The impacts of climate change and anthropogenic disturbance are increasingly evident in the structure and demographics of tropical forests, yet the response of tree reproduction remains poorly understood. As fruit and seed production is the first step in forest recruitment, this gap is critical to understanding tropical forest resilience. Tropical fruits are important in diets of numerous frugivores and are essential resources for local human communities, thus changes in fruit quantity and composition could have cascading effects on ecosystems and the people who depend on them. In this study, we demonstrate that forest fruit production is shifting across tropical sites, with negative species-level trends occurring four times more frequently than positive ones across a network of 17 sites. Nevertheless, trends in fruit production are diverse across sites. While major spatial and temporal gaps in data coverage remain, by leveraging the expanding network of long-term monitoring, collaborative research has the potential to identify current trends in tropical fruit production and their drivers. This will enable robust predictions of future trends and advance our understanding of tropical forest vulnerability to environmental change.

Mycorrhizae, a form of plant–fungal symbioses, mediate vegetation impacts on ecosystem functioning. Climatic effects on decomposition and soil quality are suggested to drive mycorrhizal distributions, with arbuscular mycorrhizal plants prevailing in low-latitude/high-soil-quality areas and ectomycorrhizal (EcM) plants in high-latitude/low-soil-quality areas. However, these generalizations, based on coarse-resolution data, obscure finer-scale variations and result in high uncertainties in the predicted distributions of mycorrhizal types and their drivers. Using data from 31 lowland tropical forests, both at a coarse scale (mean-plot-level data) and fine scale (20 × 20 metres from a subset of 16 sites), we demonstrate that the distribution and abundance of EcM-associated trees are independent of soil quality. Resource exchange differences among mycorrhizal partners, stemming from diverse evolutionary origins of mycorrhizal fungi, may decouple soil fertility from the advantage provided by mycorrhizal associations. Additionally, distinct historical biogeographies and diversification patterns have led to differences in forest composition and nutrient-acquisition strategies across three major tropical regions. Notably, Africa and Asia’s lowland tropical forests have abundant EcM trees, whereas they are relatively scarce in lowland neotropical forests. A greater understanding of the functional biology of mycorrhizal symbiosis is required, especially in the lowland tropics, to overcome biases from assuming similarity to temperate and boreal regions.

While the conservation of forest elephants is a global concern, human-elephant conflict (HEC), especially crop-raiding by elephants, is a serious threat to both human livelihoods and conservation efforts. However, only a few studies have explored elephant crop-raiding and related damage mitigation strategies in Central Africa’s forest landscapes, which are characterized by low human and high animal densities and shifting cultivation practices. This study investigates HEC in rural Gabon, where human activities are limited, and local livelihoods are severely threatened by crop-raiding elephants. Through long-term ethnographic research and an in-depth analysis of damage mitigation practices by local people, the study unveils serious crop-raiding by elephants, leading to a significant change in the local lifestyle. Most households spend the majority of days in the field protecting crops, resulting in the village becoming almost empty. In addition to the physical burden of staying in poor living conditions, there is a considerable psychological burden for local people. Establishing effective elephant conservation systems requires understanding each local situation and evaluating the various costs to local people. To sustain livelihoods and address the challenge of HEC, it is crucial for local communities and various stakeholders to act collectively.

Accurate mapping and monitoring of tropical forests aboveground biomass (AGB) is crucial to design effective carbon emission reduction strategies and improving our understanding of Earth's carbon cycle. However, existing large-scale maps of tropical forest AGB generated through combinations of Earth Observation (EO) and forest inventory data show markedly divergent estimates, even after accounting for reported uncertainties. To address this, a network of high-quality reference data is needed to calibrate and validate mapping algorithms. This study aims to generate reference AGB datasets using field inventory plots and airborne LiDAR data for eight sites in Central Africa and five sites in South Asia, two regions largely underrepresented in global reference AGB datasets. The study provides access to these reference AGB maps, including uncertainty maps, at 100 m and 40 m spatial resolutions covering a total LiDAR footprint of 1,11,650 ha [ranging from 150 to 40,000 ha at site level]. These maps serve as calibration/validation datasets to improve the accuracy and reliability of AGB mapping for current and upcoming EO missions (viz., GEDI, BIOMASS, and NISAR).

