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5,784 results for “density”
Long-term adult and juvenile periwinkle snail (Littoraria irrorata) density in mid-marsh and creekbank plots from the Georgia Coastal Ecosystems LTER Fall Monitoring Program
This data set includes long-term observation of the abundance of periwinkle snail (Littoraria irrorata) at sampling sites within the Georgia Coastal Ecosystem (GCE) LTER study area. Visual counts of adult Littoraria were conducted within 0.5m x 0.5m quadrats in-line with permanent GCE vegetation plots. Juvenile Littoraria (1-4mm shell length) were found and counted by looking inside all leaf furls within a nested quadrat (0.25m x 0.25m). In each marsh zone at each GCE LTER permanent monitoring site, we counted the number of adult and juvenile snails in 8 creekbank and 12 mid-marsh replicate quadrats. This method was started in 2012 as a new part of the annual GCE monitoring and supplements the invertebrate data that has been collected since October 2000. This data set includes observations from 2012 to 2023 and will be updated annually to include the prior year observations.
Survey of adult and juvenile periwinkle snail (Littoraria irrorata) density in mid-marsh and creekbank plots at GCE LTER study sites in October 2021.
To characterize spatial variation in the adult and juvenile density of periwinkle snails, Littoraria irrorata, within two zones in the salt marsh, the mid-marsh and creekbank, and across a gradient in salinity and distance to ocean, we surveyed snail density in October 2021. In each marsh zone at each GCE LTER permanent monitoring site, we counted the number of adult and juvenile snails in 8 creekbank and 12 mid-marsh replicate quadrats.
Survey of adult and juvenile periwinkle snail (Littoraria irrorata) density in mid-marsh and creekbank plots at GCE LTER study sites in October 2022.
To characterize spatial variation in the adult and juvenile density of periwinkle snails, Littoraria irrorata, within two zones in the salt marsh, the mid-marsh and creekbank, and across a gradient in salinity and distance to ocean, we surveyed snail density in October 2022. In each marsh zone at each GCE LTER permanent monitoring site, we counted the number of adult and juvenile snails in 8 creekbank and 12 mid-marsh replicate quadrats.
Survey of adult and juvenile periwinkle snail (Littoraria irrorata) density in mid-marsh and creekbank plots at GCE LTER study sites in October 2023.
To characterize spatial variation in the adult and juvenile density of periwinkle snails, Littoraria irrorata, within two zones in the salt marsh, the mid-marsh and creekbank, and across a gradient in salinity and distance to ocean, we surveyed snail density in October 2023. In each marsh zone at each GCE LTER permanent monitoring site, we counted the number of adult and juvenile snails in 8 creekbank and 12 mid-marsh replicate quadrats.
Georgia Salt Marsh: Soil Organic Carbon, Nitrogen, Bulk Density, Moisture, and Texture
As part of project predicting soil carbon at depth from that found at the surface using remote sensing, 28 soil cores were taken from six salt marshes along the Georgia coastline. Cores were taken as deep as possible (25 – 165 cm) and sectioned into 5 cm depths. Soils were analyzed for organic carbon (SOC), total nitrogen (N), bulk density (BD), and particle size (by horizon). Stable carbon isotopes were obtained in three marshes on Sapelo Island; a subset was also analyzed for radiocarbon.
Zooplankton density for all samples collected from Toolik Lake and lakes near the Toolik Field Station, Arctic LTER 2003-2022.
Zooplankton samples were taken on lakes with a 30 cm diameter plankton net with a 256 micron mesh netting from 2018-2022. Prior years used a 156 micron mesh netting. Density was calculated based on the number of each taxa counted in a subsample or whole sample and expanded to the volume sampled to determine the number of each zooplankton taxa per liter. All samples were collected from Toolik Lake and lakes near the University of Alaska Toolik Field Station, Fairbanks, Arctic LTER from 2003-2022.
