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90 results for “large river”
Large consumer isotope values, Shark River Slough, Everglades National Park (FCE LTER), Florida, USA, May 2005 - ongoing
This dataset provides information on the stable isotope values from multiple tissues from various consumers (especially bull sharks and American alligators) sampled within the Shark River Slough.
Indicative distribution map for Ecosystem Functional Group F1.7 Large lowland rivers
<p>This archive contains indicative distribution maps and profiles for <strong>F1.7 Large lowland rivers</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Mask of large scale river catchments
<p>The catchment mask provides information about the location of large scale river catchments on a global grid. Its purpose is the provision of a common reference for the computation of area averages, especially for the analysis of Earth System Model output.</p>
Large shark catches (Drumline), water temperatures, salinities, dissolved oxygen levels, and stable isotope values in the Shark River Slough, Everglades National Park (FCE LTER) from May 2009 to May 2011
This dataset provides information on the catches of large sharks in the Shark River Slough in relation to physical factors including dissolved oxygen, water temperature, salinity, and distance upstream. Analysis of data collected suggest that distance from the Gulf of Mexico and salinity have the largest effects on shark catch rates, with most large sharks being caught at the mouth of the estuary in high salinity waters. This dataset includes all sharks caught on drumline gear, including large coastal species such as bull sharks and lemon sharks, as well as smaller coastal species such as Atlantic sharpnose sharks and blacknose sharks.
White spruce seedling demography and browsing by snowshoe hares inside and outside the large herbivore exclosures located along the Tanana River, summer 2014
White spruce seedling height, age, basal diameter, browsing history, and density were measured inside and outside the seven remaining paired exclosure and control plots located along the Tanana River floodplain in the summer of 2014.
Figures 1–13 in New records and diagnostic notes on large carpenter bees (Hymenoptera: Apidae: genus Xylocopa Latreille), from the Amazon River basin of South America
Figures 1–13. Dorsal habitus photographs of pinned, preserved specimens of females of Xylocopa (Neoxylocopa) species from the Amazon River basin, from the USNM collection. 1) X. (N.) aeneipennis. 2) X. (N.) amazonica. 3) X. (N.) aurulenta. 4) X. (N.) carbonaria. 5) X. (N.) cearensis. 6) X. (N.) fimbriata. 7) X. (N.) frontalis. 8) X. (N.) grisescens. 9) X. (N.) hirsutissima. 10) X. (N.) orthogonaspis. 11) X. (N.) similis. 12) X. (N.) suspecta. 13) X. (N.) tegulata.
Prediction of ice duration along large rivers
<p>Contains two files:</p> <p>shp files: contain river centerline points and its associated downstream distance:</p> <p>* "name": name of the river</p> <p>* "dsDistKm": downstream distance in km</p> <p>* "dsPointId": unique id for each centerline point for a river, together with river name uniquely identify a point in the dataset</p> <p>"river_ice_duration_winter_temperature" (csv file)—contains river ice duration and winter mean temperature prediction:</p> <p>* "name" : name of the river </p> <p>* "dsPointId": unique id for each centerline point for a river, together with river name uniquely identify a point in the dataset</p> <p>* "year": year (from 2019–2099) </p> <p>* "tasmean": winter (Dec, Jan, and Feb) mean surface air temperature (from ERA5, in degree Celsius) </p> <p>* "iceDuration": ice duration in days</p> <p>Temperature and ice duration were based on CESM1-BGC under RCP8.5.</p>
A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies V1.1
