In 2023, the deforested area in the Legal Amazon in Brazil amounted to approximately 802,300 hectares. Just a year earlier, the Amazon deforested area surpassed 1.2 million hectares. What is behind the growing Amazon deforestation in Brazil? Illegal logging, expansion of agricultural areas for soybean cultivation, and an increase in wildfire outbreaks are all among the leading causes of deforestation in the Brazilian Amazon. Politics, however, has also played an important role. For example, the authorized budget for Brazil’s Ministry of the Environment has been on a mostly downward trend since 2013, when it reached a decade-long peak of nearly seven billion Brazilian reals. How big is the Brazilian deforestation issue? In 2023, Brazil registered by far the largest area of primary forest loss in the world, amounting to more than one million hectares. This was roughly the same area as the remaining top nine countries combined. As the country with the second-largest forest area worldwide, these developments are cause for concern amidst the conversation on climate change mitigation. With the global tree cover loss annually increasing, and the emission of greenhouse gases from forest areas along with it, reaching net-zero emissions targets by 2050 grows ever more challenging.

The deforested area of the Brazilian Amazon declined by over 21 percent in 2023, when compared to the previous year. This was the second drop recorded in the last six years. The deforested area in the Brazilian Amazon stood at approximately nine thousand square kilometers in 2022.

Forest fire incidents are becoming increasingly common around the world, posing a threat to the environment, economy, and social life. These wildfires are further expected to rise in their frequency and intensity, considering the global climate change and human activities. A variety of attributes must be studied in order to analyse relationships between the probable causes of fire and the characteristics of wildfire incidents, and inform decision-making. Such attributes are available or easily collectable in various regions around the world, but they are not readily available in the South American Amazon. The Amazon rainforest covers such a large area that acquiring a useful dataset necessitates extensive effort and computer intensive pre-processing. The associated study to this dataset investigates potential data sources for the Amazon, establishes a methodological baseline, and prepares a dataset of covariates thought to be contributing to the wildfire ignition process. The dataset is intended to be used for forest fire studies, specifically spatio-temporal and statistical analysis of wildfires. The study provides three sets of (i) raw data (acquired data with a global extent), (ii) pre-processed data (source data transformed to the same projection system and same file format), and (iii) working data (cropped to Amazon region extent with spatial resolution of 500 meters and monthly temporal resolution, to enable the scientific community to work with various possibilities of forest-fire analysis, and to further encourage research in study areas in the other parts of the world.

The Amazon rainforest is known for being one of the most diverse ecosystems on Earth. According to the source, this biome located in South America was home to one in every ten known species on the planet. The Amazon basin spans across nine South American countries or territories: Brazil, Bolivia, Peru, Ecuador, Colombia, Venezuela, Guyana, Suriname, and French Guiana. The region occupies an area of 2.6 million square miles, which represents around 40 percent of the total South American territory.

This dataset includes GEDI data, ICESat-2 data, auxiliary data, and intermediate results necessary to reproduce results in Milenkovic et al. 2022. The code required to process the data is on: https://github.com/MilutinMM/SecFor-Regrowth.git.

Short descriptions of files:

ATL08_gdf.json - ICESat-2 ATL08 segments in Rondonia

ATL08_gdf_Para_MG.json - ICESat-2 ATL08 segments intersecting the two calibration sites

ATL08_h5_fileNames_Rondonia.txt - A list of ICESat-2 orbits (ATL08 h5 files) intersecting Rondonia (primary input)

calibartionModels.zip - GEDI and ICESat-2 calibration models and statistics (xlsx files)

deforested_poligons_2018_2019.zip - SPH file of a deforested polygon in the calibration site

gedi_L2A_allTime_gdf_Para_MG.json - GEDI shots intersecting the two calibration sites

gedi_L2A_allTime_MG_all.csv - GEDI shots within the FN calibration site

gedi_L2A_allTime_Para_all.csv - GEDI shots within the TNF calibration site

GEDI_L2A_fileNames_Rondonia.txt - A list of GEDI orbits (L2A h5 files) intersecting Rondonia (primary input)

