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2,390 results for “rainforests”
Figure 6 in The rediscovery of Pimelodella longipinnis (Borodin, 1927), an enigmatic Atlantic Rainforest catfish species from Southeastern Brazil (Siluriformes: Heptapteridae)
Figure 6. Subaquatic photograph at Rio Quilombo, a rocky bottom stream where part of P. longipinnis specimens were collected (MZUSP 116324). Photograph by Douglas Rey, December 2, 2014. Material observed
Figure 1 in The rediscovery of Pimelodella longipinnis (Borodin, 1927), an enigmatic Atlantic Rainforest catfish species from Southeastern Brazil (Siluriformes: Heptapteridae)
Figure 1. Left lateral (A) and dorsal (B) views of Pimelodella longipinnis (Borodin, 1927), AMNH 8642, holotype of Rhamdella longipinnis, 84.6 mm SL. Photo provided by AMNH staff.
Figure 2 in The rediscovery of Pimelodella longipinnis (Borodin, 1927), an enigmatic Atlantic Rainforest catfish species from Southeastern Brazil (Siluriformes: Heptapteridae)
Figure 2. Ventral view of left pectoral-fin spine of Pimelodella longipinnis, AMNH 8642, holotype of Rhamdella longipinnis, total length of spine 11.5 mm.
Raster-based dataset for spatio-temporal analysis of forest fires in the Amazon rainforest from 2001 to 2020
<p>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. </p>
Supplementary figures and data for "Complete phylogeny of Micrathena spiders suggests multiple dispersal events among Neotropical rainforests, islands, and landmasses, and indicates Andean orogeny promotes speciation"
<p>Supplementary figures and data for "Complete phylogeny of <em>Micrathena</em> spiders suggests multiple dispersal events among Neotropical rainforests, islands, and landmasses, and indicates Andean orogeny promotes speciation" by Ivan L. F. Magalhaes, Pedro H. Martins, Bárbara T. Faleiro, Teofânia H. D. A. Vidigal, Fabrício R. Santos, Leonardo S. Carvalho, Adalberto J. Santos, submitted to <em>Cladistics</em>. </p> <p>Supplementary Figures S1 – S15</p> <p>Supplementary data S16: Complete specimen data of specimens used as vouchers for morphological scorings in Excel spreadsheet format.<br>Supplementary data S17: Discrete character matrix with morphological scorings in Nexus format.<br>Supplementary data S18: Complete specimen data of specimens used as vouchers for DNA sequencing in Excel spreadsheet format.<br>Supplementary data S19: Alignment of cytochrome oxidase I sequences in Fasta format.<br>Supplementary data S20: Alignment of histone H3 sequences in Fasta format.<br>Supplementary data S21: Alignment of 28S ribosomal sequences in Fasta format.<br>Supplementary data S22: Alignment of internal transcribed spacer sequences in Fasta format.<br>Supplementary data S23: Alignment of 16S ribosomal sequences in Fasta format.<br>Supplementary data S24: Zipped folder containing the inputs for estimating the phylogeny using morphological data in IQ-Tree: matrix in nexus format, partition file, and command file in plain text.<br>Supplementary data S25: Zipped folder containing the inputs for estimating the phylogeny using sequence data aligned with Muscle in IQ-Tree: matrix in nexus format, partition file, and command file in plain text.<br>Supplementary data S26: Zipped folder containing the inputs for estimating the phylogeny using sequence data aligned with MAFFT in IQ-Tree: matrix in nexus format, partition file, and command file in plain text.<br>Supplementary data S27: Zipped folder containing the inputs for estimating the phylogeny using morphological and sequence data aligned with MUSCLE in IQ-Tree: matrix in nexus format, partition file, and command file in plain text.<br>Supplementary data S28: Input Nexus for estimating the phylogeny using morphological data in MrBayes.<br>Supplementary data S29: Input Nexus for estimating the phylogeny using sequence data aligned with Muscle in MrBayes.<br>Supplementary data S30: Input Nexus for estimating the phylogeny using sequence data aligned with MAFFT in MrBayes.<br>Supplementary data S31: Input Nexus for estimating the phylogeny using morphological and sequence data aligned with Muscle in MrBayes.<br>Supplementary data S32: Input XML for estimating the dated phylogeny under Bayesian inference in BEAST.