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113 results for “rainforest trees”
Data from: Fine partitioning of epiphyte habitat within Johansson zones in tropical Australian rainforest trees
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Data from: Genetic differentiation in spite of high gene flow in the dominant rainforest tree of southeastern Australia, Nothofagus cunninghamii
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Diversification history of clown tree frogs in Neotropical rainforests (Anura, Hylidae, Dendropsophus leucophyllatus group)
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Landscape structure shapes the diversity of tree seedlings at multiple spatial scales in a fragmented tropical rainforest
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Data from: Next-Gen phylogeography of rainforest trees: measuring landscape-level cpDNA variation from whole-genome sequencing.
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Data from: Dry-season decline in tree sapflux is correlated with leaf turgor loss point in a tropical rainforest
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Data from: Carbon flux and forest dynamics: increased deadwood decomposition in tropical rainforest tree-fall gaps
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Seed fates of four rainforest tree species in the fragmented forests of Anamalais in the southern Western Ghats, India
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Post-disturbance conifer tree-ring δ15N reflects openness of the nitrogen cycle across temperate coastal rainforests
<p>1. Post-disturbance losses in nitrogen (N) may diminish forest productivity, and soils with inherently 'open' N cycles (high nitrification rates) are considered the most vulnerable to leaching losses of NO<sub>3</sub><sup>-</sup>. Monitoring ongoing N depletion from soil profiles is challenging, but tree-ring δ<sup>15</sup>N of regenerating stands may offer an effective method for assessing site-specific, long-term soil N dynamics. Evidence to date is mixed, however, and includes increasing, unchanging, or decreasing tree-ring δ<sup>15</sup>N in young stands following stand-level disturbances, possibly because of contrasting soil fertility among study sites. In addition, a consensus on post-disturbance N trajectories is hampered by the sometimes inconsistent patterns in tree-ring δ<sup>15</sup>N found between tree species of differing mycorrhizal association.</p> <p><a name="_Hlk38376288">2. We compared tree-ring </a>δ<sup>15</sup>N of two conifer species (<i>Picea sitchensis</i> with ectomycorrhizal fungi and <i>Thuja plicata</i> with arbuscular mycorrhiza) from a replicated silviculture trial across temperate rainforests of Vancouver Island (Canada). A natural gradient in soil fertility across the six sites, driven largely by topography and parent materials, was confirmed by an <i>in situ</i> increase in N mineralization and nitrification rates with declining C:N ratios for both organic horizons and mineral soils. </p> <p>3. Five decades after logging, the overall trend in tree-ring δ<sup>15</sup>N was positive, but among individual plots there was a wide range in δ<sup>15</sup>N slopes, ranging from nearly 0 to 0.13. We found the gains in tree-ring δ<sup>15</sup>N over time were consistent between mycorrhizal types and escalated sharply (up to 6‰) with increasing N mineralization rates, although less so on flat terrain with seasonal water tables. The most recent sapwood was also enriched in <sup>15</sup>N as soil N mineralization rates increased, perhaps slightly more so for <i>T</i>. <i>plicata</i> than <i>P</i>. <i>sitchensis</i>. </p> <p>4. <i>Synthesis</i>. The correspondence of tree-ring δ<sup>15</sup>N with soil fertility may be especially strong in regenerating forests because of tree ontogeny effects, including the expansion of rooting depth and differences in N resorption efficiency with stand age. <a name="_Hlk37683599">Sharp</a> increases in tree-ring d<sup>15</sup>N underscore the vulnerability of low C:N soils with open N cycles to post-disturbance N losses, and highlight how repeated, frequent harvest cycles may risk substantial N depletion from these productive rainforest ecosystems.</p>
Terrestrial lidar data collected from four large tropical rainforest trees in Floresta Nacional de Caxiuanã
