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1,276 results for “distribution maps”
Fig. 13. Distribution maps. A–B in The bats of the Congo and of Rwanda and Burundi revisited (Mammalia: Chiroptera)
Fig. 13. Distribution maps. A–B. Macronycteris gigas (Wagner, 1845). C–D. Macronycteris vittatus (Peters, 1852). E–F. Cardioderma cor (Peters, 1872). A, C, E. Distribution in the CRB area. B, D, F. Pan-African distribution.
Fig. 9. Distribution maps. A–B in The bats of the Congo and of Rwanda and Burundi revisited (Mammalia: Chiroptera)
Fig. 9. Distribution maps. A–B. Rousettus aegyptiacus (E. Geoffroy St.-Hilaire, 1810). C–D. Casinycteris argynnis Thomas, 1910. E–F. Scotonycteris bergmansi Hassanin et al., 2015. A, C, E. Distribution in the CRB area. B, D, F. Pan-African distribution.
Fig. 3. Distribution maps. A–B in The bats of the Congo and of Rwanda and Burundi revisited (Mammalia: Chiroptera)
Fig. 3. Distribution maps. A–B. Eidolon helvum (Kerr, 1792). C–D. Epomophorus anselli Bergmans & Van Strien, 2004. E–F. Epomophorus crypturus Peters, 1852. A, C, E. Distribution in the CRB area. B, D, F. Pan-African distribution.
Fig. 12. Distribution maps. A–B. Hipposideros camerunensis Eisentraut, 1956. C–D in The bats of the Congo and of Rwanda and Burundi revisited (Mammalia: Chiroptera)
Fig. 12. Distribution maps. A–B. Hipposideros camerunensis Eisentraut, 1956. C–D. Hipposideros fuliginosus (Temminck, 1853). E–F. Hipposideros ruber (Noack, 1893). A, C, E. Distribution in the CRB area. B, D, F. Pan-African distribution.
Fig. 1 in Malacological mapping in Austria distribution of the Austrian spring snail Bythinella austriaca (v. F , 1857) in the federal state of Salzburg
Fig. 1: Geographical map of the federal state of Salzburg including all sample points with registered occurrence of the Austrian spring snail B. austriaca. Open circles mark sample locations published in literature, whereas filled circles represent sample points of own field investigations.
Mapping present and future predicted distribution patterns for a meso-grazer guild in the Baltic Sea
<p>Baltic Sea communities consisting of key and endemic species are threatened by climate change. Using Ecological niche modelling, we map predicted distribution patterns under recent and future climate change scenarios (2050) for a food-web consisting of a guild of meso-grazers (Idotea spp.), their host algae (Fucus vesiculosus and F. radicans) and their fish predator (Gasterosteus aculeatus). Brackish water species depend on two important abiotic factors: temperature and salinity. We assess which of these environmental factors determines the distribution limits of the grazers in the Baltic Sea today. For species in a semi-enclosed sea area such as the Baltic Sea, climate-induced changes may lead to dramatic food-web effects. We assess the consequences of the predicted climate-induced habitat range changes for this unique Baltic community.<br /> </p>
FIGURES 26 – 28. Pneuminion distribution maps. — 26. P. balfourbrownei. — 27. P. impressum. — 28. P in A revision of the South African endemic water beetle genus Pneuminion Perkins (Coleoptera: Hydraenidae)
FIGURES 26 – 28. Pneuminion distribution maps. — 26. P. balfourbrownei. — 27. P. impressum. — 28. P. nanum.
FIGURES 23 – 25. Pneuminion distribution maps. — 23. P. velamen. — 24. P. semisulcatum. — 25. P. t u b u m in A revision of the South African endemic water beetle genus Pneuminion Perkins (Coleoptera: Hydraenidae)
FIGURES 23 – 25. Pneuminion distribution maps. — 23. P. velamen. — 24. P. semisulcatum. — 25. P. t u b u m.
FIGURES 21 – 22. Pneuminion distribution maps. — 21. All Pneuminion collecting sites. — 22. P in A revision of the South African endemic water beetle genus Pneuminion Perkins (Coleoptera: Hydraenidae)
FIGURES 21 – 22. Pneuminion distribution maps. — 21. All Pneuminion collecting sites. — 22. P. endroedyi.
