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7,883 results for “tropics.”
Global and tropical band averages for a selection of CMIP5 and CMIP6 models: piControl and abrupt-4xCO2 experiments
<p>This dataset provides post-processed spatial averages for a selection of CMIP5 and CMIP6 models. The experiments contained in this dataset are only the pre-industrial controls (piControl) and the experiments with a four-fold increase in the atmospheric CO$_{2}$ concentration in relation to the pre-industrial level (abrupt-4xCO2). The spatial averages are global and tropical bands from x°S to x°N, where the x value is between 5 and 40 in increments of 5°. This dataset was created to study climate sensitivity in general and the effect of stratospheric circulation changes on the tropical equilibrium climate sensitivity. It contains the following variables:</p> <ul> <li>incoming (d) short-wave (SW, s) radiative flux (RF, r) at the top of the atmosphere (TOA, t): rsdt</li> <li>outgoing (u) SW RF at TOA: rsut</li> <li>outgoing long-wave (LW, l) RF at TOA: rlut</li> <li>net (n) RF at TOA: rnt</li> <li>incoming SW RF at the surface (s): rsds</li> <li>outgoing SW RF at the surface: rsus</li> <li>incoming LW RF at the surface: rlds</li> <li>outgoing LW RF at the surface: rlus</li> <li>net RF at the surface: rns</li> <li>sensible heat flux (hfs) at the surface: hfss</li> <li>latent heat flux (hfl) at the surface: hfls</li> <li>surface temperature (t): ts</li> <li>atmospheric temperature: ta</li> <li>specific humidity: hus</li> <li>zonal component of wind: ua</li> <li>meridional component of wind: va</li> <li>lagrangian tendency of pressure (vertical component of wind in pressure per time dimensions): wap</li> <li>surface pressure: ps</li> <li>geopotential height: zg</li> </ul>
Variability and drivers of the Oxygen Minimum Zone in the Tropical Pacific during 1981-2020
<p>This dataset contains model outputs simulated by a basin-scale model (OGCM-DEEC V1.4). It is supplementary of the paper "Variability and drivers of the Oxygen Minimum Zone in the Tropical Pacific during 1981-2020". Please let me know if you need any more information.</p>
Genomes of tropical rust fungi (Araucariomyces, Coleosporium, Hamaspora, Puccinia paululla, Skierka, and Sphaerophragmium)
<p>Hi, </p> <p>Everyone is welcome to use the assemblies shared here.</p> <p>The catch is they are not perfect. The DNA was extracted from environmental samples and has contaminant contigs from hosts, bacteria and endophytic or saprophytic fungi. I've run Kraken2 to remove most host and bacterial contigs, but there are no doubt some remaining. The genomes were sequenced in the same run and the mitochondrion of <em>Sphaerophragmium</em> is present in all genomes.</p> <p>The taxa are selected for their evolutionary relationships in the Pucciniales (see Aime and McTaggart 2021, A familial rank classification of rust fungi, with notes on genera). More about each taxon is available below.<br> <em>Araucariomyces fragiforme</em>: https://collections.daff.qld.gov.au/web/key/rustfungi/Media/Html/aecidiumfragiforme.html<br> <em>Coleosporium plumeriae</em>: https://collections.daff.qld.gov.au/web/key/rustfungi/Media/Html/coleosporiumplumeriae.html<br> <em>Hamaspora acutissima</em>: https://collections.daff.qld.gov.au/web/key/rustfungi/Media/Html/hamasporaacutissima.html<br> <em>Puccinia paullula</em>: https://collections.daff.qld.gov.au/web/key/rustfungi/Media/Html/pucciniapaullula.html<br> <em>Skierka agallocha</em>: https://collections.daff.qld.gov.au/web/key/rustfungi/Media/Html/skierkaagallocha.html<br> <em>Sphaerophragmium acaciae</em>: https://collections.daff.qld.gov.au/web/key/rustfungi/Media/Html/sphaerophragmiumacaciae.html</p> <p>The raw sequencing data are available either in this Google Drive folder https://drive.google.com/drive/folders/1nDRWYLEO80R6CCxdnMzkaFY3DqUKn6Vo?usp=sharing, or contact me (alistair.mctaggart@gmail.com) if you need another option.</p> <p>If you have the fortitude to clean up these genomes, let's get them on to GenBank. I'd be delighted to collaborate on anything and currently am working on mating genes and mitochondria.</p> <p>All genomes were assembled with the below commands for Supernova. The assembly reports have also been provided.</p> <p>supernova run --id RustName --accept-extreme-coverage --localmem 350 --fastqs=$PATH/reads --maxreads=2140000000<br> supernova mkoutput --style=megabubbles --asmdir=$PATH/RustName/outs/assembly --outprefix=RustName_mkoutput</p> <p>There are more raw sequencing data for <em>Uromycladium</em> and <em>Ravenelia</em>, but nothing assembled from these yet. You're welcome to use them if you need.</p> <p>You could do me a solid and cite: McTaggart et al. (2022) Sexual reproduction is the null hypothesis in life cycles of rust fungi. PLoS Pathogens 18(5):e1010439. DOI: 10.1371/journal.ppat.1010439. We released the MAT loci based on the genomes in there, more work on RNAi pathway genes coming that can be cited as an alternative to reference these assemblies when published.</p> <p>Enjoy and keep smiling,<br> Alistair</p>
