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168 results for “biodiversity distribution”
Figure 6. from: Studies in Hawaiian Diptera III: New Distributional Records for Canacidae and a New Endemic Species of Procanace - Biodiversity Data Journal 4: e5611 (08 April 2016) https://doi.org/10.3897/BDJ.4.e5611
Figure 6. - Abdomen of P. constricta female, showing constriction between tergites 4 and 5, the lateral extension of tergite 8 beyond the apex of the ovipositor, and the ovipositor and associated structures. The specimen imaged is from collection 205268.
Figure 10. from: Studies in Hawaiian Diptera III: New Distributional Records for Canacidae and a New Endemic Species of Procanace - Biodiversity Data Journal 4: e5611 (08 April 2016) https://doi.org/10.3897/BDJ.4.e5611
Figure 10. - Distribution of P. acuminata on Oahu, Molokai, Maui and Hawaii and P. nigroviridis on Kauai.
Figure 21. from: Studies in Hawaiian Diptera III: New Distributional Records for Canacidae and a New Endemic Species of Procanace - Biodiversity Data Journal 4: e5611 (08 April 2016) https://doi.org/10.3897/BDJ.4.e5611
Figure 21. - Head of Scatella kauaiensis, showing the large face which extends over the oral cavity. The clypeus is normal in size and does not extend over the mouth. The specimen imaged was from collection 205192.
Figure 15. from: Studies in Hawaiian Diptera III: New Distributional Records for Canacidae and a New Endemic Species of Procanace - Biodiversity Data Journal 4: e5611 (08 April 2016) https://doi.org/10.3897/BDJ.4.e5611
Figure 15. - Wing of Canaceoides hawaiiensis, showing a single break at the terminus of the subcostal vein. The specimen imaged was from collection 205542.
The global distribution of known and undiscovered ant biodiversity
<p><span>Invertebrates constitute the majority of animal species and are critical for ecosystem functioning and services</span><span>. Nonetheless, global invertebrate biodiversity patterns and their congruences with vertebrates remain largely unknown</span><span>. We resolve the first high-resolution (~20-km) global diversity map for a major invertebrate clade, ants, using biodiversity informatics, range modeling, and machine learning to synthesize existing knowledge and predict the distribution of undiscovered diversity. We find that ants and different vertebrate groups have distinct features in their patterns of richness and rarity, underscoring the need to consider a diversity of taxa in conservation. However, despite their phylogenetic and physiological divergence, ant distributions are not highly anomalous relative to variation among vertebrate clades.</span> <span>Furthermore, our models predict rarity centers largely overlap (78%), suggesting that general forces shape endemism patterns across taxa</span><span>. This raises confidence that conservation of areas important for small-ranged vertebrates will benefit invertebrates while</span><span> providing a "treasure map" to guide future discovery</span><span>.</span></p>
Fig. 7 in The global distribution of known and undiscovered ant biodiversity
Fig. 7. Globalprotection status of richness andrarity centers. Richness andrarity centers (top 10% of area) are overlaid with protected areas usingdata retrieved from the World Database of Protected Areas (protectedplanet.net) and processed. Biodiversity centers for ants based on current sampling (top row), predicted ant centers under universal high sampling (second row), and vertebrate centers (bottom row) are presented.
Fig. 6 in The global distribution of known and undiscovered ant biodiversity
Fig. 6. Empirical andpredicted raritycenters of Eastern Asiaand Oceania. Raritycentersbased on currentknowledge andprojectedby a Random Forestmodelunder a "universal high sampling" scenario. See Fig. 3 for more explanation.
Fig. 3 in The global distribution of known and undiscovered ant biodiversity
Fig. 3. Machine learningpredictshowincreased samplingcouldchangeour understandingof antrichness andrarity centers. Random Forestmodelswere trained topredict ant speciesrichness andrarity values asafunction of climate (7 vars.),topography,biogeographic realm, vertebratebiodiversity, andsampling density. Wethen used the models to predict (A) richness and rarity values under a "universal high sampling" scenario, revealing which areas may drop out of the top 10% with increased global sampling (red), which are robust to sampling (purple), and which centers are predicted to enter the top 10% with increased sampling (blue). The latter represents a treasure map indicating areas that should be prioritized for future sampling. The top 10% areas for vertebrates are indicated by hatched regions. (B) Overlap fractions for empirical and projected center designations for richness and rarity, and Spearman's correlations continuous richness and rarity values.
Fig. 2 in The global distribution of known and undiscovered ant biodiversity
Fig. 2. Global patterns of ant rarity and comparison with terrestrial vertebrates. (A) The concordance of different rarity (i.e., rarity-weighted richness, a metric indicating a concentration of small-ranged species) centers (top 10% of area) for amphibians, birds, mammals, reptiles, and ants. (B) Continuous rarity maps for ants and vertebrates. (C) Spearman's correlation matrix for grid cell–level rarity across taxa.
