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Land Cover Classification for Hog Island, VA, 2018
Image processing/classification of Sentinel-2 imagery (2018 Summer scene) was used to produce a land cover classification of Hog Island, Virginia. Using Random Forests classifier the accuracy obtained was 86%. The different cover types include: 1. Trees 2. Shadow 3. Spartina patens (Sprobolus pumilus) 4. SeaWater 5. Brackish 6. Morella cerifera 7. Beach Sand 8. Inter Tidal 9. Water
Ohio Public Land Survey (PLS) Witness Tree GIS Shapefile
The United States Public Land Survey (PLS) divided land into one square mile units, termed sections. Surveyors used trees to locate section corners and other locations of interest (witness trees). As a result, a systematic ecological dataset was produced with regular sampling over a large region of the United States, beginning in Ohio in 1786 and continuing westward. We digitized and georeferenced archival hand drawn maps of these witness trees for 27 counties in Ohio. This dataset consists of a GIS point shapefile with 11,925 points located at section corners, recording 26,028 trees (up to four trees could be recorded at each corner). We retain species names given on each archival map key, resulting in 70 unique species common names. PLS records were obtained from hand-drawn archival maps of original witness trees produced by researchers at The Ohio State University in the 1960’s. Scans of these maps are archived as “The Edgar Nelson Transeau Ohio Vegetation Survey” at The Ohio State University: http://hdl.handle.net/1811/64106. The 27 counties are: Adams, Allen, Auglaize, Belmont, Brown, Darke, Defiance, Gallia, Guernsey, Hancock, Lawrence, Lucas, Mercer, Miami, Monroe, Montgomery, Morgan, Noble, Ottawa, Paulding, Pike, Putnam, Scioto, Seneca, Shelby, Williams, Wyandot. Coordinate Reference System: North American Datum 1983 (NAD83). This material is based upon work supported by the National Science Foundation under grants #DEB-1241874, 1241868, 1241870, 1241851, 1241891, 1241846, 1241856, 1241930.
Copernicus Global Land Service: Land Cover 100m: epoch 2016: Africa demo (deprecated)
<p><strong><em>This demo dataset over Africa is deprecated. Please see <a href="https://doi.org/10.5281/zenodo.3518025">this global dataset</a> instead.</em></strong></p> <p>Demonstration land cover maps over Africa at 100m resolution for epoch year 2016, from the global component of the Copernicus Land Service and derived from PROBA-V satellite observations.</p> <p>The maps include the main discrete classification (23 classes aligned with UN-FAO's LCCS), a set of cover fractions (%) for the 10 main classes and additional quality layers (e.g. density of input data).</p> <p>For the consolidated epoch 2016, the classifier and regression models of base year 2015 are used, and the time window of the classified metrics covers one full year prior (2015) and pastor (2017) data. The layers with the probability of the discrete classification and the standard deviation of the cover fractions are only provided for the base year (epoch 2015). The Change Consistency Layer that checks consistency between classifier and break detection, is only available for the most recent (near-real time) year (epoch 2018).</p> <p> </p> <p><a href="https://africa.lcviewer.vito.be/2016">View the maps and area statistics</a></p> <p><a href="https://land.copernicus.eu/global/documents/lcc100/all/pum">Product User Manual</a></p> <p><a href="https://land.copernicus.eu/global/products/lc">More land cover change product information and documentation</a></p> <p> </p>
Simulations from the CABLE-POP land surface model for the Vegetation Carbon Turnover Intercomparison
<p>Outputs from the CABLE-POP land surface model are provided for two global simulations. One forced by CRU-NCEP v7 climate data for the period 1901-2015 and one forced by bias-corrected IPSL-CM5A-LR RCP 8.5 climate data for the period 1901-2099. Landcover was fixed at 1700 values throughout the simulations. Both simulations are at 0.5 x 0.5 degree spatial resolution. Carbon turnover fluxes for live vegetation for each individual turnover-causing process in the model were outputted individually. For a full description of the simulations, please refer to the accompanying paper.</p>
Snow depth map and land cover map from satellite photogrammetry (Pleiades) in Tuolumne, California.