Density Anomalies in White Pine at Harvard Forest 2019
The density of wood is a primary determinant of the amount of carbon sequestered in forests. For boreal and Mediterranean ecosystems, anomalies from the typical intra-annual density increase in radial growth of conifers are predominantly related to drought. We took 41 wood samples at breast height, ten additional samples near branches, and seven samples from the top of 41 white pines to determine the spatial distribution of density anomalies throughout the stem. We measured the ring width, density anomaly presence, position within a ring, and arc of density anomalies at multiple heights. Even in a mesic forest density anomalies in white pine are predominantly occurring during drier growing seasons. Moreover, we examined the spatial extent of density anomalies within the stems of white pines and discovered density anomalies are more likely to occur near branches, at the top of the tree, and in wider rings. Furthermore, the position of the density anomalies within the rings was remarkably consistent with the anomalies occurring roughly 80% into the fully formed ring at all heights for high-frequency years only as well as all years Density anomalies seem to be triggered by exogenous factors but their distribution within the stem varies along endogenous gradients, thus better understanding these systematic anomalies can help us to understand how wood formation has reacted and will respond to the environment and changes therein.
Lake Mendota, Wisconsin, USA, Zebra Mussel Veliger Water Column Density 2016-2019
We sampled veliger (larval stage) zebra mussels (Dreissena polymorpha) from 2016-2019. Zebra mussels are invasive in Lake Mendota and were first detected in November 2015. Samples were taken at three different sites on Lake Mendota from June to August in 2016, and from June to November in 2018-2019, using a 0.5 m diameter, 64 micrometer mesh size plankton net for an 8 m depth tow. This dataset complements adult zebra mussel, zoobenthos, and phytobenthos data collected during the same time period, for which data is also archived with EDI.
Lake Mendota, Wisconsin, USA, Zebra Mussel Density and Biomass 2016-2018
We sampled adult zebra mussels (Dreissena polymorpha) in the benthos of Lake Mendota from 2016-2018 to track the growth of the population following its initial detection in fall 2015. We sampled along three transects inherited from Karatayev et al. (2013) at five different depths (1, 3, 5, 8, and 10 m) twice a summer (June and August) from 2016-2018. Because suitable zebra mussel substrate was limited at these sites, we also selected five 1 m depth, rocky sites (optimal zebra mussel sites) to track density and biomass where colonization was most intense. A pared-down version of this routine sampling continued from 2019 onward but is not included here. This dataset complements zoobenthos and phytobenthos data collected according to the same routine sampling structure, as well as larval zebra mussel (veliger) sampling for which data is also archived with EDI. Biomass data are modeled from lengths of up to 100 individuals that were measured in each sample. Those lengths were fed into Lake Mendota-specific length-to-weight power law equations parameterized by body size measurements (length, width, live weight, wet weight, dry weight, shell weight, shell-free weight, and ash-free dry weight) of 99 mussels collected at different sites across Lake Mendota in 2018.
Data from “Larval and juvenile Longfin Smelt diets as a function of fish size and prey density in the San Francisco Estuary”
This publication includes the raw data from the manuscript: Lojkovic Burris, Z. P., R. D. Baxter, and C. E. Burdi. 2022. Larval and juvenile Longfin Smelt diets as a function of fish size and prey density in the San Francisco Estuary. California Fish and Wildlife Journal 108:e11. http://www.doi.org/10.51492/cfwj.108.11 Data includes the diets of larval and juvenile Longfin Smelt in the San Francisco Estuary from 2005 to 2008 in the form of diet by number, diet by weight, macroinvertebrate prey lengths, prey length-weight equations, and prey weight conversions.
