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7,883 results for “tropics”
TROPRAIN: Tropical Precipitation anomalies
<p>The TROPRAIN dataset is a product containing the daily rainfall anomalies across the entire tropical region (180°W - 180°E/30°S - 30°N). These are calculated from the GOES, GPCP, CPC and TRMM data sets. The data is in NetCDF format and is 3D grids. The objective of this data set is to facilitate researchers to study rainfall anomalies during the occurrence of short and medium duration physical processes.</p>
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>
Mean current velocity sections along 11°S, 5°S, 35°W, and 23°W from shipboard measurements used in "Transports and pathways of the tropical AMOC return flow from Argo data and shipboard velocity measurements"
<p>This data set contains current velocity measurements used in the study "Transports and pathways of the tropical AMOC return flow from Argo data and shipboard velocity measurements“ by <em>Tuchen et al. (2022)</em> published at <em>Journal of Geophysical Research: Oceans</em>.</p> <p>For the meridional mean sections along 35°W and 23°W, and for the quasi-zonal sections along 11°S and 5°S, one ".mat" file is provided for each of the sections. Please note that the section along 11°S consists of a zonal part (east of 34.2°W) and a cross-shore part closer to the coast. The meridional velocities along the cross-shore part of the 11°S-section are rotated clockwise by 36° in order to derive along-shore velocities.</p> <ul> <li>11°S: meridional velocity / alongshore velocity (V), neutral density (gamma_n), longitude (LON), depth (Z)</li> <li>5°S: meridional velocity (V), neutral density (gamma_n), longitude (LON), depth (Z)</li> <li>35°W: zonal velocity (U), neutral density (gamma_n), latitude (LAT), depth (Z)</li> <li>23°W: zonal velocity (U), neutral density (gamma_n), latitude (LAT), depth (Z)</li> </ul> <p>Mean velocity data in the upper 10 m are replaced by the gridded mean surface current velocities at 1/4° horizontal resolution derived from satellite-tracked surface drifting buoys (<em>Laurindo et al. 2017</em>) that were horizontally interpolated to the resolution of the individual ship sections.</p>
Dataset to manuscript: Tailor-made biochar systems: Interdisciplinary evaluations of ecosystem services and farmer livelihoods in tropical agro-ecosystems
<p>Raw data to the manuscript entitled "Tailor-made biochar systems: Interdisciplinary evaluations of ecosystem services and farmer livelihoods in tropical agro-ecosystems" by Severin-Luca Bellè, Jean Riotte, Norman Backhaus, Muddu Sekhar, Pascal Jouquet and Samuel Abiven. </p> <p>Data files include all raw data of farmer interviews (20211209_Biochar_India_rawdata_Bellè_Abiven_farmer_interviews) and all raw data from the soil incubation study (20211209_Biochar_India_rawdata_Bellè_Abiven_soil_incubation). </p> <p>File ending with var_names is the README file. </p>
Global restoration opportunities in tropical rainforest landscapes - Supplementary Materials - Spatial Data Layers
<p><strong>Global restoration opportunities in tropical rainforest landscapes</strong></p> <p><strong>Sci Adv 5 (7), eaav3223</strong></p> <p><strong>DOI: 10.1126/sciadv.aav3223</strong></p> <p><strong><a href="https://advances.sciencemag.org/content/5/7/eaav3223">https://advances.sciencemag.org/content/5/7/eaav3223</a></strong></p> <p><strong>Supplementary Materials</strong></p> <p><strong><a href="https://advances.sciencemag.org/content/suppl/2019/07/01/5.7.eaav3223.DC1">https://advances.sciencemag.org/content/suppl/2019/07/01/5.7.eaav3223.DC1</a></strong></p> <p><strong>Spatial Data layers:</strong></p> <p><strong><a href="https://doi.org/10.5281/zenodo.3233495">https://doi.org/10.5281/zenodo.3233495</a></strong></p> <p><strong>_OutR10:</strong></p> <p><strong>r_10.img → Global restoration opportunity score (ROS)</strong></p> <p><strong>r_10_sc.img → Global restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_nt_sc.img → Neo Tropic restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_aa_sc.img → Australiasia restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_at_sc.img → Afro Tropic restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_im_sc.img → Indo Malay restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_nt_sc.img → Neo Tropic restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p> </p> <p><strong>_OutBasics:</strong></p> <p><strong>r_1.img → Study Area</strong></p> <p><strong>r_2.img → Restorable Area</strong></p> <p><strong>r_3.img → Restoration Benefits</strong></p> <p><strong>r_4.img → Restoration feasibility</strong></p> <p><br> <strong>_OutCountry:</strong></p> <p><strong>r_10_XXX_sc.tif → restoration opportunity score (ROS) for country XXX – rescaled 0-1</strong></p> <p><br> <strong>_OutHotspots:</strong></p> <p><strong>r_10_hotspot_XXX_hotspot_area_sc.tif → restoration opportunity score (ROS) for conservation hotspot area XXX – rescaled 0-1</strong></p> <p><strong>r_10_hotspots_upper60.img → Areas with restoration opportunity score (ROS) above 0.6 in conservation hotspots</strong></p> <p><br> <strong>_OutKBA:</strong></p> <p><strong>r_10_XXX_sc.tif → restoration opportunity score (ROS) for Key Biodiversity Area XXX – rescaled 0-1</strong></p> <p><strong>r_10_kba_upper60.img → Areas with restoration opportunity score (ROS) above 0.6 in Key Biodiversity Areas</strong></p> <p><br> <strong>_OutAichi:</strong></p> <p><strong>r_10_aichi_XXX.tif → Top 15% area of with highest restoration opportunity score (ROS) in country XXX</strong></p> <p><strong>r_10_aichi.img → Top 15% area of with highest restoration opportunity score (ROS) global</strong></p> <p><br> <strong>_OutBonn:</strong></p> <p><strong>r_10_XXX_Bonn.img → Area with highest restoration opportunity score (ROS) in country XXX according to their Bonn Challenge commitments</strong></p> <p> </p> <p><strong>_OutParis:</strong></p> <p><strong>r_10_at_paris.img → Area with highest restoration opportunity score (ROS) in Afro Tropic Nationally Determined Contributions to the Paris Climate Agreement</strong></p> <p><strong>r_10_im_paris.img → Area with highest restoration opportunity score (ROS) in Indo Malay Nationally Determined Contributions to the Paris Climate Agreement</strong></p> <p><strong>r_10_nt_paris.img → Area with highest restoration opportunity score (ROS) in Neo Tropic Nationally Determined Contributions to the Paris Climate Agreement</strong></p> <p><br> <strong>_OutTEOW:</strong></p> <p><strong>r_10_ECOREGION_XXX_sc.tif → restoration opportunity score (ROS) for Ecoregion XXX – rescaled 0-1</strong></p> <p><strong>r_10_ECOREGION_upper60.img → Areas with restoration opportunity score (ROS) above 0.6 in Ecoregions</strong></p> <p> </p> <p><strong>_OutAll</strong></p> <p><strong>alltargets.img → Area with highest restoration opportunity score (ROS) according to all targets (excluded from the paper)</strong></p> <p> </p>
Annual tropical forest loss during 2001-2021 in the Congo Basin
<p>The loaded dataset in Zenodo includes the following parts:</p> <p>(1) Shp file of the Congo Basin;</p> <p>(2) GeoTIFF image of the evergreen forest cover map in the Congo Basin (file name 'Congo_EvergreenForest2000_TCC70_Height5_Clip');</p> <p>(3) GeoTIFF image of the annual forest loss map produced by us (file name 'Congo_log2001_2021_L78S2_w300_cb300_theta0p2_clean120_5_new');</p> <p>(4) GeoTIFF image of the post-forest loss recovery index map produced by us (file name 'Congo_RI_Log2001_2021_L78S2_w300_cb300_400_theta0p2_clean120_5_new.tif'), and it is noted that the industrial plantation map (file name 'JRC_TMF_plantations_2022.tif') should be used to exclude any plantations in our post-forest loss recovery index map, as industrial plantations is not regarded as forest recovery.</p>
Marine heatwaves statistics for the tropical western and central Pacific Ocean
<p>Processed marine heatwave metrics are provided for the tropical western and central Pacific Ocean region (120°E-140°W, 40°S-15°N). The metrics are computed from daily sea surface temperature (SST) data, from both observations and models. The observed marine heatwave data are calculated from NOAA 0.25° daily Optimum Interpolation Sea Surface Temperature (OISST) over the period 1982-2019. The modelled marine heatwave data are from analysis of 18 model simulations as part of the Coupled Model Intercomparison Project, Phase 6 (CMIP6) over the period 1982-2100, where two future scenarios have been analysed. Marine heatwaves are computed with respect to the 1995-2014 climatology. The marine heatwave data are provided on a grid point basis across the domain. Marine heatwave timeseries metrics are also provided for three case study regions: Fiji, Samoa, and Palau.</p>