<p><strong>snow_depth_20170501_pleiades.tif</strong></p> <p>Snow depth from the difference of digital elevation models (DEMs) calculated from Pléiades images. The snow-on DEMs were acquired on 2017-05-01. The snow-off DEMs were acquired on 2017-08-08 and 2017-08-13.</p> <p> </p> <p><strong>land_cover_20170813-08_pleiades.tif land_cover_20170501_pleiades.tif</strong></p> <p>Land cover calculated from multi-spectral Pléiades images acquired on 2017-05-01 (snow-on) and 2017-08-08 and -13 (snow-off). Classes are snow (1), forest (2), bare rock/low vegetation (3), lake (4).</p>
Figure 4 in Rediscovery and re-description of Rhaphaulus assamicus Godwin-Austenı 1886ı an endemic pupinid land snail from India (Gastropoda: Pupinidae)
Figure 4. (a) Field photograph of Rhaphaulus assamicus Godwin-Austen, 1886 [1882–1920]; (b–f) the habitats of R. assamicus in Namdapha National Park, Arunachal Pradesh, India.
Data from the article "The mitochondrial phylogeny of land plants shows support for Setaphyta under non-stationary substitution models"
<p>Data from the article:</p> <p>"The mitochondrial phylogeny of land plants shows support for Setaphyta under non-stationary substitution models"</p> <p>Filipe de Sousa, Peter Civáň, João Brazão, Peter G. Foster, Cymon J. Cox</p> <p> </p> <p>These data are divided in four folders:</p> <p>* 1_36_gene_nt_alignments_&_trees - contains 36 single gene nucleotide alignments and the corresponding trees inferred from a MCMC analysis on the program p4</p> <p>* 2_36_gene_aa_alignments_&_trees - contains 36 single gene amino acid alignments and the corresponding trees inferred from a MCMC analysis on the program p4</p> <p>* 3_concatenated_alignments_&_trees - contains the nucleotide, codon-degenerate and amino acid alignments of 36 concatenated genes and the corresponding trees inferred from MCMC analyses on the programs p4 and phylobayes with composition homogeneous, tree-heterogeneous and site-heterogeneous models; trees correspond to figures S1-S7 on the online supplemental file.</p> <p>* 4_concatenated_ML_trees - contains the ML trees from the analyses of the concatenated datasets (nucleotide, codon degenerate and amino acid).</p> <p> </p> <p> </p> <p> </p>
ERA5-Land monthly averaged dataset for Galaxy Panoply training
<p><strong>ERA5-Land monthly averaged data January 2019</strong></p> <p>Dataset has been retrieved on the Copernicus Climate data Store (<a href="https://cds.climate.copernicus.eu/#!/home">https://cds.climate.copernicus.eu/#!/home</a>) and is meant to be used for teaching purposes only. This dataset is used in the Galaxy training on "Visualize Climate data with Panoply in Galaxy".</p> <p>See <a href="https://training.galaxyproject.org/">https://training.galaxyproject.org/</a> (topic: climate) for more information.</p> <p><strong>Product type: </strong>Monthly averaged reanalysis</p> <p><strong>Variable:</strong></p> <p>10m u-component of wind, 10m v-component of wind, 2m temperature, Leaf area index, high vegetation, Leaf area index, low vegetation, Snow cover, Snow depth</p> <p><strong>Year:</strong></p> <p>2019</p> <p><strong>Month:</strong></p> <p>January</p> <p><strong>Time:</strong></p> <p>00:00</p> <p><strong>Format:</strong></p> <p>NetCDF (experimental)</p>
Global Agricultural Land Resources – A High Resolution Suitability Evaluation and Its Perspectives until 2100 under Climate Change Conditions (v2.0)
<p><strong>Agricultural land resources – a global suitability evaluation</strong></p> <p><em>An inventory is required on the changing potentially suitable areas for agriculture under changing climate conditions. Within the context of the GLUES project, researchers at the Ludwig-Maximilians University (LMU) investigated the global agricultural suitability of land under changing climate conditions at high spatial resolution. The growing demand for food, feed, fiber and bioenergy increases pressure on land and causes land use/cover change and trade-offs between different uses of land and ecosystem services. In order to ensure food security, agricultural potentials need to be used more efficiently in the future. Therefore, the agricultural suitability of land are important information e.g. in order to identify todays suitable areas and possible future changes. The potential suitability of todays forested and protected areas can be used to identify possible hotspots of land use/cover change. Therefore, LMU is working on improving the knowledge of global agricultural potentials of land and better understanding the interdependencies between ecological and socio-economic systems which are driving land use/cover change.