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1,133 results for “wetlands”
Fig. 2 in A Mark-Recapture Study Of A Dog-Faced Water Snake Cerberus Schneiderii (Colubridae: Homalopsidae) Population In Sungei Buloh Wetland Reserve, Singapore
Fig. 2. Physical conditions (rainfall, air temperature) in relation to relative abundance of Cerberus schneiderii at Sungei Buloh Wetland Reserve between Jan.–Dec.2006: a, total rainfall and mean dry bulb temperature at the Changi Meteorological Station, Singapore; b, relative abundance of snakes represented by the relative number of individuals captured (i.e., # captures (# hours × # observers) –1) to standardise for sampling effort, which differed between months.
Fig. 1 in A Mark-Recapture Study Of A Dog-Faced Water Snake Cerberus Schneiderii (Colubridae: Homalopsidae) Population In Sungei Buloh Wetland Reserve, Singapore
Fig. 1. Map of Sungei Buloh Wetland Reserve showing the locations of the four brackish ponds (A3-4, A6, C2-3 and C4-5) where Cerberus schneiderii individuals were collected.
Fig. 7 in A Mark-Recapture Study Of A Dog-Faced Water Snake Cerberus Schneiderii (Colubridae: Homalopsidae) Population In Sungei Buloh Wetland Reserve, Singapore
Fig. 7. Cerberus schneiderii.. Scatterplots and fitted regression lines of initial snout-vent length (SVL0) against 'SVL after one month' (SVL1) for males (O, solid line) and females (+, dashed line).
Fig. 8 in A Mark-Recapture Study Of A Dog-Faced Water Snake Cerberus Schneiderii (Colubridae: Homalopsidae) Population In Sungei Buloh Wetland Reserve, Singapore
Fig. 8. Cerberus schneiderii. Percentage frequency distribution of microhabitat types utilised by: a, all snakes (n = 2262); b, males (n = 1206); and c, females (n = 1056).
Figure 3 in Acridomorpha (Orthoptera) species associated with the protected wetlands of Santa Lucía, Montevideo, Uruguay
Figure 3. Acridomorpha species (Orthoptera, Caelifera) found in the Área Protegida de los Humedales de Santa Lucía, Montevideo, Uruguay. A. Ronderosia bergii. B. Scotussa impudica. C. Scotussa lemniscata. D. Scotussa liebermanni. E. Aleuas lineatus. F. Leptysma argentina. G. Haroldgrantia lignosa. H. Allotruxalis gracilis. I. Metaleptea adspersa. J. Amplytropidia australis. Scale bars = 10 mm.
Figure 2 in Acridomorpha (Orthoptera) species associated with the protected wetlands of Santa Lucía, Montevideo, Uruguay
Figure 2. Acridomorpha species (Orthoptera, Caelifera) found in the Área Protegida de los Humedales de Santa Lucía, Montevideo, Uruguay. A. Orienscopia costulata. B. Orienscopia sanmartini. C. Chromacris speciosa. D. Staleochlora viridicata orientalis. E. Xyleus discoideus discoideus. F. Zoniopoda iheringi. G. Dichroplus conspersus. H. Dichroplus elongates. I. Dichroplus obscurus. J. Dichroplus pratensis. K. Leiotettix politus. L. Leiotettix pulcher. Scale bars = 10 mm.
Figure 4 in Acridomorpha (Orthoptera) species associated with the protected wetlands of Santa Lucía, Montevideo, Uruguay
Figure 4. Acridomorpha species (Orthoptera, Caelifera) found in the Área Protegida de los Humedales de Santa Lucía, Montevideo, Uruguay. A. Laplatacris dispar. B. Orphulella punctata. C. Sinipta dalmani. D. Staurorhectus longocornis longicornis. Scale bars = 10 mm.
Fig. 3 in Spatial pattern of a fish assemblage in a seasonal tropical wetland: effects of habitat, herbaceous plant biomass, water depth, and distance from species sources
Fig. 3. Partial regressions testing the effects of water depth (left) and distance from colonizing source (right) on fish species richness collected in 22 plots in Site of Long-Term Sampling (SLTS). Only statistically significant relationships are shown.
Fig. 1 in Spatial pattern of a fish assemblage in a seasonal tropical wetland: effects of habitat, herbaceous plant biomass, water depth, and distance from species sources
Fig. 1. Geographical location of the study area and the Site of Long-Term Sampling (in the area). The system is installed in the Pantanal, Brazil.