<p>Title<br> -----</p> <p>Terrestrial lidar data collected from four large tropical rainforest trees in Floresta Nacional de Caxiuanã</p> <p>Authors<br> -------</p> <p>A. Burt<br> M. Boni Vicari<br> A. C. L. da Costa<br> I. Coughlin<br> P. Meir<br> L. Rowland<br> M. Disney</p> <p>Contact<br> -------</p> <p>a.burt@ucl.ac.uk</p> <p>License<br> -------</p> <p>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.</p> <p>Overview<br> --------</p> <p>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.<br> This dataset includes: i) raw lidar data, ii) tree-level point clouds, and iii) quantitative structural models.<br> 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’.</p> <p>Acquisition<br> -----------</p> <p>Neighbouring vegetation surrounding each tree was removed before data collection.<br> Lidar data were acquired using a RIEGL VZ-400 terrestrial laser scanner.<br> A minimum of 16 scans (upright and tilt) were collected from 8 scan positions around each tree.<br> The angular step between sequentially fired pulses was 0.04 degrees, and the distance between scanner and tree varied.<br> This arrangement provided a 45 degree sampling arc around each tree, and a complete sample of the scene from each position.<br> The laser pulse has a wavelength of 1550nm, a beam divergence of 0.35mrad, and the diameter of the footprint at emission is 7mm.<br> 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. </p> <p>Processing<br> ----------</p> <p>i) Individual scans were registered onto a common coordinate system using RIEGL RiSCAN PRO (v2.7.0, http://riegl.com).<br> ii) Tree-level point clouds were extracted from the larger-area point cloud using treeseg (v0.2.0, https://github.com/apburt/treeseg).<br> iii) Points were classified as returns from wood or leaf material using TLSeparation (v1.2.1.5, https://github.com/TLSeparation).<br> iv) Points from buttresses were manually removed using CloudCompare (v2.10.3, https://cloudcompare.org).<br> 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). </p> <p>File and directory naming convention<br> ------------------------------------</p> <p>The four trees are identified: CAX-H_T1, CAX-H_T2, CAX-H_T3 and CAX-H_T4.<br> The various files and directories are described as follows: </p> <p>./CAXH-H/<br> ├───CAX-H_T1/ (Directory: tree-level directories)<br> ├───CAX-H_T2/<br> ├───CAX-H_T3/<br> ├───CAX-H_T4/<br> │ ├───2018-10-06.001.riproject/<br> │ │ ├───ScanPos001/ (Directory: individual scan directories containing raw lidar data and other auxiliary files; odd: upright, even: tilt)<br> │ │ ├───ScanPos.../<br> │ │ ├───ScanPos020/<br> │ │ │ ├───181006_194253.rxp (File: measurement data stream)<br> │ │ │ ├───181006_194253.mon.rxp (File: monitoring data stream) <br> │ │ ├───matrix/ (Directory: contains the registration matrices)<br> │ │ │ ├───001.dat<br> │ │ │ ├───....dat<br> │ │ │ ├───020.dat (File: 3x4 matrix used to rotate and translate scan 20 into the coordinate system of scan 1)<br> │ │ ├───clouds/ (Directory: contains tree-level point clouds)<br> │ │ │ ├───CAXH_T4.txt (File: point cloud of CAX-H_T4 as extracted by treeseg)<br> │ │ │ ├───CAXH_T4nb.txt (File: CAXH_T4.txt with buttress points manually removed using CloudCompare)<br> │ │ │ ├───CAXH_T4w.txt (File: CAXH_T4.txt with leafy returns removed using TLSeparation)<br> │ │ │ ├───CAXH_T4l.txt (File: CAXH_T4.txt with woody returns removed using TLSeparation)<br> │ │ │ ├───CAXH_T4wnb.txt (File: CAXH_T4.txt with buttress points manually removed using CloudCompare, and leafy returns removed using TLSeparation)<br> │ │ ├───models/ (Directory: contains quantitative structural models constructed from the tree-level point clouds)<br> │ │ │ ├───CAXH_T4.mat (File: quantitative structural model of CAXH_T4.txt)<br> │ │ │ ├───CAXH_T4nb.mat<br> │ │ │ ├───CAXH_T4w.mat<br> │ │ │ ├───CAXH_T4wnb.mat<br> │ │ │ ├───CAXH_T4.models.dat (File: reports the volume (m3) and standard deviation (m3) of the QSMs)<br> │ │ │ ├───intermediate/ (Directory: contains intermediate QSMs generated by optqsm)<br> │ │ │ │ ├───CAXH_T4/<br> │ │ │ │ ├───CAXH_T4nb/<br> │ │ │ │ ├───CAXH_T4w/<br> │ │ │ │ ├───CAXH_T4wnb/<br> │ │ │ │ │ ├───CAXH_T4wnb-1.mat<br> │ │ │ │ │ ├───CAXH_T4wnb-....mat<br> │ │ │ │ │ ├───CAXH_T4wnb-10.mat</p>
Strong genetic differentiation on a small geographic scale in the Neotropical rainforest understory tree Paypayrola blanchetiana (Violaceae)