Bonanza Creek LTER: Shrub, Seedling and Sapling Density from 1975 to Present in the Bonanza Creek Experimental Forest near Fairbanks, Alaska
This study is a survey of the density of tall shrubs, saplings, and seedlings at Bonanza Creek LTER Control Plots. Densities have been measured at some sites since 1975 while others began in the mid 1980's. In 2006 methodology was changed in attempt to better capture the spatial variability of shrub and tree species within the research sites. The current method, presented here, uses a 2 meter wide transect along two site boundaries covering 220m2. All seedlings, saplings, and tall shrubs >1m (regardless of species) are counted by species and size class along the transect. The previous method used 4m2 circular plots at 20 points on a 10mx10m grid within the control plot for an area of 80m2. The circular method measured all seedlings, saplings, and a certain set of "tall shrubs", regardless of height. The height requirement creates an issue when comparing the old and new methods for early successional stands. For Alnus and Salix this is likely only a problem in the FP0 sites as they are the only sites with a high density of shrubs shorter than 1m. Shrub community changes at these early successional sites should be analyzed using percent cover data. At the later successional sites most Alnus and Salix shrubs are greater than 1m so the transect method likely does not underrepresent density. The metadata and raw data from the circular method can be found in <a href="http://www.lter.uaf.edu/data/data-detail/id/175"> Vegetation Plots of the Bonanza Creek LTER Control Plots: Species Count (1975 - 2004) </a>.
CGP01 Gall-insect densities on selected plant species in watersheds with different fire frequencies
Long-term monitoring of gall-insect densities on Solidago canadensis, Vernonia baldwinii, and Ceanothus herbaceous. Gall abundances are censused in watersheds burned at one- to twenty- year intervals to asses the role of fire frequency and time since fire on gall-insect population dynamics. The data sets contain the following: Watershed fire frequency, number of growing seasons since last fire, plant species, number of galled stems, and number of censused stems. Censuses conducted for the 1989-1996 growing seasons except 1992 and 1994, next scheduled census is fall 1997.
Density of Seagrass in Virginia Coastal Bays, 2007-2021
This dataset contains measurements of seagrass shoot density in restored Z. marina meadows in the Virginia coastal bays. Measurements were made annually in June-July at plots in Hog Island Bay and South Bay, VA. GPS locations of sampling plots are available in the companion data set VCR11180.
Seagrass shoot density and benthic chlorophyll density from the Seagrass Recovery Experiment, South Bay, VA 2020-2022
To understand intra-meadow stability, the Seagrass Recovery Experiment was designed to ask 1) is recovery faster at sites with less thermal stress owing to greater exchange with cooler oceanic water at the meadow edge? 2) what is the shape of recovery? and 3) what are the recovery mechanisms? To conduct this experiment, aboveground seagrass biomass was removed from 28.3 m2 plots within the interior and along an edge of a restored seagrass meadow in South Bay, VA. Sites 1-3 correspond to the meadow interior while sites 4-6 correspond to the northern edge. Each site was comprised of a control (i.e., C) where no seagrass was disturbed and a treatment (i.e., T) where seagrass was removed (n = 12 sites total, e.g., 1C, 1T, 2C...). This dataset includes monthly seagrass shoot counts between June-October 2020, May-October 2021, and April-October 2022. To further characterize differences between the meadow interior and edge, benthic chlorophyll samples were also collected.