Fig. 2 in Aquatic food webs in mangrove and seagrass habitats of Centla Wetland, a Biosphere Reserve in Southeastern Mexico
Fig. 2. Mean (± S.D.) δ13C and δ15N values of consumers (fishes, crustaceans and mollusks) and primary producers in Polo Stream and San Pedrito Lagoon. Species identities are in Table 1.
Fig. 1 in Aquatic food webs in mangrove and seagrass habitats of Centla Wetland, a Biosphere Reserve in Southeastern Mexico
Fig. 1. Map depicting the location and extent of Centla Wetland Biosphere Reserve (CWBR) in Southern Mexico. The detailed study area map illustrates locations of field collections at Polo Stream and San Pedrito Lagoon along the Grijalva and Usumacinta Rivers in Tabasco, Mexico.
Development of a global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M)
<p>Seasonal and interannual variations in global wetland area is a strong driver of fluctuations in global methane (CH<sub>4</sub>) emissions. Current maps of global wetland extent vary with wetland definition, causing substantial disagreement and large uncertainty in estimates of wetland methane emissions. To reconcile these differences for large-scale wetland CH<sub>4</sub> modeling, we developed a global Wetland Area and Dynamics for Methane Modeling (WAD2M) dataset at ~25 km resolution at equator (0.25 arc-degree) at monthly time-step for 2000-2018. WAD2M combines a time series of surface inundation based on active and passive microwave remote sensing at coarse resolution (~25 km) with six static datasets that discriminate inland waters, agriculture, shoreline, and non-inundated wetlands. We exclude all permanent water bodies (e.g. lakes, ponds, rivers, and reservoirs), coastal wetlands (e.g., mangroves and seagrasses), and rice paddies to only represent spatiotemporal patterns of inundated and non-inundated vegetated wetlands. Globally, WAD2M estimates the long-term maximum wetland area at 13.0 million km<sup>2</sup> (Mkm<sup>2</sup>), which can be separated into three categories: mean annual minimum of inundated and non-inundated wetlands at 3.5 Mkm<sup>2</sup>, seasonally inundated wetlands at 4.0 Mkm<sup>2</sup> (mean annual maximum minus mean annual minimum), and intermittently inundated wetlands at 5.5 Mkm<sup>2</sup> (long-term maximum minus mean annual maximum). WAD2M has good spatial agreements with independent wetland inventories for major wetland complexes, i.e., the Amazon Lowland Basin and West Siberian Lowlands, with high Cohen’s kappa coefficient of 0.54 and 0.70 respectively among multiple wetlands products. By evaluating the temporal variation of WAD2M against modeled prognostic inundation (i.e., TOPMODEL) and satellite observations of inundation and soil moisture, we show that it adequately represents interannual variation as well as the effect of El Niño-Southern Oscillation on global wetland extent. This wetland extent dataset will improve estimates of wetland CH<sub>4</sub> fluxes for global-scale land surface modeling. </p> <p> </p> <p>Update: Oct.08.2021</p> <p>Documentation for WAD2M Version 2.0 can be found at <a href="https://drive.google.com/file/d/1adoAnuqu6uBWnTYKI8u_S4OAQSdgOtd6/view?usp=sharing">WAD2M_V2_update</a></p>
Tabular summary for the Costa Rican wetlands vulnerability index
<p>The table summarizes the data inputs used to generate the cartography the "Costa Rican wetlands vulnerability index" paper (DOI: https://doi.org/10.1177/03091333221134189). The table complements the shape for Costa Rican wetlands. </p> <p>It includes information for each one of the 10.669 wetland polygons of Costa Rica, according to the National Wetlands Inventory of Costa Rica generated by UNPD (DOI: http://dx.doi.org/10.13140/RG.2.2.10529.48485) regarding CI, HI, VI, area in hectares, if it is inside/outside/partially within Protected Areas (PA), the type of wetland and the name and unique ID (Form) of each polygon.</p>
Land cover classification data for the first Chinese wetland cities in 2015 and 2020
<p>Land cover classification data for the first Chinese wetland cities in 2015 and 2020</p> <p>A land cover dataset, which had a resolution of 10 m and included four wetland types and five non-wetland types.</p> <p>The first Chinese wetland cities include Yinchuan, Changde, Haikou, Harbin, Dongying and Changshu.</p>
