while unmanaged areas remained nearly neutral. These findings emphasize the role of sustainable forests management to enhance carbon retention in the region.

Abstract The current ecological, socioeconomic, and governance status of the Congo Basin is profoundly influenced by its historical dynamics, including the significant impacts of colonialism, postcolonialism, structural adjustments, globalization, and the emergence of a multipolar world. These historical contexts, alongside national agendas and realities, must be considered when devising optimal management strategies for the Congo Basin. We suggest that despite significant progress since the Rio Conference in 1992, a business-as-usual model will not generate sufficient development opportunities for the Congo Basin’s young, growing population to facilitate sustained development, environmental governance, and to ensure peace and security. There is a need for improved governance, a postcolonial economic model based on transformation not simply extraction and which sustains ecosystems and the services they provide, rather than degrading and destroying them. Given its natural resources and human potential, the region can and must lead the world toward a sustainable development model that promotes environmental and social resilience.

Old-growth tropical forests store vast amounts of carbon in their aboveground biomass (AGB), yet the relative roles of abiotic factors such as climate, soil, and topography in governing its spatial distribution remain poorly understood. In particular, the degree to which climate acts on AGB through forest structure is still poorly quantified at the pantropical scale. Using a pantropical dataset of more than 2,000 old-growth forest plots and a structure-explicit framework, we assess how climate influences AGB through its effects on four structural attributes: basal area, mean diameter, stem density, and basal area-weighted wood density. We find that climate shapes AGB primarily through its effects on forest structure. However, structural attributes respond to climate in opposite directions, so climate’s net effect on AGB largely cancels out, and no clear climate-AGB relationship emerges across tropical regions. Moreover, only wood density responds consistently, decreasing with annual precipitation and increasing with precipitation seasonality, whereas all other attributes respond to climate differently from one region to another. This geographical variation further obscures any global climatic signal on AGB and points to the role of biogeographic history in shaping forest structure. Our findings highlight the central role of the climate-structure nexus in explaining AGB variation, and call for structure-explicit models to improve carbon stock predictions and inform climate adaptation strategies.

Land-use drives forest loss and carbon emissions in the Congo basin, undermining its role in climate change mitigation. Here, we assess how aboveground live carbon (AGC) stocks and fluxes vary across land-use in the Congo basin rainforests by developing AGC density map for the year 2020 and integrating it with high-resolution forest cover loss data over 30 years (1990–2020) to estimate carbon emissions and removals. Our findings reveal higher forest integrity under managed disturbances, with logged forests having 7.53% (range: 4.86%–9.5%) less AGC compared to old growth, unlike 50% differences observed with slash-and-burn and other unmanaged disturbances. Unmanaged areas hold 50% of the AGC storage, implying that much of the basin remains highly vulnerable to rapid carbon loss from unmanaged land-use. Overall, the basin remained a net AGC sink of -37.5 ± 4.29 Tg C yr− 1, dominated my 98% removals from managed and intact forests within logging concessions (21.3 ± 2.39 Tg C yr− 1) and protected areas (15.73 ± 2.24 Tg C yr− 1), while unmanaged forests nearly remained carbon neutral. These findings underscore the critical need to integrate sustainable management strategies for unmanaged forests into climate mitigation frameworks such as REDD + to enhance carbon retention in the Congo basin.

Tropical forest canopies are the biosphere's most concentrated atmospheric interface for carbon, water and energy1,2. However, in most Earth System Models, the diverse and heterogeneous tropical forest biome is represented as a largely uniform ecosystem with either a singular or a small number of fixed canopy ecophysiological properties3. This situation arises, in part, from a lack of understanding about how and why the functional properties of tropical forest canopies vary geographically4. Here, by combining field-collected data from more than 1,800 vegetation plots and tree traits with satellite remote-sensing, terrain, climate and soil data, we predict variation across 13 morphological, structural and chemical functional traits of trees, and use this to compute and map the functional diversity of tropical forests. Our findings reveal that the tropical Americas, Africa and Asia tend to occupy different portions of the total functional trait space available across tropical forests. Tropical American forests are predicted to have 40% greater functional richness than tropical African and Asian forests. Meanwhile, African forests have the highest functional divergence-32% and 7% higher than that of tropical American and Asian forests, respectively. An uncertainty analysis highlights priority regions for further data collection, which would refine and improve these maps. Our predictions represent a ground-based and remotely enabled global analysis of how and why the functional traits of tropical forest canopies vary across space.

