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411 results for “Tropical rainforests”
Data from: Edge effects on components of diversity and above-ground biomass in a tropical rainforest
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Data from: Tropical nematode diversity: vertical stratification of nematode communities in a Costa Rican humid lowland rainforest
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Data from: Secondary succession has surprisingly low impact on arboreal ant communities in tropical montane rainforest
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Data from: Functional groups, species, and light interact with nutrient limitation during tropical rainforest sapling bottleneck
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Data from: Carbon flux and forest dynamics: increased deadwood decomposition in tropical rainforest tree-fall gaps
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Elevational contrast in predation and parasitism risk to caterpillars in a tropical rainforest
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Data from: Effect of distance to edge and edge interaction on seedling regeneration and biotic damage in tropical rainforest fragments: a long‐term experiment
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Data from: Nutrient scarcity strengthens soil fauna control over leaf litter decomposition in tropical rainforests
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Data from: Ants as ecological indicators of rainforest restoration: community convergence and the development of an Ant Forest Indicator Index in the Australian wet tropics
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Defaunated and invaded insular tropical rainforests will not recover alone: recruitment limitation factors disentangled by hierarchical models of spontaneous and assisted regeneration
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Data from: Persistent anthrax as a major driver of wildlife mortality in a tropical rainforest
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Data from: Drivers of tropical rainforest composition and alpha diversity patterns over a 2,520 m altitudinal gradient
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Arboreality drives heat tolerance while elevation drives cold tolerance in tropical rainforest ants
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Impacts of tropical rainforest disturbance on mammalian parasitism rates
<b>Description: </b><p>Records of parasite and parasite egg counts from small mammal faecal samples</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/19"><b>Impacts of tropical rainforest disturbance on mammalian parasitism rates</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=3973691">here</a></p><p><b>Files: </b>This dataset consists of 2 files: Template_Ladds.xlsx, ParasitePhotos.zip</p><p><b>Template_Ladds.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Small mammal parasite loads</b> (described in worksheet Data)</p><p>Description: Records of parasite identity and egg loads recorded from small mammal faecal samples</p><p>Number of fields: 17</p><p>Number of data rows: 276</p><p>Fields: </p><ul><li><b>Microscopy_date</b>: Date microscopy work was conducted (Field type: date)</li><li><b>Microscopist</b>: Name of microscopist (Field type: id)</li><li><b>Sample_date</b>: Date faecal sample was collected (Field type: date)</li><li><b>Sample_Number</b>: Unique reference code for specimen (Field type: id)</li><li><b>Point</b>: Trap location in SAFE gazeteer (Field type: location)</li><li><b>Grid</b>: Trapping grid in which trap was located (Field type: replicate)</li><li><b>Trap_num</b>: Unique trap code (Field type: replicate)</li><li><b>host</b>: Species of small mammal from which faecal sample was collected (Field type: taxa)</li><li><b>AnimalID</b>: PIT tag code for the small mammal (Field type: id)</li><li><b>Anyparasites</b>: Parasites detected? (Field type: categorical)</li><li><b>parasite</b>: Identity of parasite (Field type: taxa)</li><li><b>Parasitecount</b>: Number of parasites detected (Field type: numeric interaction)</li><li><b>Countingscale</b>: How much of the slide was sampled for counting? (Field type: categorical)</li><li><b>Picturetaken</b>: Picture taken of the parasite? (Field type: categorical)</li><li><b>Pictureref</b>: Was a photograph taken of the parasite? Images can be found in the zip file associated with this dataset. (Field type: comments)</li><li><b>Sample_Weight</b>: Weight of the faecal sample (Field type: numeric)</li><li><b>Amountused</b>: Confirmation that the entire faecal sample was used (Field type: comments)</li></ul></li></ol><p><b>ParasitePhotos.zip</b></p><p>Description: Photographs of parasites</p><p><b>Date range: </b>2015-04-16 to 2015-07-10</p><p><b>Latitudinal extent: </b>4.6430 to 4.7519</p><p><b>Longitudinal extent: </b>116.9650 to 117.5898</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> -  -  Nematoda <br> -  -  -  [nematode1] <br> -  -  -  [nematode2] <br> -  -  -  [nematode3] <br> -  -  -  [nematode4] <br> -  -  -  [nematode5] <br> -  -  -  [nematode6] <br> -  -  -  [nematode7] <br> -  -  -  [nematode8] <br> -  -  -  Secernentea <br> -  -  -  -  Rhabditida <br> -  -  -  -  -  [oxyurid A] <br> -  -  -  -  -  Heteroxynematidae <br> -  -  -  -  -  -  <i>Aspiculuris</i> <br> -  -  -  -  -  -  -  [aspiculuris] <br> -  -  -  -  Spirurida <br> -  -  -  -  -  Spiruridae <br> -  -  -  -  -  -  [spirurid A] <br> -  -  -  -  -  -  [spirurid B] <br> -  -  -  -  Ascaridida <br> -  -  -  -  -  Oxyuridae <br> -  -  -  -  -  -  <i>Syphacia</i> <br> -  -  -  -  -  -  -  [syphacia1] <br> -  -  -  Adenophorea <br> -  -  -  -  Trichocephalida <br> -  -  -  -  -  Capillariidae <br> -  -  -  -  -  -  <i>Capillaria</i> <br> -  -  -  -  -  -  -  [capillaria1] <br> -  -  -  -  -  -  -  [capillaria2] <br> -  -  -  -  -  -  -  [capillaria3] <br> -  -  -  -  -  -  -  [extra long capillaria] <br> -  -  -  -  -  Trichinellidae <br> -  -  -  -  -  -  <i>Trichuris</i> <br> -  -  -  -  -  -  -  [Trichuris] <br> -  -  Chordata <br> -  -  -  Mammalia <br> -  -  -  -  Rodentia <br> -  -  -  -  -  Muridae <br> -  -  -  -  -  -  <i>Maxomys</i> <br> -  -  -  -  -  -  -  <i>Maxomys rajah</i> <br> -  -  -  -  -  -  -  <i>Maxomys surifer</i> <br> -  -  -  -  -  -  -  <i>Maxomys baeodon</i> <br> -  -  -  -  -  -  <i>Rattus</i> <br> -  -  -  -  -  -  -  <i>Rattus tiomanicus</i> <br> -  -  -  -  -  -  -  <i>Rattus exulans</i> <br> -  -  -  -  -  -  <i>Niviventer</i> <br> -  -  -  -  -  -  -  <i>Niviventer cremoriventer</i> <br> -  -  -  -  -  -  <i>Sundamys</i> <br> -  -  -  -  -  -  -  <i>Sundamys muelleri</i> <br> -  -  -  -  -  -  <i>Chrotomys</i> <br> -  -  -  -  -  -  -  <i>Chrotomys whiteheadi</i> (as homotypic_synonym: <i>Maxomys whiteheadi</i>)<br> -  -  -  -  -  -  <i>Leopoldamys</i> <br> -  -  -  -  -  -  -  <i>Leopoldamys sabanus</i> <br> -  -  -  -  -  Sciuridae <br> -  -  -  -  -  -  <i>Callosciurus</i> <br> -  -  -  -  -  -  -  <i>Callosciurus adamsi</i> <br> -  -  -  -  -  -  <i>Sundasciurus</i> <br> -  -  -  -  -  -  -  <i>Sundasciurus lowii</i> <br> -  -  -  -  Scandentia <br> -  -  -  -  -  Tupaiidae <br> -  -  -  -  -  -  <i>Tupaia</i> <br> -  -  -  -  -  -  -  <i>Tupaia longipes</i> <br> -  -  -  -  -  -  -  <i>Tupaia tana</i> <br> -  -  -  -  -  -  -  <i>Tupaia gracilis</i> <br> -  -  Platyhelminthes <br> -  -  -  Cestoda <br> -  -  -  -  [cestode1] <br> -  -  -  -  [cestode2] <br> -  -  -  -  [cestode3] <br> -  -  -  -  [cestode4] <br> -  -  -  -  [cestode5] <br> -  Chromista <br> -  -  Myzozoa <br> -  -  -  Conoidasida <br> -  -  -  -  Eucoccidiorida <br> -  -  -  -  -  Eimeriidae <br> -  -  -  -  -  -  <i>Eimeria</i> <br> -  -  -  -  -  -  -  [Eimeria A] <br> -  -  -  -  -  -  -  [large eimeria] <br></div><p></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>
Supplementary material 2 from: Basset Y, Donoso DA, Hajibabaei M, Wright MTG, Perez KHJ, Lamarre GPA, De León LF, Palacios-Vargas JG, Castaño-Meneses G, Rivera M, Perez F, Bobadilla R, Lopez Y, Ramirez JA, Barrios H (2020) Methodological considerations for monitoring soil/litter arthropods in tropical rainforests using DNA metabarcoding, with a special emphasis on ants, springtails and termites. Metabarcoding and Metagenomics 4: e58572. https://doi.org/10.3897/mbmg.4.58572
Appendix S2
FIGURE 14. Male Tico gen. n in A new genus and two new species of planthopper in the tribe Cenchreini (Hemiptera: Auchenorrhyncha: Derbidae) from lowland tropical rainforest in Costa Rica
FIGURE 14. Male Tico gen. n. pseudosororius sp. n. wing venation; black = vein, green = cell.