Data from: Seasonal variation in wildlife roadkills in plantations and tropical rainforest in the Anamalai Hills, Western Ghats, India
<p>This dataset contains animal roadkill data (2011-13) from the Valparai Plateau and Anamalai Tiger Reserve, Western Ghats, India. Occurrence records were gathered in the field by researchers of the <a href="https://www.ncf-india.org">Nature Conservation Foundation, India</a>. The dataset corresponds to the following publication:</p> <p>Jeganathan, P., Mudappa, D., Kumar, M. A., and Raman, T. R. S. 2018. <a href="https://doi.org/10.18520/cs/v114/i03/619-626">Seasonal variation in wildlife roadkills in plantations and tropical rainforest in the Anamalai Hills, Western Ghats, India</a>. <em>Current Science</em> 114(3): 619-626. DOI: 10.18520/cs/v114/i03/619-626</p> <p>CONTACT #1<br> 1. Name: P. Jeganathan<br> 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br> 3. Work Phone: +91 821 2515601<br> 4. Email address: jegan@ncf-india.org<br> 5. ORCID: https://orcid.org/0000-0002-0238-0655</p> <p>CONTACT #2<br> 1. Name: Divya Mudappa<br> 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br> 3. Work Phone: +91 821 2515601<br> 4. Email address: divya@ncf-india.org<br> 5. ORCID: https://orcid.org/0000-0001-9708-4826</p> <p>CONTACT #3<br> 1. Name: M. Ananda Kumar<br> 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br> 3. Work Phone: +91 821 2515601<br> 4. Email address: anand@ncf-india.org<br> 5. ORCID: https://orcid.org/0000-0001-7094-1314</p> <p>CONTACT #4<br> 1. Name: T. R. Shankar Raman<br> 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br> 3. Work Phone: +91 821 2515601<br> 4. Email address: trsr@ncf-india.org<br> 5. ORCID: https://orcid.org/0000-0002-1347-3953</p> <p><strong>Keywords: </strong>tropical rainforest, plantations, Anamalai Hills, animal roadkill, linear infrastructure intrusions, highways, road ecology, animal-vehicle collisions </p> <p><strong>Geographic Coverage:</strong><br> 1. Location/Study Area: Valparai Plateau, Tamil Nadu, India; Anamalai Tiger Reserve, Tamil Nadu, India<br> 2. GPS coordinates: Valparai Plateau (10°15'- 10°22'N, 76°52' - 76°59'E); Anamalai Tiger Reserve (10°12' - 10°35'N, 76°49' - 77°24'E)</p> <p><strong>Temporal Coverage:</strong><br> 1. Begins: 2011-06-01 (Year, Month, Day)<br> 2. Ends: 2013-05-31 (Year, Month, Day)</p> <p><strong>Methods:</strong></p> <p>Methods involved repeated surveys along the road routes searching for roadkills and habitat sampling as described in <a href="https://doi.org/10.18520/cs/v114/i03/619-626">Jeganathan et al. (2018),<em> Current Science</em> 114(3): 619-626</a>, DOI: 10.18520/cs/v114/i03/619-626</p> <p><strong>Files included:</strong></p> <p>Besides this 00_README.txt file, the dataset includes the following six files as explained below:<br> 1) 01_habitat_length.csv -- details of road routes surveyed as line transects<br> 2) 02_sampling_events.csv -- details of individual line transect sample surveys along road routes<br> 3) 03_roadkill_data_final.csv -- roadkill occurrence data from sample surveys along road routes<br> 4) 04_canopy_and_habitat.csv -- canopy and habitat readings along road routes (transects) surveyed<br> 5) 05_roadkill_transects_all.kml -- KML file containing geographic tracks of 11 road routes surveyed as roadkill transects<br> 6) 06_road_transects_map.jpg -- Map of surveyed routes corresponding to Figure 1 in Jeganathan et al. (2018)</p> <p><strong>01_habitat_length.csv</strong><br> transect: name of road route surveyed as a line transect<br> route_description: description of road route<br> tlength_km: transect length along road in kilometres (km)<br> tlength_m: transect length along road in metres (m)<br> forest: extent of the road in metres (m) with forest on both sides<br> forest_tea: extent of the road in metres (m) with forest on one side, tea on the other<br> coffee_forest: extent of the road in metres (m) with forest on one side, coffee plantation on the other<br> tea: extent of the road in metres (m) with tea plantation on both sides<br> coffee: extent of the road in metres (m) with coffee plantation on both sides<br> eucalyptus: extent of the road in metres (m) with eucalyptus plantation on both sides<br> eucalyptus_tea: extent of the road in metres (m) with eucalyptus on one side, tea plantation on the other</p> <p><strong>02_sampling_events.csv</strong><br> season: monsoon (June to December 2011) or summer (March to June 2012 prior to the onset of 2012 monsoon)<br> transect: name of road route surveyed as a line transect<br> transect: name of road route surveyed as a line transect<br> tcode: unique code for each individual survey of a road route (transect) coevered on a specific date<br> eventDate: date of road survey<br> tlength: transect length along road in kilometres (km)</p> <p><strong>03_roadkilldata_final.csv</strong><br> sno: serial number of observation<br> season: monsoon (June to December 2011) or summer (March to June 2012 