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31 results for “vegetation density”
Vegetation Cover Permanent Plot Data: Bulk Density at Upper Phillips Creek marsh at the Virginia Coast Reserve 1990-1992
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A dataset of sulfur content and density of vegetation on the Tibetan Plateau
<p>As an important part of China's terrestrial ecosystem, the variation and distribution of sulfur in the vegetation of the Tibetan Plateau (TP) will have a profound impact on the national and even global sulfur cycle. We collected and sorted out the field survey and test data of the research group from 2019 to 2020. This dataset encompasses forest, grassland, shrubland, desert and other major ecosystem types, including the average sulfur content, density and storage data of different vegetation types and plant organs. The establishment of this data set provides important basic data for the assessment of regional vegetation biomass and sulfur reserves and the optimization of sulfur cycle model.</p>
Fig. 2 in Variation in diet of frugivorous bats in fragments of Brazil's Atlantic Forest associated with vegetation density
Fig. 2.—Carbon and nitrogen isotopic ratios for each bat population presented as mean and standard deviation. Each panel represents one species–season pairing: Al-H is Artibeus lituratus in the Humid season; Cp-H is Carollia perspicillata in the Humid season; Cp-S is C. perspicillata in the super-humid season; Sl-S is Sturnira lilium in the super-humid season. Fragments and population designations correspond to fragments in Brazil's Atlantic Forest in Fig. 1. Shades correspond to fragment where the sample was collected ordered by area. Darker greens are largest fragments; darkest purple are smallest fragments.
Fig. 1 in Variation in diet of frugivorous bats in fragments of Brazil's Atlantic Forest associated with vegetation density
Fig. 1.—Map of study sites situated in Brazil's Atlantic Forest, Rio de Janeiro State, Brazil. Sites in Reserva Ecologica de Guapiacu ("REGUA") and fragments (F) adjacent to this area are indicated as points. Thirteen areas were sampled and allocated as REGUA, REGUA2, REGUA3 for those sampled in the reserve (considered repeated efforts sampling in the same fragment), and 10 fragments designated as F1 through F10. This figure was constructed using ESRI base maps and existing maps available through Instituto Brasileiro de Geografia (IBGE) (SOS Mata Atlântica (2009) (www.sosma.org). Used under commons licence). Figure is adapted from Teixeira (2019).
Reducing Dietary Energy Density by Incorporating Vegetables in Order to Decrease Energy Intake
ClinicalTrials.gov study NCT01165086. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A dataset of sulfur content and density of vegetation on the Tibetan Plateau
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Soil bulk density:Effect of Fire Frequency on Grassland Vegetation and Soils
The purpose of this experiment, begun in 1983 by Johannes Knops, is to determine what effect different fire frequencies have on grassland vegetation. This experiment is being conducted in field B. There are 4 different burn treatments: 1. plots burned every year 2. plots burned every other year 3. controls which are not burned 4. plots burned every fourth year There are 6 replicates of each treatment which were randomly assigned to the 24 plots. Plots are 8 by 8 meters and are placed in a 3 by 8 grid with 2 meter walkways. Plots are marked with colored rebar at each corner.