Understanding how the traits of lineages are related to diversification is key for elucidating the origin of variation in species richness. Here, we test whether traits are related to species richness among lineages of trees from all major biogeographical settings of the lowland wet tropics. We explore whether variation in mortality rate, breeding system and maximum diameter are related to species richness, either directly or via associations with range size, among 463 genera that contain wet tropical forest trees. For Amazonian genera, we also explore whether traits are related to species richness via variation among genera in mean species-level range size. Lineages with higher mortality rates—faster life-history strategies—have larger ranges in all biogeographic settings and have higher mean species-level range sizes in Amazonia. These lineages also have smaller maximum diameters and, in the Americas, contain dioecious species. In turn, lineages with greater overall range size have higher species richness. Our results show that fast life-history strategies influence species richness in all biogeographic settings because lineages with these ecological strategies have greater range sizes. These links suggest that dispersal has been a key process in the evolution of the tropical forest flora.

Trees structure the Earth's most biodiverse ecosystem, tropical forests. The vast number of tree species presents a formidable challenge to understanding these forests, including their response to environmental change, as very little is known about most tropical tree species. A focus on the common species may circumvent this challenge. Here we investigate abundance patterns of common tree species using inventory data on 1,003,805 trees with trunk diameters of at least 10 cm across 1,568 locations1-6 in closed-canopy, structurally intact old-growth tropical forests in Africa, Amazonia and Southeast Asia. We estimate that 2.2%, 2.2% and 2.3% of species comprise 50% of the tropical trees in these regions, respectively. Extrapolating across all closed-canopy tropical forests, we estimate that just 1,053 species comprise half of Earth's 800 billion tropical trees with trunk diameters of at least 10 cm. Despite differing biogeographic, climatic and anthropogenic histories7, we find notably consistent patterns of common species and species abundance distributions across the continents. This suggests that fundamental mechanisms of tree community assembly may apply to all tropical forests. Resampling analyses show that the most common species are likely to belong to a manageable list of known species, enabling targeted efforts to understand their ecology. Although they do not detract from the importance of rare species, our results open new opportunities to understand the world's most diverse forests, including modelling their response to environmental change, by focusing on the common species that constitute the majority of their trees.

Gabon is one of 11 high-forest, low-deforestation (HFLD) countries in the world. It has the highest proportion of preserved forests in the Congo Basin and is the first country to create large forest carbon offset credits in the market. However, about 60% of forests in Gabon is allocated to logging concessions, causing concerns for forest degradation and the sustainability of carbon credits. Here, we use a combination of air- and space-borne remote sensing data and the-state-of-the-art gradient boosted regression trees to estimate forest structure and aboveground biomass carbon density (ACD) of trees at 100 m resolution for the year circa 2020. Mapping spatial variations of ACD across floristically diverse landscapes, we estimate average density and total living carbon storage of trees at the national and sub-national levels. The estimated ACD of trees in forestlands within the country was 142.12 ± 7.3 Mg C ha ^−1 with the highest values found in central Gabon (150.08 ± 5.8 Mg C ha ^−1 ) and on highlands (161.18 ± 6.7 Mg C ha ^−1 ). On average, in every region, ACD of forests found within logging concessions (149.89 ± 6.1 Mg C ha ^−1 ) was higher than unmanaged forests of unprotected areas (122.81 ± 4.4 Mg C ha ^−1 ), indicating the combined effects of logging in carbon-rich forests and increased productivity due to management. The country’s total estimated biomass carbon for trees (above and belowground) stored within the forests was 4.14 ± 0.3 Pg C with 68% found within logging concessions and 14% within protected areas. The map provides high precision and comprehensive assessment of carbon stocks of trees in Gabon’s forests, significantly improving the country’s prospects to implement climate mitigation policies and to participate in carbon markets.