gedi_L2A_gdf_Para_MG_sens_a2.json - GEDI shots intersecting the two calibration sites with sensitivities derived from the algorithm setting group 2

gedi_L2A_gdf_sens_a2.json - GEDI shots in Rondonia

gedi_L2A_MG_all_sens_a2.csv - GEDI shots within the FN calibration site (sensitivity from the alg. set. group 2)

gedi_L2A_Para_all_sens_a2.csv - GEDI shots within the TFN calibration site (sensitivity from the alg. set. group 2)

gedi_L2A_Rondonia_all_sens_a2.csv - GEDI shots in Rondonia

MG_ATL08_h5_fileNames.txt - A list of ICESat-2 orbits (ATL08 h5 files) intersecting the FN calibration site

Para_ATL08_h5_fileNames.txt - A list of ICESat-2 orbits (ATL08 h5 files) intersecting the TFN calibration site

svbr-rondonia-2018.tif - Forest age map for Rondonia (Silva Junior et al. 2020)

svbr-rondonia-2018_bw_eroded.tif - a secondary forest extent mask with removed border pixels

References:

Milenković, M., Reiche, J., Armston, J., Neuenschwander, A., De Keersmaecker, W., Herold, M., Verbesselt, J., Assessing amazon rainforest regrowth with GEDI and ICESat-2 data, Science of Remote Sensing, 2022, 100051, ISSN 2666-0172, https://doi.org/10.1016/j.srs.2022.100051.

Silva Junior, C.H.L., Heinrich, V.H.A., Freire, A.T.G. et al. Benchmark maps of 33 years of secondary forest age for Brazil. Sci Data 7, 269 (2020). https://doi.org/10.1038/s41597-020-00600-4

1. Plants are enormously diverse in their traits and ecological adaptation, even within given ecosystems, such as tropical rainforests. Accounting for this diversity in vegetation models poses serious challenges. Global plant functional trait databases have highlighted general trait correlations across species that have considerably advanced this research program. However, it remains unclear whether trait correlations found globally hold within communities, and whether they extend to drought tolerance traits.
2. For 134 individual plants spanning a range of sizes and life forms (tree, liana, understory species) within an Amazonian forest, we measured leaf drought tolerance (leaf water potential at turgor loss point, πtlp), together with 17 leaf traits related to various functions, including leaf economics traits and nutrient composition (leaf mass per area, LMA; and concentrations of C, N, P, K, Ca, and Mg per leaf mass and area), leaf area, water use efficiency (carbon isotope ratio), and time-integrated stomatal conductance and carbon assimilation rate per leaf mass and area. We tested trait coordination and the ability to estimate πtlp from the other traits through model selection. Performance and transferability of the best predictive model were assessed through cross-validation.
3. πtlp was positively correlated with leaf area, and with N, P and K concentrations per leaf mass, but not with LMA or any other studied trait. Five axes were needed to account for >80% of trait variation, but only three of them explained more variance than expected at random. The best model explained only 30% of the variation in πtlp, and out-sample predictive performance was variable across life forms or canopy strata, suggesting a limited transferability of the model.
4. Synthesis. We found a weak correlation among leaf drought tolerance and other leaf traits within a forest community. We conclude that higher trait dimensionality than assumed under the leaf economics spectrum may operate among leaves within plant communities, with important implications for species coexistence and responses to changing environmental conditions, and also for the representation of community diversity in vegetation models.

Dataset

This dataset was created by Johar M. Ashfaque

Contents

In 2021, more than 28.4 million people were estimated to reside within the Legal Amazon area in Brazil. Since 1970, the resident population in the region has quadrupled. The Legal Amazon in Brazil extends across nine Brazilian states, with the the largest area located in the state of Amazonas.