IVMOOC 2017 - GloBI Data for Interactive Tableau Map of Spatial and Temporal Distribution of Interactions
<p>Global Biotic Interactions (GloBI, www.globalbioticinteractions.org) provides an infrastructure and data service that aggregates and archives known biotic interaction databases to provide easy access to species interaction data. This project explores the coverage of GloBI data against known taxonomic catalogues in order to <em>identify ‘gaps’ in knowledge of species interactions</em>. We examine the richness of GloBI’s datasets using itself as a frame of reference for comparison and explore interaction networks according to geographic regions over time. The resulting analysis and visualizations intend to provide insights that may help to enhance GloBI as a resource for research and education.</p> <p>Spatial and temporal biotic interactions data were used in the construction of an interactive Tableau map. The raw data (IVMOOC 2017 GloBI <em>Kingdom</em> Data Extracted 2017 04 17.csv) was extracted from the project-specific SQL database server. The raw data was clean and preprocessed (IVMOOC 2017 GloBI Cleaned Tableau Data.csv) for use in the Tableau map. Data cleaning and preprocessing steps are detailed in the companion paper.</p> <p>The <strong>interactive Tableau map</strong> can be found here: https://public.tableau.com/profile/publish/IVMOOC2017-GloBISpatialDistributionofInteractions/InteractionsMapTimeSeries#!/publish-confirm</p> <p>The<strong> companion paper</strong> can be found here: doi.org/10.5281/zenodo.814979</p> <p><strong>Complementary high resolution visualizations </strong>can be found here: doi.org/10.5281/zenodo.814922</p> <p><strong>Project-specific data </strong>can be found here: doi.org/10.5281/zenodo.804103 (SQL server database)</p>
Indicative distribution map for Ecosystem Functional Group MT1.2 Muddy Shorelines
<p>This archive contains indicative distribution maps and profiles for <strong>MT1.2 Muddy Shorelines</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.1). Please refer to Keith <em>et al.</em> (2020) and Keith <em>et al.</em> (2022) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Indicative distribution map for Ecosystem Functional Group T4.5 Temperate subhumid grasslands
<p>This archive contains indicative distribution maps and profiles for <strong>T4.5 Temperate subhumid grasslands</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.1). Please refer to Keith <em>et al.</em> (2020) and Keith <em>et al.</em> (2022) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Fig. 14. Distribution map. 1 in New species and new records of terrestrial isopods (Crustacea, Isopoda, Oniscidea) of the families Philosciidae and Scleropactidae from Brazilian caves
Fig. 14. Distribution map. 1. Alboscia jotajota Campos-Filho, Bichuette & Taiti sp. nov. 2. Androdeloscia akuanduba Campos-Filho, Cardoso & Taiti sp nov. 3. Atlantoscia inflata Campos-Filho & Araujo, 2015. 4. Benthana iporangensis Lima & Serejo, 1993. 5. B. longicornis Verhoeff, 1941. 6. B. olfersii (Brandt, 1833). 7. B. picta (Brandt, 1833). 8. B. taeniata Araujo & Buckup, 1994. 9. Metaprosekia igatuensis Campos-Filho, Fernandes & Bichuette sp. nov. 10. Paratlantoscia rubromarginata (Araujo & Leistikow, 1999). 11. Amazoniscus spica Campos-Filho, Aguiar & Taiti sp. nov. 12. Circoniscus bezzii Arcangeli, 1931. Light gray areas denote Brazilian conservation units. AL = Alagoas; BA = Bahia; CE = Ceará; DF = Distrito Federal; ES = Espírito Santo; GO = Goiás; MA = Maranhão; MG = Minas Gerais; MT = Mato Grosso; PA = Pará; PB = Paraíba; PE = Pernambuco; PI = Piauí; PR = Paraná; RJ = Rio de Janeiro; RN = Rio Grande do Norte; SE = Sergipe; SP = São Paulo; TO = Tocantins.
