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5,108 results for “North America”
Data from: Accuracy of node and bud-scar counts for aging two dominant conifers in western North America
Accurately aging trees is critical for understanding tree demography and tree responses to environmental change. Given the proliferation of studies aimed at understanding the effects of climate and disturbance on forest ecosystems, it is important to understand the tradeoffs between field-based age estimates and precise dendrochronological techniques. We assessed the accuracy of age estimates from node counts in the field against precise tree-ring counts at the root-shoot boundary, in 1279 ponderosa pine and 1268 Douglas-fir seedlings sampled from across three study regions in the western U.S. We also assessed the accuracy of age estimates from bud-scar counts in the field against node counts and precise tree-ring counts in a subset of 757 seedlings from the Northern Rockies. Node counts systematically underestimated ring counts by an average of 4.1 years, with bias increasing with tree age. At annual, ±1-, ±2-, and ±5-yr precision, the accuracy of node counts was 5%, 15%, 29%, and 74% across all regions and species, respectively. Similar results were found for bud scars. Given the magnitude of the bias between field-based methods and ring counts, it is critical to select appropriate aging methods, based on the precision required to answer specific ecological questions. To improve the accuracy of field-based age estimates in these species, we provide a tool for correcting for the bias when precise dendrochronological aging is not feasible.
Species as conservation umbrellas: a case study with lesser prairie-chicken (Tympanuchus pallidicinctus) in the southern Great Plains of North America
<p><span>Qualitative index of conservation benefit conferred by management for the lesser prairie chicken on non-target at-risk wildlife species.</span></p>
Figures 1-2 in First records of Icius subinermis (Araneae: Salticidae) in North America, with notes on the local establishment of this species and its behavior in captivity
Figures 1-2. Photographs of Icius subinermis taken in Philadelphia, Pennsylvania, on a fence beside the Delaware River Trail at Washington Avenue Green. 1, Female with captured midge, May 2020. 2, Male, June 2019.
Figure 3 in First records of Icius subinermis (Araneae: Salticidae) in North America, with notes on the local establishment of this species and its behavior in captivity
Figure 3. Fence at Washington Avenue Green, beside the Delaware River, from which specimens of Icius subinermis were collected in May 2020.
Figure 5 in A new species of Epeolus Latreille, 1802 (Hymenoptera: Apidae) from western North America
Figure 5. Approximate geographic ranges of Epeolus autumnalis Rob-
Figure 8. Dissected S7 in A new species of Epeolus Latreille, 1802 (Hymenoptera: Apidae) from western North America
Figure 8. Dissected S7 (left) and S8 (right), ventral view, of male Epeolus
Figure 1 in Tyrannosaurids (Dinosauria) of Asia and North America
Figure 1. The skull of Albertosaurus libratus with cranial terminology used in this paper.
FIGURE 2 in Two New Records of Wing-reduced Tipulidae from North America
FIGURE 2. Tricyphona subaptera (Alexander). Left lateral view. Scale bar = 5 mm.
Data for irrigation impacts on urban heat stress in North America
<p>Includes all model simulation results, summaries by urban clusters, and evaluation results of the study. <br><br>The netcdf files are for various model variables and simulations (noURB for no urban simulation, IRR for irrigation simulation, and CTRL for the urban with no irrigation simulation, which is treated as the baseline), the csv files are the summaries for various cases (by urban cluster, and by world and climate zone for model evaluations), and the geotiff files are the raster images for key variables shown in the manuscript.<br><br>The python notebook has all the scripts to estimate the heat stress metrics from the netcdf files.</p>
Figs. 1-2 in Contribution to the knowledge of the genus Thaumaglossa Redtenbacher, 1867 from North and Central America (Coleoptera¡ Dermestidae¡ Megatominae).
Figs. 1-2.- Thaumaglossa cognatoi sp. nov. 1.- Terminal antennomere of male. 2.- Aedeagus.
Fig. 2 in First record of Silpha puncticollis Lucas, 1846 (Coleoptera¦ Silphidae, Silphinae) for North America.
Fig. 2.- Habitus of Silpha puncticollis Lucas, 1846 (♂).