<br>Supplementary data S33: Distribution maps for all Micrathena species included in this study, the database with the original records and their source, and the R script used to generate the maps.<br>Supplementary data S34: Zipped folder containing all the inputs for estimating ancestral ranges and performing biogeographic stochastic mapping in R. This contains (0) the scripts for performing model selection and estimating ancestral states over a distribution of posterior trees, followed by stochastic mapping; (1) the geography and tree files (MMCT, and 100 trees randomly drawn from the posterior distribution); (2) the dispersal matrix and (3) the files for performing the time-stratified analysis to test the influence of the closure of the Isthmus of Panama on Micrathena dispersal.<br>Supplementary data S35: R script for generating the in-situ-speciation-through-time graphs from the results of biogeographic stochastic maps, as seen in Fig. 5.<br>Supplementary data S36: R script for getting and plotting elevations from individual records of Micrathena.<br>Supplementary data S37: R script for performing the GeoHiSSE estimation of net diversification rates among areas.<br>Supplementary data S38: Zipped folder containing the optimal trees found as a result of analysing inputs S24–S32 (IQ-Tree, MrBayes and BEAST).<br>Supplementary data S39: Zipped folder containing the ancestral range estimation results under competing models in BioGeoBEARS.<br>Supplementary data S40: Excel spreadsheet with parameter values of competing biogeographic models in BioGeoBEARS.<br>Supplementary data S41: Excel spreadsheet with results of biogeographic stochastic mapping over 100 different trees from the posterior distribution; includes all Micrathena species.<br>Supplementary data S42: Excel spreadsheet with results of biogeographic stochastic mapping over 100 different trees from the posterior distribution; includes only Micrathena species which had their DNA sequenced.<br>Supplementary data S43: Excel spreadsheet with the geography matrix, indicating which Micrathena species may be found in each area, and indicating the proportion of species from each area that are missing from the incomplete dataset (i.e., excluding species with unavailable DNA sequences).<br>Supplementary data S44: Excel spreadsheet with parameter values of competing biogeographic models estimated in GeoHiSSE.</p>
Data from: The distribution of tree biomass carbon within the pacific coastal temperate rainforest, a disproportionally carbon dense forest
<p>Spatially explicit global estimates of forest carbon storage are typically coarsely scaled. While useful, these estimates do not account for the variability and distribution of carbon at management scales. We asked how climate, topography, and disturbance regimes interact across and within geopolitical boundaries to influence tree biomass carbon, using the perhumid region of the Pacific Coastal Temperate Rainforest, an infrequently disturbed carbon dense landscape, as a test case. We leveraged permanent sample plots in southeast Alaska and coastal British Columbia and used multiple quantile regression forests and generalized linear models to estimate tree biomass carbon stocks and the effects of topography, climate, and disturbance regimes. We estimate tree biomass carbon stocks are either 211 (SD = 163) Mg C ha<sup>-1</sup> or 218 (SD = 169) Mg C ha<sup>-1</sup>. Natural disturbance regimes had no correlation with tree biomass but logging decreased tree biomass carbon and the effect diminished with increasing time since logging. Despite accounting for 0.3% of global forest area, this forest stores between 0.63% - 1.07% of global aboveground forest carbon as aboveground live tree biomass. The disparate impact of logging and natural disturbance regimes on tree biomass carbon suggests a mismatch between current forest management and disturbance history.</p>
Data from: fire in the rainforest: a 3,200-year history of fire in a West Kalimantan, Indonesia tropical rainforest
<p>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.</p>
Fig. 11 in Ecological interactions between arthropods and small vertebrates in a lowland Amazon rainforest
Fig. 11. Juvenile Pamphobeteus sp. infested by fly larvae. Photo by Emanuele Biggi.
