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45 results for “geographic area”
Fig. 1. – Study area. A in Geographical patterns of woody plants' functional traits in Burkina Faso
Fig. 1. – Study area. A. Species richness of the 129 woody plants, distribution records and position of Burkina Faso within Africa; B. Names of landscape elements and cities mentioned in the results.
Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7). in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7).
Рис. 2. Распредение биомассы Mytilus trossulus septentrionalis на литорали дальневоcточных морей России. Здесь и далее на гистограммах по оси абцисс после географических пунктов в скобках укаЗана выборка (число иЗученных проб), по оси ординат – максимальные ЗначениЯ биомассы вида. Под Значением биомассы 0.1 г/м² подраЗумеваютсЯ качественные пробы. СокраЩениЯ (бмп) и (топ) оЗначают соответственно беринговоморское и тихоокеанское побережьЯ Восточной Камчатки. Побережье Зал. Петра Великого от устьЯ р. Туманной к северу до м. Поворотного условно отноcитсЯ к южному Приморью; побережье к северу от м. Поворотного (пос. Преображение, б. СоколовскаЯ) до б. Ольга, включительно, условно относитсЯ к среднему Приморью; побережье к северу от б. Ольга до м. Белкина и материковое побережье Татарского пролива относим к северному Приморью. Fig. 2. The distribution of biomass of Mytilus trossulus septentrionalis in the intertidal zone of the Far Eastern seas of Russia. Here and throughout on histograms, on the abcissa is the number of studied samples (numbers in parentheses following the names geographic localities), on the ordinate is the maximum biomass of species. The number 0.1 g wet wt m-2 means the qualitative samples. Abbreviations (bmp) and (top) mean the Bering Sea coast and the Pacific coast of eastern Kamchatka. The coast of Peter the Great Bay from the mouth of the Tumannaya River to Cape Povorotny is conditionally referred to as southern Primorye; the area north of Cape Povorotny (Preobrazhenie Settlement, Sokolovskaya Bay) to Olga Bay inclusive is conditionally referred to as middle Primorye; north of Olga Bay to Cape Belkin and the mainland coast of the Tatar Strait to as northern Primorye. in Bivalve mollusks of the intertidal zone of the Far Eastern seas of Russia
Рис. 2. Распредение биомассы Mytilus trossulus septentrionalis на литорали дальневоcточных морей России. Здесь и далее на гистограммах по оси абцисс после географических пунктов в скобках укаЗана выборка (число иЗученных проб), по оси ординат – максимальные ЗначениЯ биомассы вида. Под Значением биомассы 0.1 г/м² подраЗумеваютсЯ качественные пробы. СокраЩениЯ (бмп) и (топ) оЗначают соответственно беринговоморское и тихоокеанское побережьЯ Восточной Камчатки. Побережье Зал. Петра Великого от устьЯ р. Туманной к северу до м. Поворотного условно отноcитсЯ к южному Приморью; побережье к северу от м. Поворотного (пос. Преображение, б. СоколовскаЯ) до б. Ольга, включительно, условно относитсЯ к среднему Приморью; побережье к северу от б. Ольга до м. Белкина и материковое побережье Татарского пролива относим к северному Приморью. Fig. 2. The distribution of biomass of Mytilus trossulus septentrionalis in the intertidal zone of the Far Eastern seas of Russia. Here and throughout on histograms, on the abcissa is the number of studied samples (numbers in parentheses following the names geographic localities), on the ordinate is the maximum biomass of species. The number 0.1 g wet wt m-2 means the qualitative samples. Abbreviations (bmp) and (top) mean the Bering Sea coast and the Pacific coast of eastern Kamchatka. The coast of Peter the Great Bay from the mouth of the Tumannaya River to Cape Povorotny is conditionally referred to as southern Primorye; the area north of Cape Povorotny (Preobrazhenie Settlement, Sokolovskaya Bay) to Olga Bay inclusive is conditionally referred to as middle Primorye; north of Olga Bay to Cape Belkin and the mainland coast of the Tatar Strait to as northern Primorye.
Fig. 4 in Distributions and phylogeographic data of rheophilic freshwater fishes provide evidences on the geographic extension of a central-brazilian amazonian palaeoplateau in the area of the present day Pantanal Wetland
Fig. 4. Haplotype network showing the occurrence of three groups (upper rio Xingu, upper rio Paraguay and upper rio Tapajós). Traces show the number of mutational steps from two adjacent haplotypes. Circle diameters are proportional to the number of individuals, which each haplotype and the colors represent the locality were those haplotypes were found. Upper rio Xingu= Pink (1: dark pink); upper rio Paraguay = Blue (2: light blue; 3: navy blue; 4: dark blue; 5: light pink; 6: orange; 7: light purple; 8: dark purple; 9: white; 10: yellow; 11: light green; 12: dark green); and upper rio Tapajós= Gray (13: light gray and 14: dark gray).