Aim

The exceptionally rich biodiversity found in tropical rainforest is under threat from anthropogenic climate change. We recognize the threat, yet we have little knowledge of the capacity of tropical species to adjust their climate sensitivity in response to it. One indicator of a species' capacity to adjust to different climates is the amount of intraspecific variation observed in its climate-relevant traits; if a climate-relevant trait varies, and this variation is correlated with local climates, it suggests the species can adjust the trait to different conditions through either phenotypic plasticity or evolutionary adaptation. Here, we test for intraspecific variation in climate-relevant traits in a rainforest specialist to shed light on the capacity of such species to adjust to different climates. Location

The Wet Tropics Bioregion, Australia. Methods

We studied 12 populations of a lizard that is a tropical rainforest specialist, the rainforest sunskink (Lampropholis coggeri), testing for intraspecific variation in four traits that are potentially important in determining a species' climate sensitivity. The measured traits were as follows: critical thermal minimum, critical thermal maximum, thermal optimum for sprinting, and desiccation rate. Results

We found substantial variation both through time and across space in the measured traits, suggesting both strong plasticity and substantial geographic variation. Moreover, trait variation was correlated with local climate variables, suggesting variation reflects adjustment to local conditions. Main conclusions

If physiological lability similar to that observed in rainforest sunskinks occurs in tropical rainforest species more generally, these taxa may not be as climatically specialized, and so not as vulnerable to climate change, as previously thought.

Title

Terrestrial lidar data collected from four large tropical rainforest trees in Floresta Nacional de Caxiuanã

Authors

A. Burt

M. Boni Vicari

A. C. L. da Costa

I. Coughlin

P. Meir

L. Rowland

M. Disney

Contact

a.burt@ucl.ac.uk

License

These data are distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC BY 4.0) - see the LICENSE file for details.

Overview

Terrestrial lidar data were acquired from four large tropical rainforest trees prior to harvest (diameter range: 0.6-1.2m, height range: 30-46m) in a natural closed forest stand in Floresta Nacional de Caxiuanã, Pará, Brazil (approx. coordinates in the WGS-84 datum: -1.798, -51.435 degrees), during August/October 2018.

This dataset includes: i) raw lidar data, ii) tree-level point clouds, and iii) quantitative structural models.

A complete description of the four trees, these data, and the companion destructive harvest data can be found in our paper entitled: ‘New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar’.

Acquisition

Neighbouring vegetation surrounding each tree was removed before data collection.

Lidar data were acquired using a RIEGL VZ-400 terrestrial laser scanner.

A minimum of 16 scans (upright and tilt) were collected from 8 scan positions around each tree.

The angular step between sequentially fired pulses was 0.04 degrees, and the distance between scanner and tree varied.

This arrangement provided a 45 degree sampling arc around each tree, and a complete sample of the scene from each position.

The laser pulse has a wavelength of 1550nm, a beam divergence of 0.35mrad, and the diameter of the footprint at emission is 7mm.

The instrument was in ‘High Speed Mode’ (pulse repetition rate: 300kHZ), ‘Near Range Activation’ was off (minimum measurement range: 1.5m), and waveforms were not stored.

Processing

i) Individual scans were registered onto a common coordinate system using RIEGL RiSCAN PRO (v2.7.0, http://riegl.com).

ii) Tree-level point clouds were extracted from the larger-area point cloud using treeseg (v0.2.0, https://github.com/apburt/treeseg).

iii) Points were classified as returns from wood or leaf material using TLSeparation (v1.2.1.5, https://github.com/TLSeparation).

iv) Points from buttresses were manually removed using CloudCompare (v2.10.3, https://cloudcompare.org).

v) Quantitative structural models were constructed using TreeQSM (v2.3.2, https://github.com/InverseTampere/TreeQSM) via optqsm (v0.1.0, https://github.com/apburt/optqsm).

File and directory naming convention

The four trees are identified: CAX-H_T1, CAX-H_T2, CAX-H_T3 and CAX-H_T4.