<p>A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies. GSHA covers 21,568 watersheds from 13 agencies for as long as 43 years based on the discharge observations scraped from the web. GSHA includes yearly streamflow characteristics derived from daily discharge observations, daily meteorological variables (including precipitation, 2-m air temperature, long- and shortwave radiation, wind speed, actual and potential evapotranspiration (AET and PET)), daily or weekly water storage terms (4 layers of soil moisture, groundwater, and snow depth water equivalence), daily vegetation index (leaf area index (LAI)), yearly LULC characteristics (urban, cropland, and forest fraction), and yearly reservoir information (degree of regulation (DOR) and reservoir capacity). For each meteorological variable, multiple independent data sources are incorporated to provide uncertainty estimates. Static attributes like land physiography, soils, and geology are not additionally extracted, as similar efforts have been made by other researchers, so we directly matched our gauge locations to the HydroATLAS dataset by providing the river ID match table.</p> <p>For more details of GSHA, please refer to a companion research article submitted to ESSD.</p> <p>Please access the variables in version 1.0. Monthly streamflow indices files do not include Chinese basins.</p> <p>Citation: <strong> </strong>Yin, Z., Lin, P., Riggs, R., Allen, G. H., Lei, X., Zheng, Z., and Cai, S.: A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-256, in review, 2023.</p> <p> </p>
Data for: Landscape diversity promotes stable food web architectures in large rivers
<p>Uncovering relationships between landscape diversity and species interactions is crucial for predicting how ongoing land-use change and homogenization will impact the stability and persistence of communities. However, such connections have rarely been quantified in nature. We coupled high-resolution river sonar imaging with annualized energetic food webs to quantify relationships between habitat diversity, energy flux, and trophic interaction strengths in large-river food web modules that support the endangered Pallid Sturgeon. Our results demonstrate a clear relationship between habitat diversity and species interaction strengths, with more diverse foraging landscapes containing higher production of prey and a greater proportion of weak and potentially stabilizing interactions. Additionally, rare patches of large and relatively stable river sediments intensified these effects and further reduced interaction strengths by increasing prey diversity. Our findings highlight the importance of landscape characteristics in promoting stabilizing food-web architectures and provide direct relevance for future management of imperiled species in a simplified and rapidly changing world.</p>
Рис. 2. Продольный (А–Д) и поперечный (Е–З) среЗы череЗ наружный покров ноги моллюска с раЗными типами складок: А, Б – Широкие складки в виде плато, В, Г – длинные иЗвилистые складки, Д, Е – складки с округлыми и бокаловидными клетками в субЭпителиальном слое, Ж, З – слабовыраженные складки с больШими полостЯми (синусами) длЯ гемолимфы под субЭпителиальным слоем. МасШтабные линейки 200 мкм (А, В, Ж, З) и 100 мкм (Б, Г–Е). вК – клетки с вакуолЯми, сэ – субЭпителиальный слой, БК – бокаловиднаЯ клетка, сг – синусы длЯ гемолимфы, ф – фолликулы, а – ацинусы. Fig. 2. Saggital (А–Д) and transverse (Е–З) sections of pedal integument with different types of plicae: А, Б – broad plateau-shaped plicae, В, Г – long, tortuous plicae, Д, Е – plicae with round and goblet cells in the subepithelial layer, Ж, З – mild plicae with large cavities (sinuses) for hemolymph under subepithelial layer. Scale bars 200 µm (А, В, Ж, З) and 100 µm (Б, Г–Е). вК – cells with vacuoles, сэ – subepithelial layer, БК – goblet cell, сг – sinuses for hemolymph, ф – follicles, а – acini. in Nodularia vladivostokensis (Bivalvia: Unionidae) from Razdolnaya River (Primorye, Russia)
Рис. 2. Продольный (А–Д) и поперечный (Е–З) среЗы череЗ наружный покров ноги моллюска с раЗными типами складок: А, Б – Широкие складки в виде плато, В, Г – длинные иЗвилистые складки, Д, Е – складки с округлыми и бокаловидными клетками в субЭпителиальном слое, Ж, З – слабовыраженные складки с больШими полостЯми (синусами) длЯ гемолимфы под субЭпителиальным слоем. МасШтабные линейки 200 мкм (А, В, Ж, З) и 100 мкм (Б, Г–Е). вК – клетки с вакуолЯми, сэ – субЭпителиальный слой, БК – бокаловиднаЯ клетка, сг – синусы длЯ гемолимфы, ф – фолликулы, а – ацинусы. Fig. 2. Saggital (А–Д) and transverse (Е–З) sections of pedal integument with different types of plicae: А, Б – broad plateau-shaped plicae, В, Г – long, tortuous plicae, Д, Е – plicae with round and goblet cells in the subepithelial layer, Ж, З – mild plicae with large cavities (sinuses) for hemolymph under subepithelial layer. Scale bars 200 µm (А, В, Ж, З) and 100 µm (Б, Г–Е). вК – cells with vacuoles, сэ – subepithelial layer, БК – goblet cell, сг – sinuses for hemolymph, ф – follicles, а – acini.
Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance - Data
<p>Datasets and R code related to manuscript entitled, "Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance". See '0_READ_ME.rtf' file for additional description of available files.</p>
Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance - Data Revision
<p>Datasets and R code related to manuscript entitled, "Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance". See '0_READ_ME.rtf' file for additional description of available files.</p>
Assessing suspended sediment fluxes with acoustic doppler current profilers: case study from large rivers in Russia
<p>The dataset contains measurements of water discharge by Teledyne RDInstruments RioGrande WorkHorse ADCP unit with a working frequency of 600kHz mounted on a moving boat in 6 areas over large rivers of Russia. The dataset comprises the four largest Arctic Siberian rivers and included continuous ADCP measurements done in 2018-2020 at constant crossection at each river located upper from the impact of recipient seas (tides, surges) near the cities of Salekhard (Ob River), Igarka (Yenisey River), Zhigansk (Lena river) and Chersky (Kolyma River). Another area includes ADCP measurements over 20 transects (named S1…S26, fig. 2) in the lower 200 km of the river Selenga on 27-31July 2018. Additionally, the dataset contains ADCP measurements at 38 points along the Moskva River (named M1, M2…) and 17 tributaries (named T01, T02…) done during 2019-2020.</p> <p>This is a supporting material to a manuscript submitted to «Big Earth Data» journal</p>
Geomorphology shapes relationships between animal communities and ecosystem function in large rivers
<p class="MsoNormal"><span>Understanding how the Earth's surface (i.e., 'nature's stage') influences connections between biodiversity and ecosystem function (BEF) is a central objective in ecology. Despite recent calls to examine these connections at multiple trophic levels and at more complex and realistic scales, little is known about how landscape structure shapes BEF relationships among animal communities in nature. We coupled high-resolution habitat mapping with extensive field sampling to quantify connections among the geophysical habitat templet, invertebrate assemblages, and secondary production in two large North American riverscapes. Patterns of sediment size governed invertebrate assemblage structure, with particularly strong effects on composition, richness, and taxonomic and functional diversity. These relationships propagated to drive positive relationships between biodiversity and secondary production that were modified by scale, context-dependencies, and anthropogenic modification. Finally, leveraging spatially explicit descriptions of geophysical and biological properties, we uncovered distinct and nested spatial scales of biodiversity and secondary production, and suggest that multiple geophysical processes simultaneously influence these patterns at different scales. Together, our findings advance our understanding of relationships between the physical templet and patterns of BEF, and help to predict </span>how perturbations to the Earth's surface may propagate to influence biodiversity and energy flux through food webs.<span> </span></p>
Large effect loci mediate rapid adaptation of salmon body size after river regulation
<p>Understanding the potential of natural populations to adapt to altered environments is becoming increasingly relevant in evolutionary research. Currently, our understanding of adaptation to human alteration of the environment is hampered by lack of knowledge on the genetic basis of traits, lack of time series, and little or no information on changes in optimal trait values. Here we used time series data spanning nearly a century to investigate how body mass of Atlantic salmon (<em>Salmo salar</em>) adapts to river regulation. We found that the change in body mass followed the change in waterflow, both decreasing to ~1/3 of their original values. Allele frequency changes at two loci in the regions of <em>vgll3</em> and <em>six6 </em>predicted more than 80% of the observed body mass reduction. Modelling the adaptive dynamics revealed that the population mean lagged behind its optimum before catching up ~6 salmon generations after the initial waterflow reduction. Our results demonstrate rapid adaptation mediated by large effect loci and provide insight into the temporal dynamics of evolutionary rescue following human disturbance.</p>
FIGURE 1 in First study of food webs in a large glacial river: the trophic role of invasive trout
FIGURE 1 | Sampling areas in the Santa Cruz River, Argentina. Upstream area corresponds to the locally known "Labyrinth", and Midstream area correspond to "Estancia San Ramon". Map created by the authors, upper picture taken from Google Earth (R).
Fig. 4 in Short-term changes in energy allocation by Hemiodontidae fish after the construction of a large reservoir (Lajeado Dam, Tocantins River)
Fig. 4. Variation in feeding activity (standard residuals, regression between LS x WS), visceral fat storage, body condition (standard residuals, regression between LS x TW) and reproductive effort (GSR) of Hemiodus unimaculatus, before (Pre-1 and 2) and after (Post-1 and 2) the construction of Lajeado Dam.
Fig. 3 in Short-term changes in energy allocation by Hemiodontidae fish after the construction of a large reservoir (Lajeado Dam, Tocantins River)
Fig. 3. Variation in feeding activity (standard residuals, regression between LS x WS), visceral fat storage, body condition (standard residuals, regression between LS x TW) and reproductive effort (GSR) of Hemiodus microlepis, before (Pre-1 and 2) and after (Post-1 and 2) the construction of Lajeado Dam.
Fig. 2 in Short-term changes in energy allocation by Hemiodontidae fish after the construction of a large reservoir (Lajeado Dam, Tocantins River)
Fig. 2. Variation in feeding activity (standard residuals, regression between LS x WS), visceral fat storage, body condition (standard residuals, regression between LS x TW) and reproductive effort (GSR) of Argonectes robertsi, before (Pre-1 and 2) and after (Post-1 and 2) the construction of Lajeado Dam.
Fig. 1 in Short-term changes in energy allocation by Hemiodontidae fish after the construction of a large reservoir (Lajeado Dam, Tocantins River)
Fig. 1. Relative abundance of A. robertsi, H. microlepis, and H. unimaculatus in Pre- and Post-impoundment periods, in sites distributed along the reservoir (combined within zones: Fluvial, Transition, and Lacustrine).
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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.