FIGURE 6. Male Tico gen. n in A new genus and two new species of planthopper in the tribe Cenchreini (Hemiptera: Auchenorrhyncha: Derbidae) from lowland tropical rainforest in Costa Rica
FIGURE 6. Male Tico gen. n. emmettcarri sp. n. wing venation; black = vein, green = cell.
Data from: Trade-offs for butterfly alpha and beta diversity in human-modified landscapes and tropical rainforests
The accelerating expansion of human populations and associated economic activity across the globe have made maintaining large, intact natural areas increasingly challenging. The difficulty of preserving large intact landscapes in the presence of growing human populations has led to a growing emphasis on landscape approaches to biodiversity conservation with a complementary strategy focused on improving conservation in human-modified landscapes. This, in turn, is leading to intense debate about the effectiveness of biodiversity conservation in human-modified landscapes and approaches to better support biodiversity in those landscapes. Here, we compared butterfly abundance, alpha richness, and beta diversity in human-modified landscapes [urban, sugarcane] and natural, forested areas to assess the conservation value of human-modified landscapes within the Wet Tropics bioregion of Australia. We used fruit-baited traps to sample butterflies and analyzed abundance and species richness in respective land uses over a one-year period. We also evaluated turnover and spatial variance components of beta diversity to determine the extent of change in temporal and spatial variation in community composition. Forests supported the largest numbers of butterflies, but were lowest in each, alpha species richness, beta turnover, and the spatial beta diversity. Sugarcane supported higher species richness, demonstrating the potential for conservation at local scales in human-modified landscapes. In contrast, beta diversity was highest in urban areas, likely driven by spatial and temporal variation in plant composition within the urban landscapes. Thus, while improving conservation on human-modified landscapes may improve local alpha richness, conserving variation in natural vegetation is critical for maintaining high beta diversity.
Data from: The insect-focused classification of fruit syndromes in tropical rainforests: an inter-continental comparison
We propose a new classification of rainforest plants into eight fruit syndromes, based on fruit morphology and other traits relevant to fruit-feeding insects. This classification is compared with other systems based on plant morphology or traits relevant to vertebrate fruit dispersers. Our syndromes are based on fruits sampled from 1,192 plant species at three Forest Global Earth Observatory plots: Barro Colorado Island (Panama), Khao Chong (Thailand) and Wanang (Papua New Guinea). The three plots differed widely in fruit syndrome composition. Plant species with fleshy, indehiscent fruits containing multiple seeds were important at all three sites. However, in Panama a high proportion of species had dry fruits, while in New Guinea and Thailand, species with fleshy drupes and thin mesocarps were dominant. Species with dry, winged seeds that do not develop as capsules were important in Thailand, reflecting the local importance of Dipterocarpaceae. These differences can also determine differences among frugivorous insect communities. Fruit syndromes and colours were phylogenetically flexible traits at the scale studied, as only three of the eight seed syndromes, and one of the 10 colours, showed significant phylogenetic clustering at either genus or family levels. Plant phylogeny was, however, the most important factor explaining differences in overall fruit syndrome composition among individual plant families or genera across the three study sites.
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OpenNeuro
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