Self-incompatible plants as well as common plant species are expected to be especially vulnerable to the deleterious effects of fragmentation on genetic diversity. Paypayrola blanchetiana (Violaceae) is a common, self-incompatible understory tree in the East Brazilian Atlantic forest. Its autochorous seed dispersal and occurrence in dense, well-separated clusters makes it an interesting model for studies of gene flow and genetic structuring on a small geographic scale. A previous study has found remarkably low fruit set and frequent seed abortions in this species in several populations situated in forest fragments. We tested the hypothesis that P. blanchetiana is affected by inbreeding and loss of genetic diversity in a fragmented landscape. Nine nuclear and three plastid microsatellite loci were genotyped for 285 individuals (149 adult trees and 136 saplings) across seven populations in five forest fragments in a 240 km2 sugarcane-rainforest matrix. We found a low to moderate genetic diversity in nuclear loci of P. blanchetiana, population structuring on a small geographical scale and high levels of inbreeding. Haplotype distributions confirmed that seed dispersal is very limited. There were, however, no signs for lower genetic diversity or higher inbreeding in populations situated in the smallest forest fragments. Furthermore, genetic diversity was not lower in the sapling cohort, which was created in post-fragmentation condition. Therefore, we may be witnessing the genetic consequences of this species biology, rather than immediate effects of fragmentation.
Genotyping by sequencing data of five legume tree species widespread in the rainforests of West and Central Africa
<p>Although today the forest cover is continuous in Central Africa this may have not always been the case, as the scarce fossil record in this region suggests that arid conditions might have significantly reduced tree density during the Ice Ages. Our aim was to investigate whether the dry ice-age periods left a genetic signature on tree species that can be used to infer the date of the past fragmentation of the rainforest. We sequenced reduced representation libraries of 182 samples representing five widespread Legume trees and seven outgroups. Phylogenetic analyses identified an early divergent lineage for all species in West Africa (Upper Guinea), and two clades in Central Africa: Lower Guinea-North and Lower Guinea-South. As the structure separating the Northern and Southern clades -congruent across species- cannot be explained by geographic barriers, we tested other hypotheses with demographic model testing using ∂a∂I. The best estimates indicate that the two clades split between the Upper Pliocene and the Pleistocene, a date compatible with forest fragmentation driven by ice-age climatic oscillations. Furthermore, we found remarkably older split dates for the shade-tolerant tree species with non-assisted seed dispersal than for light-demanding species with long-distance wind dispersal. Different recolonisation abilities after recurrent cycles of forest fragmentation seem to explain why species with long-distance dispersal show more recent genetic admixture between the two clades than species with limited seed dispersal. Despite their old history, our results depict the African rainforests as a dynamic biome where tree species have expanded relatively recently after the last glaciation.</p>
Data from: Trade-offs in juvenile growth potential vs. shade tolerance among subtropical rainforest trees on soils of contrasting fertility
Plant adaptation to gradients of light availability involves a well-studied functional trade-off, as does adaptation to gradients of nutrient availability. However, little is known about how these two major trade-offs interact, and thus, it remains unclear whether and how the nature of the growth–shade tolerance trade-off differs on soils of contrasting fertility. We asked whether juvenile growth–shade tolerance trade-offs differed in slope and elevation between tree assemblages on nutrient-rich basalt and nutrient-poor rhyolite soils in an Australian subtropical rain forest. We measured the growth of, and the range of light environments occupied by, juveniles (40–120 cm tall) of eight basalt specialists, six rhyolite specialists, and one generalist that was common on both substrates. In situ minimum light requirements were estimated from the 5th