prior to the onset of 2012 monsoon)<br> transect: name of road route surveyed as a line transect<br> tcode: unique code for each individual survey of a road route (transect) coevered on a specific date<br> eventDate: date of road survey<br> fielddate: date of road survey as initially noted (for two surveys completed over two successive days, the initial date was recorded as eventDate for 2011-06-17 = 2011-06-16 and eventDate for 2011-07-06 = 2011-07-05<br> tlength: transect length along road in kilometres (km)<br> verbatimIdentification: original identification of roadkilled taxon<br> vernacularName: common name of taxon<br> scientificName: scientific name of taxon for corresponding taxonomic level of identification<br> taxonRank: rank of taxon indicating for corresponding taxonomic level of identification<br> taxonRemarks: category of taxon as noted for analysis<br> verbatimCoordinateSystem: coordinate system used for initial data collection<br> verbatimSRS: SRS of the location data collected (EPSG:32643/WGS84)<br> georeferenceRemarks: note indicating locations were converted from UTM (zone 43 N) to latitude longitude using QGIS software<br> verbatimLongitude: UTM longitude (Easting) as originally recorded<br> verbatimLatitude: UTM latitude (Northing) as originally recorded<br> decimalLongitude: longitude in decimal degree East<br> decimalLatitude: latitude in decimal degrees North<br> habitat: habitat on either side of the road (forest - forest on both sides; forest_tea - forest on one side, tea on the other; human - human settlements; coffee - coffee plantation on both sides; coffee_forest - coffee on one side, forest on the other; eucalyptus - eucalyptus plantation on both sides; eucalyptus_tea - eucalyptus on one side, tea on the other; tea - tea plantation)<br> individualCount: number of individuals recorded as roadkill (0 if no roadkills in that survey)<br> occurrenceStatus: indicated as 'present' for roadkills, or 'absent' if no roadkills recorded<br> occurrenceRemarks: notes and remarks if any</p> <p><strong>04_canopy_and_habitat.csv</strong><br> transect: name of road route surveyed as a line transect<br> verbatimroute: route name as originally noted<br> sno: serial number<br> canopycover: 0 if tree canopy absent, 1 if tree canopy present above point of observation<br> canopyoverlap: horizontal overlap of tree canopy above point of observation ranked as 0 - no canopy above; 1 canopy present but barely touching or overrlapping; 2 - canopy overlapping with sky still visible through leaves; 3 - canopy overlaps overhead densely with sky scarcely visible<br> verticaloverlap: vertical gap between canopy or branches of trees above point of observation ranked as 0 - very wide; 1 - barely touching, 2 - significant vertical overlap, 3 - substantial and dense vertical overlap<br> habcode: two letter alphabetical code with each letter indicating habitat on one side of the road at the point of observation with f - forest, t - tea, c - coffee, e - eucalyptus, v - village or human habitation, m - dam or reservoir<br> habno: numeric category for habitat on either side coded as 1 for monocultures (ee, tt); 2 for mixed forest and plantation (ef, ft, etc.); 3 for forest (ff), and 4 for coffee plantation (cc)<br> longitude: longitude in decimal degrees east<br> latitude: latitude in decimal degrees north</p> <p><strong>05_roadkill_transects_all.kml</strong><br> This KML file contains all 11 road routes surveyed as roadkill transects.</p> <p><strong>06_road_transects_map.jpg</strong><br> This map illustrating the surveyed road routes corresponds to Figure 1 in <a href="https://doi.org/10.18520/cs/v114/i03/619-626">Jeganathan et al. (2018), <em>Current Science</em> 114(3): 619-626</a>, DOI: 10.18520/cs/v114/i03/619-626</p>
Dataset for the ``Fast atmospheric response to a cold oceanic mesoscale patch in the north-western tropical Atlantic" publication
<p>The dataset presented here contains the files needed to produce the results presented in the publication "Fast atmospheric response to a SST mesoscale cold patch in the north-western subtropical Atlantic" submitted to the <em>Journal of Geophysical Research: Atmospheres</em>. The scripts that read and produce these files are publicly available at <a href="https://github.com/ClauClouds/SST-impact/">https://github.com/ClauClouds/SST-impact/</a> and can also be found in this repository (code_python.zip). This Zenodo data repository includes the following datasets:</p> <ul> <li> <p>Radiosonde data from 2-3 February 2020 (Stephan et al., 2021)</p> </li> <li> <p>Doppler lidar, and ARTHUS Raman lidar variables data from 2-3 February 2020,</p> </li> <li> <p>GOES-East (Geostationary Operational Environmental Satellite - East) Binary Cloud Mask (BCM) and Cloud Optical Depth (COD) products, provided at 2 km grid spacing every 10 minutes. They come from the GOES-R Advanced Baseline Imager (ABI) (Schmit et al., 2017), available at <a href="https://www.ncei.noaa.gov/products/satellite/goes-r-series.Data">https://www.ncei.noaa.gov/products/satellite/goes-r-series.Data</a> and they are provided for the 2-3 February 2020.