Northern and central Walker Lane horizontal velocities in North America and Sierra Nevada reference frames.
<p>The data in this file contains east and north velocities covering the northern and central sections of the Walker Lane, USA. The velocities were obtained from GPS time series, available at Nevada Geodetic Laboratory (NGL): http://geodesy.unr.edu/NGLStationPages/gpsnetmap/GPSNetMap.html (24 hour final solutions). The position time series were manually screened and corrected for offsets, and a local common-mode filter was applied following the methodology of Kreemer & Blewitt (2021, https://doi.org/10.1007/s00190-020-01466-5). Velocities in the North America frame were obtained using MIDAS, a robust median trend estimator (Blewitt et al., 2016, https://doi.org/10.1002/2015JB012552). Two sets of stations were used to rotate the velocities into the Sierra Nevada frame: CAOV, CAPV, P140, P276, P310 for the northern section and CMBB, P245, P305, P308, P512 for the central section.</p> <p>The file contains the following columns:</p> <ul> <li>sta: station ID</li> <li>lon: longitude of station (decimal degree)</li> <li>lat: latitude of station (decimal degree)</li> <li>ve_NA: east velocity in North America frame (mm/yr)</li> <li>vn_NA: north velocity in North America frame (mm/yr) </li> <li>ve_SN: east velocity in Sierra Nevada frame (mm/yr) </li> <li>vn_SN: north velocity in Sierra Nevada frame (mm/yr) </li> <li>region: denotes whether the set of stations used to rotate the velocities into the Sierra Nevada frame are located in the northern (N) or central (C) Walker Lane</li> <li>sve: east velocity uncertainty (mm/yr)</li> <li> svn: north velocity uncertainty (mm/yr)</li> <li>network: who operates the station</li> </ul>
Land facet data for North America at 100m resolution.
<p>We developed a dataset categorizing the North American continent into physical habitat types at 100m resolution. The input data used included elevation and soil type. Further details on methodology are available <a href="http://www.cacpd.org/Facets/landfacetmethods.pdf">here</a>. The data is provided via a link to a zipfile containing TIFF (.tif) format files that can be imported into ArcGIS or other GIS applications. Projection information is available <a href="http://www.cacpd.org/Facets/lazea.prj">here</a>.</p> <p>One potential strategy for protecting biodiversity in a changing climate is based on the idea of protecting the diversity of abiotic conditions that influence patterns of biodiversity. In this strategy, conservation features (the “targets” considered in the conservation planning process) are derived from data on physical features such as topography, soils, and geology. The approach has been described as “conserving the ecological stage” or protecting “land facets” or “enduring features”. Species distributions, communities, ecosystems, and broader patterns of biodiversity are clearly influenced by abiotic drivers such as soils, geology, topography, and climate. Although climates will change relatively rapidly over the coming century, soils, geology, and topography will not. Thus, local, and some regional, climate patterns and gradients influenced by topography will persist (e.g., higher elevations will still be cooler than lower elevations, although both will likely be warmer) as climates change. The hypothesis underlying use of land facets in climate adaptation planning is that by protecting a diversity of land facets, it may be possible to protect areas that will foster a diversity of biota in the future, albeit different biota than those areas would protect today. Although land facets are clearly an imperfect coarse-filter surrogate for biodiversity, physical habitat diversity may still represent a useful additional source of data that can augment biodiversity data in conservation planning processes.</p>
Thermal regimes, but not mean temperatures, drive patterns of rapid climate adaptation at a continent-scale: evidence from the introduced European earwig across North America
<p>Full data set + R script</p>
Fig. 30 in Synopsis of the extinct Batrachia, Reptilia and Aves of North America
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Fig. 33 in Synopsis of the extinct Batrachia, Reptilia and Aves of North America
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Fig. 32 in Synopsis of the extinct Batrachia, Reptilia and Aves of North America
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Fig.31 in Synopsis of the extinct Batrachia, Reptilia and Aves of North America
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Fig.32a in Synopsis of the extinct Batrachia, Reptilia and Aves of North America
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Fig. 34 in Synopsis of the extinct Batrachia, Reptilia and Aves of North America
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ScienceDex guides
Understand access before you commit
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.