The various files and directories are described as follows:

./CAXH-H/

├───CAX-H_T1/ (Directory: tree-level directories)

├───CAX-H_T2/

├───CAX-H_T3/

├───CAX-H_T4/

│ ├───2018-10-06.001.riproject/

│ │ ├───ScanPos001/ (Directory: individual scan directories containing raw lidar data and other auxiliary files; odd: upright, even: tilt)

│ │ ├───ScanPos.../

│ │ ├───ScanPos020/

│ │ │ ├───181006_194253.rxp (File: measurement data stream)

│ │ │ ├───181006_194253.mon.rxp (File: monitoring data stream)

│ │ ├───matrix/ (Directory: contains the registration matrices)

│ │ │ ├───001.dat

│ │ │ ├───....dat

│ │ │ ├───020.dat (File: 3x4 matrix used to rotate and translate scan 20 into the coordinate system of scan 1)

│ │ ├───clouds/ (Directory: contains tree-level point clouds)

│ │ │ ├───CAXH_T4.txt (File: point cloud of CAX-H_T4 as extracted by treeseg)

│ │ │ ├───CAXH_T4nb.txt (File: CAXH_T4.txt with buttress points manually removed using CloudCompare)

│ │ │ ├───CAXH_T4w.txt (File: CAXH_T4.txt with leafy returns removed using TLSeparation)

│ │ │ ├───CAXH_T4l.txt (File: CAXH_T4.txt with woody returns removed using TLSeparation)

│ │ │ ├───CAXH_T4wnb.txt (File: CAXH_T4.txt with buttress points manually removed using CloudCompare, and leafy returns removed using TLSeparation)

│ │ ├───models/ (Directory: contains quantitative structural models constructed from the tree-level point clouds)

│ │ │ ├───CAXH_T4.mat (File: quantitative structural model of CAXH_T4.txt)

│ │ │ ├───CAXH_T4nb.mat

│ │ │ ├───CAXH_T4w.mat

│ │ │ ├───CAXH_T4wnb.mat

│ │ │ ├───CAXH_T4.models.dat (File: reports the volume (m3) and standard deviation (m3) of the QSMs)

│ │ │ ├───intermediate/ (Directory: contains intermediate QSMs generated by optqsm)

│ │ │ │ ├───CAXH_T4/

│ │ │ │ ├───CAXH_T4nb/

│ │ │ │ ├───CAXH_T4w/

│ │ │ │ ├───CAXH_T4wnb/

│ │ │ │ │ ├───CAXH_T4wnb-1.mat

│ │ │ │ │ ├───CAXH_T4wnb-....mat

│ │ │ │ │ ├───CAXH_T4wnb-10.mat

The Tropical Rainforest Plot Network Rainforest Understorey Floristic Data contains understorey floristic data from all 20 of its 0.5 ha (100 m x 50 m) permanent rainforest plots in Northern Queensland, Australia. Data was collected for all plots at the time of establishment before 1981 and data was again collected for half of the plots post-2000. The CSIRO permanent rainforest plots are located within 60 km of the north Queensland coast between Mackay (21.5ºS, 149ºE) and the Iron Range on Cape York Peninsula (12.5ºS, 143ºE). The plots have a rainfall range of 1200 to 3500 mm, represent eleven vegetation types, six parent materials, and range from 15 m to 1200 m above sea level. Except for minor disturbances associated with selective logging on two plots, the plots were established in old growth forest and all plots have thereafter been protected. In conjunction with the Tree Demographic Data packages which have been collected by the Tropical Rainforest Plot Network, the data collected from these 20 plots provides an insight into the floristical composition, structure and long term forest dynamics of Australian tropical rainforests and allows direct comparisons to be made with long-term monitoring plots at a global scale. For further background data please refer to Bradford, M.G., Murphy, H.T., Ford, A.J., Hogan, D. and Metcalfe, D.J. (2014) "Long-term stem inventory data from tropical rain forest plots in Australia", Ecology, 95(8): 2362. Ecological Archives E095-209-D1. https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1890/14-0458R.1 A synopsis of related data packages which have been collected as part of the Tropical Rainforest Plot Network’s full program is provided at http://www.ltern.org.au/index.php/ltern-plot-networks/tropical-rainforest. Data can be sourced from: http://doi.org/10.4225/08/59475c67be7a4 or https://data.csiro.au/dap/landingpage?pid=csiro:6638

