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149 results for “tropical soils”
Dataset to manuscript: Soil organic carbon stocks and quality in small-scale tropical, sub-humid and semi-arid watersheds under shrubland and dry deciduous forest in southwestern India
<p>Raw data to the manuscript entitled "Soil organic carbon stocks and quality in small-scale tropical, sub-humid and semi-arid watersheds under shrubland and dry deciduous forest in southwestern India" by Severin-Luca Bellè, Jean Riotte, Muddu Sekhar, Laurent Ruiz, Marcus Schiedung and Samuel Abiven.</p> <p>Data files include all raw data of soil cores (20211111_Raw_data.zip), data measured on composited samples (20211111_Composite_data.zip) and DRIFT spectra (20211111_DRIFT_data.zip).</p> <p>Files ending with var_names are the README files.</p>
Model outputs from the study "A scalable framework for soil property mapping tested across a highly diverse tropical data-scarce region"
<p>Model outputs from the study "A scalable framework for soil property mapping tested across a highly diverse tropical data-scarce region". The study is published as open access and can be found at the following link: <a href="https://www.sciencedirect.com/science/article/pii/S2950289625000326">https://www.sciencedirect.com/science/article/pii/S2950289625000326</a></p> <p> </p> <p>The file "SWAT_USERSOIL.csv" was included to facilitate the assimilation of the soil mapping data into the Soil & Water Assessment Tool (SWAT, https://swat.tamu.edu/) for hydrological modeling. </p> <p> </p> <p>Regarding the raster files, please note:</p> <p>a) All values in these datasets have been multiplied by 10,000 to optimize file sizes.</p> <p>b) Files are named using the variable acronym, followed by the corresponding soil layer. For outputs derived from pedotransfer functions (PTFs), the PTF reference is appended after the variable acronym.</p> <p>c) Available data decrease with increasing soil layer number. This occurs because not all locations (grid cells) have the same soil depth or number of soil layers.</p> <p> </p> <p>If you have any questions about the dataset or its use, please don't hesitate to contact us.</p> <p> </p> <p> </p>
Extracellular polymeric substances are closely related to land cover, microbial communities, and enzyme activity in tropical soils
<p>These are datasets and R codes linked to the paper: Extracellular polymeric substances are closely related to land cover, microbial communities, and enzyme activity in tropical soils. </p>
Variation in the Composition of Understory Vegetation in a Tropical Rain Forest as a Function of Soil and Topographic Position. 1986 - 1990
Understory plants are a major contribution to the high plant species diversity of Neotropical rain forests. Shrubs, understory trees, saplings of overstory trees, and herbs occupy a habitat of generally low light levels and high humidity in which there seem to be few obvious mechanisms to support habitat partitioning. Moreover several plant families are characterized by a high number of co-occurring understory species. In 1987-1989 we sampled understory vegetation in 18 sites at the La Selva Biological Station of the Organization for Tropical Studies in Heredia Province, Costa Rica. At each site we used 20 nested quadrats to investigate the effects of soil type on replicated sites of mapped alluvial and residual volcanic soils (5 map units) and topographic positions (ridges, midslopes and flats) on composition, density and diversity of small (1m tall to 5cm dbh, 25 m2 quadrat) and large(5-10cm dbh, 100 m2 quadrat) understory plants. We also measured fine litter dry mass, extractable P, total organic matter, percent slope and percent incident light radiation in each quadrat.
Fig. 5 in Camerobiid mites (Acariformes: Raphignathina: Camerobiidae) inhabiting epiphytic bromeliads and soil litter of tropical dry forest with analysis of setal homology in the genus Neophyllobius
Fig. 5. Neophyllobius tepoztlanensis sp. nov., ♀, holotype. A. Palp. B. Subcapitulum. C. Dorsal idiosoma. D. Ventral idiosoma. E. Trochanter–tibia of leg I. F. Tarsus I.
