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214 results for “suitable habitat”
Contrasting the suitability of shade coffee agriculture and native forest as overwinter habitat for Canada Warbler (Cardellina canadensis) in the Colombian Andes.
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Data from: Suitability of Laurentian Great Lakes for invasive species based on global species distribution models and local habitat
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Data from: Suitable habitat of the wild Asian elephant in the western Terai of Nepal
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Data from: Habitat suitability and connectivity modeling reveal priority areas for Indiana bat (Myotis sodalis) conservation in a complex habitat mosaic
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Predicting hedgehog mortality risks on British roads using habitat suitability modelling
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Global Habitat Suitability for Framework-Forming Cold-Water Corals - Environmental Data
<p>Environmental data layers used in the production of global habitat suitability models for several species of Scleractinian corals from the publication:</p> <p><strong>Davies, A.J. & Guinotte, J.M. (2011) Global Habitat Suitability for Framework-Forming Cold-Water Corals. PLoS ONE 6(4): e18483. doi:10.1371/journal.pone.0018483</strong></p> <p>Files are ASCII raster layers, zipped using 7zip. Each raster has a WGS 1984 coordinate system.</p> <p>Brief methodology and outline of variables provided are presented below, for full details refer to the above manuscript in PLoS ONE:</p> <p><strong><em>Terrain variables</em></strong></p> <p>Several terrain attributes were extracted from the SRTM30 data (Table 1) following techniques and algorithms described in Wilson et al (2007). Individual approaches are described within the footnote of Table 1, however, briefly the extraction process and description of each variable is described here. Bathymetric position index (BPI) is an approach to determine topographical features based on their relative position within a neighbourhood, and can be calculated over fine or broad scales to capture smaller or larger terrain features respectively. This calculation has been developed into an ArcGIS tool by Wright et al. (2005). Slope was calculated using DEM Tools for ArcGIS developed by Jenness (2012), in particular the 4-cell method of calculating slope, which is accepted as the most accurate approach (Jones 1998). In this manuscript, slope is defined as the gradient in the direction of the maximum slope. Curvature attempts to describe terrain features and may provide an indication of how water would interact with the terrain. In this manuscript, plan and tangential curvature can describe how water would converge or diverge as it flows over relief, whilst profile curvature describes how water would accelerate or decelerate as it flows over relief (Jenness 2012). Aspect is defined as the direction of maximum slope and was converted to continuous radians following Wilson et al. (2007). Rugosity, terrain ruggedness index and roughness all generally describe the variability of the relief of the seafloor (Wilson et al. 2007). Rugosity is defined as the ratio of the surface area to the planar area across a neighbourhood of a central pixel (Jenness 2012). Terrain ruggedness index is defined as the mean difference between a central pixel and its surrounding cells and roughness which is the largest inter-cell difference of a central pixel and its surrounding cell (Wilson et al. 2007).</p> <p><strong><em>Environmental variables</em></strong></p> <p>Environmental variables were created using the variable up-scaling approach presented within (Davies & Guinotte 2011). This approach takes gridded layers of an environmental variable and drapes them over bathymetry to provide an indication of conditions near the seabed, it has been proven to work well over global and regional scales (Davies & Guinotte 2011, Guinotte & Davies 2012). In this manuscript, these environmental layers must be considered as representations of general conditions, as likely, the highly variable topography of the canyon will not yield a good prediction of environmental variables at such a small spatial scale. Limited CTD profiles were collected using a SeaBird 911+, collating data for turbidity (Seapoint, formazin turbidity units), dissolved oxygen (mg L<sup>-1</sup>), depth (m), conductivity (Siemens/m), temperature (°C), salinity, and pH. These casts were compared with the modelled layers (specifically dissolved oxygen, salinity and temperature) to determine their relative accuracy for certain areas of the seafloor</p> <p><strong>Variable name // Filename // Units // Reference (see original paper)</strong></p> <p>Terrain Variables</p> <p>Aspect // aspect // Degree // Jenness (2012)<br> Aspect – Eastness // eastness // // Wilson et al. (2007)<br> Aspect – Northness // northness // // Wilson et al. (2007)<br> Bathymetry // srtm30 // m // Becker et al. (2009)<br> Curvature – Profile // profilecurve // // Jenness (2012)<br> Curvature – Plan // plancurve // // Jenness (2012)<br> Curvature – Tangential // tangcurve // // Jenness (2012)<br> Roughness // roughness // // Wilson et al. (2007)<br> Rugosity // rugosity // // Jenness (2012)<br> Slope // slope // Degrees // Jenness (2012)<br> Terrain Ruggedness Index // tri // // Wilson et al. (2007)<br> Topographic Position Index // tpi // // Wilson et al. (2007) </p> <p>Environmental variables<br> Alkalinity // alk_stein // μmol l-1 // Steinacher et al. (2009)<br> Apparent oxygen utilisation // woaaoxu // mol O2 m-3 // Garcia et al. (2006b)<br> Chlorophyll a // modismin, modismean, modismax // mg m-3 // NASA Ocean Color<br> Dissolved inorganic carbon // dic_stein // μmol l-1 // Steinacher et al. (2009)<br> Dissolved oxygen // woadiso2 // ml l-1 // Garcia et al.