</p> </li> <li> <p>Multi-scale Ultra-high Resolution (MUR) product (JPL MUR MEaSUREs Project, 2015,183 (Chin et al., 2017)) averaged between the 2nd and 3rdfor the 2nd of February 2020. The MUR product is an analysis product provided on a daily basis that combines different satellite (infrared at high and medium resolutions and microwave products) and in-situ data (Chin et al., 2017).</p> </li> <li> <p>W-band radar data post-processed for the purposes of the publication. The original W-band radar data used are publicly accessible at <a href="https://howto.eurec4a.eu/merian_cloudradar.html">https://howto.eurec4a.eu/merian_cloudradar.html</a> and can be downloaded via <a href="https://eurec4a.aeris-data.fr/">AERIS data portal</a>. See more details and specific DOI below.</p> </li> </ul> <p>The present dataset is structured as follows:</p> <ul> <li> <p>diurnal_cycle_removed_vars: files containing the time series of the variables without noise and diurnal cycle (filenames with extended dates 20200202 and 20200203)</p> </li> <li> <p>diurnal_cycle: files containing the diurnal cycle of each variable used in the publication</p> </li> <li> <p>binned_sst_vars: files containing variables binned in terms of SST, used to derive the plots in the paper.</p> </li> <li> <p>satellite_data: a folder containing all satellite data used in the publication</p> </li> </ul> <p>Additional data used in the publication, that are processed via the scripts contained in the link mentioned above, are available online at the following urls:</p> <ul> <li> <p>cloud radar observations can be directly obtained from the public dataset identifiable via DOI: <a href="https://doi.org/10.25326/235">https://doi.org/10.25326/235</a> (Acquistapace et al., 2022)</p> </li> <li> <p>ASCAT wind field data and corresponding MUR SST data are available from the NASA JPL PODAAC platform (<a href="https://podaac.jpl.nasa.gov/">https://podaac.jpl.nasa.gov/</a>)</p> </li> <li> <p>hourly ERA5 (Hersbach et al., 2020) gridded fields (available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=form, last accessed March 2022) of the following variables: SST, water vapor mixing ratio, air temperature, and horizontal wind components. </p> </li> </ul> <p><br> </p> <p>References;</p> <p>Acquistapace et al., 2022, ESSD, <a href="https://doi.org/10.25326/235">https://doi.org/10.25326/235</a>.</p> <p>Schmit, T. et al., 2017, QJRMS, <a href="https://doi.org/10.1175/BAMS-D-15-00230.1">https://doi.org/10.1175/BAMS-D-15-00230.1</a></p> <p>Hersbach et al., 2020, QJRMS, <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3803">https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3803</a></p> <p>Stephan et al., 2021, ESSD, <a href="https://doi.org/10.5194/essd-13-491-2021">https://doi.org/10.5194/essd-13-491-2021</a></p> <p>Chin, T. M. et al., (2017), RS, <a href="https://doi.org/10.1016/j.rse.2017.07.029">https://doi.org/10.1016/j.rse.2017.07.029</a></p>
Tropical house simulation made with ENERGYPLUS
<p>This is an excel file of the simulation of a house located in Tahiti, french Polynesia, made with ENERGYPLUS.</p> <p>Weather data used in the simulation were measured in Tahiti.</p> <p>For further information on the house properties please visit https://doi.org/10.3390/hydrogen2020011</p>
Temperature Profiles from the Eastern Tropical Pacific (0-300m) from January-February 2023
<p>Vertical temperature profiles taken as part of a research expedition to Clipperton Atoll. Water column temperature profiles were measured down to 300m depth by deploying a <em>RBRduet<sup>3</sup> T.D.</em> sensor<sup> </sup>(Range -5°C to 35°C; Initial accuracy ±0.002°C; Resolution <0.00005°C; time constant <1s). Data (downcast and upcast) is averaged by depth into 1 meter bins, and the standard deviation and number of measurements in each bin are included as columns in the data.</p> <p> </p> <p>This data is supplemented locally for the shallow waters of Clipperton Atoll with 21 water column profiles measured using a Mares Puck Pro dive computer (Range: -10 °C to +50 °C; Resolution: 1°C; Accuracy: ± 2 °C) worn by one of the expedition divers.</p>
Appendix S3 from: Droissart V, Dauby G, Hardy OJ, Deblauwe V, Harris DJ, Janssens S, Mackinder BA, Blach-Overgaard A, Sonké B, Sosef MSM, Stévart T, Svenning J-C, Wieringa JJ, Couvreur TLP (2018) Beyond trees: biogeographical regionalization of tropical Africa. Journal of Biogeography. DOI:10.1111/jbi.13190
