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349 results for “global distribution”
Soil dissolved organic carbon in terrestrial ecosystems: global budget, spatial distribution and controls
<p><strong>Aims: </strong>Soil dissolved organic carbon (DOC) is a primary form of labile carbon in terrestrial ecosystems and therefore plays a vital role in soil carbon cycling. This study aims to quantify the budgets of soil DOC at biome- and global levels and to examine the variations in soil DOC and their environmental controls. Location: Global Time period: 1981 - 2019 Method: We compiled a global dataset and analyzed the concentration and distribution of DOC across 10 biomes.</p> <p><strong>Results: </strong>Large variations in DOC are found among biomes across space and the soil DOC concentration declines exponentially along soil depths. Tundra has the highest soil DOC concentration in 0 - 30 cm soils (453.75 (95% confidence interval: 324.95 – 633.5) mg·kg-1); whereas tropical and temperate forests have relatively lower DOC concentrations, ranging from 30.20 (24.78 - 36.80) mg·kg-1 to 54.54 (49.77 – 59.77) mg·kg-1. DOC generally accounts for < 1% of total organic carbon in soils, and DOC in 0 - 30 cm contributes more than half of total DOC in 0 - 100 cm soil profile. Furthermore, variations in DOC are primarily controlled by soil texture, moisture, and total organic carbon.</p> <p><strong>Main conclusion: </strong>A global synthesis is combined with an empirical model to extrapolate the DOC concentration along soil profiles across the globe, and global budgets of DOC are estimated as 7.20 Pg C in top 0 - 30 cm and 12.97 Pg C in 0 - 100 cm, respectively, with a considerable variation among biomes. The strong soil texture control but weak TOC control on DOC variations suggest that the investigation of physical protection of soil organic carbon might need to expand to consider the labile C in soils. The global maps of DOC concentration serve as a benchmark for validating land surface models in estimating carbon storage in soils.</p>
Derivative products: "IBEX Ribbon Separation Using Spherical Harmonic Decomposition of the Globally Distributed Flux" by Swaczyna et al.
<p>Derivative products from: Swaczyna et al. 2022, "IBEX Ribbon Separation Using Spherical Harmonic Decomposition of the Globally Distributed Flux", ApJS, 258:6</p>
Accompanying data for "Non-cyanobacterial diazotrophs: Global diversity, distribution, ecophysiology, and activity in marine waters"
<p>Data accompanying the manuscript "Non-cyanobacterial diazotrophs: Global diversity, distribution, ecophysiology, and activity in marine waters," submitted to FEMS Microbiology Reviews.</p> <p>Contents include:</p> <p>- A compilation of available marine water column qPCR/ddPCR nifH gene abundance data from published studies with metadata colocalized using the Simons Collaborative Marine Atlas Project (see NCDReview_SupplTable4.pdf for a description of variables): NCDReview_qPCR_database.xlsx</p> <p>- Nucleotide and amino acid sequences for the assembled nifH gene catalog (NCDReview_SupplTable1_caption.docx, NCDReview_TableS1_nt.fasta, NCDReview_TableS1_aa.fasta)</p> <p>- Table of nifH clusters and subclusters, with corresponding phyla and genera (NCDReview_Table1.xlsx)</p>
Global distribution and climate sensitivity of the tropical montane forest nitrogen cycle
<p>Tropical forests are pivotal to global climate and biogeochemical cycles, yet the geographic distribution of nutrient limitation to plants and microbes across the biome is unresolved. One long-standing generalization is that tropical montane forests are nitrogen (N)-limited whereas lowland forests tend to be N-rich. However, empirical tests of this hypothesis have yielded equivocal results. Here we evaluate the topographic signature of the ecosystem-level tropical N cycle by examining climatic and geophysical controls of surface soil N content and stable isotopes (δ15N) from elevational gradients distributed across tropical mountains globally. We document steep increases in soil N concentration and declining δ15N with increasing elevation, consistent with decreased microbial N processing and lower gaseous N losses. Temperature explained much of the change in N, with an apparent temperature sensitivity (Q10) of ~1.9. Although montane forests make up 11% of forested tropical land area, we estimate they account for > 17% of the global tropical forest soil N pool. Our findings support the existence of widespread microbial N limitation across tropical montane forest ecosystems and high sensitivity to climate warming.</p>