Рис. 1. Карта района иссΛеΑований. 1 – граница зон раститеΛьности; 2 – граница поΑзон раститеΛьности; 3 – места сбора материаΛа; I – южная тайга; II – среΑняя тайга; III – северная тайга; IV – крайнесеверная тайга; V – ΛесотунΑра; VI – южная тунΑра; VII – северная тунΑра. Fig. 1. Map of the studied area. 1 – boundary of vegetation zones; 2 – boundary of vegetation subzones; 3 – collection points; I – southern taiga; II – middle taiga; III – northern taiga; IV – extremely northern taiga; V – forest tundra; VI – southern tundra; VII – northern tundra. in Fauna and landscape-zonal distribution of Orthoptera in the Komi Republic (Russia)
Рис. 1. Карта района иссΛеΑований. 1 – граница зон раститеΛьности; 2 – граница поΑзон раститеΛьности; 3 – места сбора материаΛа; I – южная тайга; II – среΑняя тайга; III – северная тайга; IV – крайнесеверная тайга; V – ΛесотунΑра; VI – южная тунΑра; VII – северная тунΑра. Fig. 1. Map of the studied area. 1 – boundary of vegetation zones; 2 – boundary of vegetation subzones; 3 – collection points; I – southern taiga; II – middle taiga; III – northern taiga; IV – extremely northern taiga; V – forest tundra; VI – southern tundra; VII – northern tundra.
Maps of the diversity and distribution of Raunkiær's life forms in European vegetation
<p>This repository contains raster files (TIF format) with a 50 km × 50 km resolution (over UTM grid EPSG:32633), showcasing the diversity and distribution of Raunkiær’s life forms in European vegetation. The maps are based on two key metrics: (i) the proportion (%) of species within each life form and (ii) the diversity of life forms, including richness and evenness.</p> <p>To generate these maps, we averaged plot-level metric values across a comprehensive dataset comprising 546,501 vegetation plots sourced from the European Vegetation Archive (EVA; Project 163; <a href="https://euroveg.org" target="_new">https://euroveg.org</a>). These plots cover diverse habitats, including 173,190 forests, 260,884 grasslands, 52,517 scrubs, and 59,910 wetlands.</p> <p>The maps encompass the entire dataset, offering a visualization of the geographical distribution patterns of life forms across Europe. Additionally, we created habitat-specific maps by subsetting the dataset to explore unique patterns within each habitat type (forest, grassland, scrub, and wetland).</p> <p>Furthermore, we generated additional maps based on standardised effect sizes (SES) of diversity metrics. Through 500 species identity shuffles without replacement, specific to each habitat type, we examined the deviations from random expectations. SES values outside the range of -1.96 to 1.96 indicate significantly lower or higher metric values than expected at random, respectively. </p> <p> </p> <table> <tbody> <tr> <td><strong>Folder name</strong></td> <td><strong>Description of TIF raster values</strong></td> </tr> <tr> <td>full.div</td> <td>Mean richness and evenness of life forms across all habitat types</td> </tr> <tr> <td>full.mean.rel.prop</td> <td>Mean proportion of each life form across all habitat types</td> </tr> <tr> <td>habitat.div</td> <td>Mean richness and evenness of life forms across separate habitat types (forest, grassland, scrub, and wetland)</td> </tr> <tr> <td>habitat.mean.rel.prop</td> <td>Mean proportion of each life form across separate habitat types (forest, grassland, scrub, and wetland)</td> </tr> <tr> <td>SES.full.div</td> <td>Mean richness and evenness of life forms across all habitat types measured with standardized effect sizes (SES)</td> </tr> <tr> <td>SES.full.mean.rel.prop</td> <td>Mean proportion of each life form across all habitat types measured with standardized effect sizes (SES)</td> </tr> <tr> <td>SES.habitat.div</td> <td>Mean richness and evenness of life forms across separate habitat types (forest, grassland, scrub, and wetland) measured with standardized effect sizes (SES)</td> </tr> <tr> <td>SES.habitat.mean.rel.prop</td> <td>Mean proportion of each life form across separate habitat types (forest, grassland, scrub, and wetland) measured with standardized effect sizes (SES)</td> </tr> </tbody> </table> <p><br>Additional information is available in our publication:<br><br>Midolo, G., Axmanová, I., Divíšek, J., Dřevojan, P., Lososová, Z., Večeřa, M., Karger, D. N., Thuiller, W., Bruelheide, H., Aćić, S., Attorre, F., Biurrun, I., Boch, S., Bonari, G., Čarni, A., Chiarucci, A., Ćušterevska, R., Dengler, J., Dziuba, T., Garbolino, E., Jandt, U., Lenoir, J., Marcenò, C., Rūsiņa, S., Šibík, J., Škvorc, Ž., Stančić, Z., Stanišić-Vujačić, M., Svenning, J. C., Swacha, G., Vassilev, K., & Chytrý, M. (2024) Diversity and distribution of Raunkiær’s life forms in European vegetation.<em> Journal of Vegetation Science. </em>Accepted on the 10th of December 2023</p>