Vegetation N:P ratio stoichiometric is a driver of negative density dependence in a succession series of a semi-arid area
<p>Plant negative density dependence is the result of interactions between plants and between plants and the environment. We selected a succession series, i.e., early successional, mid-successional and late successional stages<i> </i>for <i><span>Artemisia ordosica</span></i>, <i><span>Sophora alopecuroides</span></i> and <i><span>Stipa bungeana</span></i> communities, respectively, in a semi-arid area. We investigated the density and biomass and determined the nitrogen (N) and phosphorus (P) content of every plant species for each quadrat of 225 quadrats, and calculated the N and P content of vegetation using biomass as a weighted coefficient. The results show that, total plant density of the <i><span>A. ordosica</span></i><i> </i>community increased with the increase of vegetation N:P ratio, while total plant density of the <i><span>S. bungeana</span></i> community decreased with the increase of vegetation N:P ratio, which took on negative density dependence at the late successional stage. In the early and mid-successional stages of the community succession, the stagnation point of the quadratic function relationship between plant total density and vegetation N/P ratio was (16.6, 353.3), that was, if the N:P ratio of the vegetation was greater than 16.6, which was characterized by negative density dependence. The analysis shows that the negative density dependence is due to P limitation. These findings reveal that the vegetation N:P ratio in a semi-arid region is the driving force for negative density dependence.</p>
Vegetation N:P ratio stoichiometric is a driver of negative density dependence in a succession series of a semi-arid area
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Gridded GEDI Vegetation Structure Metrics and Biomass Density at Multiple Resolutions
This dataset consists of near-global, analysis-ready, multi-resolution gridded vegetation structure metrics derived from NASA Global Ecosystem Dynamics Investigation (GEDI) Level 2 and 4A products associated with 25-m diameter lidar footprints. This dataset provides a comprehensive representation of near-global vegetation structure that is inclusive of the entire vertical profile, based solely on GEDI lidar, and validated with independent data. The GEDI sensor, mounted on the International Space Station (ISS), uses eight laser beams spaced by 60 m along-track and 600 m across-track on the Earth surface to measure ground elevation and vegetation structure between approximately 52 degrees North and South latitude. Between April 17th 2019 and March 16th 2023, GEDI acquired 11 and 7.7 billion quality waveforms suitable for measuring ground elevation and vegetation structure, respectively. This dataset provides GEDI shot metrics aggregated into raster grids at three spatial resolutions: 1 km, 6 km, and 12 km. In addition to many of the standard L2 and L4A shot metrics, several additional metrics have been derived which may be particularly useful for applications in carbon and water cycling processes in earth system models, as well as forest management, biodiversity modeling, and habitat assessment. Variables include canopy height, canopy cover, plant area index, foliage height diversity, and plant area volume density at 5 m strata. Eight statistics are included for each GEDI shot metric: mean, bootstrapped standard error of the mean, median, standard deviation, interquartile range, 95th percentile, Shannon's diversity index, and shot count. Quality shot filtering methodology that aligns with the GEDI L4B Gridded Aboveground Biomass Density, Version 2.1 was used. In comparison to the current GEDI L3 dataset, this dataset provides additional gridded metrics at multiple spatial resolutions and over several temporal periods (annual and the full mission duration). Files are provided in cloud optimized GeoTIFF format.
The impact of planting density on the morphological traits and peak pullout force of vegetation and slope stability
<p><a href="../api/records/10937858/draft/files/NC_morphological.xls/content" target="_blank" rel="noopener noreferrer">NC_morphological.xl</a>s and <a href="../api/records/10937858/draft/files/NC_morphological.xls/content" target="_blank" rel="noopener noreferrer">MS_morphological.xls files include all morphological traits of two species, </a><a href="../api/records/10937858/draft/files/ms_force_space.RData/content" target="_blank" rel="noopener noreferrer">ms_force_space.RData</a><a href="../api/records/10937858/draft/files/NC_morphological.xls/content" target="_blank" rel="noopener noreferrer"> and nc</a><a href="../api/records/10937858/draft/files/ms_force_space.RData/content" target="_blank" rel="noopener noreferrer">_force_space.RData files include the uprooting force - time data.</a></p> <p><a href="../api/records/10937858/draft/files/ms_force_space.RData/content" target="_blank" rel="noopener noreferrer">In ms_force_space.RData</a><a href="../api/records/10937858/draft/files/NC_morphological.xls/content" target="_blank" rel="noopener noreferrer"> and nc</a><a href="../api/records/10937858/draft/files/ms_force_space.RData/content" target="_blank" rel="noopener noreferrer">_force_space.RData, Time means sampling time, AI1.01 means uprooting force, sample means the sampling ID, test means different planting densities. </a></p> <p><span>This study examines the influence of planting density on the morphological traits and peak pullout force of two herbaceous plant species, <em>Medicago sativa</em> (MS) and <em>Nepeta cataria</em> (NC), and their collective impact on slope stability. Through field experiments across six planting densities (8, 11, 16, 25, 44, and 100 plants/m²), we evaluated changes in plant morphology, root and shoot biomass, and root anchorage strength. The peak pullout force was analyzed using the infinite slope stability model to assess its correlation with slope stability coefficients.</span></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)
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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.