Global coastal transect type classification associated to "Global distribution and dynamics of muddy coasts", Nat. Coms 2023
<p>This dataset is associated with the scientific article "Global distribution and dynamics of muddy coasts" (https://www.nature.com/articles/s41467-023-43819-6)</p> <p>The dataset contains the following items:</p> <ul> <li>VisualizeResults.qgz: QGIS project with the formatted datasets ready for viewing (tested in QGIS v3.28.2.)</li> <li>PredictedClasses_MuddyBeaches.shp (and associated files): Shapefile containing predicted classes of coastal transects differentiating the following types: <ul> <li>0: Sandy coastal transect</li> <li>1: Muddy coastal transect</li> <li>2: Rocky coastal transect</li> <li>3: Vegetated coastal transect</li> <li>4: Other undefined type</li> </ul> </li> <li>MuddyZonesFleming.shp (and associated files): Shapefile containing hand-drawn polygons based on the description of muddy coastline concentrations by (Flemming, B. W. Geographic distribution of muddy coasts, 2002).</li> <li>TransectTrainingDataframe.csv : comma separated value file containing 1867 hand-labelled coastal type transects randomly distributed around the world. Each transect contains the following entries: <ul> <li>transect_id: individual transect ID</li> <li>box_id: tiled ID</li> <li>country_name</li> <li>continent</li> <li>New_Start_lon: longitude transect start</li> <li>New_Start_lat: latitude transect start</li> <li>New_End_lon: longitude transect end</li> <li>New_End_lat: latitude transect end</li> <li>Center_lon</li> <li>Center_lat</li> <li>Length: transect length</li> <li>label: hand-labeled coastal type, Str (Sandy, Muddy, Rocky, Vegetated, Other)</li> <li>abs lat: latitude</li> <li>flag_sandy: bool, classified as a sandy area (Luiijendijk et al, 2018)</li> <li>sand: % of pixels classified as sand from Sentinel 2 Multispectral land-type classification</li> <li>mud: % of pixels classified as muddy from Sentinel 2 Multispectral land-type classification</li> <li>water: % of pixels classified as water from Sentinel 2 Multispectral land-type classification</li> <li>vegetation: % of pixels classified as vegetation from Sentinel 2 Multispectral land-type classification</li> <li>dry: % of pixels classified as dry-vegetation from Sentinel 2 Multispectral land-type classification</li> <li>other: % of pixels classified as other from Sentinel 2 Multispectral land-type classification</li> <li>turbid % of pixels classified as turbid-water from Sentinel 2 Multispectral land-type classification</li> <li>height max: max height in transect (MERIT-DEM)</li> <li>height var: variance height in transect (MERIT-DEM)</li> <li>mangrove: bool, mangrove presence (Global Mangrove Forest Distributions)</li> <li>intertidal: intertidal range Murray et al 2019.</li> <li>gsw: Global Surface Water dataset, Pekel et al 2016</li> <li>maxtemp: max temperature WorldClim V1 Bioclim dataset</li> <li>mintemp: min temperature WorldClim V1 Bioclim dataset</li> <li>mhhw: Tide MHHW Deltares GTSM model</li> <li>mllw: Tide MLLW Deltares GTSM model</li> <li>tidal range: Tidal range Deltares GTSM model</li> </ul> </li> <li>PixelTrainingDataframe.csv : comma separated value file containing 3240 hand-labelled land-type pixel randomly distributed around the world. These are derived from a Sentinel 2 TOA images as a 2020 composite. Bands (BX) refer to the Sentinel-2 MSI Multispectral Instrument, level-2A spectral bands names. Each entry contains: <ul> <li>Aerosols: B1</li> <li>Blue: B2</li> <li>Green: B3</li> <li>Red: B4</li> <li>Red Edge 1: B5</li> <li>Red Edge 2: B6</li> <li>Red Edge 3: B7</li> <li>NIR: B8</li> <li>Red Edge 4: B8A</li> <li>SWIR 1: B11</li> <li>SWIR 2: B12</li> <li>NDWI: Normalized Differential Water Index ((B3-B8)/(B3+B8)</li> <li>NDVI ((B8-B4)/(B8+B4)</li> <li>num: hand-label type differentiating the following types (1) sandy beaches, (2) mudflats, (3) clear water, (4) 111 turbid (brown) water, (5) green vegetation, (6) dry vegetation, and (7) other (containing clouds, snow, buildings, and all other indefinable points)</li> </ul> </li> </ul>