In lowland tropical rainforest, hundreds of tree species typically occur within mesoscale landscapes (50-500 ha). There is no consensus ecological theory that accounts for the coexistence of so many species with similar morphologies and the same fundamental requirements of light, nutrients, water, and physical space. In part this is due to the limited understanding of post-establishment ecology for the vast majority of tropical tree species. Of even more concern is the lack of understanding of how these trees are responding to on-going atmospheric and climatic changes. Here we present long-term data on the post-establishment ecology of ten species of tropical rainforest trees that span a broad life-history spectrum. The study site was upland (non-swamp) old-growth tropical wet forest at the La Selva Biological Station (N.E. Costa Rica). Focal individuals from established seedlings to mature trees were assessed annually, with an emphasis on accuracy and long-term consistency of the observations. The annual time-step, rare for longterm studies in tropical rainforest, captures the typically abrupt changes in forest structure and light environments, the frequent instances of major physical damage, and the trees' responses to these events and to interannual and long-term climatic variation. With the completion of the study in 2016, the data for survivorship, growth, and microsite conditions span 4499 individuals and 34 years. The first ten years of these data were published as an Ecology/Ecological Archives data paper in 2000 (Clark and Clark 2000), with two subsequent update publications (Clark and Clark 2006, 2012). This final update adds the final six years of observations, digitized field comments, and histories of points of measurement on the trees. The metadata now include the scanned original field data-sheets for the entire study and a narrative detailing the annual qa/qc of the data. The data set is unique for its scope (years of continuous annual measurements, number of monitored individuals), the in-depth documentation, and the unrestricted data access. The data have been used to study life history patterns, tree ecology through ontogeny, and effects on tree performance from interannual and long-term climatic and atmospheric change. They have also contributed to numerous remote-sensing studies.

These data are the rain gauge time series components of a curated set of historical daily rainfall and streamflow data for a large region spanning the southern Amazonian rainforest and tropical savanna biomes of Brazil.

Data (columns) include: the year, month, and day ("year", "month", "day") of observations; the depth of daily rainfall in mm/day ("value"); a site ID ("site"); and the state within which the rain gauge is located ("state"). Missing values are set to -9999. For each unique site, there exists a corresponding rain gauge location record (see "Rain Gauge Locations for the Brazilian Rainforest-Savanna Transition Zone"); these are identified by the same site ID numbers. Date ranges are different for each site.

Rainfall data were obtained from free, publicly-available historical records from the Brazilian water management agency: Agência Nacional de Águas (ANA).

Additional data package information and contents, including raw data files, documentation of data acquisition and processing, and related programmatic scripts, are available via Figshare: https://doi.org/10.6084/m9.figshare.3100912.v1

Background: Future climate-change effects on plant growth are most effectively studied using microclimate-manipulation experiments, the design of which has seen much advance in recent years. For tropical forests, however, such experiments are particularly hard to install and have hence not been widely used. We present a system of active heating and CO2 fertilisation for use in tropical forest understoreys, where passive heating is not possible. The system was run for two years to study climate-change effects on epiphytic bryophytes, but is also deemed suitable to study other understorey plants.

Methods: Warm-air and CO2 addition were applied in 1.6-m tall, 1.2-m diameter hexagonal open-top chambers and the microclimate in the chambers compared to outside air. Warming was regulated with a feedback system while CO2 addition was fixed.

Results: The setup successfully heated the air by 2.8K and increased CO2 by 250 ppm, on average, with +3K and +300 ppm as the targets. Variation was high,...