percentile of the distribution of naturally regenerated juveniles in relation to daily light transmittance. Stem growth was measured for 12–16 months across a wide range of light environments to estimate the light compensation point of growth of each species. Light compensation points of growth showed nearly a 1 : 1 correspondence with in situ minimum light requirements of species, indicating that whole-plant carbon balance is a key driver of ecological success in low light. Minimum light requirements were negatively correlated with relative growth rate in low light, but correlated positively with growth in high light. Soil type had no effect on either the slope or the elevation of this trade-off, all species aligning around a common growth–shade tolerance trade-off, but our results do show a wider range of growth rates and shade tolerance on the nutrient-rich basalt soil than on the nutrient-poor rhyolite. Our results suggest that adaptation to light availability involves fundamentally similar trade-offs on these two substrates of differing fertility. However, a wider range of growth rates and shade tolerance on the nutrient-rich basalt soil than on the nutrient-poor rhyolite may help to explain the higher species richness and greater structural complexity of forest stands on the former substrate.
Data from: Distribution models predict climate-related range alteration or extinction of eleven threatened tropical rainforest trees in the Western Ghats
<p>This dataset contains information related to species occurence data and species distribution modeling (SDM) analysisr of eleven threatened tree species. Occurrences are compiled from extensive field surveys in the Anamalai Hills along with data from the Global Biodiversity Information Facility (GBIF.org) and earlier work done within the southern Western Ghats, India.</p> <p>References:<br>Page, N. V., & Shanker, K. (2020). Climatic stability drives latitudinal trends in range size and richness of woody plants in the Western Ghats, India. PLOS ONE, 15(7), e0235733. https://doi.org/10.1371/journal.pone.0235733</p> <p>GBIF.org (2022) GBIF Occurrence Download, 2 August 2022. DOI:10.15468/dl.gnvuxj</p> <p><br>AUTHOR #1<br>1. Name: A.P. Madhavan<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Email address: madhavan@ncf-india.org<br>4. ORCID: https://orcid.org/0009-0009-2754-8256</p> <p>AUTHOR #2<br>1. Name: Kshama Bhat<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Email address: kshama@ncf-india.org<br>4. ORCID: ORCID: https://orcid.org/0000-0002-6190-2687</p> <p>AUTHOR #3<br>1. Name: Srinivasan Kasinathan<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Email address: srini@ncf-india.org<br>4. ORCID: https://orcid.org/0000-0001-7323-6653</p> <p>AUTHOR #4<br>1. Name: Divya Mudappa <br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Email address: divya@ncf-india.org <br>4. ORCID: https://orcid.org/0000-0001-9708-4826</p> <p>AUTHOR #5<br>1. Name: Navendu Page<br>2. Work Address: Wildlife Institute of India, Post Box No. 18, Chandrabani, Dehradun, Uttarakhand 248001, India<br>3. Email address: navendu.page@gmail.com<br>4. ORCID: ORCID: https://orcid.org/0000-0002-9413-7571</p> <p>AUTHOR #6<br>1. Name: T. R. Shankar Raman <br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Email address: trsr@ncf-india.org <br>4. ORCID: https://orcid.org/0000-0002-1347-3953</p> <p>Keywords: tropical rainforest, climate change, tree distributions, species distribution models, range shifts, Western Ghats</p> <p><br>Geographic Coverage:<br>1. Location/Study Area: Southern Western Ghats Montane Rain Forests, Southern Western Ghats Moist Deciduous Forests, India<br>2. GPS coordinates: SWG (73.95° – 80.33° E, 8.06° – 13.11°N) </p> <p>Temporal coverage<br>Starts: 2020-08-01<br>Ends: 2024-03-28</p> <p>Besides this README.txt file, the dataset includes three comma-delimited text files (csv); two R scripts, and 1 kml file of surveyed trails.