Red knot occurrence, prey density, island morphology, and climate change in the Virginia Barrier Islands (2009-2023)
Global climate change is reshaping dynamic coastal ecosystems, with uncertain consequences for migratory shorebirds such as the federally threatened red knot (Calidris canutus rufa) that rely on coastal staging sites during migration. Understanding how sea-level rise and changing climate drivers affect red knot foraging ecology is critical for informing conservation and management at coastal staging sites. We integrated long-term biological, geomorphological, and climatological data to examine the direct and indirect pathways influencing red knots and their prey at intertidal foraging sites on the Virginia Barrier Islands during spring migration (May 21 - 28, 2009-2023). Using piecewise structural equation modeling, we tested hypothesized two causal networks linking 1) red knot occurrence and 2) densities of their main invertebrate prey to habitat characteristics, island morphology, geomorphic change, and climate drivers of ecosystem change. Red knots were indirectly affected by geomorphic change and climate drivers through bottom-up effects on invertebrate communities mediated by island morphology. Accelerated shoreline change narrowed islands, reducing invertebrate density and richness and indirectly decreasing red knot occurrence. Storms interacted with global climate oscillations to drive erosion or accretion of beaches, with variable effects on invertebrate density and red knot occurrence. Invertebrate responses were taxon-specific: shoreline change directly increased blue mussel density but indirectly reduced coquina clam and crustacean densities by narrowing island width, while storms impacts on crustacean density were mediated by beach width. Our findings suggest that accelerated ecosystem change under future climate scenarios may alter foraging conditions for red knots and other migratory shorebirds in the Virginia Barrier Islands, with broader implications for long-term population resilience.
Dataset of "Asparagine-Modified Magnetic Graphene Oxide: An Efficient and Green Nanocatalyst for Synthesis of 5-oxodihydropyrano[3,2-c]chromenes and dihydropyrano[2,3- c]pyrazole derivatives and the Density functional theory calculation".
<p>The primary focus of this study involved the fabrication of a novel nanocatalyst Fe3O4-supported asparagine functionalized graphene oxide (Fe3O4@GO-N-(Asparagine)). The catalyst was synthesized through a four-step procedure.</p>
Datasets for testing the robustness of LiDAR vegetation metrics to varying point densities
<p><span>The calculation of vegetation metrics from LiDAR point clouds might be affected by the available point density of a dataset. Testing how the same LiDAR vegetation metrics differ with different point densities can therefore inform about their robustness for upscaling metrics to other areas or other LiDAR point clouds. The datasets made available here were generated to test the robustness of LiDAR vegetation metrics to varying point densities and spatial resolutions (i.e., plots of 1 × 1 m, 2 × 2 m, 5 × 5 m and 10 × 10 m size). A total of 25 LiDAR vegetation metrics representing different aspects of vegetation height, vegetation cover and structural complexity were tested (see metric definition in Kissling et al. 2023, </span><span><a href="https://doi.org/10.1016/j.dib.2022.108798"><span>https://doi.org/10.1016/j.dib.2022.108798</span></a></span><span>). The metric calculation was similar to the metric calculation in the Laserchicken software (Meijer et al. 2020, </span><span><a href="https://doi.org/10.1016/j.softx.2020.100626"><span>https://doi.org/10.1016/j.softx.2020.100626</span></a></span><span>) and the Laserfarm workflow (Kissling et al. 2022, https://doi.org/10.1016/j.ecoinf.2022.101836). The Dutch AHN4 dataset from the years 2020–2022 with a point density of 20–30 points/m<sup>2</sup> was used. Initially, 100 plots (i.e., squared polygons around centre points) were randomly placed across the Netherlands in Dutch Natura 2000 sites that predominantly contain woodland habitats (using shapefiles from the European Environmental Agency). For each centre point, square polygons of the desired resolutions (i.e., 1 × 1 m, 2 × 2 m, 5 × 5 m or 10 × 10 m plot size) were generated. The square polygons were subsequently used to clip the LiDAR point clouds from the Dutch AHN4 point cloud dataset. Since not all locations of the 100 randomly placed plots contained points, the actual sample sizes were slightly smaller than 100, i.e., 94 plots for the 1 × 1 m, 2 × 2 m and 5 × 5 m resolution and 95 plots for the 10 × 10 m resolution. Metrics were calculated with the original point density of the Dutch AHN4 dataset (20–30 points/m2) and with six systematically down-sampled point clouds for the same plots (i.e., keeping 5%, 10%, 20%, 40%, 60% and 80% of the points in the original point clouds). For each clipped point cloud of a plot at a given resolution, the points were first sorted according to their GPS acquisition time (from earliest to latest). Points were then systematically discarded and only 5%, 10%, 20%, 40%, 60% and 80% of the points in the original point clouds were kept. The kept points were used for calculating the 25 LiDAR vegetation metrics. </span></p>