Despite its perceived historical rarity, fire is an important disturbance in tropical rainforests. Very large rainforest fires have been observed multiple times in recent decades, often during years of strong El Niño-Southern Oscillation droughts. Fire in rainforest has major short-term consequences for humans and wildlife by converting forest to fire-prone fern, shrub, and grass, but the long-term effects remain to be seen. Borneo's indigenous groups have been using fire to clear land for centuries, yet the prevalence and spatial patterns of pre-modern fire across forest types in Borneo is not well understood. This research set out to reconstruct fire in a 1500-ha primary rainforest spanning 800 m of elevation in Indonesian Borneo with the goal of elucidating the role humans have played in rainforest fire. We found that humans played an important role in the occurrence of fire in recent centuries. Evidence of fire—charcoal >2 mm—is more abundant in forest types where humans would be more likely to live and/or practice swidden agriculture. However, pyrogenic material is ubiquitous across the study area, showing that all forest types have experienced fire. A set of 50 radiocarbon dates showed that in lowland areas—where human-caused fire is most likely—fire occurred throughout the last 3,200 years, peaking 1300-1600 CE. The upland areas lacked evidence of fire before 1250 CE but otherwise had a similar pattern to the lowlands. The period of high fire coincides with regional demographic changes as well as regional droughts documented elsewhere in Southeast Asia. In upland areas, fires likely burned only under regional drought when fires could more easily spread upslope. Although forest plot studies at this site show little structural evidence of past fires, tree diversity is lower than expected in the most burned areas (alluvial benches). Thus, our results suggest that land clearance was a major source of fire, but the current intact state of these rainforests indicates that they were largely resilient to fires and land use hundreds of years ago. Recent fires mirror patterns of fire spread that occurred hundreds of years ago, though their severity and extent is likely much greater.

In August 2024, around 660 square kilometers of forest in the Brazilian Amazon were destroyed, the highest monthly figure recorded that year. In 2024, in total, almost 3,800 square kilometers were deforested in the region. This represents a decline of over seven percent in comparison to the same period in the previous year. Most of the deforested area in the Brazilian Amazon occurs in private land areas.

Accurate soil organic carbon (SOC) maps are needed to predict the terrestrial SOC feedback to climate change, one of the largest remaining uncertainties in Earth system modeling. Over the last decade, global scale models have produced varied predictions of the size and distribution of SOC stocks, ranging from 1,000 to > 3,000 Pg of C within the top 1 m. Regional assessments may help validate or improve global maps because they can examine landscape controls on SOC stocks and offer a tractable means to retain regionally-specific information, such as soil taxonomy, during database creation and modeling. We compile a new transboundary SOC stock database for coastal watersheds of the North Pacific coastal temperate rainforest, using soil classification data to guide gap-filling and machine learning approaches used to explore spatial controls on SOC and predict regional stocks. Precipitation and topographic attributes controlling soil wetness were found to be the dominant controls of SOC, underscoring the dependence of C accumulation on high soil moisture. The random forest model predicted stocks of 4.5 Pg C (to 1 m) for the study region, 22% of which was stored in organic soil layers. Calculated stocks of 228 ± 111 Mg C ha-1 fell within ranges of several past regional studies and indicate 11-33 Pg C may be stored across temperate rainforest soils globally. Predictions compared very favorably to regionalized estimates from two spatially-explicit global products (Pearson's correlation: ρ = 0.73 vs. 0.34). Notably, SoilGrids250m was an outlier for estimates of total SOC, predicting 4-fold higher stocks (18 Pg C) and indicating bias in this global product for the soils of the temperate rainforest. In sum our study demonstrates that CTR ecosystems represent a moisture-dependent hotspot for SOC storage at mid-latitudes.

Biomass carbon sequestration and sink capacities of tropical rainforests are crucial for addressing climate change. However, accurate canopy height estimation is necessary to determine carbon sink potential and implement effective forest management. This study compares the performance of four advanced machine learning algorithms—Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Convolutional Neural Network (CNN), and Backpropagation Neural Network (BP)—in predicting forest canopy height in Hainan Tropical Rainforest National Park. The study uses 140 field survey samples and 315 unmanned aerial vehicle photogrammetry samples, along with multi-modal remote sensing datasets, including GEDI and ICESat satellite LiDAR data, Landsat imagery, and environmental information.The following is a description of the data package:The data in the folder (Model) includes the trained models of the four machine learning algorithms (BP, CNN, GBDT, RF).The data in the folder (Figures) includes the technology roadmap of the research, distribution map of the study area, and data point maps used in the study, along with scatter plots of RH80, RH85, RH90, and RH95 internal and external validation for the four machine learning algorithms (BP, CNN, GBDT, RF).The data in the folder (Datasets) includes the filtered modeling data and external validation data from GEDI and ICESat.The data in the folder (Code) includes the training code and prediction code for four machine learning algorithms (BP, CNN, GBDT, RF).If you require raw high-resolution raster data from other periods, please contact Dr. Qiu Zixuan at zixuanqiu@hainanu.edu.cn.