This Zenodo repository contains the code and the processed dataset used in the research paper titled "Tropical tree ectomycorrhiza are distributed independently of soil nutrients" published in Nature Ecology & Evolution. In this study, we investigate the distribution and abundance of ectomycorrhizal (EcM) trees in lowland tropical forests and their relationship with soil quality. Key Finding: EcM-associated trees' distribution and abundance in lowland tropical forests are independent of soil quality. Contents: Code (CODE_COARSE_SCALE.R and CODE_FINE_SCALE.R): Contains the R scripts used for data analysis. Processed Dataset (PCs_prop_EcM.csv): Includes the processed data for the fine-scale analysis. README: Provides detailed information on how to use the code, interpret the dataset, and access additional required information. Data Access Information: To perform the full analyses, please request additional data from the Principal Investigators (PIs) of the plots. ForestGEO plot data can be obtained upon request through the ForestGEO portal at http://ctfs.si.edu/datarequest/. Refer to Extended Data Table 1 in the manuscript for a comprehensive list of data sources.

One mechanism proposed to explain high species diversity in tropical systems is strong negative conspecific density dependence (CDD), which reduces recruitment of juveniles in proximity to conspecific adult plants. Although evidence shows that plant-specific soil pathogens can drive negative CDD, trees also form key mutualisms with mycorrhizal fungi, which may counteract these effects. Across 43 large-scale forest plots worldwide, we tested whether ectomycorrhizal tree species exhibit weaker negative CDD than arbuscular mycorrhizal tree species. We further tested for conmycorrhizal density dependence (CMDD) to test for benefit from shared mutualists. We found that the strength of CDD varies systematically with mycorrhizal type, with ectomycorrhizal tree species exhibiting higher sapling densities with increasing adult densities than arbuscular mycorrhizal tree species. Moreover, we found evidence of positive CMDD for tree species of both mycorrhizal types. Collectively, these findings indicate that mycorrhizal interactions likely play a foundational role in global forest diversity patterns and structure.

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.

In 2015 and 2016, the AfriSAR campaign was carried out as a collaborative effort among international space and National Park agencies (ESA, NASA, ONERA, DLR, ANPN and AGEOS) in support of the upcoming ESA BIOMASS, NASA-ISRO Synthetic Aperture Radar (NISAR) and NASA Global Ecosystem Dynamics Initiative (GEDI) missions. The NASA contribution to the campaign was conducted in 2016 with the NASA LVIS (Land Vegetation and Ice Sensor) Lidar, the NASA L-band UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar). A central motivation for the AfriSAR deployment was the common AGBD estimation requirement for the three future spaceborne missions, the lack of sufficient airborne and ground calibration data covering the full range of ABGD in tropical forest systems, and the intercomparison and fusion of the technologies. During the campaign, over 7000 km2 of waveform Lidar data from LVIS and 30,000 km2 of UAVSAR data were collected over 10 key sites and transects. In addition, field measurements of forest structure and biomass were collected in sixteen 1-hectare sized plots. The campaign produced gridded Lidar canopy structure products, gridded aboveground biomass and associated uncertainties, Lidar based vegetation canopy cover profile products, Polarimetric Interferometric SAR and Tomographic SAR products and field measurements. Our results

Arbuscular mycorrhizal (AM) and ectomycorrhizal (EcM) associations are critical for host-tree performance. However, how mycorrhizal associations correlate with the latitudinal tree beta-diversity remains untested. Using a global dataset of 45 forest plots representing 2,804,270 trees across 3840 species, we test how AM and EcM trees contribute to total beta-diversity and its components (turnover and nestedness) of all trees. We find AM rather than EcM trees predominantly contribute to decreasing total beta-diversity and turnover and increasing nestedness with increasing latitude, probably because wide distributions of EcM trees do not generate strong compositional differences among localities. Environmental variables, especially temperature and precipitation, are strongly correlated with beta-diversity patterns for both AM trees and all trees rather than EcM trees. Results support our hypotheses that latitudinal beta-diversity patterns and environmental effects on these patterns are highly dependent on mycorrhizal types. Our findings highlight the importance of AM-dominated forests for conserving global forest biodiversity.