Spotted hyaena distribution currently widely encompasses sub-Saharan Africa, apart from the Congo Basin. Formerly described as residents of Gabon but considered extinct, vagrant individuals have been recorded since 2003, but no systematic species presence assessment has been made. Based on records of killed individuals, tracks and camera-trap sightings, we show that not only vagrant individuals are roaming in Gabon, but a small resident population occurs in the North-East of the country. The records collated here formed the basis for spotted hyaenas to be listed as protected in Gabon, were included in the IUCN Red List species' range map update and showcased the importance of large-scale by-catch data analysis in updating species distributions. Actuellement, l'aire de r & eacute;partition de la hy & egrave;ne tachet & eacute;e couvre largement l'Afrique subsaharienne, & agrave; l'exception du Bassin du Congo. Anciennement d & eacute;crits comme r & eacute;sidents du Gabon mais consid & eacute;r & eacute;s comme & eacute;teints, des individus vagabonds ont & eacute;t & eacute; enregistr & eacute;s depuis 2003, mais aucune & eacute;valuation syst & eacute;matique de la pr & eacute;sence de l'esp & egrave;ce n'a & eacute;t & eacute; faite. En se basant sur des enregistrements d'individus tu & eacute;s, des traces et des observations par pi & egrave;ges photographiques, nous montrons que non seulement des individus vagabonds errent au Gabon, mais qu'une petite population r & eacute;sidente est pr & eacute;sente dans le nord-est du pays. Les donn & eacute;es rassembl & eacute;es ici ont servi de base & agrave; l'inscription des hy & egrave;nes tachet & eacute;es sur la liste des esp & egrave;ces prot & eacute;g & eacute;es au Gabon, ont & eacute;t & eacute; incluses dans la mise & agrave; jour de la carte de l'aire de r & eacute;partition des esp & egrave;ces de la liste rouge de l'UICN et ont montr & eacute; l'importance de l'analyse des donn & eacute;es sur les prises accessoires & agrave; grande & eacute;chelle pour la mise & agrave; jour de la r & eacute;partition des esp & egrave;ces.

Assessing abundance and monitoring ecology and population trends are of critical importance for animal species of conservation concern. For sea turtles, annual nest counts represent the most common method of estimating population size. However, to develop a better understanding of population trends, these data need to be complemented by other reproductive parameters, which are lacking for many nesting populations across central Africa.To this end, an intensive capture-mark-recapture programme was conducted spanning 21 years (1997-2018) in the most important nesting sites on the Atlantic coast of central Africa (Gabon and Congo) for leatherback (Dermochelys coriacea) and olive ridley (Lepidochelys olivacea) sea turtles.A total of 18,105 encounters of 14,109 D. coriacea individuals and 2678 encounters of 2427 L. olivacea individuals were recorded. Biological and technical parameters such as clutch frequency, inter-nesting interval, remigration interval, annual survival rate, somatic growth, size trends, tag loss and number of annual nesting females were estimated through a variety of methods and models.The study detected a decline in body size and low survival probability (not due to tag loss) in both species but no clear decline in estimated annual number of nesting females. High fidelity to nesting sites (<30 km for both species) implies that the current conservation strategy, protecting the main nesting areas, could be effective.We recommend that local conservation managers promote: (i) continued monitoring of the nesting activity of the two species through capture-mark-recapture programmes; (ii) continued nest counts at long-term monitoring sites, which may also detect possible spatial shifts; and (iii) strengthening of cross-border cooperation between Gabon and Republic of the Congo given the observed connectivity between nesting sites of the two countries.

Efforts to preserve, protect and restore ecosystems are hindered by long delays between data collection and analysis. Threats to ecosystems can go undetected for years or decades as a result. Real-time data can help solve this issue but significant technical barriers exist. For example, automated camera traps are widely used for ecosystem monitoring but it is challenging to transmit images for real-time analysis where there is no reliable cellular or WiFi connectivity. We modified an off-the-shelf camera trap (Bushnell (TM)) and customised existing open-source hardware to create a 'smart' camera trap system. Images captured by the camera trap are instantly labelled by an artificial intelligence model and an 'alert' containing the image label and other metadata is then delivered to the end-user within minutes over the Iridium satellite network. We present results from testing in the Netherlands, Europe, and from a pilot test in a closed-canopy forest in Gabon, Central Africa. All reference materials required to build the system are provided in open-source repositories. Results show the system can operate for a minimum of 3 months without intervention when capturing a median of 17.23 images per day. The median time-difference between image capture and receiving an alert was 7.35 min, though some outliers showed delays of 5-days or more when the system was incorrectly positioned and unable to connect to the Iridium network. We anticipate significant developments in this field and hope that the solutions presented here, and the lessons learned, can be used to inform future advances. New artificial intelligence models and the addition of other sensors such as microphones will expand the system's potential for other, real-time use cases including real-time biodiversity monitoring, wild resource management and detecting illegal human activities in protected areas.