Indicative distribution map for Ecosystem Functional Group TF1.1 Tropical flooded forests and peat forests
<p>This archive contains indicative distribution maps and profiles for <strong>TF1.1 Tropical flooded forests and peat forests</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Indicative distribution map for Ecosystem Functional Group T6.5 Tropical alpine grasslands and herbfields
<p>This archive contains indicative distribution maps and profiles for <strong>T6.5 Tropical alpine grasslands and herbfields</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Indicative distribution map for Ecosystem Functional Group T3.1 Seasonally dry tropical shrublands
<p>This archive contains indicative distribution maps and profiles for <strong>T3.1 Seasonally dry tropical shrublands</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Topography drives microgeographic adaptations of closely-related species in two tropical tree species complexes
<p>Combining LiDAR-derived topography, tree inventories, and single nucleotide polymorphisms (SNPs) from gene capture experiments, we explored genome-wide population genetic structure, covariation of environmental variables, and genotype-environment association to assess microgeographic adaptations to topography within the species complexes <em>Symphonia</em> (Clusiaceae), and <em>Eschweilera</em> (Lecythidaceae) with three species per complex and 385 and 257 individuals genotyped, respectively.</p>
QBO: monthly zonal stratospheric winds from tropical radiosonde data (mainly Singapore)
<p><strong>Monthly Tropical Stratospheric Zonal Winds from Radiosondes</strong></p> <p><strong>Data Source and Processing:</strong></p> <p>Monthly mean zonal wind data for the tropics are provided as a service for the global QBO and trend analysis communities. The original data source and processing chain were established by the Free University of Berlin (FUB). Currently, the data is processed at the Karlsruhe Institute of Technology (KIT, ROR:04t3en479), Institute of Meteorology and Climate Research (IMK), Germany with the tools developed at FUB.</p> <p><strong>Data Description:</strong></p> <p>The dataset includes monthly mean zonal wind values at pressure levels 100, 90, 80, 70, 60, 50, 45, 40, 35, 30, 25, 20, 15, 12, and 10 hPa, derived from radiosonde observations at four equatorial stations:</p> <ul> <li>Kiribati (Canton Island) - data from 1953 to 1967 (closed)</li> <li>Maldives (Gan Island) - data from 1967 to 1975 (closed)</li> <li>Singapore (Payalebar) - data from 1975 to 1989</li> <li>Singapore (Changi) - data from 1989 onwards</li> </ul> <p><strong>Important Notes:</strong></p> <ul> <li>Values for 100 hPa from October 1967 are solely from Singapore (Changi).</li> <li>Values for 100 hPa before October 1967 are from Kiribati (Canton Island) when available.</li> <li>Detailed information about the radiosonde stations and their periods of operation is provided below.</li> </ul> <p><strong>Additional Information:</strong></p> <ul> <li>Access the data in other formats also published here: <a href="https://www.atmohub.kit.edu/english/807.php" target="_blank" rel="noopener noreferrer">https://www.atmohub.kit.edu/english/807.php</a></li> </ul> <p><strong>Detailed List of Radiosonde Stations:</strong></p> <div> <div> <div> <div> <table> <tbody> <tr> <th>Station Name</th> <th>Location (Lat, Lon)</th> <th>Data Period</th> <th>Pressure Levels (hPa)</th> </tr> <tr> <td>Kiribati (Canton Island)</td> <td>-2.7667, -171.7167</td> <td>1953 - 1967 (closed)</td> <td> <p>Above 100 (until August 1967)</p> <p>100 (until September 1967)</p> </td> </tr> <tr> <td>Maldives (Gan Island)</td> <td>-0.6933, 73.1556</td> <td>1967 - 1975 (closed)</td> <td>Above 100 (September 1967 to December 1975)</td> </tr> <tr> <td>Singapore (Payalebar)</td> <td>1.3667, 103.9167</td> <td>1975 - 1989</td> <td>Above 100 (January 1976 to May 1989)</td> </tr> <tr> <td>Singapore Upper Air Observatory</td> <td>1.3404, 103.8879</td> <td>1989 - present</td> <td> <p>100 (October 1967 to May 1989), </p> <p>All levels from June 1989</p> </td> </tr> </tbody> </table> </div> </div> </div> </div> <div> </div>
Trellis-forming stems of a tropical liana Condylocarpon guianense (Apocynaceae): a plant-made safety net constructed by simple "start-stop" development