Legumes provide an essential service to ecosystems by capturing nitrogen from the atmosphere and delivering it to the soil, where it may then be available to other plants. However, this facilitation by legumes has not been widely studied in global tropical forests. Demographic data from 11 large forest plots (16–60 ha) ranging from 5.25° S to 29.25° N latitude show that within forests, leguminous trees have a larger effect on neighbor diversity than non-legumes. Where soil nitrogen is high, most legume species have higher neighbor diversity than non-legumes. Where soil nitrogen is low, most legumes have lower neighbor diversity than non-legumes. No facilitation effect on neighbor basal area was observed in either high or low soil N conditions. The legume–soil nitrogen positive feedback that promotes tree diversity has both theoretical implications for understanding species coexistence in diverse forests, and practical implications for the utilization of legumes in forest restoration.

Aim Mapping tree species richness across the tropics is of great interest for effective conservation and biodiversity management. In this study, we evaluated the potential of full-waveform lidar data for mapping tree species richness across the tropics by relating measurements of vertical canopy structure, as a proxy for the occupation of vertical niche space, to tree species richness. Location Tropics. Time period Present. Major taxa studied Trees. Methods First, we evaluated the characteristics of vertical canopy structure across 15 study sites using (simulated) large-footprint full-waveform lidar data (22 m diameter) and related these findings to in-situ tree species information. Then, we developed structure-richness models at the local (within 25-50 ha plots), regional (biogeographical regions) and pan-tropical scale at three spatial resolutions (1.0, 0.25 and 0.0625 ha) using Poisson regression. Results The results showed a weak structure-richness relationship at the local scale. At the regional scale (within a biogeographical region) a stronger relationship between canopy structure and tree species richness across different tropical forest types was found, for example across Central Africa and in South America [R(2)ranging from .44-.56, root mean squared difference as a percentage of the mean (RMSD%) ranging between 23-61%]. Modelling the relationship pan-tropically, across four continents, 39% of the variation in tree species richness could be explained with canopy structure alone (R-2 = .39 and RMSD% = 43%, 0.25-ha resolution). Main conclusions Our results may serve as a basis for the future development of a set of structure-richness models to map high resolution tree species richness using vertical canopy structure information from the Global Ecosystem Dynamics Investigation (GEDI). The value of this effort would be enhanced by access to a larger set of field reference data for all tropical regions. Future research could also support the use of GEDI data in frameworks using environmental and spectral information for modelling tree species richness across the tropics.

The CoFor-AGB dataset is distributed as a four layers GeoTIFF file. The file projection system is World Mercator (EPSG: 3395). Layers’ information are provided below.Layer 1: Mean pixel AGB (in Mg.ha-1) Layer 2: Number of field plots within the pixelLayer 3: Mean date of plots inventory, expressed in fractional year Layer 4: Inter-plot variance (in Mg2.ha-2) Layer 5: Intra-plot variance (in Mg2.ha-2)See the associated Data Descriptor for details : Ploton et al., 2020, Scientific data. doi:10.1038/s41597-020-0561-0.

ForestGEO is a network of scientists and long-term forest dynamics plots (FDPs) spanning the Earth's major forest types. ForestGEO's mission is to advance understanding of the diversity and dynamics of forests and to strengthen global capacity for forest science research. ForestGEO is unique among forest plot networks in its large-scale plot dimensions, censusing of all stems >= 1 cm in diameter, inclusion of tropical, temperate and boreal forests, and investigation of additional biotic (e.g., arthropods) and abiotic (e.g., soils) drivers, which together provide a holistic view of forest functioning. The 71 FDPs in 27 countries include approximately 7.33 million living trees and about 12,000 species, representing 20% of the world's known tree diversity. With >1300 published papers, ForestGEO researchers have made significant contributions in two fundamental areas: species coexistence and diversity, and ecosystem functioning. Specifically, defining the major biotic and abiotic controls on the distribution and coexistence of species and functional types and on variation in species' demography has led to improved understanding of how the multiple dimensions of forest diversity are structured across space and time and how this diversity relates to the processes controlling the role of forests in the Earth system. Nevertheless, knowledge gaps remain that impede our ability to predict how forest diversity and function will respond to climate change and other stressors. Meeting these global research challenges requires major advances in standardizing taxonomy of tropical species, resolving the main drivers of forest dynamics, and integrating plot-based ground and remote sensing observations to scale up estimates of forest diversity and function, coupled with improved predictive models. However, they cannot be met without greater financial commitment to sustain the long-term research of ForestGEO and other forest plot networks, greatly expanded scientific capacity across the world's forested nations, and increased collaboration and integration among research networks and disciplines addressing forest science.