Data sources and code for: "Species-specific acclimation capacity of key traits explains global vertical distributions of seagrass species"
<p>Minguito-Frutos_etal_2023_Data1.xlsx contains the data for analyzing the relationship between plant size and seagrass growth reproductive strategy and the species-specific vertical distribution of seagrasses. </p> <p>Minguito-Frutos_etal_2023_Data2.xlsx contains the data for the meta-analityc approach studying the relationship between the vertical distribution of seagrass species and the plasticity of their traits (physiological, morphological, structural and growth). </p> <p>Scripts_Minguito_Frutos_etal_2023_GEB_Ref.GEB-2022-0592.R contains the R reproducible code to run all the analyses carried out in this study. </p>
Global Distribution and Morphology of Small Seamounts
<p><strong>Abstract</strong></p> <p>Seamounts are isolated elevations in the seafloor with circular or elliptical plans, comparatively steep slopes, and relatively small summit area (Menard, 1964). The vertical gravity gradient (VGG), which is the curvature of the ocean surface topography derived from satellite altimeter measurements, has been used to map the global distribution of seamounts (Kim & Wessel, 2011). We used the latest grid of VGG to update and refine the global seamount catalog; we identified 19325 new seamounts, expanding a previously published catalog having 24643 seamounts. 739 well-surveyed seamounts, having heights ranging from 421 m to 2500 m, were used to estimate the typical radially-symmetric seamount morphology. First, an Empirical Orthogonal Function (EOF) analysis was used to demonstrate that these small seamounts have a basal radius that is linearly related to their height – their shapes are scale invariant. Two methods were then used to compute this characteristic base to height ratio: an average Gaussian fit to the stack of all profiles and an individual Gaussian fit for each seamount in the sample. The first method combined the radial normalized height data from all 739 seamounts to form median and median-absolute deviation. These data were fit by a 2-parameter Gaussian model that explained 99.82% of the variance. The second method used the Gaussian function to individually model each seamount in the sample and further establish the Gaussian model. Using this characteristic Gaussian shape we show that VGG can be used to estimate the height of small seamounts to an accuracy of ~270 m.</p> <p><strong>Methods:</strong> This directory contains the files that were used to locate the SIO seamounts in Gevorgian et al., 2022 (Manuscript in Revision).</p> <ol> <li> <p>VGG30.kmz, VGG32.kmz, and VGG32a.kmz: Files of the vertical gravity gradient to view on Google Earth. VGG32a.kmz has a smaller gray-scale saturation range of -42 to +28 Eotvos (Sandwell et al., 2021).</p> </li> <li> <p>srtm15_V2.kmz: File of seafloor bathymetry to view on Google Earth (Tozer et al., 2019).</p> </li> <li> <p>KWSMTSV0.1.kml: Previous Seamount Picks (Kim & Wessel, 2011).</p> </li> <li> <p>SSPs-ridges.kmz and FZs.kmz: Digitized see-saw propagators, ridges, and fracture zones for Google Earth (Matthews et al., 2011; Wessel et al., 2015).</p> </li> </ol> <p><strong>Seamounts_Modeled: </strong>This directory contains the seamounts in the combined SIO and Kim-Wessel (2011) (KW) catalogs. </p> <p>There are five categories of seamounts: all.nxhrdnc, good.nxhrdnc, shallow.nxhrdnc, short.nxhrdnc, tall.nxhrdnc. Each file has seven columns: longitude, latitude, height, radius, base depth, name, and charted/uncharted. More information can be found in the README.txt file. </p> <p>The seamounts_modeled.kmz file can be used to view the seamount catalog in Google Earth. </p> <p>KW_badlist.txt is a text file which has names of 514 seamounts from the KW catalog that no longer show a signal in the VGG (Version 30).</p> <p>The smtdata_739.xlsx has the analysis results for 739 seamounts which were modeled through two methods (Gevorgian et al., 2022).</p>
Distribution and environmental drivers of fungal denitrifiers in global soils
<p>Meta- and source data as well as newick phylogeny associated to the article 'Distribution and Environmental Drivers of Fungal Denitrifiers in Global Soils '.</p>
Unveiling global species abundance distributions - Callaghan et al. 2023 - Nature Ecology and Evolution