Fig. 4 in Camerobiid mites (Acariformes: Raphignathina: Camerobiidae) inhabiting epiphytic bromeliads and soil litter of tropical dry forest with analysis of setal homology in the genus Neophyllobius
Fig. 4. Schematic tarsal setations of Neophyllobius cibyci sp. nov. A–D. ♀, holotype. A. Tarsus I. B. Tarsus II. C. Tarsus III. D. Tarsus IV. E–H. ♁, paratype (CNAC009238). E. Tarsus I. F. Tarsus II. G. Tarsus III. H. Tarsus IV. I–L. Protonymph, paratype (CNAC009241). I. Tarsus I. J. Tarsus II. K. Tarsus III. L. Tarsus IV. M–O. Larva, paratype (CNAC009242). M. Tarsus I. N. Tarsus II. O. Tarsus III.
Fig. 1 in Camerobiid mites (Acariformes: Raphignathina: Camerobiidae) inhabiting epiphytic bromeliads and soil litter of tropical dry forest with analysis of setal homology in the genus Neophyllobius
Fig. 1. Neophyllobius cibyci sp. nov., ♀, holotype. A. Palp. B. Subcapitulum. C. Dorsal idiosoma. D. Ventral idiosoma. E. Trochanter–tibia of leg I. F. Tarsus I.
Fig. 2 in Camerobiid mites (Acariformes: Raphignathina: Camerobiidae) inhabiting epiphytic bromeliads and soil litter of tropical dry forest with analysis of setal homology in the genus Neophyllobius
Fig. 2. Neophyllobius cibyci sp. nov. A–B. ♁, paratype (CNAC009238). A. Dorsal idiosoma. B. Ventral idiosoma. C–D. Protonymph, paratype (CNAC009241). C. Dorsal idiosoma. D. Ventral idiosoma. E–F. Larva, paratype (CNAC009242). E. Dorsal idiosoma. F. Ventral idiosoma.
Fire promotes functional plant diversity and modifies soil carbon dynamics in tropical savanna
<p>The dataset associated with the manuscript "Fire promotes functional plant diversity and modifies soil carbon dynamics in tropical savanna" (Teixeira et al.) includes 6 different datasets, for which we provided one metadata.<br> </p> <p><strong>Version 2</strong> includes an update of the biomass data set, including the correct transformation to g/m2 on fine roots biomass data.<br><br><strong>Version 3 </strong>includes an update of the belowground traits data set based on correcting formatting errors in the belowground traits data. <br><br><strong>Version 4 </strong>Sorry for the inconvenience. This version includes the correct updated belowground traits data file based on the correct formatting errors in the belowground trait data. <br><br>fluxes: it includes data related to net ecosystem C and water exchange. NEE and ET from each plot were measured using the LiCOR 7500 infrared gas analyzer (Li-Cor Inc.). See the method section in the manuscript for full details.</p> <p>soil_carbon: it includes carbon soil data.<br><br>biomass_v2: it includes data related to aboveground and belowground biomass. Aboveground data were collected in 0.5m2 subplot and belowground at 0.25m2 at 20cm depth both within 1m2 sampling plot. See the method section in the manuscript for full details.</p> <p>aboveground_traits: all aboveground functional traits from plant species. See the method section in the manuscript for full details.</p> <p>belowground_traitsv3: all roots functional traits from plant species. See the method section in the manuscript for full details.</p> <p>species_composition: plant community composition. See the method section in the manuscript for full details.</p> <p><br><strong>Abstract</strong><br>Fire is an evolutionary environmental filter in tropical savanna ecosystems altering functional diversity and associated C pools in the biosphere and fluxes between the atmosphere and biosphere. Therefore, alterations in fire regimes (e.g. fire exclusion) will strongly influence ecosystem processes and associated dynamics. In those ecosystems, C dynamics and functions are underestimated by the fire-induced offset between C output and input. To determine how fire shapes ecosystem C pools and fluxes in an open savanna across recently burned and fire excluded areas, we measured the following metrics: (I) plant diversity including taxonomic (i.e. richness, evenness) and