Fig. 3 in Distributions and phylogeographic data of rheophilic freshwater fishes provide evidences on the geographic extension of a central-brazilian amazonian palaeoplateau in the area of the present day Pantanal Wetland
Fig. 3. Phylogenetic tree showing relationships among major lineages of Jupiaba acanthogaster from the upper rio Paraguay, upper rio Tapajós and upper rio Xingu, obtained by a maximum likelihood partitioned analysis. Numbers at each of the main nodes represents percentage of bootstrap support obtained by maximum parsimony analysis (1000 bootstrap pseudoreplicates).
Fig. 2 in Distributions and phylogeographic data of rheophilic freshwater fishes provide evidences on the geographic extension of a central-brazilian amazonian palaeoplateau in the area of the present day Pantanal Wetland
Fig. 2. Distribution of sampled localities for Jupiaba acanthogaster in the upper rio Paraguay, rio Tapajós and rio Xingú basins. The drainages of the rio Tocantins, rio Araguaia and upper rio Paraná are also illustrated. Drainage boundaries delimited by a continuous black line.
Fig. 1 in Distributions and phylogeographic data of rheophilic freshwater fishes provide evidences on the geographic extension of a central-brazilian amazonian palaeoplateau in the area of the present day Pantanal Wetland
Fig. 1. Map of the upper rio Paraguay basin and adjoining areas showing the distribution of Leporinus octomatulatus, Jubiaba acanthogaster, Oligosarcus perdido, Moenkhausia cosmops, and Hypostomus cochliodon, exemplifying distributional pattern discussed in this paper.
Text-fig. 1. Geographic situation of the source area of the Glenarea cretacea holotype. a, Řetenice locality of A. E. Reuss, b, Teplice-Stínadla locality, c, Teplice-Písečný vrch locality. For detail, see 'type locality of Glenarea cretacea' chapter. in The Scleractinian Coral Genus Glenarea (Bohemian Cretaceous Basin)
Text-fig. 1. Geographic situation of the source area of the Glenarea cretacea holotype. a, Řetenice locality of A. E. Reuss, b, Teplice-Stínadla locality, c, Teplice-Písečný vrch locality. For detail, see 'type locality of Glenarea cretacea' chapter.
Text-fig. 1. The Czech Republic with the position of the Příbram-Jince Basin (A), distribution of Cambrian rocks of the Jince Formation in the Příbram-Jince Basin (B), geographic position of discussed localities (C), stratigraphic ranges of Condylopyge in the Jince Formation of the Příbram-Jince Basin (D). 1. foot of the slope known as Vinice near Jince (locality 15 in Fatka and Kordule 1992); lowermost levels of the Acadolenus snajdri Zone sensu Fatka and Szabad (2014). 2. locality Potůček near Rejkovice (= locality 12 in Fatka and Kordule 1992); lower levels of the Paradoxides (Eccaparadoxides) pusillus Zone sensu Fatka and Szabad (2014). Specimens CGS CW 17 and CGS FK 63. 3. foot of the slope known as Vinice near Jince (locality 20 in Fatka and Kordule 1992); lower levels of the Onymagnostus hybridus Biozone sensu Fatka and Szabad (2014). Specimen CGS CW 18. in Condylopyge Hawle Et Corda, 1847 In The Příbram-Jince Basin (Barrandian Area, The Czech Republic, Agnostida)
Text-fig. 1. The Czech Republic with the position of the Příbram-Jince Basin (A), distribution of Cambrian rocks of the Jince Formation in the Příbram-Jince Basin (B), geographic position of discussed localities (C), stratigraphic ranges of Condylopyge in the Jince Formation of the Příbram-Jince Basin (D). 1. foot of the slope known as Vinice near Jince (locality 15 in Fatka and Kordule 1992); lowermost levels of the Acadolenus snajdri Zone sensu Fatka and Szabad (2014). 2. locality Potůček near Rejkovice (= locality 12 in Fatka and Kordule 1992); lower levels of the Paradoxides (Eccaparadoxides) pusillus Zone sensu Fatka and Szabad (2014). Specimens CGS CW 17 and CGS FK 63. 3. foot of the slope known as Vinice near Jince (locality 20 in Fatka and Kordule 1992); lower levels of the Onymagnostus hybridus Biozone sensu Fatka and Szabad (2014). Specimen CGS CW 18.