<p>Data supporting article describing mechanical and structural organisation of a climin g plant trellis system sin the tropical rainforest of French Guiana</p> <p>Tropical vines and lianas have evolved mechanisms to avoid mechanical damage during their climbing life histories. We explore the mechanical properties and stem development of a tropical climber that develops trellises in tropical rain forest canopies. We measured the young stems of <em>Condylocarpon guianensis</em> (Apocynaceae) that construct complex trellises via self-supporting shoots, attached stems and unattached pendulous stems. The results suggest that in this species there is a size (stem diameter) and developmental threshold at which plant shoots will make the developmental transition from stiff young shoots to later flexible stem properties. Shoots that do not find a support remain stiff, becoming pendulous and retaining numerous leaves. The formation of a second TYPE II (lianoid) wood is triggered by attachment, guaranteeing increased flexibility of light-structured shoots that transition from self-supporting searchers to inter-connected net-like trellis components. The results suggest that this species shows a “hard-wired” development that limits self-supporting growth among the slender stems that make up a liana trellis. The strategy is linked to a stem-twining climbing mode and promotes a rapid transition to flexible trellis elements in cluttered densely branched tropical forest habitats. These are situations that are prone to mechanical perturbation via wind action, tree falls and branch movements. The findings suggest that some twining lianas are mechanically fine-tuned to produce trellises in specific habitats. Trellis building is carried out by young shoots that can perform very different functions via subtle development changes in order to ensure a safe space occupation of the liana canopy.</p>
Spatial and Temporal Availability of Cloud-free Optical Observations in the Tropics
<p>These data comprise three layers describing the spatial and temporal distribution of cloud-free optical observations over the tropics. The test datasets shared here are derived from the combination of Landsat and Sentinel-2 satellite data and represent a portion of the full datasets. The test data correspond to year 2020 over Mesoamerica. </p> <ul> <li>Spatial data: the Meso2020_validObs contains one band 'valid_obs' indicating the number of cloud-free observations at the pixel level. </li> <li>Temporal data: the maximumWaitDate2020 contains two bands 'max' and 'maxDay' corresponding to the number of maximum consecutive days without data in a year and final date in which the maximum number of consecutive days without data occurred, respectively.</li> </ul>
Detecting small changes in tropical forests from space... data and code for thesis chapter 4
<p>SAR and UAV-LiDAR data used in chapter 4 of my thesis <em>Detecting small changes in tropical forests from space: experiments using synthetic aperture radar. </em>This content has also been submitted for peer review in Frontiers in Remote Sensing.</p> <p>DEM_timeseries_3m contains phase height and coherence from TanDEM-X InSAR high-resolution spolight images, processed by Jose-Luis Bueso-Bello at DLR. NetCDF format, dimensions latitude, longitude, time.</p> <p>TDX_descending_intensity contains intensity from the same TanDEM-X time series, covering an area of the Madre de Dios region in Peru. These data were processed by Harry Carstairs using ESA's SNAP software.</p> <p>UAV_change_1m_mask is a raster showing the change in canopy height at the study site between June 2019 and July 2021, according to two UAV LiDAR campaigns, with 1m pixels, and with areas with low point density masked out.</p> <p>CODE.zip contains python scripts and notebooks used to collate the data, create change detection metrics, develop SAR models of canopy height, and produce the figures.</p> <p>Funded by European Research Council (ERC) grant to the Tropical Forest Degradation Experiment (FODEX).</p>
Input data to replicate "The social cost of tropical cyclones"