Data from: Structure and composition of a canopy-beetle community (Coleoptera) in a Neotropical lowland rainforest in southern Venezuela
<p>Species richness, community structure, and taxonomic composition are important characteristics of biodiversity. Beetle communities show distinct diversity patterns according to habitat attributes. Tropical rainforest canopies, which are well known for their richness in Coleoptera, represent such a conspicuous life zone. Here, I describe a canopy-inhabiting beetle community associated with 23 tree species in a Neotropical lowland rainforest. Adult beetles were sampled manually and in aerial traps using a large tower crane for a cumulative year. The sample revealed 6738 adult beetles, which were assigned to 862 (morpho-)species in 45 families. The most species-rich beetle families were Curculionidae (<em>n</em> = 246), Chrysomelidae (<em>n</em> = 121), and Cerambycidae (<em>n</em> = 89). The most abundant families were Curculionidae (<em>n</em> = 2746) and Chrysomelidae (<em>n</em> = 1409). Dominant beetle families were found in most assemblages. The beetle community consisted of 400 singletons (46.4%). A similar proportion was evident for assemblages of single tree species. I found that 74.5% of all beetle species were restricted in their occurrence on host trees to the phenological season and time of the day. This daily and seasonal migration causes patterns similar to mass effects, and therefore accounts for the high proportion of singletons.</p>
Microhabitat selection of the big-headed turtle Platysternon megacephalum in the Hainan Tropical Rainforest National Park, China
<p>Understanding species habitat requirements is vital for ensuring the success of targeted conservation and habitat restoration measures. The big-headed turtle (<em>Platysternon megacephalum</em>)<em> </em>is a freshwater species which is distributed across Southeast Asia. Due to the human threats posed by illegal pet trade and overharvesting for food and medicinal purposes, the species has undergone rapid decline. Furthermore, limited research has been conducted on this species, particularly regarding habitat preferences on Hainan Island, China. Therefore, this study examined the microhabitat selection of <em>P. megacephalum</em> using cage and sample plot methods in the Diaoluo Mountain area of the Hainan Tropical Rainforest National Park. Our results indicated that big-headed turtles select stream microhabitats at higher altitudes, in proximity to rocky substrates, several caves, and high diversity of food sources. Microhabitat utilization did not differ significantly between adults and juveniles. This suggests that protecting microhabitats and main food sources is important for the conservation of <em>P. megacephalum</em>. Our findings provide a reference for the protection of this species in Jianfeng, Yingge Ridge, and other areas in the Hainan Tropical Rainforest National Park.</p>
Indicative distribution map for Ecosystem Functional Group T1.3 Tropical/Subtropical montane rainforests
<p>This archive contains indicative distribution maps and profiles for <strong>T1.3 Tropical/Subtropical montane rainforests</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
dataset for: Fire activity and drought increases ozone-plant damage to the Amazon rainforest.
<h3>Abstract</h3> <div> <p><strong>This dataset relates to work undertaken for publication 'Fire activity and drought increases ozone-plant damage to the Amazon rainforest.'</strong></p> <p><strong>Datasets and jupyter notebooks allow the figures and data to be reproduced.</strong></p> <p> </p> <p><strong>ABSTRACT: </strong>Human activity is exposing the Amazon rainforest to increasing stressors, including plant damage due to elevated ozone (O<sub>3</sub>) pollution. O<sub>3</sub>-plant damage reduces plant photosynthesis and the land carbon sink thus increasing atmospheric CO<sub>2</sub>. Factors that control O<sub>3</sub>-plant damage in the Amazon have not been characterised so damage may be exacerbated by recurrent extreme drought and fires. We identify drivers of interannual variability in O<sub>3</sub>-plant damage using a land surface model validated against satellite products. We find that O<sub>3</sub>-plant damage increases with fire activity, with additional interactive effects during major droughts. The indirect increase in atmospheric CO<sub>2</sub> from fire-driven O<sub>3</sub>-plant damage is ~50% the magnitude of direct CO<sub>2</sub> emissions from fires in non-drought years, suggesting the negative impact of fires on the Amazon carbon budget is severely underestimated. During droughts, leaves close their stomata, which reduces O<sub>3</sub> uptake and should protect against O<sub>3</sub> damage. However, due to higher fire activity, and elevated O<sub>3</sub> concentrations, O<sub>3</sub>-plant damage during droughts is up to 77 Tg-C (or over 2 times) greater than average. As droughts are set to become more frequent, our results demonstrate that high fire activity associated with drought increases the O<sub>3</sub>-plant damage risk to the rainforest, especially in remote areas.</p> </div>
Do logging roads impede small mammal movement in Borneo's tropical rainforests?