<p>This repository represents some data and code to reproduce the main figures from Callaghan et al. 2023. Unveiling the global species abundance distributions of Eukaryotes. Nature Ecology and Evolution.</p>
Climate and ant diversity explain the global distribution of ant-plant mutualisms
<p>Biotic interactions play an important role in shaping species geographic distributions and diversity patterns. However, the role of mutualistic interactions in shaping global plant diversity patterns remains poorly understood, particularly with respect to interactions with invertebrates. It is unclear how the nature of different mutualisms interacts with abiotic drivers and affects the distribution of mutualistic organisms. Here, we present a global-scale biogeographic analysis of three distinct ant-plant mutualisms, differentiating between plants bearing domatia, extrafloral nectaries (EFNs), and elaiosomes, based on comprehensive geographic distributions of ~19,000 flowering plants and ~13,000 ant species. Domatia and extrafloral nectaries involve indirect plant defences provided by ants, while elaiosomes attract ants to disperse seeds. Our results reveal distinct biogeographic patterns of different ant-plant mutualisms, with domatium- and EFN-bearing plant diversity decreasing sharply from the equator towards the poles, while elaiosome-bearing plants prevail at mid-latitudes. Present climate, especially mean annual temperature and precipitation, emerge as the strongest predictors of ant-associated plant diversity. In hot and moist regions, typically the tropics, the representation of EFN-bearing plants increases with the proportion of potential ant partners while domatium-bearing plants show no correlation with ants. In dry regions, plants with elaiosomes are strongly linked to interacting ant seed dispersers. Our results suggest that ants in combination with climate drive the spatial variation of plants bearing domatia, extrafloral nectaries, and elaiosomes, highlighting the importance of mutualistic interactions for understanding plant biogeography.</p>
Environmental drivers and distribution of cold-water corals in the global ocean - Habitat Suitability Models
<p><strong>Publication Abstract</strong></p> <p>Species distribution models (SDMs) are useful tools for identifying the distribution of marine species in data limited environments. Outputs from SDMs have been used to identify areas for spatial management, analyzing trawl closures, quantitatively measuring the risk of bottom trawling, and evaluating protected areas for improving conservation management. Cold-water corals are globally distributed habitat forming organisms that are vulnerable to anthropogenic impacts and climate change, but data deficiency remains an ongoing issue for the effective spatial management of these important ecosystem engineers. In this study, we constructed 11 environmental seabed variables at 500m resolution based on the latest multi-depth global datasets and high-resolution bathymetry. Ensemble modeling methods were used to predict the global habitat suitability for ten widespread cold-water coral species, including six reef Scleractinian framework-forming species and four large gorgonian species. Temperature, depth, salinity, terrain ruggedness index, carbonate saturation state and chlorophyll were the most important factors in determining the global distributions of these species. The Scleractinian species <em>Madrepora oculata</em> showed the widest niche breadth, whilst most other species demonstrated somewhat limited niche breadth. The shallowest study species, <em>Oculina varicosa</em>, had the most distinctive niche of the group. The model outputs from this study represent the highest resolution global predictions for these species to date and are valuable in aiding the management, conservation and continued research into cold-water coral species.</p> <p><strong>Data description</strong></p> <p>These datasets (compressed Zip archives) contain the habitat suitability model outputs generated for the publication Tong et al., (2023) doi: 10.3389/fmars.2023.1217851, please refer to the manuscript for methodological details. These files are provided in an ArcGIS compatible TIFF format that is readable by various GIS packages and can be imported to R. </p> <p>AA.zip = <em>Acanella arbuscula</em><br> DP.zip = <em>Desmophyllum pertusum</em> (former and now unaccepted synonym <em>Lophelia pertusa</em>)<br> ER.zip = <em>Enallopsammia rostrata</em><br> GD.zip = <em>Goniocorella dumosa</em><br> MO.zip = <em>Madrepora oculata</em><br> OV.zip = <em>Oculina varicosa</em><br> PA.zip = <em>Paragorgia arborea</em><br> PP.zip = <em>Paramuricea placomus</em><br> PR.zip = <em>Primnoa resedaeformis</em><br> SV.zip = <em>Solenosmilia variabilis</em></p>
