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Dataset results
21 results for “richness mapping”
Global map of soil bacterial richness
<p>This repository contains global model estimates of soil bacterial richness (Fig.4) as described in:</p> <p>Bickel, Samuel, Xi Chen, Andreas Papritz, and Dani Or. “A Hierarchy of Environmental Covariates Control the Global Biogeography of Soil Bacterial Richness.” <em>Scientific Reports</em> 9, no. 1 (August 20, 2019): 1–10. <a href="https://doi.org/10.1038/s41598-019-48571-w">https://doi.org/10.1038/s41598-019-48571-w</a>.</p>
Data from: mapping endemic freshwater fish richness to identify high priority areas for conservation: an ecoregion approach
<p>Freshwater ecosystems are experiencing accelerating global biodiversity loss. Thus, knowing where these unique ecosystems' species richness reaches a peak can facilitate their conservation planning. By hosting more than 290 freshwater fishes, Iran is a major freshwater fish hotspot in the Middle East. Considering the accelerating rate of biodiversity loss, there is an urgent need to identify species rich areas and understanding of the mechanisms driving biodiversity distribution. In this study, we gathered distribution records of all endemic freshwater fishes of Iran (85 species) to develop their richness map and determine the most critical drivers of their richness patterns from an ecoregion approach. We performed a generalized linear model (GLM) with quasi-Poisson distribution to identify contemporary and historical determinants of endemic freshwater fish richness. We also quantified endemic fish similarity among the 15 freshwater ecoregions of Iran. Results showed that endemic freshwater fish richness is highest in the Zagros Mountains while moderate level of richness was observed between Zagros and Alborz Mountains. High, moderate and low richness of endemic freshwater fish match with Upper Tigris & Euphrates, Namak, and Kavir & Lut Deserts ecoregions respectively. Kura - South Caspian Drainages and Caspian Highlands were the most similar ecoregions and Orumiyeh was the most unique ecoregion according to endemic fish presence. Precipitation and precipitation change velocity since the Last Glacial Maximum were the most important predictors of endemic freshwater fish richness. Areas identified to have the highest species richness have high priority for the conservation of freshwater fish in Iran, therefore, should be considered in future protected areas development.</p>
Data from: mapping endemic freshwater fish richness to identify high priority areas for conservation: an ecoregion approach
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Mapping species richness using opportunistic samples: a case study on ground-floor bryophyte species richness in the Belgian province of Limburg
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Mapping breeding bird species richness at management-relevant resolutions across the United States
<p>Human activities alter ecosystems everywhere, causing rapid biodiversity loss and biotic homogenization. These losses necessitate coordinated conservation actions guided by biodiversity and species distribution spatial data that cover large areas yet have fine-enough resolution to be management-relevant (i.e., ≤ 5 km). However, most biodiversity products are too coarse for management or are only available for small areas. Furthermore, many maps generated for biodiversity assessment and conservation do not explicitly quantify the inherent tradeoff between resolution and accuracy when predicting biodiversity patterns. Our goals were to 1) generate predictive models of overall breeding bird species richness and species richness of different guilds based on nine functional or life history-based traits across the conterminous US at three resolutions (0.5, 2.5, and 5 km), and 2) quantify the tradeoff between resolution and accuracy, and hence relevance for management, of the resulting biodiversity maps. We summarized eighteen years of North American Breeding Bird Survey data (1992-2019) and modeled species richness using random forests, including 66 predictor variables (describing climate, vegetation, geomorphology, and anthropogenic conditions), 20 of which we newly derived. Among the three spatial resolutions, the percent variance explained ranged from 27% to 60% (median = 54%; mean = 57%) for overall species richness and 12% to 87% (median = 61%; mean = 58%) for our different guilds. Overall species richness and guild-specific species richness were best explained at 5-km resolution using approximately 24 predictor variables based on percent variance explained, symmetric mean absolute percentage error, and root mean squared error values. However, our 2.5-km resolution maps were almost as accurate and provided more spatially detailed information, which is why we recommend them for most management applications. Our results represent the first consistent, occurrence-based, and nationwide maps of breeding bird richness with a thorough accuracy assessment that are also spatially detailed enough to inform local management decisions. More broadly, our findings highlight the importance of explicitly considering tradeoffs between resolution and accuracy to create management-relevant biodiversity products for large areas.</p>
Supplementary material 1 from: Minoli I, Morando M, Avila LJ (2015) Reptiles of Chubut province, Argentina: richness, diversity, conservation status and geographic distribution maps. ZooKeys 498: 103-126. https://doi.org/10.3897/zookeys.498.7476
Specimens examined from LJAMM-CNP herpetological collection: Explanation note: Specimens examined from LJAMM-CNP herpetological collection, museum voucher and bibliography data for the reptile's distribution in Chubut province, Patagonia, Argentina. Additional figures of the results section.