<p>Input data for the <a href="https://dx.doi.org/10.5281/zenodo.8056520">scripts</a> that replicate the results of <a href="https://doi.org/10.1038/s41467-023-43114-4">Krichene et al. 2023</a>.</p> <p>To run the <a href="https://dx.doi.org/10.5281/zenodo.8056520">scripts</a>, the following files from this repository need to be placed in the <code>./data/input/</code> subdirectory of the project folder containing the <a href="https://dx.doi.org/10.5281/zenodo.8056520">scripts</a>:</p> <ul> <li><code>GMT.nc</code>: Global mean temperature time series as used by the <a href="https://gitlab.pik-potsdam.de/tovogt/tc_emulator">tropical cyclone emulator</a>.</li> <li><code>GrowthClimateDataset.dta</code>: The <a href="https://purl.stanford.edu/wb587wt4560">input data</a> of <a href="https://dx.doi.org/10.1038/nature15725">Burke et al. 2015</a>.</li> <li><code>IHME_GLOBAL_GDP_ESTIMATES_1950_2015.csv</code>: Historical GDP per capita data from <a href="https://doi.org/10.1186/1478-7954-10-12">James et al. 2012</a> (downloaded from <a href="https://ghdx.healthdata.org/record/ihme-data/gross-domestic-product-gdp-estimates-country-1950-2015">IHME</a>).</li> <li><code>mean_temperature_gswp3-w5e5.csv</code>: Population-weighted average national temperature time series for the historical period.</li> <li><code>pulse_response_ricke_caldeira_2014.csv</code>: The global mean temperature response of an additional emission pulse according to <a href="https://dx.doi.org/10.1088/1748-9326/9/12/124002">Ricke & Caldeira 2014</a>.</li> <li><code>tcdata/TCE-DAT_historic-exposure_1950-2015.csv</code> and <code>tcdata/TotalPopulation.csv</code>: Historical (national) numbers of people affected by tropical cyclones according to <a href="https://doi.org/10.5880/pik.2017.011">TCE-DAT</a> with the corresponding total population counts.</li> <li><code>tcdata/emulator/</code>: Projected (national) shares of people affected by tropical cyclones according to the <a href="https://gitlab.pik-potsdam.de/tc_cost/tc_emulator">tropical cyclone emulator</a> as computed by the scripts in the <a href="https://gitlab.pik-potsdam.de/tc_cost/tc_people_affected">corresponding repository</a>.</li> <li><code>wid_all_data.zip</code>: A bulk data set from the <a href="https://wid.world/bulk_download/wid_all_data.zip">World Inequality Database</a>.</li> </ul> <p>For more information, see <a href="https://doi.org/10.1038/s41467-023-43114-4">Krichene et al. 2023</a> and the README file provided with the <a href="https://dx.doi.org/10.5281/zenodo.8056520">scripts</a>.</p>
Indicative distribution map for Ecosystem Functional Group T1.4 Tropical heath forests
<p>This archive contains indicative distribution maps and profiles for <strong>T1.4 Tropical heath forests</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
DeepSurge storm surge predictions for HighResMIP tropical cyclones
<p>DeepSurge is a newly presented deep-learning approach to modeling the storm surge generated by a tropical cyclone (TC). This dataset is a collection of DeepSurge outputs for synthetic TCs in the North Atlantic generated by the HighResMIP project (Haarsma et al. 2016) for a simulated historical (1950-2014) and future (2015-2050) climate under the climate scenario SSP585.</p> <p>The data generation process and data analysis is detailed in an upcoming publication. The storm surge data presented here intentionally does not include the effects of sea level rise, rainfall, or other factors, in order to isolate the effects of changing TC climatology on future storm surge risk.</p> <h4>Dataset format</h4> <p>The data comes in the form of maximum surge levels at 2846 near-coastal locations for each synthetic TC. Each TC is defined by the corresponding track in the HighResMIP TempestExtremes dataset (Roberts 2019). The data is presented in NetCDF format, with two dimensions: </p> <ul> <li>'nodes', the number of near-coastal locations, always 2846.</li> <li>'tracks', the number of tracks in the simulation, which is different in each file.</li> </ul> <p>There are 6 variables in each file:</p> <ul> <li>'lons' and 'lats', the coordinates of the nodes in degrees North and East respectively.</li> <li>'track_valid' is a binary indicator (zero for false, one for true) indicating whether the TC occurs within the region of interest (HighResMIP tracks are global, but we only simulate those in the North Atlantic)</li> <li>'track_done' is another binary indicator for whether the track has been simulated. It should indicate true for all tracks for which 'track_valid' is true.</li> <li>'max_zeta' provides the predicted maximum surge height, in meters, for each storm at all 2846 nodes. This data is only valid in entries for which the corresponding 'track_done' and 'track_valid' indicators are true.</li> <li>'years' is the year in which each simulated TC occurs.</li> </ul>