</em></p> <p><strong>Determining Agricultural Suitability</strong></p> <p>Local climate, soil and topography determine the available energy, water and nutrient supply for agricultural crops and thus their natural suitability. In order to allow for computing the natural agricultural constraints on the globe at 30 arc seconds (1km) spatial resolution, the following high resolution data were applied:</p> <p>Daily data for temperature, precipitation and solar radiation from the global climate model ECHAM5. Soil data comes from the Harmonized World Soil Database (HWSD). Considered soil properties are texture, proportion of coarse fragments and gypsum, base saturation, pH content, organic carbon content, salinity, sodicity. Topography data was applied from the Shuttle Radar Topography Mission (SRTM). Irrigation has strong impact on the crop’s suitability. It is considered on todays irrigated areas as given by the FAO Aquastat Global Maps of Irrigated Areas (GMIA) dataset. The determinant factors are contrasted with the crop-specific requirements, using a fuzzy-logic approach. The crop requirements are taken from literature.</p> <p><strong>Agricultural Suitability</strong></p> <p>General agricultural suitability at a spatial resolution of 30 arcsec, considering rainfed conditions and irrigation on currently irrigated areas. The agricultural suitability represents for each pixel the maximum suitability value of the considered 16 plants. The dataset contains four time periods (1961-1990, 1981-2010, 2011-2040, 2071-2100).</p> <p><strong>Suitability Change due to Climate until 2100</strong></p> <p>Change in agricultural suitability and crop suitability due to climate change for SRES A1B scenario conditions for 16 crops between 1981-2010 and 2071-2100 at a spatial resolution of 30 arcsec.</p> <p><strong>Multiple Cropping</strong></p> <p>Potential number of suitable crop cycles for 16 crops at a spatial resolution of 30 arcsec, considering rainfed conditions and irrigation on currently irrigated areas. The dataset contains four time periods (1961-1990, 1981-2010, 2011-2040, 2071-2100).</p> <p><strong>Growing Cycle</strong></p> <p>Start of the growing cycle for 16 crops at a spatial resolution of 30 arcsec, considering rainfed conditions and irrigation on currently irrigated areas. In case of multiple cropping, the start of the first growing cycle is shown. The dataset contains four time periods (1961-1990, 1981-2010, 2011-2040, 2071-2100).</p> <p><strong>Further information</strong></p> <p>Detailled information are available in the following publication:<br> Zabel F., Putzenlechner B., Mauser W. (2014): <strong>Global agricultural land resources – a high resolution suitability evaluation and its perspectives until 2100 under climate change conditions. </strong> Online available: <a href="http://dx.plos.org/10.1371/journal.pone.0107522">PLOS ONE</a>. DOI: 10.1371/journal.pone.0107522</p> <p><strong>Improvements in v2.0</strong></p> <p>Compared to previous versions, v2.0 uses updated input data for soil and minor improvements of the statistical downscaling and the bias correction of the climate model data.</p> <p><strong>Contact</strong></p> <p>Please contact: Dr. Florian Zabel, <a href="mailto:f.zabel@lmu.de">f.zabel@lmu.de</a>, Department für Geographie, LMU München (<a href="http://www.geografie.uni-muenchen.de">www.geografie.uni-muenchen.de</a>)</p>
Land surface phenology based crop maps for Continental United States 2000-2018