<p>This dataset corresponds to GIS file that were generated in the study published by Droissart, Dauby et al. in <em>Journal of Biogeography</em>:</p> <p>Droissart V, Dauby G, Hardy OJ, Deblauwe V, Harris DJ, Janssens S, Mackinder BA, Blach-Overgaard A, Sonké B, Sosef MSM, Stévart T, Svenning J-C, Wieringa JJ, Couvreur TLP (2018) Beyond trees: biogeographical regionalization of tropical Africa. <em>Journal of Biogeography. </em>DOI:10.1111/jbi.13190</p> <p><em>Please cite the aforementioned article and the dataset herein, when using of any of these files in this dataset.</em></p> <p> </p> <p>The GIS file is referred in the paper as <strong>Appendix S3</strong> and correspond to the map presented in Figure 1. Each polygons of the shapefile correspond to the main floristic bioregions and transition zones of tropical Africa delimited using bipartite network clustering analysis of 24,719 plant species.</p> <p>The coordinate system of the ESRI shapefile is GCS_WGS_1984. Field descriptions for the associate table are:</p> <ul> <li><strong>bionames</strong>: name of the bioregions as given in Table S1.1.</li> <li><strong>bioreg_ID</strong>: identifier of the bioregions as given in Table S1.1 and Fig. 1. T= Transition zones</li> <li><strong>cluster_ID</strong>: identifier of clusters delimited using bipartite network clustering on the 24,719 plant species of the RAINBIO database, as given in Table S1.1 and Fig. S2.1.</li> </ul>
CO2 emissions, water table and temperature time series from an undrained tropical peatland
<p>Supplement to: Hoyt, A. M., Gandois, L. , Eri, J. , Kai, F. M., Harvey, C. F. and Cobb, A. R. (2019), CO2 emissions from an undrained tropical peatland: Interacting influences of temperature, shading and water table depth. <em>Global Change Biology</em>. https://doi.org/10.1111/gcb.14702</p>
Wind fields from aggregated retrievals from the WIRA-C Doppler wind radiometer in tropical and arctic lattitudes
<p>These data sets contain the retrieved wind fields from aggregated retrievals from the WIRA-C Doppler wind radiometer from two campaigns.</p> <p>The first campaign took place in the southern hemisphere at the Maïdo observatory on La Réunion Island (France), located in the Indian ocean at 21°S, 55°E. Data from April, May and June 2017 are included.</p> <p>For the second (and still ongoing) campaign, WIRA-C is located at the ALOMAR observatory on Andøya (Norway) at 69°N, 16°E. Data from September, October and November are included.</p>
DATASET : Thresholds of fire response to moisture and fuel load differ between tropical savannas and grasslands across continents
<p><strong>Abstract </strong></p> <p><strong>Aim:</strong> An emerging framework for tropical ecosystems states that fire activity is either ‘<em>fuel build-up limited</em>’ or ‘<em>fuel moisture limited</em>’ i.e. as you move up along rainfall gradients, the major control on fire occurrence switches from being the amount of fuel, to the moisture content of the fuel. Here we used remotely sensed datasets to assess whether interannual variability of burned area is better explained by annual rainfall totals driving fuel build-up, or by dry season rainfall driving fuel moisture.</p> <p><strong>Location:</strong> Pantropical savannas and grasslands</p> <p><strong>Time period:</strong> 2002-2016</p> <p><strong>Methods:</strong> We explored the response of annual burned area to interannual variability in rainfall. We compared several linear models to understand how <em>fuel moisture </em>and <em>fuel build-up effect </em>(accumulated rainfall during 6 and 24 months prior to the end of the burning season respectively) determine the interannual variability of burned area and explore if tree cover, dry season duration and human activity modified these relationships.</p> <p><strong>Results:</strong> Fuel and moisture controls on fire occurrence in tropical savannas varied across continents. Only 24% of South American savannas were <em>fuel build-up limited</em> against 61% of Australian savannas and 47% of African savannas. On average, South America switched from fuel limited to moisture limited at 500 mm yr<sup>-1</sup>, Africa at 800 mm yr<sup>-1</sup> and Australia at 1000 mm yr<sup>-1 </sup>of mean annual rainfall.</p> <p><strong>Main conclusions:</strong> In 42% of tropical savannas (accounting for 41% of current area burned) increased drought and higher temperatures will not increase fire, but there are savannas, particularly in South America, that are likely to become more flammable with increasing temperatures. These findings highlight that we cannot transfer knowledge of fire responses to global change across ecosystems/regions – local solutions to local fire management issues are required, and different tropical savanna regions may show contrasting responses to the same drivers of global change.</p>
MADERA: A standardized Pan-Amazonian dataset for tropical timber species