Atmospheric methane since the LGM was driven by wetland sources
<p>Companion data set to Kleinen et al. (2023):<br> Thomas Kleinen, Sergey Gromov, Benedikt Steil, and Victor Brovkin<br> Atmospheric methane since the LGM was driven by wetland sources<br> Climate of the Past, 2023</p> <p>Model output from the MPIESM model, model experiments base and MWM.<br> See Kleinen et al. (2023) for details.</p> <p>Timeseries data plotted in all Figures:<br> Global mean temperature, total land carbon; CH4 concentrations and fluxes; NO and RC fluxes; atmospheric lifetimes.</p> <p>Time axis in netcdf files is negative years before present, i.e. year -20000 is 20000 years before present (present=1950 CE).<br> Time is represented as absolute time YYMMDD.f, with YY negative year BP, MM mmonth and DD day, f is fractional daytime.<br> </p>
Global Wetlands: Luderick Seagrass Dataset - Test Set Image Patches
<p>This dataset is a test dataset of image patches created from the 'novel-test' split of the Global Wetlands Luderick-Seagrass dataset. The original images were divided as a grid into 50 image patches. The image patches were manually labeled into 'Background', 'Fish' and 'Seagrass' sets. The images were otherwise unaltered. </p> <p>We contribute this test dataset of underwater image patches to facilitate evaluation of coarse segmentation seagrass methods.</p> <p>Original dataset description: "This dataset comprises of annotated footage of Girella tricuspidata in two estuary systems in South East Queensland, Australia. This data is suitable for a range of classification and object detection research in unconstrained underwater environments."</p> <p>Original dataset citation: Ditria, Ellen M; Connolly, Rod M; Jinks, Eric L; Lopez-Marcano, Sebastian (2021)<strong>:</strong> Annotated video footage for automated identification and counting of fish in unconstrained marine environments. <em>PANGAEA</em>, <a href="https://doi.org/10.1594/PANGAEA.926930">https://doi.org/10.1594/PANGAEA.926930</a>.</p> <p>The original dataset is available at: <br> https://github.com/globalwetlands/luderick-seagrass<br> https://download.pangaea.de/dataset/926930/files/Fish_automated_identification_and_counting.zip<br> https://globalwetlands.blob.core.windows.net/globalwetlands-public/datasets/luderick-seagrass/luderick-seagrass.zip</p>
wetland-soc
<p>Data supporting "Setting a reference for wetland carbon: the importance of accounting for hydrology, topography, and natural variability"</p>
Fig. 3 in The Importance Of Artificial Wetlands In The Conservation Of Wetland Birds And The Impact Of Land Use Attributes Around The Wetlands: A Study From The Ajara Conservation Reserve, Western Ghats, India
Fig. 3. Classification of wetland birds based on feeding guild recorded at five artificial wetlands during 2011– 2015: A — Gavase wetland; B — Dhangarmola wetland; C — Khanapur wetland; D — Erandol wetland; E — Ningudage wetland.
Fig. 1 in The Importance Of Artificial Wetlands In The Conservation Of Wetland Birds And The Impact Of Land Use Attributes Around The Wetlands: A Study From The Ajara Conservation Reserve, Western Ghats, India
Fig. 1. Map of study sites: A — Gavase wetland, B — Dhangarmola wetland, C — Khanapur wetland, D — Erandol wetland, E — Ningudage wetland. Adopted from Patil & Choudaj (2022).
Fig. 4 in The Importance Of Artificial Wetlands In The Conservation Of Wetland Birds And The Impact Of Land Use Attributes Around The Wetlands: A Study From The Ajara Conservation Reserve, Western Ghats, India
Fig. 4. Photographs of some of the wetland birds: a — Ruddy Shelduck Tadorna ferruginea; b — Little Ringed Plover Charadrius dubius; c — Small Pratincole Glareola lacteal; d — Painted Stork Mycteria leucocephala; e — Black-headed Ibis Threskiornis melanocephalus; f — Asian Openbill Anastomus oscitans; g — Eurasian Spoonbill Platalea leucorodia; h — Black-winged Stilt Himantopus himantopus; i — River Tern Sterna aurantia.
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.