FIGURE. Map of Mexico, indicating the number of families, genera, and species of Mexican Vascular Epiphytes by state. in Mexican Vascular Epiphytes: Richness and Distribution
FIGURE. Map of Mexico, indicating the number of families, genera, and species of Mexican Vascular Epiphytes by state.
Data from: High-resolution and large-extent mapping of plant species richness using vegetation-plot databases
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Mapping breeding bird species richness at management-relevant resolutions across the United States
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FIG. 3. — Species richness map for Xylopia L in A revision of Xylopia L. (Annonaceae): the species of Madagascar and the Mascarene islands
FIG. 3. — Species richness map for Xylopia L. on Madagascar and the Mascarene Islands, plotted by 0.5° × 0.5° grid squares.
Appendix B of Berg, Afruni et al. 2024, entitled:' Mapping the spatial extent of HI-rich absorbers using MgII absorption along gravitational arcs', accepted for publication on A&A
<p>J1527 velocity profiles</p>
Appendix C of Berg, Afruni et al. 2024, entitled:' Mapping the spatial extent of HI-rich absorbers using MgII absorption along gravitational arcs', accepted for publication on A&A
<p>J0033 velocity profiles</p>
Figure. Map of Georgia with main administrative divisions delimited. Shade intensity indicates the richness of mayfly species for respective administrative unit. Dots indicate localities sampled for mayflies prior to this study. in The first annotated checklist of mayflies (Ephemeroptera: Insecta) of Georgia with new distribution data and a new record for the country
Figure. Map of Georgia with main administrative divisions delimited. Shade intensity indicates the richness of mayfly species for respective administrative unit. Dots indicate localities sampled for mayflies prior to this study.
Figure 3 from: Minoli I, Morando M, Avila LJ (2015) Reptiles of Chubut province, Argentina: richness, diversity, conservation status and geographic distribution maps. ZooKeys 498: 103-126. https://doi.org/10.3897/zookeys.498.7476
Figure 3 - Imagery source: Blue Marble Next Generation (true-color), Web Map Service (WMS) layer from CREAF MAP SERVER (open-gis), EPSG: 4326. A Records of Cheloniidae, Dipsadidae and Viperidae. Green dot: Chelonia mydas; light blue dot: Erythrolamprus sagittifer sagittifer; magenta dot: Phalotris bilineatus; red dots: Bothrops ammodytoides; orange dots: Oxyrhopus rhombifer; black dots: Pseudotomodon trigonatus; blue dots: Philodryas patagoniensis; yellow dots: Philodryas trilineata; grey dots: Tachymenis chilensis B Records of lizards. Black dots: Amphisbaena plumbea; light blue dot: Amphisbaena kingii; red dot: Aurivela longicauda; magenta dots: Diplolaemus bibronii; blue dots: Diplolaemus darwinii; orange dots: Diplolaemus sexcinctus; yellow dots: Leiosaurus bellii; green dots: Pristidactylus nigroiugulus C Records of Homonota darwinii D Records of some Liolaemus species. Blue dots: Liolaemus bibronii; red dots: Liolaemus boulengeri E Records of some Liolaemus species. Magenta dots: Liolaemus camarones; black dots: Liolaemus canqueli; green dots: Liolaemus chehuachekenk; orange dots: Liolaemus darwinii; light blue dots: Liolaemus elongatus; yellow dots: Liolaemus fitzingerii; blue dots: Liolaemus gracilis; white dots: Liolaemus shehuen F Records of some Liolaemus species. Light blue dots: Liolaemus kingii; red dot: Liolaemus kriegi; yellow dots: Liolaemus lineomaculatus; green dots: Liolaemus melanops; magenta dots: Liolaemus morandae; blue