<p>We compiled and presented a dataset for all timber species reported in the Amazon region from all nine South American Amazonian countries. This was based on official information from every country, as well as from two substantial scientific references. We verified the standard taxonomic names from each individual source, using the Taxonomic Name Resolution Service (TNRS) and considered all Amazonian tree species with DBH ≥ 10 cm. We also obtained estimates of the current population size for most species from a published approach based on data from 1,900 tree inventory plots (1-hectare each) distributed across the Amazon region and part from the Amazon Tree Diversity Network (ATDN). We then identified the hyperdominant timber species. In addition, we overlapped our timber species list with data for species that are used for commercial purposes, according to the International Tropical Timber Organization (ITTO), the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) and the International Union for Conservation of Nature (IUCN) taxa assessment and Red List categories. Finally, we also included IUCN Red List categories based on combined deforestation, and climate change scenarios for these species. Our final Amazonian timber species dataset contains 1,112 unique species records, which belong to 337 genera and 72 families from the lowland Amazonian rainforest, with associated information related to population, conservation, and trade status of each species. The authors of this research expect that the information provided will be useful to strengthen the public forestry policies of the Amazon countries, inform ecological studies, as well for forest management purposes. </p>
Projected distribution of invasive plant species in the tropical Andes under climate change
<p>Distribution maps of 11 invasive species now and in the future (2040-70). The projections were the result of the assembly of three algorithms: Adaptive Boosting (AdaBoost), Boosted Regression Trees (BRT), and Extreme Gradient Boosting (XGBoost). Future projections were made for three global circulation models and three climate change scenarios, each with low (SSP126), medium (SSP370), and high (SSP585) levels of carbon emission.</p> <p>Habitat suitability and presence/absence maps are also included. The threshold for establishing a species as present was determined to be the value that maximized the TSS. </p> <p>For more information, see the article accompanying the dataset by González-Trujillo et al. Mapping the threat: Projecting invasive plant distribution in the tropical Andes under climate change</p> <p>List of modeled invasive plant species and their known impacts in the tropics.</p> <table> <tbody> <tr> <td> <p><strong>Species </strong></p> </td> <td> <p><strong>Biogeographic origin</strong></p> </td> <td> <p><strong>Impacts </strong></p> </td> <td> <p><strong>References</strong></p> </td> <td> <p><strong>GBIF data (DOIs)</strong></p> </td> </tr> <tr> <td> <p><em>Acacia decurrens </em></p> </td> <td> <p>Australian</p> </td> <td> <p>Create regular layers of litter on the ground, inhibit or redirect successional processes, inhibit the expression of seed banks, and limit resource supply, leading to displacement of native plants and animals and increasing the frequency of fires.</p> </td> <td> <p> (Cárdenas López et al., 2017; Le Maitre et al., 2011)</p> </td> <td> <p>https://doi.org/10.15468/dl.mjyxhw</p> </td> </tr> <tr> <td> <p><em>Acacia melanoxylon</em></p> </td> <td> <p>Australian</p> </td> <td> <p>Alter the structure and function of their ecosystems, thereby displacing their native flora. It also causes soil erosion and alters hydrological cycles, negatively affecting agriculture.</p> </td> <td> <p>(Kumschick and Jansen, 2023; Le Maitre et al., 2011)</p> <p> </p> </td> <td> <p>https://doi.org/10.15468/dl.4cugnk</p> </td> </tr> <tr> <td> <p><em>Arundo donax</em></p> <p><em> </em></p> </td> <td> <p>Holarctic</p> </td> <td> <p>Alter<em> </em>the natural vegetation structure, outcompete native plant species and diminish the diversity and abundance of animals such as arthropods and birds. It also drives out soil, fuels forest fires, displaces native species, and increases the invasion of ticks that affect livestock.</p> </td> <td> <p>(Cárdenas López et al., 2017; Girotto et al., 2021; Lambert et al., 2010)</p> </td> <td> <p>https://doi.org/10.15468/dl.bfep4t</p> </td> </tr> <tr> <td> <p><em>Genista monspessulana</em></p> </td> <td> <p>Holarctic</p> </td> <td> <p>Alter fire regime and nutrient cycling displace native species and decrease native diversity by forming dense monospecific stands. It also facilitates the establishment of other invasive species and produces seeds that are toxic to livestock and humans.</p> </td> <td> <p>(Cárdenas López et al., 2017; Herrera et al., 2016; Pauchard et al., 2008)</p> </td> <td> <p>https://doi.org/10.15468/dl.gyhnxh</p> </td> </tr> <tr> <td> <p><em>Hedychium coronarium </em></p> </td> <td> <p>Indo-Malesian</p> </td> <td> <p>Alter hydrological and nutrient cycles in soil. It forms thickets that suppress the successional and regeneration processes of native species, thus affecting the native flora and crops.</p> </td> <td> <p>(Cárdenas López et al., 2017; Costa et al., 2019)</p> </td> <td> <p>https://doi.org/10.15468/dl.6z2jgb</p> </td> </tr> <tr> <td> <p><em>Melinis minutiflora</em></p> </td> <td> <p>African</p> </td> <td> <p>Increases the occurrence of fires, displaces native species, and alters soil properties and decomposition. It also inhibits the growth of native species.