<p>This data collection contains annual maps of crop types for Continental United States for the period 2000-2018 at spatial resolution of ~231m derived using <a href="https://doi.org/10.3334/ORNLDAAC/1299">MODIS Land Surface Phenology</a>. This data collection is a companion to the paper <strong><em>Konduri, V., Kumar, J., Hargrove, W. W., Hoffman, F. M., Ganguly, A. R. (2020) <a href="https://doi.org/10.1016/j.rse.2020.112048">Mapping Crops Within the Growing Season Across the United States. Remote Sensing of Environment</a>, Vol 251, 2020 </em></strong><a href="https://doi.org/10.1016/j.rse.2020.112048">https://doi.org/10.1016/j.rse.2020.112048</a>, which describes the methodology for development of these datasets, validation metrics and analysis.</p> <p> </p> <p><strong><strong>Files in collection (28): </strong></strong></p> <ul> <li><strong><em>crop_map_predicted_[YEAR].nc</em>: </strong>Annual crop type maps for YEAR = 2000-2018</li> <li><strong><em>Crop_map_legend.csv</em>: </strong>Legend for crop type categories in the maps (Category numbers and legends for crop types are consistent with those used by <a href="https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php">USDA Crop Data Layer</a>)</li> <li>Maps of earliest date (Day of Year) of classification for eight dominant crop types for year 2015: <ul> <li><strong>earliest_classification_date_corn_CONUS.nc</strong> : Earliest date of classification for corn</li> <li><strong>earliest_classification_date_soybeans_CONUS.nc</strong> : Earliest date of classification for soybeans</li> <li><strong>earliest_classification_date_winter_wheat_CONUS.nc</strong> : Earliest date of classification for winter wheat</li> <li><strong>earliest_classification_date_fallow_CONUS.nc</strong> : Earliest date of classification for fallow</li> <li><strong>earliest_classification_date_other_hay_non_alfalfa_CONUS.nc</strong> : Earliest date of classification for other hay/non-alfalfa</li> <li><strong>earliest_classification_date_alfalfa_CONUS.nc</strong> : Earliest date of classification for alfalfa</li> <li><strong>earliest_classification_date_sorghum_CONUS.nc</strong> : Earliest date of classification for sorghum</li> <li><strong>earliest_classification_date_rice_CONUS.nc</strong> : Earliest date of classification for rice</li> </ul> </li> </ul> <p> </p> <p><strong><strong>Data formats:</strong></strong></p> <ul> <li>All map products are in gridded <a href="https://www.unidata.ucar.edu/software/netcdf/">NetCDF</a> format.</li> <li>Annual crop type maps use category types described in Crop_map_legend.csv. </li> <li>Earliest date of classification maps are encoded as Day of the Year.</li> </ul> <p> </p> <p><strong><strong>Projection for geospatial data</strong> (in <a href="https://live.osgeo.org/en/overview/proj4_overview.html">PROJ4 format</a>):</strong></p> <pre><code class="language-bash">PROJCS["US_National_Atlas_Equal_Area", GEOGCS["sphere", DATUM["unknown", SPHEROID["Spherical_Earth",6370997,"inf"]], PRIMEM["Greenwich",0], UNIT["degree",0.0174532925199433]], PROJECTION["Lambert_Azimuthal_Equal_Area"], PARAMETER["latitude_of_center",45], PARAMETER["longitude_of_center",-100], PARAMETER["false_easting",0], PARAMETER["false_northing",0], UNIT["Meter",1]]</code></pre> <p><strong>Paper Citation:</strong></p> <blockquote> <p><strong><em>Konduri, V., Kumar, J., Hargrove, W. W., Hoffman, F. M., Ganguly, A. R. (2020) Mapping Crops Within the Growing Season Across the United States. Remote Sensing of Environment (in revision)</em></strong></p> </blockquote>
Multispectral and augmented Landsat data with land cover labels
<p>Benchmark set at 77.1% O.A at: https://doi.org/10.1117/1.JRS.14.048503</p> <p>The dataset consists of 60,000 images, corresponding to Landsat patches of 33x33 pixels with 102 bands. Randomly selected from Mexico (country). Each patch is labeled with one of 12 Land Use and Vegetation classes according to the classification described at https://doi.org/10.3390/rs6053923.</p> <p>The zip file contains 12 folders numbered 1-12 and each contains 5,000 .npy python files (can be loaded with the NumPy library).</p> <p>The labeled classes correspond to the following identifier.