Combining camera trap surveys and IUCN range maps to improve knowledge of species distributions
<p><span>Reliable maps of species distributions are fundamental for biodiversity research and conservation. Range maps created by the International Union for Conservation of Nature (IUCN) Red List are often considered authoritative but may not match species occurrence data. We tested concordance between occurrences from camera trap surveys and predicted occurrence from IUCN maps for 510 medium- to large-bodied mammalian species in 80 camera-trap sampling areas. Across all areas, cameras detected 39% of the species that were expected to occur based on IUCN ranges. The probability of mismatches between camera traps and IUCN range maps was significantly higher for smaller-bodied mammals and habitat specialists in the Neotropics and Indomalaya, and in areas with shorter canopy forests. Our results indicate that in many areas within their range map distributions species may be rare or absent. We suggest that combining range map data with accumulating data from ground-based biodiversity sensors, such as camera traps, acoustic recorders, and eDNA surveys, provides a richer knowledge base for conservation mapping and planning.</span></p>
Fig. 8. Heat map resulting from the Species Distribution Model using MaxEnt, where 1 in A revision of the genus Armillipora Quate (Diptera: Psychodidae) with the descriptions of two new species
Fig. 8. Heat map resulting from the Species Distribution Model using MaxEnt, where 1 is equal to the highest probability of distribution, while 0 is the lowest probability.
The Adult Adansonia digitata L. (baobab tree) distribution map derived from very high resolution satelite imagery for 2010s across the Sahel at 1km resolution
<p>The baobab tree (<em>Adansonia digitata</em> <em>L.</em>) is an integral part of rural livelihoods throughout the African continent. However, the combined effects of climate change and increasing global demand for baobab products are currently exerting pressure on the sustainable utilization of these resources. Here we employ sub-meter resolution satellite imagery to identify nearly 3 million baobab trees in the Sahel, a dryland region of 1.5 million km<sup>2</sup>. This achievement is considered an essential step towards improving valuable woody species' management and monitoring system. The map's overall underestimate bias is 0.27. To prevent mismanagement of this specific tree species, we aggregated every single adult baobab tree map to 1 × 1 km grids. We also classified the baobab trees using the tree crown diameters( small: 3-9m; medium 9m-13m; large: >13m). The baobab tree count map is also available for these three different size classes. </p>
Fig. 2. Distribution maps. A in A revision of the Adenophorus Group and other glandular-leaved species of Croton (Euphorbiaceae) from northern Madagascar and Mayotte, including three new species
Fig. 2. Distribution maps. A. Croton nudatus Baill. (white), C. stanneus Baill. (yellow); B. Croton adenophorus Baill. (green) and C. tsiampiensis Leandri (white); C. Croton bathianus Leandri (light blue), C. loucoubensis Baill. (yellow), and Croton scoriarum Leandri (red); D. Croton orangeae Kainul. & Berry (red) and C. sahafariensis Kainul. & Berry (yellow); E. Croton mayottae P.E. Berry & Kainul. [Google Earth Image © 2017 DigitalGlobe. Reproduced per attribution guidelines]
Fig. 10. – Distribution maps. A in Revision of the group previously known as Panicum L. (Poaceae: Panicoideae) in Madagascar
Fig. 10. – Distribution maps. A. Panicum luridum Hack. (stars), P. manongarivense A. Camus (triangles), P. mitopus K. Schum. (circles) and P. novemnerve Stapf (squares); B. Panicum palackyanum A. Camus (stars), P. perrieri A. Camus (triangles), P. pleianthum Peter (circles) and P. spergulifolium A. Camus (squares).
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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.