The purpose of this data set was to compile distributional, general life-history characteristics and phylogenies for Australian tropical rain forest vertebrates to inform a wide range of comparative studies on the determinants of biodiversity patterns and to assess the impacts of global climate change. We provide three distinct data sets: (1) a table of species-specific distributional and life-history traits for 242 vertebrate species found in the rain forests of the Australian Wet Tropics; (2) species distribution maps (GIS raster files) for 202 of the species displaying both the realized and potential distributions; and (3) phylogenies for these species. These species represent 93 birds, 31 amphibians, 31 mammals (including one monotreme), and 47 reptiles. Where information exists, the distributional and life-history data compiled here present information on: indices of environmental specialization (ENFA), habitat specialization, average body mass and size, sexual dimorphism, reproductive characteristics such as age at first reproduction, clutch/litter size, number of reproductive bouts per year and breeding seasonality, longevity, time of day when most active, and dispersal ability; distributional characteristics such as range size (potential and realized for both total and core ranges) and observed ranges in temperature, precipitation, and elevation; and niche attributes such as environmental marginality and specialization. The distribution maps provided represent a combination of presence-only ecological niche modeling (using MaxEnt) to estimate the potential distribution of a species followed by biogeographic clipping by expert opinion based on extensive field data and a subregional classification relevant to the topography and biogeographic history of the region to produce best-possible estimates of the realized distribution. Our assemblage contains many species with a shared evolutionary history, and thus many analyses of these data will need to account for phylogeny. Although a comprehensive phylogeny with branch length information does not exist for this diverse group of species, we present a best-estimate composite phylogeny constructed primarily from recently published molecular phylogenies of included groups.

This metadata record is an extract from the authorive metadata records maintained by the tropical data hub (http://tropicaldatahub.org/data/6e58cda4-a2c9-4193-bb22-708e25054e3c).

These are summarised plot data from fifteen 40 m by 40 m sample plots established in Oban Division of Cross River National Park, Nigeria, between 23rd August 2019 and 9th September 2019. We have also included data summaries and RStudio codes used for analysis and generating results for the manuscript entitled: "Impact of human foraging on tree diversity, composition and abundance in a tropical rainforest", submitted for publication as an original research article in Biotropica. All data and R code required to generate the results as shown in the manuscript have been included. Complete tree species and plot data can be accessed at https://forestplots.net/. Methods Fifteen permanent sample plots of 40 by 40 m dimension were established along an elevational gradient rising from about 100 m to 500m above sea level between 23rd August 2019 and 9th September 2019. Five plot clusters with three plots each were established in three areas of the forest: one cluster in Erukut, two in Aking and two in Osomba, making a total of 15 sample plots with a total area of 2.4 hectares. Due to the steep topography of Aking and Osomba locations, plots were established at closer horizontal intervals based on elevational differences of about 20 m. All trees with a minimum of 10 cm dbh in all 15 plots were measured and tagged with a unique number. The collected data included plot information (plot number and GPS coordinate of the four corners of plots using Garmin eTrex 10 Outdoor Handheld GPS Unit), tree dbh (diameter of a tree at 1.30 m from the base) using a diameter tape and LaserAce 1000 rangefinder for points of measurement too high for a diameter tape, species name, assigned tag number, and species group (edible and inedible species). Measurement of buttress trees, folk trees, leaning trees or trees on a slope was completed using the African Tropical Rainforest Observation Network (AfriTRON) protocol. Trees were identified to species level by a field taxonomist and in cases where there was uncertainty in the identification of species, tree locations were noted, and specimens were collected and stored in herbarium presses for further taxonomic work.