</p> <p>1, Temperate Coniferous forest<br> 2, Temperate Decidius Forest<br> 3, Temperate Mixed Forest<br> 4, Tropical Evergreen Forest<br> 5, Tropical Deciduous Forest<br> 6, Scrubland<br> 7, Wetland Vegetation<br> 8, Agriculture<br> 9, Grassland<br> 10, Water body<br> 11, Barren Land<br> 12, Urban Area</p> <p>To build that dataset, we take the information of the National Continuum of Land Use and Vegetation series number 5 generated by the National Institute of Statistics and Geography from Mexico (INEGI) from The National Commission for the Knowledge and Use of Biodiversity (CONABIO) web page (http://geoportal.conabio.gob.mx/metadatos/doc/html/usv250s5ugw.html).</p> <p>The file used for this dataset construction is the shape format file with geographic coordinates located in http://www.conabio.gob.mx/informacion/gis/maps/geo/usv250s5ugw.zip.<br> Later, a transformation to Albers equal-area conic projection was done with the followings parameters:</p> <p>Fake east: 2500000.0<br> Fake North: 0.0<br> Origin longitude: -102.0º<br> Origin latitude: 12.0º<br> First standard parallel: 17.5º<br> Second standard parallel: 29.5º<br> Linear unit: Meter (1.0)<br> Reference ellipsoid: GRS80</p> <p><br> Once the data was projected, using the classes identified in the National Continuum of Land Use and Vegetation, correspondence was applied to the classes identified in https://doi.org/10.3390/rs6053923, these classes being: Agriculture, Barren land, Grassland, Scrubland, Temperate coniferous forest, Temperate deciduous forest, Temperate mixed forest, Tropical deciduous forest, Tropical evergreen forest, Urban area, Waterbody and Wetland vegetation.</p> <p>Once the information layer was generated with the 12 classes indicated above, the reference layer was rasterized.<br> Thus, a national grid of 1,975,940 regions of 1 x 1 kilometers was generated and the percentage of pixels of the dominant class in each corresponding 1 km region was associated.</p> <p>A total of cells with 70% or more pixels from one dominant class corresponds to 1,640,827 which represents a total of 83% of the Mexican territory. That means, only 17% of cells have less than 70% of their pixels from one dominant class.<br> Then, 5000 regions were randomly selected from each land cover class at the national level. For this random selection only were selected the regions in which cells have 70% or more of their pixels from one dominant class. The above, for looking to have consistent and reliable data for the automatic classification task. This random selection generates a total of 60,000 regions selected.</p> <p>Image patches were extracted from the selected regions in the sample.</p> <p>The image used is the result of the application of multiple time series analysis algorithms on a cube of image data with mainly Tier 1 (T1) quality and a few Tier 2 (T2) as described in https: // www. usgs.gov/land-resources/nli/landsat/landsat-collection-1. An Open Data Cube (ODC, https://www.opendatacube.org/) was constructed from 3,515 Landsat 5 and 7 images corresponding to the year 2011, which is the same reference year of the National Continuum of Land Use and Vegetation Series 5.</p> <p>From the analysis of the ODC images, the Geomedian (https://doi.org/10.1109/TGRS.2017.2723896) was calculated, which generated a national cloud-free mosaic from 2011, pixels at 30 meters resolution and 6 spectral bands (blue, green, red, nir, swir 1, swir 2). Finally, 15 spectral indices were calculated for each pixel in the image. This resulted in 15 national mosaics from the analysis of the time series of each pixel available for the year 2011 using all the combinations of normalized difference indices, which were possible with the 6 bands that were incorporated into the data cube, with which resulted in 102 information channels. Since Landsat images have a resolution of 30 meters, we have images of 33 pixels x 33 pixels for each region of 1 km x 1 km.