dots: Liolaemus petrophilus G Records of some Liolaemus species. Magenta dots: Liolaemus pictus argentinus; orange dots: Liolaemus rothi; green dots: Liolaemus senguer; yellow dot: Liolaemus somuncurae; light blue dots: Liolaemus telsen; blue dots: Liolaemus uptoni; red dots: Liolaemus xanthoviridis H Records of Phymaturus species. Red dots: Phymaturus calcogaster; white dot: Phymaturus camilae; green dot: Phymaturus castillensis; blue dot: Phymaturus felixi; yellow dots: Phymaturus indistinctus; light blue dots: Phymaturus patagonicus; orange dots: Phymaturus somuncurensis; magenta dot: Phymaturus videlai.
Figure 1 from: Minoli I, Morando M, Avila LJ (2015) Reptiles of Chubut province, Argentina: richness, diversity, conservation status and geographic distribution maps. ZooKeys 498: 103-126. https://doi.org/10.3897/zookeys.498.7476
Figure 1 - A Presence of reptiles recorded for central Patagonia, based on a spatial grid. Blue gradient grid: representing the number of localities sampled within each cell; brown lines: roads from a vector line shapefile; department's names and main geographic references are presented B Species richness of reptiles recorded for central Patagonia, analyzed based on a spatial grid. Green gradient grid: representing the richness within each cell C Species richness of reptile recorded for central Patagonia, analyzed based on phytogeographic provinces. White circles: representing the richness within each phytogeographic province polygon; map legend: total species per phytogeographic province. References: magenta dots: localities with accurate location information.
Nucleosome position mapping by micrococcal nuclease analysis of S. cerevisiae anchor-away Sth1, Swi2 and/of TBP cells in raffinose and galactose-rich media in rapamycin or DMSO
GEO Series GSE190737. Saccharomyces cerevisiae. 34 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Genome-wide maps of DNA cytosine methylation state in during normal differentiation commitment in hematopoietic stem cells (HSC) and in Special AT-rich sequence-binding protein1 (Satb1) - deficient HS
GEO Series GSE44304. Mus musculus. 6 samples. Type: Methylation profiling by high throughput sequencing.
Nucleosome position mapping by micrococcal nuclease analysis in S. cerevisiae of Top1-AID/Top2-AID strains with auxin in galactose-rich media
GEO Series GSE196945. Saccharomyces cerevisiae. 11 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Figure 2 from: Minoli I, Morando M, Avila LJ (2015) Reptiles of Chubut province, Argentina: richness, diversity, conservation status and geographic distribution maps. ZooKeys 498: 103-126. https://doi.org/10.3897/zookeys.498.7476
Figure 2 - Reptile species conservation status per genus for central Patagonia, Argentina.
West Africa Coastal Vulnerability Mapping: Subset of Global Mammal and Amphibian Richness Grids, 2015 Release
The West Africa Coastal Vulnerability Mapping: Subset of Global Mammal and Amphibian Richness Grids, 2015 Release was extracted from the NASA Socioeconomic Data and Applications Center (SEDAC) Gridded Species Distribution collection created from vector data files acquired from the International Union for Conservation of Nature (IUCN) Red List collection. Threatened species include any species classified as vulnerable, endangered, or critically endangered. The data represent the density of threatened mammals and amphibians within 200 kilometers of the coast at a one kilometer resolution.
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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.