</p> <p>CSV files with the data in columns as explained below:</p> <p>1) Focal_Tree_Dat.csv</p> <p>Comp: Number identifier<br>FT_ID: Unique tree no for each individual<br>Focal_tree: Scientific name of species<br>Date: Date of occurrence observation<br>Place: Area/locality description<br>Trail: Unique trail ID<br>Waypoint: Waypoint number <br>Time: Time in hh:mm format <br>Location: Specific description of occurrence locality <br>Latitude: Latitude in decimal degrees N <br>Longitude: Longitude in decimal degrees E <br>Elevation: Elevation in metres <br>Slope: Cateory of slope <br>ID_Notes: Notes on identification<br>Phenophase: Phenophase expression at the time of observation <br>GBH: Girth at breast height in centimetres (comma separated list of numbers in case of multi-stemmed trees) <br>Tree_ht: Tree height in metres<br>Canopy_ht: Maximimum height of the surrounding canopy in metres<br>Substrate: Soil substrate composition<br>Invasives: Name of invasive species (if present) <br>Stature: Vegetation strata position <br>Relatively: Stature of focal individual relative to other surrounding individuals <br>Deadwood: Description of deadwood on the tree <br>Damage: Description of damage on the bole <br>Shape: Description of tree canopy shape<br>Closure: Canopy closure at focal tree <br>Seedlings: Number of conspecific seedlings present in 5 m radius of focal tree <br>Saplings: Number of conspecific saplings present in 5 m radius of focal tree<br>Trees: Number of conspecific trees present in 5 m radius of focal tree<br>Remarks: Remarks </p> <p>2) Ffspecies.csv</p> <p>Source: Source of occurrence <br>ID: State/location of occurrence<br>Region: Biogeographic region of occurrence <br>decimalLatitude: Latitude in decimal degrees N<br>decimalLongitude: Longitude in decimal degrees E<br>species: Scientific name of species</p> <p>4) ft_surveys.csv</p> <p>Date: Date of survey of sample trail<br>Prot_type: Category indicating whether protected area or fragment <br>Place: Area/locality description<br>Route_description: Specific landmark description of trail<br>Trail: Unique trail ID <br>Trail_distance: Tracked distance of trail in km <br>Corrected_trail_distance: Corrected distance of trail in km<br>Track_filename_kml: File name of gps track<br>Sample_collected: Name of species if sample collected <br>Observers: Name of observers <br>Remarks: Remarks</p> <p>ANALYSES SCRIPTS<br>flexsdm_script.R<br>Script containing the analysis of all maxent distribution modeling and associated analysis</p> <p>Franklinia_density.Rmd<br>Script of density and abundance related analysis</p> <p> </p>
Verification of the accuracy of the recent 50 years of tree growth and long-term change in intrinsic water-use efficiency using xylem Δ14C and δ13C in trees in an aseasonal tropical rainforest
<p>Growth analysis based on tree-ring chronology is difficult in trees in aseasonal tropical rain forests, because annual growth rings may be unclear or completely absent. Fortunately, tree growth history recorded in xylem tissue is capable of providing valuable information on the responses of trees and forests to past and present environmental changes, including global warming.</p> <p>We have developed a new technique for aseasonal tropical forest trees which derives their growth rates from xylem Δ<sup>14</sup>C, and verified its accuracy. We also determined, from xylem δ<sup>13</sup>C, the intrinsic water-use efficiency (iWUE) in the past 50 years. We analyzed changes in xylem Δ<sup>14</sup>C and δ<sup>13</sup>C in 23 canopy trees of 12 species in 6 families growing in Pasoh Forest Reserve, Malaysia; each stem diameter at breast height (DBH) was recorded 14 times from 1969 to 2011.</p> <p>We found a significant positive relationship between the growth rates determined by <sup>14</sup>C dating and the past DBH data. On the other hand, leaf-internal CO<sub>2</sub> (C<sub>i</sub>) content did not change with increasing atmospheric CO<sub>2</sub> (C<sub>a</sub>). Thus, the iWUE increased significantly over the last 50 years in all the families and species tested.</p> <p>This study showed that the simultaneous measurements of xylem Δ<sup>14</sup>C and δ<sup>13</sup>C could reveal a long-term change in tree growth and iWUE during the past 50 years with high accuracy in various species and/or individuals in aseasonal tropical rainforests exhibiting high species diversity.</p>
Floral attraction and flower visitors of a subcanopy tropical rainforest tree, F. picrosperma_Data