Tropical forests are characterized by large carbon stocks and high biodiversity, but they are increasingly threatened by human activities. Since structure strongly influences the functioning and resilience of forest communities and ecosystems, it is important to quantify it at fine spatial scales. Here, we propose a new simulation-based approach, the "Canopy Constructor", with which we quantified forest structure and biomass at two tropical forest sites, one in French Guiana, the other in Gabon. In a first step, the Canopy Constructor combines field inventories and airborne lidar scans to create virtual 3D representations of forest canopies that best fit the data. From those, it infers the forests' structure, including crown packing densities and allometric scaling relationships between tree dimensions. In a second step, the results of the first step are extrapolated to create virtual tree inventories over the whole lidar-scanned area. Across the French Guiana and Gabon plots, we reconstructed empirical canopies with a mean absolute error of 3.98 m [95% credibility interval: 3.02, 4.98], or 14.4%, and a small upwards bias of 0.66 m [-0.41, 1.8], or 2.7%. Height-stem diameter allometries were inferred with more precision than crown-stem diameter allometries, with generally larger heights at the Amazonian than the African site, but similar crown-stem diameter allometries. Plot-based aboveground biomass was inferred to be larger in French Guiana with 400.8 t ha(-1) [366.2-437.9], compared to 302.2 t ha(-1) in Gabon [267.8-336.8] and decreased to 299.8 t ha(-1) [275.9-333.9] and 251.8 t ha(-1) [206.7-291.7] at the landscape scale, respectively. Predictive accuracy of the extrapolation procedure had an RMSE of 53.7 t ha(-1) (14.9%) at the 1 ha scale and 87.6 t ha(-1) (24.2%) at the 0.25 ha scale, with a bias of -17.1 t ha(-1) (-4.7%). This accuracy was similar to regression-based approaches, but the Canopy Constructor improved the representation of natural heterogeneity considerably, with its range of biomass estimates larger by 54% than regression-based estimates. The Canopy Constructor is a comprehensive inference procedure that provides fine-scale and individual-based reconstructions even in dense tropical forests. It may thus prove vital in the assessment and monitoring of those forests, and has the potential for a wider applicability, for example in the exploration of ecological and physiological relationships in space or the initialisation and calibration of forest growth models.

Forest biomass is key in Earth carbon cycle and climate system, and thus under intense scrutiny in the context of international climate change mitigation initiatives (e.g. REDD+). In tropical forests, the spatial distribution of aboveground biomass (AGB) remains, however, highly uncertain. There is increasing recognition that progress is strongly limited by the lack of field observations over large and remote areas. Here, we introduce the Congo basin Forests AGB (CoFor-AGB) dataset that contains AGB estimations and associated uncertainty for 59,857 1-km pixels aggregated from nearly 100,000 ha of in situ forest management inventories for the 2000 – early 2010s period in five central African countries. A comprehensive error propagation scheme suggests that the uncertainty on AGB estimations derived from c. 0.5-ha inventory plots (8.6–15.0%) is only moderately higher than the error obtained from scientific sampling plots (8.3%). CoFor-AGB provides the first large scale view of forest AGB spatial variation from field data in central Africa, the second largest continuous tropical forest domain of the world.

Mapping tree species diversity is increasingly important in the face of environmental change and biodiversity conservation. We explore a potential way of mapping this diversity by relating forest structure to tree species diversity in Gabon. First, we test the relation between canopy height, as a proxy for niche volume, and tree species diversity. Then, we test the relation between vertical canopy structure, as a proxy for vertical niche occupation, and tree species diversity. We use large footprint full-waveform airborne lidar data collected across four study sites in Gabon (Lopé, Mabounié, Mondah, and Rabi) in combination with in situ estimates of species richness (S) and Shannon diversity (H′). Linear models using canopy height explained 44% and 43% of the variation in S and H′ at the 0.25 ha resolution. Linear models using canopy height and the plant area volume density profile explained 71% of this variation. We demonstrate applications of these models by mapping S and H′ in Mondah using a simulated GEDI-TanDEM-X fusion height product, across the four sites using wall-to-wall airborne lidar data products, and across and between the study sites using ICESat lidar waveforms. The modeling results are encouraging in the context of developing pan-tropical structure-diversity models applicable to data from current and upcoming spaceborne remote sensing missions.

