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Dataset results
13 results for “midwestern United States”
Linked collectors and determiners for: A new vernal species of Hesperapis from the lower Midwestern United States (Hymenoptera: Melitidae).
Natural history specimen data linked to collectors and determiners held within, "A new vernal species of Hesperapis from the lower Midwestern United States (Hymenoptera: Melitidae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/4d538ad9-9b93-44fa-ae7d-48ffa05413f9">https://bionomia.net/dataset/4d538ad9-9b93-44fa-ae7d-48ffa05413f9</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/4d538ad9-9b93-44fa-ae7d-48ffa05413f9">https://gbif.org/dataset/4d538ad9-9b93-44fa-ae7d-48ffa05413f9</a>. Formatted as a Frictionless Data package.
Figures 10–13 in A new vernal species of Hesperapis from the lower Midwestern United States (Hymenoptera: Melitidae)
Figures 10–13. Male genitalia of Hesperapis (Carinapis) infuscata, new species. 10. Ventral view. 11. Dorsal view. 12. Lateral view. 13. Apical view.
Figures 7–9 in A new vernal species of Hesperapis from the lower Midwestern United States (Hymenoptera: Melitidae)
Figures 7–9. Male terminalia of Hesperapis (Carinapis) infuscata, new species. 7. Sixth sternum. 8. Seventh sternum. 9. Eighth sternum.
Figures 3–4 in A new vernal species of Hesperapis from the lower Midwestern United States (Hymenoptera: Melitidae)
Figures 3–4. Male of Hesperapis (Carinapis) infuscata, new species. 3. Lateral habitus. 4. Dorsal habitus.
Figures 1–2 in A new vernal species of Hesperapis from the lower Midwestern United States (Hymenoptera: Melitidae)
Figures 1–2. Female of Hesperapis (Carinapis) infuscata, new species. 1. Lateral habitus. 2. Dorsal habitus.
Figure 14 in A new vernal species of Hesperapis from the lower Midwestern United States (Hymenoptera: Melitidae)
Figure 14. Map of lower Midwestern states, with confirmed occurrences of Hesperapis (Carinapis) infuscata, new species, mapped. Mapped using SimpleMappr (Shorthouse, 2010).
DayCent-UV grassland productivity across the midwestern and western conterminous United States.
<p>This dataset provides information about daily and annual simulated aboveground plant productivity (ANPP) in grasslands grid cells across the midwestern and western conterminous United States from 1984 to 2015. Annual ANPP estimates from Rangeland Analysis Platform (RAP) are included for comparison.</p> <p><strong>Files Description:</strong></p> <ul> <li><strong>Grid_Cells_Shapefile.zip</strong></li> </ul> <p>Contains<strong> </strong>the spatial distribution for each simulated grassland grid cell. Each grid cell is identified by a <code>grid</code> number ranging from 0 to 2712, a <code>col</code> number increasing from west to east, and a <code>row</code> number increasing from south to north. </p> <ul> <li><strong>Daily_ANPP_DayCent-UV.zip:</strong></li> </ul> <p>Each NetCDF file contains the daily ANPP information for each grid cell during a specifc year. Daily ANPP for a grid cell can be acessed in the <code>cropANPP</code> variable, which depends on the grid cell identifier <code>row</code> and <code>col</code>, and <code>time</code> for the day of the year.</p> <p>For example, using the library <em>xarray</em> from Python, the daily ANPP value for a California grassland grid cell (grid : 1207, row: 78, col: 24) in day 150 in year 2000 can be obtained by using the following command lines:</p> <p>import xarray as xr</p> <p>dataset = xr.open_dataset( folder_directory + '/Daily_ANPP_DayCent-UV/daycent_cwrf_2000.nc')</p> <p>time = 150</p> <p>lon_index = 24</p> <p>lat_index = 78 </p> <p>value = dataset.cropANPP[time, lat_index, lon_index].values</p> <p>To calculate annual ANPP for each grid cell, sum the <code>cropANPP</code> values across all days within a file. </p> <ul> <li><strong>Annual_ANPP_DAYCENT-UV_1986-2015.csv</strong></li> </ul> <p>Contains the simulated annual ANPP in the <code>cum_ANPP</code> column (gC/m²), for each <code>year</code> and each grid cell identified by <code>grid</code>, <code>lon</code>, <code>lat</code> ,<code>row</code>, <code>col</code> (see shapefile description)</p> <ul> <li><strong>Annual_ANPP_RAP_1986-2015.csv</strong></li> </ul> <p>Provides RAP's annual ANPP estimates for each grid cell, identified by its <code>grid</code> number, by using two different equations: </p> <ul> <li> <ul> <li>Gerardhi & Sala equation: RAP-ANPP estimates are located in column <code>RAP_Sala_biomass</code> (g Biomass/m²), together with the annual fraction of carbon allocated to shoots in <code>fANPP_Sala</code></li> <li>Hiu equation: RAP-ANPP estimates are located in column <code>RAP_Hiu_biomass</code>, together with the annual fraction of carbon allocated to shoots in <code>fANPP_Hiu</code>.