<b>Description: </b><p>Data from small mammal trap grid adjacent to roads and an associated movement/translocation experiment</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/37"><b>Do logging roads impede small mammal movement in Borneo's tropical rainforests?</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=113">here</a></p><p><b>Files: </b>This consists of 1 file: template_Heon.xlsx</p><p><b>template_Heon.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Road crossing field data</b> (described in worksheet RoadCrossing)</p><p>Description: Data from trap grid plus translocation experiment; NB: Paper accompanying this dataset used data from Site1-7 only; no translocations were conducted at Site8 so this was not used in the paper.</p><p>Number of fields: 23</p><p>Number of data rows: 3456</p><p>Fields: </p><ul><li><b>Site</b>: Site code (sites not georeferenced) (Field type: Location)</li><li><b>Treatment</b>: Experimental treatment trap was assigned to (Field type: Categorical)</li><li><b>Road.width</b>: Width of the road adjacent to trap grid (Field type: Numeric)</li><li><b>Date</b>: Date trap was set (Field type: Date)</li><li><b>Trap.Night</b>: Number of nights trap had been set at this site for (Field type: Numeric)</li><li><b>Trap.Number</b>: For field reference on distance and location of each trap. (Field type: ID)</li><li><b>Set.distance</b>: Distance from the road's edge or the start point in control sites. (Field type: Numeric)</li><li><b>Rat.Present</b>: Trap status (Field type: Categorical)</li><li><b>Distance.fromroad</b>: distance of the trap site from a road (Field type: Numeric)</li><li><b>Trap.Status</b>: Basic information about captured individuals (Field type: Categorical)</li><li><b>Species</b>: Species identity (Field type: Taxa)</li><li><b>Weight</b>: Body mass (Field type: Numeric)</li><li><b>Hind.foot</b>: Length of hind foot (Field type: Numeric)</li><li><b>Ear</b>: Length of ear (Field type: Numeric)</li><li><b>AGD</b>: Anal-genital distance (Field type: Numeric)</li><li><b>Sex</b>: Sex of the individual (Field type: Categorical)</li><li><b>Age</b>: Age of the individual (Field type: Categorical)</li><li><b>Release.distance</b>: Distance on the trap grid that the rat was released from (Field type: Numeric)</li><li><b>Returned.translocation</b>: Was the rat recaptured after translocation? (Field type: Categorical)</li><li><b>Recaptured.trap</b>: Trap ID code (Field type: ID)</li><li><b>Recaptured.Date</b>: Date rat was recaptured on trap grid after translocation (Field type: Date)</li><li><b>Recaptured.Distance</b>: Distance on the trap grid where the rat was re-captured (returned) from translocation (Field type: Numeric)</li><li><b>Recaptured.Day</b>: Day the rat is re-captured /return from translocation to trap grid (Field type: Numeric)</li></ul></li></ol><p><b>Date range: </b>2016-04-19 to 2016-07-20</p><p><b>Latitudinal extent: </b>4.6893 to 4.7472</p><p><b>Longitudinal extent: </b>117.5817 to 117.6158</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Chordata<br> -  - Mammalia<br> -  -  - Rodentia<br> -  -  -  - Muridae<br> -  -  -  -  - <i>Chrotomys</i><br> -  -  -  -  -  - <i>Chrotomys whiteheadi</i><br> -  -  -  -  - <i>Leopoldamys</i><br> -  -  -  -  -  - <i>Leopoldamys sabanus</i><br> -  -  -  -  - <i>Maxomys</i><br> -  -  -  -  -  - <i>Maxomys baeodon</i><br> -  -  -  -  -  - <i>Maxomys rajah</i><br> -  -  -  -  -  - <i>Maxomys surifer</i><br> -  -  -  -  - <i>Niviventer</i><br> -  -  -  -  -  - <i>Niviventer cremoriventer</i><br> -  -  -  -  - <i>Rattus</i><br> -  -  -  -  -  - <i>Rattus exulans</i><br> -  -  -  -  - <i>Sundamys</i><br> -  -  -  -  -  - <i>Sundamys muelleri</i><br></div><p></p>
Movement patterns of invertebrates in tropical rainforest