plant functional diversity (i.e. functional diversity, functional richness, functional dispersion and community weighted means); (II) structure (i.e. above- and below-ground biomass, litter accumulation); and (III) functions related to C balance (i.e. net ecosystem carbon dioxide (CO<sub>2</sub>)<sub> </sub>exchange (NEE), ecosystem transpiration (ET), soil respiration (soil CO<sub>2</sub> efflux), ecosystem water use efficiency (eWUE) and total soil organic C (SOC). We found that fire promoted aboveground live and belowground biomass, including belowground organs, and coarse and fine root biomass, and contributed to higher biomass allocation belowground. Fire also increased both functional diversity and dispersion. NEE and total SOC were higher in burned plots compared to fire-excluded plots whereas soil respiration recorded lower values in burned areas. Both ET and eWUE were not affected by fire. Fire strongly favored functional diversity, fine root, and belowground organ biomass in piecewise SEM models but the role of both functional diversity and ecosystem structure to mediate the effect of fire on ecosystem functions remain unclear. Fire regime will impact C balance, and fire exclusion may lead to lower C input in open savanna ecosystems.</p>
Land management controls on soil carbon fluxes in Asia's largest tropical grassland
<p>Data files for 'Land management controls on soil carbon fluxes in Asia’s largest tropical grassland', submitted to Ecological Indicators on 15 May 2024.</p>
APPENDIX 3. — Maximum likelihood phylogram inferred from 47 taxa and 3314 in Mucoralean fungi in Thailand: novel species of Absidia from tropical forest soil
APPENDIX 3. — Maximum likelihood phylogram inferred from 47 taxa and 3314 characters based on LSU, SSU and ACT-1 matrix using GTR+G model. ML bootstrap support (≥ 70%) are indicated above the branches or near the nodes. Tree is artificially rooted using Cunninghamella homothallica (CBS 168.53), C. phaeospora (CBS 692.68), and C. bainieri (FSU319). The new species are in black bold and the type species in the dataset are indicated using T. (-) represent bootstrap support lower than 70%. (*) indicates unrecovered branching.
FIG. 7 in Mucoralean fungi in Thailand: novel species of Absidia from tropical forest soil
FIG. 7. — Mycelial growth of A. edaphica V.GHurdeal., E.Gentekaki., H.B.Lee & K.D.Hyde, sp. nov.(MFLUCC 20-0088, ex-type) and A. soli V.GHurdeal., E.Gentekaki., H.B.Lee & K.D.Hyde, sp. nov. (MFLUCC 20-0086, ex-type) in various media at room temperature (around 26°C to 27°C) after two days: A-D, colonies on MEA; E-H, colonies on PDA; I-L, colonies on CMA; M-P, colonies on YMA. The first two rows represent colonies of of A. edaphica sp. nov. and the bottom two rows A. soli sp. nov (obverse (first and third rows) and reverse (second and fourth rows).
FIG. 6 in Mucoralean fungi in Thailand: novel species of Absidia from tropical forest soil
FIG. 6. — Mycelial growth of Absidia edaphica V.GHurdeal., E.Gentekaki., H.B.Lee & K.D.Hyde, sp. nov. and Absidia soli V.GHurdeal., E.Gentekaki., H.B.Lee & K.D.Hyde, sp. nov. in different media at 25°C.
Figure 4 in Population Dynamics of Amoeboid Protists in a Tropical Desert: Seasonal Changes and Effects of Vegetation and Soil Conditions
Figure 4. Relationship between amoeboid protist richness and soil parameters during the wet season in three microhabitats by CCA: PL: Pr. laevigata, PP: Pa. praecox, and BS: bare soil. The names and abbreviations of the amoeboid protist species can be found in table 3.
Figure 2 in Population Dynamics of Amoeboid Protists in a Tropical Desert: Seasonal Changes and Effects of Vegetation and Soil Conditions
Figure 2. Cumulative richness plots of amoeboid protists present under Pr. laevigata (PL), Pa. praecox (PP) and bare soil (BS) during dry and wet seasons at 0–30 cm. a) eruptive pseudopods, and b) acanthopodial pseudopods. ND: not determined.