</p> </td> <td> <p>(Cárdenas López et al., 2017; Nogueira et al., 2019; Sandoval et al., 2022)</p> </td> <td> <p>https://doi.org/10.15468/dl.fsqwsv</p> </td> </tr> <tr> <td> <p><em>Pteridium aquilinum</em></p> </td> <td> <p>Holarctic</p> </td> <td> <p>Alter vegetation success processes affect crops and cause livestock poisoning. It also produces acids that inhibit root growth in native and cultivated species.</p> </td> <td> <p> (Berget et al., 2015; Cárdenas López et al., 2017; Valdez-Ramírez et al., 2020)</p> <p> </p> </td> <td> <p>https://doi.org/10.15468/dl.sp4uuv</p> </td> </tr> <tr> <td> <p><em>Ricinus communis</em></p> </td> <td> <p>African</p> </td> <td> <p>Alter vegetation success processes affect crops and cause livestock poisoning. It also produces acids that inhibit root growth in native and cultivated species.</p> </td> <td> <p>(Cárdenas López et al., 2017; Sandoval et al., 2022; Silva and Fabricante, 2022)</p> </td> <td> <p>https://doi.org/10.15468/dl.dhbphb</p> </td> </tr> <tr> <td> <p><em>Senecio madagascariensis</em></p> </td> <td> <p>African</p> </td> <td> <p>Alter soil nutrient cycles, damage to agricultural crops, and outcompete native species. It also contains substances that are toxic to both animals and humans. </p> </td> <td> <p>(Wijayabandara et al., 2021)</p> </td> <td> <p>https://doi.org/10.15468/dl.7e8eyx</p> </td> </tr> <tr> <td> <p><em>Thunbergia alata</em></p> </td> <td> <p>African</p> </td> <td> <p>Displace native species and reduce habitat heterogeneity, thereby affecting the structure and function of native ecosystems.</p> </td> <td> <p>(Cárdenas López et al., 2017; Quijano-Abril et al., 2021)</p> </td> <td> <p>https://doi.org/10.15468/dl.g9zybc</p> </td> </tr> <tr> <td> <p><em>Ulex europeaus</em></p> </td> <td> <p>Holarctic</p> </td> <td> <p>Dry soil and increase the occurrence of fires. Inhibits vegetative growth, including pastures in agricultural and livestock lands.</p> </td> <td> <p>(Anderson and Anderson, 2009; Cárdenas López et al., 2017)</p> </td> <td> <p>https://doi.org/10.15468/dl.6642q9</p> </td> </tr> </tbody> </table>
ARTICLE DATASET - ANTHROPOGENIC MICROPARTICLES ACCUMULATION IN SMALL-BODIED SEAGRASS MEADOWS: THE CASE OF TROPICAL ESTUARINE SPECIES IN BRAZIL
<p>This dataset with the data analysis script refers to the publication <a href="https://www.sciencedirect.com/science/article/abs/pii/S0025326X24007768?via%3Dihub"><strong>https://doi.org/10.1016/j.marpolbul.2024.116799</strong></a>.</p> <p>The dataset consists of a spreadsheet containing 9 tabs with survey data described below and their respective captions, found in the first line of each tab.</p> <p>The script for statistical analysis in the R language contains descriptive and statistical analyses, in addition to the functions for creating the graphs displayed in the article.</p> <p><strong>Description of the spreadsheet dataset tabs --------------------------------------------------------------------------------</strong></p> <ol> <li>description - Contains general information about the article</li> <li>meadows - Contains properties related to the characteristics of the multispecific grassland.</li> <li>ap_samples - Contains properties related to the abundances of anthropogenic microparticles, as to classifications by shape, size (mm) and color in units, kg and frequency of occurrence.</li> <li>ap_categories - Contains raw data related to the classifications of anthropogenic microparticles, in terms of shape, size (mm) and color.</li> <li>sediment - Contains raw data related to the classifications of sediment particles.</li> <li>granulometry - Contains properties related to sediment particles in µm, and their classifications by predominance, frequency and kg.</li> <li>shape - Contains raw data related to the classification of the shapes of anthropogenic microparticles in units and kg.</li> <li>size - Contains raw data related to the classification of the sizes of anthropogenic microparticles in units and kg.</li> <li>color - Contains raw data related to the classification of the colors of anthropogenic microparticles in units and kg.</li> </ol>
Data to reproduce figures in "Tropical thermocline helps power Pacific equatorial upwelling"
<p>A set of netcdf include results of the energetics in the Pacific STC region. </p> <p>A jupyter notebook uses all those dataset to reproduce the main plots in the paper. Code used to compute the energetics can be found within the 'Tailleux' class inside this module: https://github.com/inciente/EastPac/blob/main/KE_tools.py</p> <p>Please feel free to reach out if you're trying to use the data, or apply the energetics framework to your own simulations.</p>
Suppl. Information to "The tropical coral Pocillopora acuta displays an unusual chromatin structure and shows histone H3 clipping plasticity upon bleaching"