(2006a)<br> Nitrate // woanit // μmol l-1 // Garcia et al. (2006b)<br> Omega aragonite // arag_stein // ΩARAG // Steinacher et al. (2009)<br> Omega aragonite // arag_orr // ΩARAG // Orr et al. (2005)<br> Omega calcite // calc_stein // ΩCALC // Steinacher et al. (2009)<br> Omega calcite // calc_orr // ΩCALC // Orr et al. (2005)<br> Percent oxygen saturation // woapoxs // % O2S // Garcia et al. (2006b)<br> Phosphate // woaphos // μmol l-1 // Garcia et al. (2006b)<br> Regional current velocity // regfl // m s-1 // Carton et al. (2005)<br> Salinity // woasal // pss // Boyer et al. (2005)<br> Silicate // woasil // μmol l-1 // Garcia et al. (2006b)<br> Seasonal variation index // lutzsvi // // Lutz et al. (2007)<br> Temperature // woatemp // °C // Boyer et al. (2005)<br> Particulate organic carbon // poc // g Corg m-2 yr-1 // Lutz et al. (2007)<br> Vertical current velocity // vertfl // m s-1 // Carton et al. (2005)<br> Vertically generalised productivity model // vgpmmin, vgpmmean, vgpmmax // mg C m-2 d-1 // Behrenfeld and Falkowski (1997)</p>
The dataset of predicting the potential habitat suitability of Saussurea species in China under future climate change using the optimized Maximum Entropy (MaxEnt) model
<p><strong>Description:</strong></p> <p>This dataset accompanies the study on the Saussurea species, renowned for its biodiversity and medicinal significance in high-elevation regions, which faces endangerment due to climate change and human activities. Despite its importance, conservation research on Saussurea has been limited. To address this gap, the study employed the optimized MaxEnt model to simulate Saussurea's habitat suitability and analyze key environmental factors influencing its distribution.</p> <p>The dataset includes:</p> <ol> <li><strong>Model and Parameter Optimization Code</strong>: The code used for optimizing the MaxEnt model parameters, ensuring reproducibility of the habitat suitability models.</li> <li><strong>Saussurea Distribution Points</strong>: Georeferenced points indicating the observed locations of Saussurea species.</li> <li><strong>Current Environmental Variables</strong>: Data on key environmental factors influencing Saussurea distribution, such as Elevation, Isothermality (Bio3), and Temperature Annual Range (Bio7).</li> <li><strong>Future Environmental Variables: </strong>Data on key environmental factors influencing Saussurea distribution under SSP126, SSP245, SSP370 and SSP585 in 2020-2100s.</li> </ol>
Data from: Phylogeography, historical demography, and habitat suitability modelling of freshwater fishes inhabiting seasonally fluctuating Mediterranean river systems: a case study using the Iberian cyprinid Squalius valentinus
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Environmental gradients, covering coastal central California, which are relevant to modeling habitat suitability of Deinandra increscens subsp. villosa
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FIG. 7 in A Lentic Breeder in Lotic Waters: Sierra Nevada Yellow-Legged Frog (Rana sierrae) Habitat Suitability in Northern Sierra Nevada Streams
FIG. 7. Mean predicted probability of microhabitat use by adult R. sierrae (includes subadults) for all possible values from a given predictor variable from the 1,000 bootstrapped logistic regression models at (A) Independence Creek and (B) South Fork Tributaries. Shading and bars represent 95% credible intervals. Substrate categories are silt (Slt), sand (Snd), fine gravel (FGrav), coarse gravel (CGrav), cobble (Cob), boulder (Bld), and bedrock (Bed).
FIG. 9 in A Lentic Breeder in Lotic Waters: Sierra Nevada Yellow-Legged Frog (Rana sierrae) Habitat Suitability in Northern Sierra Nevada Streams
FIG. 9. Mean predicted probability of microhabitat use by tadpoles of R. sierrae for all possible values from a given predictor variable from the 1,000 bootstrapped logistic regression models at (A) Independence Creek and (B) South Fork Tributaries. Shading and bars represent 95% credible intervals. Substrate categories are silt (Slt), sand (Snd), fine gravel (FGrav), coarse gravel (CGrav), cobble (Cob), boulder (Bld), and bedrock (Bed).
FIG. 1 in A Lentic Breeder in Lotic Waters: Sierra Nevada Yellow-Legged Frog (Rana sierrae) Habitat Suitability in Northern Sierra Nevada Streams
FIG. 1. Study site locations in the northern portion of the range of R. sierrae. One study site is located on each of Lone Rock Creek (LRC) and Independence Creek (IND), one study site is located on the mainstem South Fork Rock Creek (SFRC), and two study sites are located on tributaries to SFRC (SFT [South Fork Tributary] and SFTT [South Fork Tadpole Tributary]).
Dataset of "Impact of climate change on the distribution and habitat suitability of the world's main commercial squids"
<p>Data of the manuscript "Impact of climate change on the distribution and habitat suitability of the world’s main commercial squids"</p>
Data for: Extreme shifts in habitat suitability under contemporary climate change for a high-Arctic herbivore
<p>Data and code associated with MaxEnt analyses to quantify shifts in habitat suitability of muskoxen in the Northeast Greenland National Park. Details on how to use the files are provided in the README.docx file</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.