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1,133 results for “wetlands”
Figure 3 in Nest characteristics and breeding success of Sarus Crane, Antigone antigone (Linnaeus, 1758) (Aves: Gruidae) in different habitats at Dhanauri Wetland, Uttar Pradesh, India
Figure 3. Nest of Sarus Crane in perennial wetland.
Figure 4 in Nest characteristics and breeding success of Sarus Crane, Antigone antigone (Linnaeus, 1758) (Aves: Gruidae) in different habitats at Dhanauri Wetland, Uttar Pradesh, India
Figure 4. Female individual maintaining nest.
Figure 6 in Nest characteristics and breeding success of Sarus Crane, Antigone antigone (Linnaeus, 1758) (Aves: Gruidae) in different habitats at Dhanauri Wetland, Uttar Pradesh, India
Figure 6. Correlation between mean water depth and nest height.
Figure 1 in Nest characteristics and breeding success of Sarus Crane, Antigone antigone (Linnaeus, 1758) (Aves: Gruidae) in different habitats at Dhanauri Wetland, Uttar Pradesh, India
Figure 1. Camera trap (in red circle) used during the study.
Figure 1 in On some avifauna from Jojari River - A Wastewater Wetland in Semi-Urban Area of Jodhpur City, Rajasthan
Figure 1. Location of Jojari River in Jodhpur city of Rajasthan.
Figure 2 in On some avifauna from Jojari River - A Wastewater Wetland in Semi-Urban Area of Jodhpur City, Rajasthan
Figure 2. Number of order of bird species found in Jojari River, Rajasthan.
Figure 3 in Recently resighted population of Blue-breasted Quail (Synoicus chinensis) in and around East Kolkata Wetland is under threat due to development activities
Figure 3. Showing LULC of the area where the BBQ were sighted.
Figure 1 in Waterbirds of Arunachal Pradesh with special reference to high altitude rivers and wetlands
Figure 1. Map of the study Namdapha Tiger Reserve, Arunachal Pradesh.
Fig. 2A in Enumeration of Herpetofaunal assemblage of Surajpur Wetland, National Capital Region (India)
Fig. 2A. Asian Common Toad Duttaphrynus melanostictus.
Fig. 1 in Enumeration of Herpetofaunal assemblage of Surajpur Wetland, National Capital Region (India)
Fig. 1. Map of the study area showing terrestrial and aquatic habitats.
Fig. 2O in Enumeration of Herpetofaunal assemblage of Surajpur Wetland, National Capital Region (India)
Fig. 2O. Checkered Keelback Xenochrophis piscator.
Fig. 2K in Enumeration of Herpetofaunal assemblage of Surajpur Wetland, National Capital Region (India)
Fig. 2K. Bengal Monitor Varanus bengalensis.
Figure 5 in Structural characteristics of the soil fauna community in beach wetlands of the Poyang Lake region
Figure 5. Margalef index (D) of soil fauna community at different sampling sites.
Figure 5 in Diet of the Lesser Spotted Eagle (Clanga pomarina) in Amvrakikos Wetlands National Park, Greece
Figure 5. Comparison of the main prey groups between Valaoritis and Zalongo by biomass (%).
Figure 4 in Diet of the Lesser Spotted Eagle (Clanga pomarina) in Amvrakikos Wetlands National Park, Greece
Figure 4. Comparison of the main prey groups between Valaoritis and Zalongo by numbers (%).
Figure 2 in Plant diversity and conservation value of wetlands along a rural-urban gradient
Figure 2. NMDS ordination for the average cover-abundance per transect per site of all species.
National Wetland Plant List: National Wetland Plant List 2016
The National Wetland Plant List (and the information implied by its wetland plant species status ratings) is used extensively in wetland delineation, wetland restoration and research, and the development of compensatory mitigation goals, as well as in providing general botanical information about wetland plants. The NWPL covers all 50 U.S. states, the District of Columbia, and the U.S. Caribbean and Pacific islands that are considered to be territories of the U.S. However, to provide additional insights regarding plant distribution awareness, we have included the total geographic range for each taxon throughout North America north of Mexico. The wetland plant data are organized into ten regions that coincide with Corps wetland delineation regions. For more information, see <p></p>http://wetland-plants.usace.army.mil/nwpl_static/v33/home/home.html# Lichvar, R.W., D.L. Banks, W.N. Kirchner, and N.C. Melvin. 2016. The National Wetland Plant List: 2016 wetland ratings. Phytoneuron 2016-30: 1-17. Published 28 April 2016. ISSN 2153 733X
Wetland sediment soil organic carbon sequestration data to support radiometric technique comparisons
<p>This workbook shows the ID, the geographical location, the year of sampling, and sediment core information in samples collected from undisturbed wetlands situated across four provinces of Canada (Alberta, Saskatchewan, Manitoba, and Ontario) from 2016 to 2019.</p>
Data - The contribution of boreal wetlands to the Northern hemisphere carbonyl sulfide sink
<p>COS fluxes in pmolm-2s-1, LAI data and model output data used in the article "The contribution of boreal wetlands to the Northern hemisphere carbonyl sulfide sink". </p>
Mapping Russian Wetlands and Estimating Methane Fluxes
<h3>Mapping Russian Wetlands and Estimating Methane Fluxes</h3> <p><strong>Introduction</strong></p> <p>Wetlands are crucial in regulating the Earth’s climate, acting as both carbon sinks and significant methane sources. Russian wetlands represent one of the largest and most diverse wetland complexes globally, extending across biomes from Arctic tundra to boreal forests. Despite their importance, these wetlands remain underexplored, particularly in terms of their spatial distribution and greenhouse gas contributions. This dataset provides a detailed typological map of Russian wetlands and accompanying methane flux estimates, representing the most comprehensive methane emissions dataset for Russian wetlands to date. The maps and calculations were developed in Google Earth Engine (GEE) through a combination of multi-seasonal Landsat composites, PALSAR radar imagery, and extensive field-based validation data from peatland sites across Western Siberia.