<p>1. Flowering plants in tropical rainforests rely heavily on pollen vectors for successful reproduction. Research into pollination systems in tropical rainforests is dominated by canopy species, while subcanopy plant-pollinator interactions remain under-represented. The microclimate beneath the rainforest canopy is characterised by low light levels and is markedly different from the canopy environment that receives more light energy.</p> <p>2. We studied the floral attractants and floral visitors of a dioecious, subcanopy tree, Fontainea picrosperma (Euphorbiaceae) in the Wet Tropics bioregion of northern Queensland, Australia.</p> <p>3. We found that wind pollination is rare and male and female flowers do not produce nectar. Female flowers are likely pollinated due to their perceptual similarity to pollen-offering male flowers. Female flowers had the same scent profile as male flowers and floral scent was an important floral attractant that acted to regulate pollinator behaviour. The two most abundant scent compounds present in the floral bouquet were benzyl alcohol and 4-oxoisophorone. These compounds are ubiquitous in nature and are known to attract a wide variety of insects. Both day-time and night-time pollinators contributed to successful pollen deposition on the stigma and diurnal flower visitors were identified from several orders of insects including beetles, flies, predatory wasps and thrips. Fontainea picrosperma is therefore likely to be pollinated by a diverse array of small insects.</p> <p>4. Synthesis. Our data indicates that F. picrosperma has a generalist, entomophilous pollination syndrome. The rainforest subcanopy is a distinctive environment characterised by low light levels, low or turbulent wind speeds and relatively high humidity. Female flowers of F. picrosperma exhibit cost saving strategies by not producing nectar and mimicking the smell of reward-offering male flowers. Insects opportunistically forage on, or inhabit flowers and pollination occurs from a pool of small insects with low-energy requirements that are found beneath the rainforest canopy.</p>
FIGURE 25 in Oecanthus buxixu sp. nov. (Orthoptera: Grylloidea: Oecanthidae): A new species of tree cricket from Brazilian Amazon rainforest
FIGURE 25. Map of Oecanthus buxixu sp. nov. species geographical localization.
Data from: Host associations and beta diversity of fungal endophyte communities in New Guinea rainforest trees
Processes shaping the distribution of foliar fungal endophyte species remain poorly understood. Despite increasing evidence that these cryptic fungal symbionts of plants mediate interactions with pathogens and herbivores, there remain basic questions regarding the extent to which dispersal limitation and host specificity might shape fungal endophyte community composition in rainforests. To assess the relative importance of spatial pattern and host specificity, we isolated fungi from a sample of mapped trees in lowland Papua New Guinea. Sequences of the internal transcribed spacer (ITS) region were obtained for 2,079 fungal endophytes from three sites and clustered into molecular operational taxonomic units (MOTUs) at 95% similarity. Multivariate analyses suggest that host affinity plays a significant role in structuring endophyte community composition whereas there was no evidence of endophyte spatial pattern at the scale of tens to hundreds of meters. Differences in endophyte communities between sampled trees were weakly correlated with variation in foliar traits but not with tree species relatedness. The dominance of relatively few generalist endophytes and the presence of a large number of rare MOTUs was a consistent observation at three sites separated by hundreds of kilometers and regional turnover was low. Host specificity appears to play a relatively weak but more important role than dispersal limitation in shaping the distribution of fungal endophyte communities in New Guinea forests. Our results suggest that in the absence of strong ecological gradients and host turnover, beta diversity of endophyte communities could be low in large areas of contiguous forest.
Nest microhabitats and tree size mediate shifts in ant community structure across elevation in tropical rainforest canopies
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Floral attraction and flower visitors of a subcanopy tropical rainforest tree, F. picrosperma_Data
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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
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International Brain Laboratory public data
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OpenNeuro
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