Interactions between plants and fungi and their roles in decay rates and CO2 release in five tropical leaf species
A microcosm experiment was used to test for the effects of interactions between particular plant and fungal decomposer species on rates of leaf decomposition. Each microcosm contained one species of leaf that was sterilized with gamma irradiation and then inoculated with a single fungus. Five plant species and ten fungal species (two dominants from each of the litter types) were used in all possible combinations. Plant species were selected for pair-wise comparisons based on phylogenetic relationships and litter quality characteristics. Decomposition was measured by both mass loss and CO2 release. Differences in weight loss and CO2 evolution were highly significant for plants, fungal species, and their interactions. Mass loss was positively correlated with CO2 evolution. Contrary to our hypotheses, however, microfungal dominants did not decompose their source leaves faster than microfungal dominants from other leaf species, nor were responses to other types of specificity detected. Matching of fungi to leaf substrates by their source, by phylogenetic relationships, or by chemical, physical and structural characteristics was not associated with consistent increases in decomposition. Although previously documented differences in microfungal species composition and dominance among decomposing leaves of different trees were confirmed in this study, such differences apparently do not directly affect the rates of ecosystem processes. The presence in a few of the microcosms of a generalist basidiomycete that had ligninolytic enzymes, Melanotus eccentricus, significantly accelerated the rate of decomposition. Non-specific basidiomycetes may therefore have a stronger effect on early stages of leaf litter decomposition than host-selective microfungi. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Pue
Recovery of a tropical stream after a harvest-related chlorine poisoning event
1. Harvest-related poisoning events are common in tropical streams, yet research on stream recovery has largely been limited to temperate streams and generally does not include any measures of ecosystem function, such as leaf breakdown. 2. We assessed recovery of a second-order, high-gradient stream draining the Luquillo Experimental Forest, Puerto Rico, three months after a chlorine-bleach poisoning event. The illegal poisoning of freshwater shrimps for harvest caused massive mortality of shrimps and dramatic changes in those ecosystem properties influenced by shrimps. We determined recovery potential using an established recovery index and assessed actual recovery by examining whether the poisoned reach returned to conditions resembling an undisturbed upstream reference reach.3. Recovery potential was excellent (score=729 out of a possible 729) and can be attributed to nearby sources of organisms for colonization, the mobility of dominant organisms, unimpaired habitat, rapid flushing and processing of chlorine, and location within a national forest.4. Actual recovery was substantial. Comparison of the reference reach with the formerly poisoned reach indicated: (1) complete recovery of xiphocaridid and palaemonid shrimp population abundances, shrimp size distributions, leaf breakdown rates, and abundances of oligochaetes and mayflies on leaves, and (2) only small differences in atyid shrimp abundance and community and ecosystem properties influenced by atyid shrimps (standing stocks of epilithic fine inorganic and organic matter, chlorophyll a, and abundances of chironomids and copepods on leaves). 5. There was no detectable pattern between any measured variables and distance downstream from the poisoning. However, shrimp size-distributions indicated that the observed recovery may represent a source-sink dynamic, in which the poisoned reach acts as a sink which depletes adult shrimp populations from surrounding undisturbed habitats. Thus, the rapid recovery observe
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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.