</p> <h3>Data Overview</h3> <p><strong>Input Layers</strong></p> <p>The wetland mapping relied on seasonal Landsat composites (spring, summer, fall) and PALSAR radar data to capture the distinct structural and hydrological characteristics of each wetland type. Additional layers, such as GMTED topographic slope and Hansen’s TreeCover, were included to exclude non-wetland areas and to enhance the classification by distinguishing forested from non-forested wetlands.</p> <p><strong>Training Points</strong></p> <p>A comprehensive training site database was created, integrating field knowledge, high-resolution imagery, and georeferenced photos. Approximately 2,450 representative points were selected to capture 12 primary wetland types across Russia, with each point validated against high-resolution imagery to ensure accuracy. Points were collected to represent the wide-ranging wetland ecosystems in Russia, from open water and patterned bogs to swampy and forested fens, providing robust ground-truth data for training the classification model.</p> <p><strong>Random Forest Classifier</strong></p> <p>The random forest classifier was chosen for its capacity to handle large datasets and complex relationships among input layers. Optimized for Landsat and PALSAR inputs, the classifier used over 100 trees, each making independent predictions based on subsets of data, which were averaged to produce the final classification. This ensemble approach minimized overfitting, a crucial factor for the varied ecological regions across Russia.</p> <p><strong>Russian Wetlands Map</strong></p> <p>The final <strong>Russian Wetlands Map</strong> encompasses 12 wetland types, detailing their distribution and extent across the country:</p> <ul> <li> <p><strong>Total Wetland Area</strong>: 173.96 million hectares of mapped wetlands, capturing diverse ecosystems, including bogs, fens, and swampy areas.</p> </li> <li> <p><strong>Open Water Area</strong>: Lakes, rivers, and smaller water bodies within wetland zones were separately mapped, totaling 42.6 million hectares.</p> </li> </ul> <h3>Emission Modeling and Ecosite Analysis</h3> <p><strong>Ecosite Proportions for Methane Emission Modeling</strong></p> <p>Each wetland type was further divided into <strong>ecosite units</strong> representing distinct, smaller areas with uniform hydrological and geochemical properties. This level of detail enabled precise methane emission estimates by capturing the variability within complex wetland ecosystems. For instance, ridges and hollows within patterned bogs exhibit unique methane emission dynamics due to differences in vegetation and water levels. Ecosite proportions for methane emission were calculated from 20-30 representative field sites per wetland type, capturing the typical area breakdown of each wetland type across Russia.</p> <p><strong>Methane Emission Period Calculation</strong></p> <p>To estimate seasonal methane emission periods across Russia’s climatic zones, the average summer temperature (Bio10) parameter from WorldClim data was used. Bio10 values reflect seasonal variation in emission potential, correlating with longer and warmer summers in southern regions versus shorter, cooler summers in the north. Using these data, an emission period was calculated for<strong> each 50 km x 50 km grid</strong> cell based on a regression model derived from Western Siberia data:<br>Emission Period (hours) = 303 * Bio10 – 675</p> <p>This equation, which explained 98% of the variation in emission duration, provided a dynamic method for estimating emission periods across Russia’s diverse landscape.</p> <h3>Methane Emission Estimates</h3> <p><strong>Calculation Approach</strong></p> <p>Methane emission estimates were derived from a multi-step approach that incorporated ecosystem-specific emission factors, ecosystem area, and the estimated emission period:</p> <ol> <li> <p><strong>Ecosystem Area Calculation</strong>: Area estimates for each ecosite type were derived from field-based proportions applied to the classified wetland map.</p> </li> <li> <p><strong>Emission Period</strong>: Calculated for each grid cell based on Bio10 data, varying continuously across climatic zones.</p> </li> <li> <p><strong>Methane Flux Values</strong>: Based on quantiles from field measurements within three main zones (Tundra, Northern Taiga, and Southern Taiga) to account for natural variability in methane emissions.</p> </li> </ol> <p>Using this approach, methane emissions were calculated for each 50 km per 50 km grid cell, factoring in the unique emission characteristics of each wetland type and zone. This produced a spatially detailed estimate of methane fluxes, reflective of the temperature and vegetation gradients across Russia.</p> <p> </p> <p><strong>Resulting National Estimate</strong></p> <ul> <li> <p><strong>Total Annual Methane Emissions</strong>: 11.39 MtCH₄ per year from all mapped wetland areas.</p> </li> <li> <p><strong>Open Water Contributions</strong>: 2.54 MtCH₄ per year from open water bodies, including intra-wetland lakes and rivers.</p> </li> </ul> <h3>Data Highlights</h3> <ul> <li> <p><strong>High-resolution wetland classification</strong> covering 173.96 million hectares across diverse wetland ecosystems.</p> </li> <li> <p><strong>Detailed methane emission data</strong> derived from multi-year field measurements and validated against climatic data, providing spatially continuous methane flux estimates across Russia.</p> </li> <li> <p><strong>50x50 km² grid cell calculations</strong>, accounting for methane emission rates, emission periods, and ecosystem proportions for each cell.</p> </li> </ul> <p>This dataset serves as an essential tool for environmental scientists, climate modelers, and conservationists, supporting further research into wetland carbon dynamics, climate mitigation strategies, and regional land-use planning. The high resolution data availbale at url: https://code.earthengine.google.com/d6a9d4045255fd84298777e56a38ae03</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.