Environmental drivers and distribution of cold-water corals in the global ocean - Environmental Data Layers
<p><strong>Publication Abstract</strong></p> <p>Species distribution models (SDMs) are useful tools for identifying the distribution of marine species in data limited environments. Outputs from SDMs have been used to identify areas for spatial management, analyzing trawl closures, quantitatively measuring the risk of bottom trawling, and evaluating protected areas for improving conservation management. Cold-water corals are globally distributed habitat forming organisms that are vulnerable to anthropogenic impacts and climate change, but data deficiency remains an ongoing issue for the effective spatial management of these important ecosystem engineers. In this study, we constructed 11 environmental seabed variables at 500m resolution based on the latest multi-depth global datasets and high-resolution bathymetry. Ensemble modeling methods were used to predict the global habitat suitability for ten widespread cold-water coral species, including six reef Scleractinian framework-forming species and four large gorgonian species. Temperature, depth, salinity, terrain ruggedness index, carbonate saturation state and chlorophyll were the most important factors in determining the global distributions of these species. The Scleractinian species <em>Madrepora oculata</em> showed the widest niche breadth, whilst most other species demonstrated somewhat limited niche breadth. The shallowest study species, <em>Oculina varicosa</em>, had the most distinctive niche of the group. The model outputs from this study represent the highest resolution global predictions for these species to date and are valuable in aiding the management, conservation and continued research into cold-water coral species.</p> <p><strong>Data description</strong></p> <p>These datasets (compressed Zip archives) contain the ten global environmental layers that were generated for the publication Tong et al., (2023) doi: 10.3389/fmars.2023.1217851, using a trilinear interpolation approach based on the 500m GEBCO bathymetric data product. These layers are representations of seafloor conditions. Please refer to the manuscript for methodological details. These files are provided in an ArcGIS compatible TIFF format that is readable by various GIS packages and can be imported to R. </p> <p>aoxu.zip = Apparrent Oxygen Utilization<br> diso2.zip = Dissolved Oxygen<br> nit.zip = Nitrate<br> oa.zip = Omega Aragonite<br> oc.zip = Omega Calcite<br> ph.zip = pH<br> phos.zip = Phosphate<br> sal.zip = Salinity<br> sil.zip = Silicate<br> temp.zip = Temperature</p>
Modelling the potential global distribution of suitable habitat for the biological control agent Heterorhabditis indica
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Data from: Climate change is predicted to impact the global distribution and richness of pines (genus Pinus) by 2070
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Soil dissolved organic carbon in terrestrial ecosystems: global budget, spatial distribution and controls
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Evolution in response to climate in the native and introduced ranges of a globally distributed plant
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GARD 1.7 - updated global distributions for all terrestrial reptiles
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Land use drives the distribution of free, physically protected, and chemically protected soil organic carbon storage at a global scale
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Data from: The Tara Oceans voyage reveals global diversity and distribution patterns of marine planktonic ciliates
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Climate and ant diversity explain the global distribution of ant-plant mutualisms
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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.