Questions We examined the spatial patterns of dominant canopy species in a tropical forest to investigate: (a) what is the niche occupancy of canopy species with respect to topographic gradients; (b) what are the dominant ecological processes that explain their distribution; (c) what are the interactions among the most prevalent canopy species; and (d) what are the interactions between canopy species adults and juveniles trees? Location Rabi permanent CTFS‐ForestGEO plot, Gabon. Methods We selected the four most abundant canopy species and one timber species. We used Berman's test to determine the effect of three topographic variables on the distribution of each species and univariate analysis to model the spatial pattern of each species using either an inhomogeneous Poisson process or an inhomogeneous Cox process. We also used a bivariate form of the pair correlation function (PCF) to determine the spatial interaction between species and the correlation among conspecific adult and juvenile trees. Results Four of the five species had aggregated spatial patterns while Lophira alata showed spatial randomness. Most of the variance in the local tree density was explained by within‐population dispersal processes rather than environmental factors. Bivariate PCF tests showed significant segregation between species associations. Two species exhibited aggregation at small distances between young and adult trees, while others showed either complete spatial randomness at small inter‐tree distances or segregation at large distances. Conclusions This study showed that the spatial pattern in the majority of canopy species was aggregation. Seed dispersal limitation mainly explained the observed aggregation pattern. Habitat filtering, as evidenced by the influence of topographic variables on niche occupancy, marginally, yet significantly, explained this pattern. The different spatial patterns of the principal species permit their coexistence. Spatial segregation among adult and juvenile trees reveals a strong pattern of either species‐specific seed predation or pathogens.

Forest biomass is an essential indicator for monitoring the Earth's ecosystems and climate. It is a critical input to greenhouse gas accounting, estimation of carbon losses and forest degradation, assessment of renewable energy potential, and for developing climate change mitigation policies such as REDD+, among others. Wall-to-wall mapping of aboveground biomass (AGB) is now possible with satellite remote sensing (RS). However, RS methods require extant, up-to-date, reliable, representative and comparable in situ data for calibration and validation. Here, we present the Forest Observation System (FOS) initiative, an international cooperation to establish and maintain a global in situ forest biomass database. AGB and canopy height estimates with their associated uncertainties are derived at a 0.25 ha scale from field measurements made in permanent research plots across the world's forests. All plot estimates are geolocated and have a size that allows for direct comparison with many RS measurements. The FOS offers the potential to improve the accuracy of RS-based biomass products while developing new synergies between the RS and ground-based ecosystem research communities.

Climate is widely recognised as an important determinant of the latitudinal diversity gradient. However, most existing studies make no distinction between direct and indirect effects of climate, which substantially hinders our understanding of how climate constrains biodiversity globally. Using data from 35 large forest plots, we test hypothesised relationships amongst climate, topography, forest structural attributes (stem abundance, tree size variation and stand basal area) and tree species richness to better understand drivers of latitudinal tree diversity patterns. Climate influences tree richness both directly, with more species in warm, moist, aseasonal climates and indirectly, with more species at higher stem abundance. These results imply direct limitation of species diversity by climatic stress and more rapid (co-)evolution and narrower niche partitioning in warm climates. They also support the idea that increased numbers of individuals associated with high primary productivity are partitioned to support a greater number of species.

Tropical forests are a key component of the global carbon cycle.Yet, there are still high uncertainties in forest carbon stock and flux estimates, notably because of their spatial and temporal variability across the tropics.Several upcoming spaceborne missions have been designed to address this gap.High-quality ground data are essential for accurate calibration/validation so that spaceborne biomass missions can reach their full potential in reducing uncertainties regarding forest carbon stocks and fluxes.The BIOMASS mission, a P-band SAR satellite from the European Space Agency (ESA), aims at improving carbon stock mapping and reducing uncertainty in the carbon fluxes from deforestation, forest degradation, and regrowth.In situ activities in support of the BIOMASS mission were carried out in French Guiana and Gabon during the TropiSAR and AfriSAR campaigns.During these campaigns, airborne P-band SAR, forest inventory, and lidar data were collected over six study sites.This paper describes the methods used for forest inventory and lidar data collection and analysis, and presents resulting plot estimates and aboveground biomass maps.These reference datasets along with intermediate products (e.g., canopy height models) can be accessed through ESA's Forest Observation System and the Dryad data repository and will be useful for BIOMASS but also to other spaceborne biomass missions such as GEDI, NISAR, and Tandem-L for calibration/validation purposes.During data quality control and analysis, prospects for reducing uncertainties have been identified, and this paper finishes with a series of recommendations for future tropical forest field campaigns to better serve the remote sensing community.