</p> <p>The 102 channels in the patches correspond to:</p> <p>Geomedian Bands (6): blue, green, red, nir, swir 1, swir 2<br> Geomedian Based Indexes (15): evi, bu, sr, arvi, ui, ndbi, ibi, ndvi, ndwi, mndwi, nbi, brba, nbai, baei, bi<br> Geomedian Based Tasseled cap transformation (6): brightness, greenness, wetness, fourth, fifth, sixth</p> <p>2011 Landsat Time Analysis Series by Pixel</p> <p>(red-swir 1)/(red+swir 1); (5): min, mean, max, std, median<br> (red-nir)/( red+nir); (5): min, mean, max, std, median<br> (swir 1-swir 2)/( swir 1+swir 2); (5): min, mean, max, std, median<br> (nir-swir 2)/(nir+swir 2); (5): min, mean, max, std, median<br> (nir-swir 1)/( nir+swir 1); (5): min, mean, max, std, median<br> (red-swir 2)/( red+swir 2); (5): min, mean, max, std, median<br> (green-swir 2)/(green+swir 2); (5): min, mean, max, std, median<br> (green-swir 1)/(green+swir 1); (5): min, mean, max, std, median<br> (green-red)/(green+red); (5): min, mean, max, std, median<br> (green-nir)/(green+nir); (5): min, mean, max, std, median<br> (blue-swir 2)/(blue+swir 2); (5): min, mean, max, std, median<br> (blue-swir 1)/(blue+swir 1); (5): min, mean, max, std, median<br> (blue-red)/(blue+red); (5): min, mean, max, std, median<br> (blue-nir)/(blue+nir); (5): min, mean, max, std, median<br> (blue-green)/( blue+green); (5): min, mean, max, std, median</p>
Data for "Coherence-Guided InSAR Deformation Analysis in the Presence of Ongoing Land Surface Change in the Imperial Valley, California"
<p>Interferometric Synthetic Aperture Radar (InSAR) observations of the surface velocity field in the Imperial Valley, California, over the period of 2015-2019, derived from the European Satellite Agency’s (ESA) Sentinel-1a/b satellite imagery.</p> <p>This dataset includes the following:</p> <p>1. Downsampled results in geographic coordinates (a grid spacing of 2”). The average (secular) velocity (in mm/yr) is estimated along the satellite line-of-sight (LOS) directions from descending (D173) and ascending (A166) tracks over the spatial extent covering the Imperial Valley (S/N/W/E: 32.4/33.6/-116.2/-115). GeoTIFF files contain velocity values or color-coded RGB values using the Matplotlib colormap RdBu_r (ranging from -25 to 25 mm/yr). Masked pixels are assigned with NaN values.</p> <p>2. Original high-resolution results processed in radar coordinates (~28 m in range and ~42 m in azimuth). HDF5 files contain matrices of the same size for the longitude, latitude, LOS secular velocity, and LOS residual displacements at each ground pixel from both tracks. The temporal error of displacement time series at each pixel (in mm) is represented by the root-mean-square residual of data fits to a linear trend. Masked pixels are assigned with NaN values.</p>
Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm
<p>This package supplements the following paper entitled “Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm” published with Science China Earth Sciences.</p>
GLASS Land Surface Temperature product (1981-2000): Orbital Drift Corrected LST
<ul> <li>GLASS Land Surface Temperature product (1981-2000): the ODC LST product is an orbital drift corrected (ODC) version of the instantaneous GLASS LST product. To compensate the effect of orbital drift on LST, an improved ODC method was used to normalize the instantaneous GLASS LSTs to 14:30 solar time.</li> <li>The dataset is organized by year and a sample data is provided in simple.zip</li> <li>Further details can be found in the readme.pdf.</li> </ul>
GLASS Land Surface Temperature product (1981-2000): monthly averaged LST
<ul> <li>The LST product contains monthly averages of GLASS ODC LST.</li> <li>The dataset is organized by year and a sample data is provided in simple.zip</li> <li>Further details can be found in the readme.pdf.</li> </ul>
Result data related to "Tröndle (2020) -- Supply-side options to reduce land requirements of fully renewable electricity in Europe"
<p>The dataset contains the optimisation model and aggregated result data of my study. See `README.md` for more information.</p> <p>If you use this data in an academic publication, please cite the following article:</p> <blockquote> <p>Tröndle, T. (2020). Supply-side options to reduce land requirements of fully renewable electricity in Europe. PLOS ONE.</p> </blockquote>
Raw Data of "Runoff and erosive responses to different land-cover types in semiarid environment: Scale effects and controlling factors"