Database of Geographic Information: Canals in the Phoenix metropolitan area (1996-1998)
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Locations of GPS-collared moose and geographic correlates, as well as for random points within study area
<p>Moose are among the many species that are vulnerable to both direcdt and indirect effects of climate change. Habitat selection is one framework to assist investigators in disentangling the various factors (including weather) that ultimately dictate how animals respond to their environment. We investigated patterns of winter habitat selecdtion by adult female moose in southerwestern MOntana, USA, during 2007-2010, and how that selection was affected by snow (quantified by snow water equivalent) and winter temperatures. We used data from GPS colalrs and a suite of environmental covariates to quantify winter habitat selection at both study area (2nd order) and home range (3rd order) spatial scales using resource selection functions. Moose strongly select for the willow (<em>Salix</em> spp.) cover type, and against grassland cover. Moose use of conifer cover at the home range scale increased when either amount of snow or ambient temperature was higher, altough the latter only during periods of the day when conifer pathces were likely to have been cooler than cover types lacking a canopy. Wildlife conservatoin and management naturally focuses on preferred habitats, particularly those that fulfill essentially all forgaing requirements. However, habitats used preferentially under stresful weather conditions, even if used rarely overall, can also form a critical part of a species' overall needs.,</p>
Figure 4 in Geographic distribution patterns of galling insects in a protected area of Atlantic forest (southeast, Brazil)
Figure 4. The fit of the count part of the "hurdle" model to the relationship between galling species richness and plant genus species richness.
Figure 2 in Geographic distribution patterns of galling insects in a protected area of Atlantic forest (southeast, Brazil)
Figure 2. Boxplots with jittered illustrating the galling species richness between year season, with jitered raw values strung vertical corresponding to plots (a, b, c), numbers the plots (N) and sites (n), and mean ± SD bars.
Figure 3 in Geographic distribution patterns of galling insects in a protected area of Atlantic forest (southeast, Brazil)
Figure 3. The fit of the count part of the ZIP model to the relationship between galling species richness and plant family species richness.
Locations of GPS-collared moose and geographic correlates, as well as for random points within study area
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Mapping Areas Invaded by Pinus sp. from Geographic Object-Based Image Analysis (GEOBIA) Applied on RPAS (Drone) Color Images
<p><strong>Abstract: </strong>Invasive alien species reduce biodiversity. In southern Brazil, the genus <em>Pinus</em> is considered invasive, and its dispersal by humans has resulted in this species reaching ecosystems that are more sensitive and less suitable for cultivation, as it was the case for the restingas on the island of Santa Catarina. Invasion control requires persistent efforts to identify and treat each new invasion case as a priority. In this study, areas invaded by <em>Pinus</em> sp. in restingas were mapped using images taken by a remotely piloted aircraft system (RPAS, or drone) to identify the invasion areas in great detail, enabling management to be planned for the most recently invaded areas, where management is simpler, more effective, and less costly. Geographic object-based image analysis (GEOBIA) was applied on images taken from a conventional RGB camera embedded in a RPAS, which resulted in a global accuracy of 89.56%, a mean kappa index of 0.86, and an F-score of 0.90 for the <em>Pinus</em> sp. Processing was conducted with open source software to reduce operational costs.</p>
Distribution. CE Madagascar, but the geographic distribution is poorly known; at present known only from its type locality of Andasibe (= Périnet) and neighboring areas (e.g. Maromizaha Forest), Analamazaotra Special Reserve, Anjozorobe-Angavo Protected Area, and Mantadia National Park. in Cheirogaleidae
Distribution. CE Madagascar, but the geographic distribution is poorly known; at present known only from its type locality of Andasibe (= Périnet) and neighboring areas (e.g. Maromizaha Forest), Analamazaotra Special Reserve, Anjozorobe-Angavo Protected Area, and Mantadia National Park.
Distribution. CE Madagascar, known only from its type locality, the Sahafina Forest (29-230 m above sea level), a lowland rainforest fragment of 15-6 km2, and its surrounding "savoka" (fallow farmland with cultivated trees), about 58 km E of Andasibe-Mantadia National Park and 18 km W of the Indian Ocean. The geographic range is presumably limited to the lowland areas (below 700 m) between the Mangoro River to the S and the Rianila River to the N, an area of about 7600 km?2. in Cheirogaleidae
Distribution. CE Madagascar, known only from its type locality, the Sahafina Forest (29-230 m above sea level), a lowland rainforest fragment of 15-6 km2, and its surrounding "savoka" (fallow farmland with cultivated trees), about 58 km E of Andasibe-Mantadia National Park and 18 km W of the Indian Ocean. The geographic range is presumably limited to the lowland areas (below 700 m) between the Mangoro River to the S and the Rianila River to the N, an area of about 7600 km?2.
MUSES Leaf Area Index (LAI) 16-Day 30m Geographic Grid over Beijing Since 1984
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES LAI product at 30 m spatial resolution and 16-day temporal resolution over Beijing. The MUSES LAI product is provided on Geographic grid and spans from 1984 to 2021 (continuously updated). It was generated from time-series Landsat surface reflectance data using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: 115.416599º E – 117.508219º E, 39.441929º N – 41.059283º N</li> <li>Temporal Coverage: 1984 – 2021</li> <li>Spatial Resolution: 0.000269469º (approximately 30 m)</li> <li>Temporal Resolution: 16 days</li> <li>Projection: Geographic</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
Interior points for various geographic areas as defined TIGER/LINE 2006
<p>Interior points (however defined) created for various geographic areas based on TIGER/LINE 2006. Created by Matthew Graham (no warranties). Posted by Lars Vilhuber.</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)
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.