Figure 1 in Population Dynamics of Amoeboid Protists in a Tropical Desert: Seasonal Changes and Effects of Vegetation and Soil Conditions
Figure 1. Study area, showing vegetation patches in the desert of Tehuacán, Puebla, Mexico. In addition, the analyzed microhabitats are shown: Pr. laevigata, Pa. praecox and bare soil.
Soil microbial communities from tropical forest and oil palm
<div> <h1>Description</h1> <p>A study examining the impact of selective logging and forest conversion to oil palm on soil microbial community composition. Soil samples were collected from old growth forest, selectively logged forest and oil palm plantations. Soil bacterial, protistan and fungal community composition were measured and summarised by calculating richness. </p> <h1>Projects</h1> <p> This dataset was collected as part of the following projects: </p><ul> <li><a href="https://safeproject.net/projects/project_view/124">https://safeproject.net/projects/project_view/124</a> </li> </ul> <p></p> <h1>Funding</h1> <p> These data were collected as part of research funded by: </p> <ul> <li>UK NERC-funded Biodiversity And Land-use Impacts on Tropical Ecosystem Function (BALI) consortium (Standard grant , NE/K016377/1 ) </li> </ul> <p></p> <p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p> <h1>Permits</h1> <p>These data were collected under permit from the following authorities:</p> <ul> <li>Sabah Biodiversity Centre ( Research licence JKM/MBS.1000-2/2 JLD.5 (20))</li> <li>Sabah Biodiversity Centre ( Export licence JKM/MBS.1000-2/3 JLD.2 (70))</li> </ul> <p></p> <h1>Files</h1> <p>This dataset consists of 1 file: SAFE_Dataset_Richness.xlsx</p> <h2>SAFE_Dataset_Richness.xlsx</h2> <p>This file contains dataset metadata and 1 data tables:</p> <h3>Soil_Microbial_Communities</h3> <ul> <li>Worksheet: Soil_Microbial_Communities</li> <li>Description: Summary richness statistics from bacterial 16S, protistan 18S and fungal ITS biomarker microbial sequencing from DNA extracted from soils</li> <li>Number of fields: 10</li> <li>Number of data rows: 225</li> <ul> <li>PlotName: Plot name corresponding to the GEM Carbon plot where soils were sampled (type: id)</li> <li>ForestType: Old-growth, Logged or Oil palm (type: categorical)</li> <li>ForestPlotsCode: Plot name as listed in ForestPlots database (type: id)</li> <li>Replicate: replicate identifier for which soil core taken from each subplot (type: replicate)</li> <li>location_name: Name of subplot where soils were collected (type: location)</li> <li>Bacteria_Richness: Number of observed bacterial taxa from sequencing of 16S marker genes from soil samples (type: numeric)</li> <li>Protist_Richness: Number of observed protistan taxa from sequencing of 18S marker genes from soil samples (type: numeric)</li> <li>Fungal_Richness: Number of observed fungal taxa from sequencing of 16S marker genes from soil samples (type: numeric)</li> <li>EcM_Fungal_Richness: Number of observed Ectomycorrhizal fungal taxa from sequencing of ITS marker genes from soil samples (type: numeric)</li> <li>AMF_Fungal_Richness: Number of observed Arbuscular Mycorrhizal fungal taxa from sequencing of ITS marker genes from soil samples (type: numeric)</li> </ul> </ul> <h1>Extents</h1> <ul> <li>Date range: 2014-10-01 to 2018-09-01</li> <li>Latitudinal extent: 4.64° to 4.954°</li> <li>Longitudinal extent: 116.95° to 117.796°</li> </ul> </div>
Soil physical quality of a tropical sandy Ferralsol
<p>Dados de pesquisa da dissertação de mestrado do primeiro autor, vinculado à Universidade Estadual de Maringá, Maringá, BR.</p>
Soil and litter chemistry, soil microbial communities and litter decomposition from tropical forest and oil palm