</li> </ul> </li> </ul> <p>We assumed a factor of 2.4 (gBiomass/m²)/(gC) to convert gC/m² to gBiomass/m².</p>
Figures 5–6 in A new vernal species of Hesperapis from the lower Midwestern United States (Hymenoptera: Melitidae)
Figures 5–6. Facial views of Hesperapis (Carinapis) infuscata, new species. 5. Female. 6. Male.
Data from: Environmental DNA metabarcoding for whole community inventories of vertebrates in rivers of the midwestern United States
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Data from: The walk is never random: subtle landscape effects shape gene flow in a continuous white-tailed deer population in the Midwestern United States
One of the pervasive challenges in landscape genetics is detecting gene flow patterns within continuous populations of highly mobile wildlife. Understanding population genetic structure within a continuous population can give insights into social structure, movement across the landscape and contact between populations, which influence ecological interactions, reproductive dynamics, or pathogen transmission. We investigated the genetic structure of a large population of deer spanning the area of Wisconsin and Illinois, USA, affected by chronic wasting disease. We combined multi-scale investigation, landscape genetic techniques and spatial statistical modeling to address the complex questions of landscape factors influencing population structure. We sampled over 2,000 deer and used spatial autocorrelation and a spatial principal components analysis to describe the population genetic structure. We evaluated landscape effects on this pattern using a spatial auto-regressive model within a model selection framework to test alternative hypotheses about gene flow. We found high levels of genetic connectivity, with gradients of variation across the large continuous population of white-tailed deer. At the fine scale, spatial clustering of related animals was correlated with the amount and arrangement of forested habitat. At the broader scale, impediments to dispersal were important to shaping genetic connectivity within the population. We found significant barrier effects of individual state and interstate highways and rivers. Our results offer an important understanding of deer biology and movement that will help inform the management of this species in an area where over-abundance and disease spread are primary concerns.
King Rail (Rallus elegans) presence in the Midwestern United States is predicted by local-scale factors and avian community
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Data from: The walk is never random: subtle landscape effects shape gene flow in a continuous white-tailed deer population in the Midwestern United States
Open the record for dataset details and reuse information.
Simulating δ15N of NOx emission within Midwestern United States
<p>A new emission dataset of NO<sub>x</sub>, which incorporates the <sup>15</sup>N isotope in all emission sectors of SMOKE (Sparse Matrix Operator Kernel Emission) outputs, before merging into CMAQ (The Community Multiscale Air Quality Modeling System)-ready emission input dataset. Using this dataset to simulate the δ<sup>15</sup>N values in the 3-D photochemical transport models, such as CAMQ, and compare to the corresponding measurements, researchers could explore the evolution of δ<sup>15</sup>N values along the “journey” of atmospheric NO<sub>x</sub>, thus evaluating and improving the accuracy of the NO<sub>x</sub> emission inventory using <sup>15</sup>N.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.