Fig. 4 in The global distribution of known and undiscovered ant biodiversity
Fig. 4. Empirical and predicted raritycenters of the Western Hemisphere. Rarity centersbased on currentknowledge and projectedbya Random Forestmodelunder a "universal high sampling" scenario. See Fig. 3 for more explanation.
Fig. 1 in The global distribution of known and undiscovered ant biodiversity
Fig. 1. Globalantspeciesrichness patternsincomparison withterrestrialvertebrates. (A) Species richnesscenters (top 10% of area) for amphibians, birds, mammals, reptiles, and ants, indicating areas of congruence and incongruence of biodiversity centers across taxa. (B) Species richness maps based on stacking individual species range estimates for ants and vertebrates. (C) Spearman's correlation matrix for grid cell–level species richness across taxa.
Fig. 5 in The global distribution of known and undiscovered ant biodiversity
Fig. 5. Empirical and predicted rarity centers of Europe, Africa, and West Asia. Rarity centers based on current knowledge and projected by a Random Forest model under a "universal high sampling" scenario. See Fig.3 for more explanation.
Fig.3 in Biodiversity survey, ecology and new distribution records of Marchantiophyta in a remnant of Brazilian Atlantic Forest
Fig.3. Graphical representation of substrates colonized by liverwort species in the fragment of dense montane ombrophilous forest studied in the National Park of Boa Nova, Bahia, Brazil.
Fig. 2 in The Latitudinal Distribution Of Sphingid Species Richness In Continental Southeast Asia: What Causes The Biodiversity 'Hot Spot' In Northern Thailand?
Fig. 2. Estimated local species richness (ACE) from nine quantitative light trapping sites. Fisher's α, an alternative measure of local diversity (not shown), is lowest at the Malaysian sites (α = 7–13) and highest at a montane site in Northwestern Thailand (α = 30), whereas the Vietnam sample and other Thai sites score intermediately (α = 11–21).
Fig. 1. A in The Latitudinal Distribution Of Sphingid Species Richness In Continental Southeast Asia: What Causes The Biodiversity 'Hot Spot' In Northern Thailand?
Fig. 1. A, Estimated species richness (simplified from Beck & Kitching, 2004); B, Sampling intensity (kernels of original distribution records, smoothed; software by Hooge et al., 1999); C, altitudinal zonation (from digital elevation model, http://www.ngdc.noaa.gov/mgg/global/ seltopo.html). Elevation classes are [m]: 0–500 (white), 501–1000, 1001–1500, 1501–2000,>2001 (black); D, Landscape types (simplified from remote sensing data, http://www-gvm.jrc.it/glc2000). Agricultural and highly disturbed areas are printed in light grey, mosaic and bush in dark grey and closed forests in black.
Fig. 3 in The Latitudinal Distribution Of Sphingid Species Richness In Continental Southeast Asia: What Causes The Biodiversity 'Hot Spot' In Northern Thailand?
Fig. 3. Abundance (number of species, y-axis) and the latitudinal mean of their range in four regions. Black bars indicate the approximate latitudinal extend of the regions under investigation.
The global distribution of known and undiscovered ant biodiversity
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Figure 1. from Extending Marine Species Distribution Maps Using Non-Traditional Sources - Biodiversity Data Journal 3: e4900 (17 April 2015) https://doi.org/10.3897/BDJ.3.e4900
Figure 1. - The IUCN Red List Review Process (IUCN 2014a). Steps refer to the DOCUMENTATION STANDARDS AND CONSISTENCY CHECKS FOR IUCN RED LIST ASSESSMENTS AND SPECIES ACCOUNTS (IUCN 2013).
Supplementary file pwt: Female length of Tylorida striata per taxonomic reference, examined syntypes and specimens from India from Bridging the distributional gap of Tylorida striata (Thorell, 1877) and new synonymy (Araneae: Tetragnathidae) - Biodiversity Data Journal 3: e4878 (26 March 2015) https://doi.org/10.3897/BDJ.3.e4878
Female length of Tylorida striata per taxonomic reference, examined syntypes and specimens from India
Figure 3. from Bridging the distributional gap of Tylorida striata (Thorell, 1877) and new synonymy (Araneae: Tetragnathidae) - Biodiversity Data Journal 3: e4878 (26 March 2015) https://doi.org/10.3897/BDJ.3.e4878
Figure 3. - Tyloridastriata, vulva, dorsal view, specimen from India (BNHS Sp. 139) CD- copulatory duct, FD- fertilization duct, S- spermatheca (Scale=0.2 mm).
ScienceDex guides
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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.