<p><strong>Supplementary File 1:</strong> Multiple alignment for protein sequences of core histones with Pocillopora acuta, Pocillopora damicornis, Acropora digitifera, Nematostella vectensis, Hydra vulgaris, Schistosoma mansoni and Mus musculus. A. Histone H2A; B. Histone H2B; C. Histone H3; D. Histone H4. An asterisk (*) means that the amino acid is conserved between all species.</p> <p><strong>Supplementary File 2</strong>: Original (uncropped and unedited) images used for Figures 1 to 4.</p> <p><strong>Supplementary File 3:</strong> <em>P. acuta</em> nuclei and <em>Symbiodinium</em> count on a Thoma cell counting chamber done over three different nuclei extractions. For each extraction, two counts were performed. P. acuta nuclei were stained with Hoechst 33342 and display a blue fluorescence at 350 nm. Symbiodinium are not damaged by our extraction method and are not permeable to Hoechst. They display a red fluorescence because of their chlorophyl content. Observations were done on a Leica DMLB with objective PL Fluotar 40x and 100x. A text version of the data in the Excel file below.</p> <p>Extraction #1 replicate 1: 102 <em>P. acuta</em> nuclei (Blue) ; 2 <em>Symbiodinium</em> (Red)<br> Extraction #1 replicate 2: 112 <em>P. acuta</em> nuclei (Blue) ; 2 <em>Symbiodinium</em> (Red)</p> <p>Extraction #1 replicate 1: 42 <em>P. acuta </em>nuclei (Blue) ; 0 <em>Symbiodinium</em> (Red)<br> Extraction #1 replicate 2: 55 <em>P. acuta </em>nuclei (Blue) ; 1 <em>Symbiodinium</em> (Red)</p> <p>Extraction #1 replicate 1: 215 <em>P. acuta </em>nuclei (Blue) ; 3 <em>Symbiodinium</em> (Red)<br> Extraction #1 replicate 2: 257 <em>P. acuta</em> nuclei (Blue) ; 5 <em>Symbiodinium</em> (Red)</p> <p>Made at IHPE.</p>
Tri-hourly dataset of wind and wave anomalies of the GFS and WAVEWATCH III models in the entire tropic region (TROPWA).
<p>This dataset contains the anomalies of the total height and peak period of the waves and of the zonal and meridional components of the wind at 10 m above the sea surface obtained from the outputs of the GFS and WAVEWATCH III coupled global models in the entire tropical region (180°W to 178.75°E longitude/30°S to 30°N latitude), with a spatial resolution of 1.25°x1°. It is made up of two files in NetCDF format, where the data for wind anomalies (speed module and its zonal and meridional components) and wave anomalies (total wave height and peak period) are contained separately. This dataset was created in order to study all types of tropical storms, cold fronts and other physical processes that influence significant changes in wave parameters, as well as for the design and construction of coastal engineering works. The authors thanks to Tropical Atlantic Interdisciplinary Laboratory on physical, biogeochemical, ecological and human dynamics (IJL TAPIOCA).</p>
Tropical Atmospheric Anomalies on the Surface (TROPAAS)
<p>This dataset contains the monthly, daily and 6-hour anomalies of the wind speed at 10 m, air temperature at 10 m, incident long-wave radiation, incident solar radiation, atmospheric pressure at sea level, the specific humidity at 10 m and precipitation in the entire tropical region (180°W – 178.125°E / 31.4281°S - 31.4281°N), calculated from the CORE v2 dataset (Coordinated Ocean-ice Reference Experiments) experiment with the correction of CIAF v2 (Corrected Inter-Annual Forcing). Covers the period from 1948 to 2009.</p> <p> </p> <p>There are 3 compressed files, each of which contains 6 3D grids in NetCDF files format with the following name description:</p> <p> </p> <p>anom_<frequency>_<variablename>_TROPAAS.nc</p> <p> </p> <p><frequency>:</p> <p>mm – Monthly means.</p> <p>dm – Daily means.</p> <p>6hrs – Each 6-hours.</p> <p> </p> <p><variablename>:</p> <p>precip – Precipitation.</p> <p>q - Specific humidity at 10 m.</p> <p>rad - Incident long-wave radiation and incident solar radiation.</p> <p>Slp - Pressure at sea level.</p> <p>T - Temperature at 10 m.</p> <p>winds - Zonal and meridional components and the module of the wind speed at 10 m.</p> <p> </p> <p>All the grids are made up of 192 x 34 nodes, with a spatial resolution of 1.875 x 1.904733 degrees.</p> <p> </p>
Flow-topography interactions in the western tropical Atlantic boundary off Northeast Brazil.
<p>Figures and other media files of the paper Flow-topography interactions in the western tropical Atlantic boundary 2off Northeast Brazil.</p>
Crop Prices and Deforestation in the Tropics
<p>We provide the stata files that allow to reproduce the results presented in the paper <br> "Crop Prices and Deforestation in the Tropics" by Nicolas Berman, Mathieu Couttenier, Antoine Leblois and Raphaël Soubeyran.</p> <p>The replication folder contains different files:<br> 1- *.dta file: database<br> 2- *.do file: do-file containing the codes to replicate the results (figures and tables)<br> 3- * Ancillary data: <br> .csv file: data needed to produce a map of the initial forest cover (in 2000). <br> .dta additional files to run sensitivity analysis</p> <p>Simply change the path to files (on line 25 of the replication_code.do file) to re-run the analysis:<br> ** Change pathway to load and save the data<br> global dir ".../Replication_files_BCLS_2022"</p> <p><br> Stata 17 was used for this work.</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.