<b>Description: </b><p>Community Data for directional malaise trapping across and along rivers from around the SAFE landscape</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/20"><b>Movement patterns of invertebrates in tropical rainforest</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=266">here</a></p><p><b>Files: </b>This consists of 1 file: Invert_Movement_Dataset_JW.xlsx</p><p><b>Invert_Movement_Dataset_JW.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Community Data for directional malaise trapping</b> (described in worksheet DF)</p><p>Description: Community Data for directional malaise trapping above rivers</p><p>Number of fields: 60</p><p>Number of data rows: 82</p><p>Fields: </p><ul><li><b>SAFESiteCode</b>: location code (Field type: Location)</li><li><b>Orientation</b>: trap orientation (Field type: Categorical)</li><li><b>Date_Out</b>: trap set date (Field type: Date)</li><li><b>Date_In</b>: trap collection date (Field type: Date)</li><li><b>Width_River_Along</b>: width of river at along trap (Field type: Numeric)</li><li><b>Width_River_Across</b>: width of river at across trap (Field type: Numeric)</li><li><b>Width_River_Middle</b>: width of river at the midpoint between the traps (Field type: Numeric)</li><li><b>Width_River_Average</b>: average river width (of three previous columns) (Field type: Numeric)</li><li><b>Height_Along</b>: height of across trap above the river (Field type: Numeric)</li><li><b>Height_Across</b>: height of along trap above the river (Field type: Numeric)</li><li><b>Rainfall</b>: rainfall collected (Field type: Numeric)</li><li><b>Forest_Type</b>: Surrounding forest type (Field type: Categorical)</li><li><b>Canopy_Across_L</b>: canopy cover (% from densiometer) on the left side (facing upstream) of the across trap (Field type: Numeric)</li><li><b>Canopy_Across_R</b>: canopy cover (% from densiometer) on the right side (facing upstream) of the across trap (Field type: Numeric)</li><li><b>Canopy_Across_LR</b>: average canopy cover (% from densiometer) on the left and right sides (facing upstream) of the across trap (Field type: Numeric)</li><li><b>Canopy_Across_River</b>: canopy cover (% from densiometer) above the river at the across trap (Field type: Numeric)</li><li><b>Canopy_Along_L</b>: canopy cover (% from densiometer) on the left side (facing upstream) of the along trap (Field type: Numeric)</li><li><b>Canopy_Along_R</b>: canopy cover (% from densiometer) on the right side (facing upstream) of the along trap (Field type: Numeric)</li><li><b>Canopy_Along_LR</b>: average canopy cover (% from densiometer) on the left and right sides (facing upstream) of the along trap (Field type: Numeric)</li><li><b>Canopy_Along_River</b>: canopy cover (% from densiometer) above the river at the along trap (Field type: Numeric)</li><li><b>Canopy_Middle_L</b>: canopy cover (% from densiometer) on the left side (facing upstream) of the midpoint between traps (Field type: Numeric)</li><li><b>Canopy_Middle_R</b>: canopy cover (% from densiometer) on the right side (facing upstream) of the midpoint between traps (Field type: Numeric)</li><li><b>Canopy_Middle_LR</b>: average canopy cover (% from densiometer) on the left and right sides (facing upstream) of the midpoint between traps (Field type: Numeric)</li><li><b>Canopy_Middle_River</b>: canopy cover (% from densiometer) above the river at the midpoint between traps (Field type: Numeric)</li><li><b>Canopy_Average_L</b>: left bank canopycover average (% from densiometer) (Field type: Numeric)</li><li><b>Canopy_Average_R</b>: right bank canopycover average (% from densiometer) (Field type: Numeric)</li><li><b>Canopy_Average_LR</b>: both banks canopycover average (% from densiometer) (Field type: Numeric)</li><li><b>Canopy_Average_River</b>: river canopycover average (% from densiometer) (Field type: Numeric)</li><li><b>Basal_Area_Across_L</b>: across trap, left bank (looking upriver), tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Basal_Area_Across_R</b>: across trap, right bank (looking upriver), tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Basal_Area_Across</b>: across trap, average of both banks, tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Basal_Area_Along_L</b>: along trap, left bank (looking upriver), tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Basal_Area_Along_R</b>: along trap, right bank (looking upriver), tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Basal_Area_Along</b>: along trap, average of both banks, tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Basal_Area_Middle_L</b>: midpoint, left bank (looking upriver), tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Basal_Area_Middle_R</b>: midpoint, right bank (looking upriver), tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Basal_Area_Middle</b>: midpoint,both