Summary R code and main functions used in the manuscript 'Functional susceptibility of tropical forests to climate change'

Significance Tree diversity is fundamental for forest ecosystem stability and services. However, because of limited available data, estimates of tree diversity at large geographic domains still rely heavily on published lists of species descriptions that are geographically uneven in coverage. These limitations have precluded efforts to generate a global perspective. Here, based on a ground-sourced global database, we estimate the number of tree species at biome, continental, and global scales. We estimated a global tree richness (≈73,300) that is ≈14% higher than numbers known today, with most undiscovered species being rare, continentally endemic, and tropical or subtropical. These results highlight the vulnerability of global tree species diversity to anthropogenic changes.

Correction for "The number of tree species on Earth," by Roberto Cazzolla Gatti, Pramod Kumar Khare, Timothy J. Kileen, Hyun Seok Kim, Henn Korjus, Amit Lewis, Natalia Lukina, Brian S. Maitner, Yadvinder Malhi, Andrew R. Marshall, Olga V. Martynenko, Abel L. Monteagudo Mendoza, Petr V. Ontikov, Edgar OrtizMalavasi, Nadir C. Pallqui Camacho, Alain Paquette, Minjee Park, Narayanaswamy Phillips, Nicolas Picard, Daniel Piotto, Lourens Poorter, John R. Poulsen, Hans

Significant gaps remain in understanding the response of plant reproduction to environmental change. This is partly because measuring reproduction in long-lived plants requires direct observation over many years and such datasets have rarely been made publicly available. Here we introduce MASTREE+, a data set that collates reproductive time-series data from across the globe and makes these data freely available to the community. MASTREE+ includes 73,828 georeferenced observations of annual reproduction (e.g. seed and fruit counts) in perennial plant populations worldwide. These observations consist of 5971 population-level time-series from 974 species in 66 countries. The mean and median time-series length is 12.4 and 10 years respectively, and the data set includes 1122 series that extend over at least two decades (>= 20 years of observations). For a subset of well-studied species, MASTREE+ includes extensive replication of time-series across geographical and climatic gradients. Here we describe the open-access data set, available as a.csv file, and we introduce an associated web-based app for data exploration. MASTREE+ will provide the basis for improved understanding of the response of long-lived plant reproduction to environmental change. Additionally, MASTREE+ will enable investigation of the ecology and evolution of reproductive strategies in perennial plants, and the role of plant reproduction as a driver of ecosystem dynamics.

More refined knowledge of how tropical forests respond to changes in the abiotic environment is necessary to mitigate climate change, maintain biodiversity, and preserve ecosystem services. To evaluate the unique response of diverse Afrotropical forest communities to disturbances in the abiotic environment, we employ country-wide tree species inventories, remotely sensed climate data, and future climate predictions collected from 104 1-ha plots in the central African country of Gabon. We predict a 3–8% decrease in Afrotropical forest species richness by the end of the century, in contrast to the 30–50% loss of plant diversity predicted to occur with equivalent warming in the Neotropics. This work reveals that forecasts of community species composition are not generalizable across regions, and more representative studies are needed in understudied diverse biomes. This study serves as an important counterpoint to work done in the Neotropics by providing contrasting predictions for Afrotropical forests with substantially different ecological, evolutionary, and anthropogenic histories.

Introduction. Aucoumea klaineana Pierre is the most harvested timber species in Central Africa and plays a key role in the economy of Gabon. This tree is considered “Vulnerable” for more than three decades by the IUCN because its population is assumed to have been reduced by at least 50% as a result of logging. The objective of this review is to synthesize recent and relevant knowledge in order to update this status. It focuses more specifically on ecology, population dynamics, silviculture and impacts of logging. Literature. Its natural range mainly covers Gabon. It is a light-demanding and gregarious species that forms root anastomoses. It principally establishes itself in abandoned fields and savannahs. In mature forest its regeneration is rare and limited to large gaps. Selective logging only allows significant regeneration along roads. However, at the scale of Gabon, the species is abundant and its renewal is ensured. Legal logging does not threaten the species. However, after decades of logging focusing on best shaped trees, a production of lower quality is feared. The implementation of a thoughtful silviculture could be a solution. Conclusions. Aucoumea klaineana is not vulnerable under IUCN A1 criterion. However, the maintenance of a high-quality production over the long term calls for the implementation of silviculture based on a thorough knowledge of the factors affecting stand dynamics, especially the role of the rhizosphere. Although long described, the functioning of root anastomoses has never been studied in depth.

NASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI’s footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI’s waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.