Dataset for "The magnetized (2+1)-dimensional Gross-Neveu model at finite density"
<p>We perform a lattice study of the (2+1)-dimensional Gross-Neveu model in a background magnetic field <em>B</em> and at non-zero chemical potential <em>μ</em>. The complex-action problem arising in our simulations using overlap fermions is under control. For <em>B</em>=0 we observe a first-order phase transition in <em>μ</em> even at non-vanishing temperatures. Our main finding, however, is that the rich phase structure found in the limit of infinite flavor number <em>N</em>f is washed out by the fluctuations present at <em>N</em>f=1. We find no evidence for inverse magnetic catalysis, i.e., the decrease of the order parameter of chiral symmetry breaking with <em>B</em> for <em>μ</em> close to the chiral phase transition. Instead, the magnetic field tends to enhance the breakdown of chiral symmetry for all values of <em>μ</em> below the transition. Moreover, we find no trace of spatial inhomogeneities in the order parameter. We briefly comment on the potential relevance of our results for QCD.</p> <p>If you use this data, please cite the corresponding paper:<br> https://doi.org/10.48550/arXiv.2304.14812 (or better the not-yet-existing published version)</p>
Salt River Wetlands denitrification rate, dissimilatory nitrate reduction to ammonium rate, dissolved organic carbon concentration in June 2016 as well as soil porosity and bulk density
Raw and derived data used to calculate denitrification and dissimilatory nitrate to ammonium (DNRA) from push-pull experiments with added isotopically labelled nitrate. Experiments were conducted in 2016 in the Salt River Accidental Wetlands in three different patch types: Unvegetated, dominated by Ludwigia peploides, and dominated by Typha species (T. domingensis and T. latifolia). Data include start and end of incubation concentration of nitrate, ammonium, atom percent 15N in ammonium, dissolved organic carbon, excess mass 29-N2, and excess mass 30-N2. Soil data was collected from the same patch types including soil moisture, porosity, and bulk density.
LTREB: Aboveground biomass, plant density, annual aboveground productivity, plant heights and snail observations in control and fertilized plots in a Spartina alterniflora-dominated salt marsh, North Inlet, Georgetown, SC: 1984-2025
Aboveground biomass and plant density were measured non-destructively as a component of a long-term project seeking to understand how salt marsh primary production and sediment chemistry respond to anthropogenic (e.g. eutrophication) and natural (e.g. sea-level rise) environmental change. Feedbacks between plants, sediments, nutrients and flooding were investigated with particular attention to mechanisms that keep marshes in equilibrium with sea level. Biomass was calculated from plant height measurements using allometric equations. Annual productivity was calculated from approximately-monthly biomass estimates. In addition to plant height measurements, observations of snails in sample plots were recorded. Other data collected as part of the project include marsh surface elevation and porewater nutrient concentrations. These data have been used to develop the Marsh Equilibrium Model, an important tool for coastal resource managers. Sampling occurred at Spartina alterniflora-dominated salt marsh sites in North Inlet, a relatively pristine estuary near Georgetown, SC on the SE coast of the United States. North Inlet is a tidally-dominated, bar-built estuary, with a semi-diurnal mixed tide and a tidal range of 1.4m. The 25-km2 estuary is comprised of about 20.5 km2 of intertidal salt marsh and mudflats, and 4.5 km2 of open water. Sampling began at one location in 1984, and at three additional locations in 1986. Sampling occurred approximately monthly through 2025. The study is on-going. There are four sampling locations at two sites. Two locations are in the low marsh; two locations are in the high marsh. One high marsh location had control sampling plots in addition to plots fertilized with nitrogen and phosphorus.
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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.