<p>The raw data of manuscript "Runoff and erosive responses to different land-cover types in semiarid environment: Scale effects and controlling factors" </p>
Data from: Quaternary land bridges have not been universal conduits of gene flow
<p>Quaternary climate oscillations are a well-known driver of animal diversification, but their effects are most well studied in areas where glaciations lead to habitat fragmentation. In large areas of the planet, however, glaciations have had the opposite effect, but here their impacts are much less well understood. This is especially true in Southeast Asia, where cyclical changes in land distribution have generated enormous land expansions during glacial periods. In this study, we selected a panel of five songbird species complexes covering a range of ecological specificities to investigate the effects Quaternary land bridges have had on the connectivity of Southeast Asian forest biota. Specifically, we combined morphological and bioacoustic analysis with an arsenal of population-genomic and modelling approaches applied to thousands of genome-wide DNA markers across a total of hundreds of individuals. Our analyses show that species dependent on forest understorey exhibit deep differentiation between Borneo and western Sundaland, with no evidence of gene flow during the land bridges accompanying the last 1-2 ice ages. In contrast, dispersive canopy species and habitat generalists have experienced more recent gene flow. Our results argue that there remains much cryptic species-level diversity to be discovered in Southeast Asia even in well-known animal groups such as birds, especially in non-dispersive forest understorey inhabitants. We also demonstrate that Quaternary land bridges have not been equally suitable conduits of gene flow for all species complexes, and that life history is a major factor in predicting relative population divergence time across Quaternary climate fluctuations.</p>
Data from: Anthropogenic noise does not surpass land cover in explaining habitat selection of Greater Prairie-Chicken (Tympanuchus cupido)
Over the last century, increasing human populations and conversion of grassland to agriculture have had severe consequences for numbers of Greater Prairie-Chickens (Tympanuchus cupido). Understanding Greater Prairie-Chicken response to human disturbance, including the effects of anthropogenic noise and landscape modification, is vital for conserving remaining populations because these disturbances are becoming more common in grassland systems. Here, we evaluate the effect of low-frequency noise emitted from a wind energy facility on habitat selection. We used the Normalized Difference Soundscape Index, a ratio of human-generated and biological acoustic components, to determine the impact of the dominant acoustic characteristics of habitat relative to physical landscape features known to influence within-home range habitat selection. Female Greater Prairie-Chickens avoided wooded areas and row crops but showed no selection or avoidance of wind turbines based on the availability of these features across their home range. Although the acoustic environment near the wind energy facility was dominated by anthropogenic noise, our results show that acoustic habitat selection is not evident for this species. In contrast, our work highlights the need to reduce the presence of trees which have been historically absent from the region, as well as decrease the conversion of grassland to row crop agriculture. Our findings suggest physical landscape changes surpass altered acoustic environments in mediating Greater Prairie-Chicken habitat selection.
Supplementary material 1 from: Yeung NW, Slapcinsky J, Strong EE, Kim JR, Hayes KA (2020) Overlooked but not forgotten: the first new extant species of Hawaiian land snail described in 60 years, Auriculella gagneorum sp. nov. (Achatinellidae, Auriculellinae). ZooKeys 950: 1-31. https://doi.org/10.3897/zookeys.950.50669
Non-type material examined for Auriculella auricula, A. minuta, A. perpusilla, A. perversa, and A. tenella
ScienceDex guides
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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.