<b>Description: </b><p>A study examining the interactions between soil chemistry, litter chemistry and soil microbial decomposers as controls on rates of litter decomposition across a tropical land use disturbance gradient. Co-located soil and litter samples were collected from old growth forest, moderately logged forest, heavily logged forest and oil palm plantations. Soil and litter were chemically characterised and soil bacterial and fungal community composition and abundance were measured. These were then combined in fully factorial ex-situ microcosms and measured litter decomposition rates at 3 time points during different stages of decomposition.</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/124"><b>Biodiversity and land-use impacts on tropical ecosystem function (BALI): Quantifying biogeochemistry across forest disturbance gradients in Sabah</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>UK NERC-funded Biodiversity And Land-use Impacts on Tropical Ecosystem Function (BALI) consortium (Standard grant, NE/K016377/1)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Centre (Research licence JKM/MBS.1000-2/2 JLD.5 (20))</li><li>Sabah Biodiversity Centre (Export licence JKM/MBS.1000-2/3 JLD.2 (70))</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3929632">here</a></p><p><b>Files: </b>This consists of 1 file: SAFE_Dataset.xlsx</p><p><b>SAFE_Dataset.xlsx</b></p><p>This file contains dataset metadata and 5 data tables:</p><ol><li><p><b>Soil_Properties</b> (described in worksheet Soil_Properties)</p><p>Description: Basic measured soil properties</p><p>Number of fields: 9</p><p>Number of data rows: 20</p><p>Fields: </p><ul><li><b>Plot</b>: Plot name corresponding to the GEM Carbon plot where soils were sampled (Field type: id)</li><li><b>Plot_ID</b>: Plot ID indicating land use as referenced in the Frontiers in forests and global change publication "Soil microbial community and litter quality controls on decomposition across a tropical forest disturbance gradient" (Field type: categorical)</li><li><b>location_name</b>: Name of subplot where soils were collected (Field type: location)</li><li><b>gravimetric moisture content</b>: Soil moisture content at the time of sample collection (Field type: numeric)</li><li><b>soil_pH</b>: Soil pH measured on fresh soils (Field type: numeric)</li><li><b>soil_N</b>: Total soil Nitrogen (Field type: numeric)</li><li><b>soil_C</b>: Total soil Carbon (Field type: numeric)</li><li><b>soil_C.N</b>: Soil carbon to nitrogen ratio (Field type: numeric)</li><li><b>soil_P</b>: soil inorganic phosphorus (Field type: numeric)</li></ul></li><li><p><b>Litter_Chemistry</b> (described in worksheet Litter_Chemistry)</p><p>Description: Litter chemistry data of mixed forest floor litter, collected, sorted to remove humified material, woody debris and dried</p><p>Number of fields: 20</p><p>Number of data rows: 40</p><p>Fields: </p><ul><li><b>Plot</b>: Plot name corresponding to the GEM Carbon plot where soils were sampled (Field type: id)</li><li><b>Plot_ID</b>: Plot ID indicating land use as referenced in the Frontiers in forests and global change publication "Soil microbial community and litter quality controls on decomposition across a tropical forest disturbance gradient" (Field type: categorical)</li><li><b>location_name</b>: Name of subplot where soils were collected (Field type: location)</li><li><b>Pretreatment</b>: Whether the litter sample was sterilised by autoclaving or not (Field type: categorical)</li><li><b>leaf_K</b>: leaf potassium concentration (Field type: numeric)</li><li><b>leaf_Ca</b>: leaf Calcium concentration (Field type: numeric)</li><li><b>leaf_Mg</b>: leaf Magnesium concentration (Field type: numeric)</li><li><b>leaf_Al</b>: leaf aluminium concentration (Field type: numeric)</li><li><b>leaf_P</b>: leaf phosphorus concentrations (Field type: numeric)</li><li><b>solubles</b>: leaf soluble cell content (Field type: numeric)</li><li><b>hem_pro_cel_lig_rec</b>: leaf hemicellulose, proteins, cellulose, lignin and recalcitrant fibres (Field type: numeric)</li><li><b>hem_pro</b>: leaf hemicellulose and proteins (Field type: numeric)</li><li><b>cel_lig_rec</b>: leaf cellulose, lignin and recalcitrant fibres (Field type: numeric)</li><li><b>cel</b>: leaf cellulose (Field type: numeric)</li><li><b>lig_rec</b>: leaf lignin and recalcitrants (Field type: numeric)</li><li><b>leaf_N</b>: leaf nitrogen concentration (Field type: numeric)</li><li><b>leaf_C</b>: leaf carbon concentration (Field type: numeric)</li><li><b>c.n</b>: leaf carbon to nitrogen ration (Field type: numeric)</li><li><b>d13c</b>: leaf carbon stable isotope ratio (Field type: numeric)</li><li><b>d15n</b>: leaf nitrogen stable isotope ratio (Field type: numeric)</li></ul></li><li><p><b>PLFA_Concentrations</b> (described in worksheet PLFA_Concentrations)</p><p>Description: Phospolipid Fatty Acid (PLFA) concentrations as biomarkers of soil bacteria and fungi. Extracted from freeze dried soils prior to the microcosm experiment</p><p>Number of fields: 10</p><p>Number of data rows: 20</p><p>Fields: </p><ul><li><b>Plot</b>: Plot name corresponding to the GEM Carbon plot where soils were sampled (Field type: id)</li><li><b>Plot_ID</b>: Plot ID indicating land use as referenced in the Frontiers in forests and global change publication "Soil microbial community and litter quality controls on decomposition across a tropical forest disturbance gradient" (Field type: categorical)</li><li><b>location_name</b>: Name of subplot where soils were collected (Field type: location)</li><li><b>Total_PLFA</b>: Total PLFA concentrations extracted from soil samples (Field type: numeric)</li><li><b>Fungal_PLFA</b>: Fungal PLFA biomarker concentrations extracted from soils (Field type: numeric)</li><li><b>Bacteria_PLFA</b>: Bacteria PLFA biomarkers extracted from soils (Field type: numeric)</li><li><b>Fungal:Bacteria</b>: Ratio of fungal to bacteria PLFAs (Field type: numeric)</li><li><b>Gram_Pos_PLFA</b>: Gram Positive PLFA Biomarker concentrations extracted from soil (Field type: numeric)</li><li><b>Gram_Neg_PLFA</b>: Gram Negative PLFA Biomarker concentrations extracted from soil (Field type: numeric)</li><li><b>GramPos:GramNeg</b>: Gram positive to Gram negative PLFA ratios (Field type: numeric)</li></ul></li><li><p><b>Soil_Microbial_Communities</b> (described in worksheet Soil_Microbial_Communities)</p><p>Description: Summary diversity statistics from bacterial 16S and fungal ITS biomarker microbial sequencing. DNA extracted from soils prior to microcosm experiment</p><p>Number of fields: 9</p><p>Number of data rows: 20</p><p>Fields: </p><ul><li><b>Plot</b>: Plot name corresponding to the GEM Carbon plot where soils were sampled (Field type: id)</li><li><b>Plot_ID</b>: Plot ID indicating land use as referenced in the Frontiers in forests and global change publication "Soil microbial community and litter quality controls on decomposition across a tropical forest disturbance gradient" (Field type: categorical)</li><li><b>location_name</b>: Name of subplot where soils were collected (Field type: location)</li><li><b>Bacteria_Richness</b>: Number of observed bacterial taxa from sequencing of 16S marker genes from soil samples (Field type: numeric)</li><li><b>Bacteria_Shannon</b>: Bacterial Shannon diversity from 16S Marker gene sequencing (Field type: numeric)</li><li><b>Fungal_Richness</b>: Number of observed fungal taxa from sequencing of 16S marker genes from soil samples (Field type: numeric)</li><li><b>Fungal_Shannon</b>: Fungal Shannon diversity from 16S Marker gene sequencing (Field type: numeric)</li><li><b>Saprotrophic_Fungal_Richness</b>: Number of observed saprotrophic fungal taxa from sequencing of 16S marker genes from soil samples (Field type: numeric)</li><li><b>Saprotrophic_Fungal_Shannon</b>: Saprotrophic Fungal Shannon diversity from 16S Marker gene sequencing (Field type: numeric)</li></ul></li><li><p><b>Ex_Situ_Litter_Decomposition</b> (described in worksheet Ex_Situ_Litter_Decomposition)</p><p>Description: Fully factorial litter decomposition experiment. 