banks, tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Basal_Area</b>: average tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Eph_Ab</b>: Ephemeroptera (Field type: Abundance)</li><li><b>Odo_Ab</b>: Odonata (Field type: Abundance)</li><li><b>Ple_Ab</b>: Plecoptera (Field type: Abundance)</li><li><b>Ort_Ab</b>: Orthoptera (Field type: Abundance)</li><li><b>Der_Ab</b>: Dermaptera (Field type: Abundance)</li><li><b>Dic_Ab</b>: Blattodea (Field type: Abundance)</li><li><b>Iso_Ab</b>: Isoptera (Field type: Abundance)</li><li><b>Hem_Ab</b>: Hemiptera (Field type: Abundance)</li><li><b>Thy_Ab</b>: Thysanoptera (Field type: Abundance)</li><li><b>Col_Ab</b>: Coleoptera (Field type: Abundance)</li><li><b>Mec_Ab</b>: Mecoptera (Field type: Abundance)</li><li><b>Dip_Ab</b>: Diptera (Field type: Abundance)</li><li><b>Lep_Ab</b>: Lepidoptera (Field type: Abundance)</li><li><b>Tri_Ab</b>: Trichoptera (Field type: Abundance)</li><li><b>Hym_Ab</b>: Hymenoptera (Field type: Abundance)</li><li><b>Uni_Ab</b>: Unidentified individuals (Field type: Numeric)</li><li><b>Abundance</b>: Total Abundance (Field type: Numeric)</li><li><b>AbundanceUni</b>: Total Abundance without unidentified individuals (Field type: Numeric)</li><li><b>Staph_Ab</b>: subset of coleoptera that were staphylinids (Field type: Abundance)</li><li><b>Richness</b>: species richness (Field type: Numeric)</li><li><b>Mass</b>: total mass (Field type: Numeric)</li><li><b>Mass_Outlier</b>: total mass without individuals weighing over 0.25g (Field type: Numeric)</li></ul></li></ol><p><b>Date range: </b>2015-05-18 to 2015-07-14</p><p><b>Latitudinal extent: </b>4.6358 to 4.7342</p><p><b>Longitudinal extent: </b>117.4576 to 117.6411</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Arthropoda<br> -  - Insecta<br> -  -  - Blattodea<br> -  -  - Coleoptera<br> -  -  -  - Staphylinidae<br> -  -  - Dermaptera<br> -  -  - Diptera<br> -  -  - Ephemeroptera<br> -  -  - Hemiptera<br> -  -  - Hymenoptera<br> -  -  - Isoptera<br> -  -  - Lepidoptera<br> -  -  - Mecoptera<br> -  -  - Odonata<br> -  -  - Orthoptera<br> -  -  - Plecoptera<br> -  -  - Thysanoptera<br> -  -  - Trichoptera<br></div><p></p>
Amazon Rainforest dataset for semantic segmentation
<p>This database contains images used for the semantic segmentation of forest and non-forest areas from a fully convolutional neural network U-Net .</p> <p>1. <strong>Training dataset: </strong>it contains 30 GeoTIFF images with 512x512 pixels and associated PNG masks (forest indicated in white and non-forest in black color).</p> <p>2. <strong>Validation dataset</strong>: it contains 15 GeoTIFF images with 512x512 pixels and associated PNG masks used for U-Net validation step.</p> <p>3. <strong>Test dataset: </strong>it contains 15 GeoTIFF images 512x512 pixels for testing.</p>
Persistent effects of fragmentation on tropical rainforest canopy structure after 20 years of isolation
<p>Data of Ecological Application paper "Persistent effects of fragmentation on tropical rainforest canopy structure after 20 years of isolation"</p>
Logging of rainforest and conversion to oil palm reduces bioturbator diversity but not levels of bioturbation.
<p>This dataset contains data about bioturbation performed by different soil animals in three types of differently degraded habitats in Sabah, Borneo. This include not only levels of bioturbation, but also data about the numbers, growth and turnover of termite mounds plus litter depth for each sampling point. </p>
Fig. 2 in Metapolybia araujoi, a new species of swarming social wasp from the Brazilian Amazon rainforest (Vespidae: Polistinae)
Fig. 2. Labels found attached to the specimen.
Figure 1 in Lajonquierea koshkini sp. nov. from lowland rainforests of northern Borneo, Malaysia (Lepidoptera: Lasiocampidae)
Figure 1. Lajonquierea koshkini sp. nov. from Borneo: A) Holotype male; B) Male genitalia.
Population Dynamics of a Subpopulation of Lion-tailed Macaques in a Rainforest Fragment, in the southern Western Ghats of India
<p>The dataset includes absolute group counts of five groups of lion-tailed macaques inhabiting the Puthuthottam forest fragment of Valparai, Tamil Nadu, India. The counts were conducted over a period of three years between 2018-2020 at six-monthly intervals. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.