16 unique soil and litter combinations (4x4) were incubated in petri dishes at constant temperature and moisture and mass loss measured after 31, 105 and 398 days.</p><p>Number of fields: 8</p><p>Number of data rows: 240</p><p>Fields: </p><ul><li><b>location_name</b>: Name of subplot where soils were collected (Field type: location)</li><li><b>Soil_ID</b>: Soil ID indicating which land use soil was collected from (Field type: categorical)</li><li><b>Litter_Location</b>: Location of which GEM carbon plot the litter was collected from. Litter was collected from the 5 carbon subplots as per soil collection and homogenised into one composite sample per carbon plot (Field type: location)</li><li><b>Litter_ID</b>: Litter ID indicating which land use litter was collected from (Field type: categorical)</li><li><b>Experimental_Block</b>: Which experimental block the microcosm was assigned to. N= 5 (Field type: replicate)</li><li><b>Timepoint</b>: At what timepoint the litter was harvested from each microcosm (Field type: categorical)</li><li><b>Mass_Loss</b>: The mass loss of litter relative to the starting mass of 1g (Field type: numeric)</li><li><b>home_away</b>: Descriptor for whether the soil and litter combination in microcosm (Field type: categorical)</li></ul></li></ol><p><b>Date range: </b>2014-10-01 to 2018-09-01</p><p><b>Latitudinal extent: </b>4.6402 to 4.9539</p><p><b>Longitudinal extent: </b>117.4518 to 117.7942</p>
Data from: Multi-population seedling and soil transplants show possible responses of a common tropical montane tree species (Weinmannia bangii) to climate change
<p>A possible response of many plant species to global warming is migration to higher elevations. However, these migrations may not be required if species can tolerate higher temperatures, or may be prevented if there are other factors such as changes in soil conditions that make upslope areas unsuitable.</p> <p>We used a set of 3-year field transplant experiments in the remote Peruvian Andes to simulate two possible responses of an abundant tropical montane cloudforest tree species (Weinmania bangii) to global warming: (1) "upward migration", in which case seedlings of W. bangii's were grown at their current elevation/temperature but in soils transplanted from higher elevations; and (2) "migration failure", in which case seedlings were transplanted downslope along with their home soils into areas that are 1°C or 2°C warmer. We conducted separate experiments with populations from the upper/leading edge, middle and lower/trailing edges of W. bangii's elevational/thermal range to assess the influence of local adaptation on responses to changes in temperature or soil.</p> <p>We found that seedling survival and growth were not affected by changes in soil conditions, regardless of the origin population. However, seedling survival decreased with temperature. A simulated warming of 1°C caused a significant reduction in the survival of seedlings transplanted from the mid-range population, and 2°C warming caused a severe decrease in the survival of seedlings transplanted from both the mid-range and bottom-edge populations.</p> <p>Synthesis. Our findings reveal that rising temperatures are a serious threat to plants, especially in populations growing in the hotter portion of their species' range. At least in the case of W. bangii, novel soil conditions will not limit the establishment or growth of seedlings at higher elevations. As such, decreases in the survivorship at lower elevations may be offset through upward migrations as temperatures continue to increase.</p>
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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
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.