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Figure 3 in Well-known species, unexpected results: high genetic diversity in declining Vipera ursinii in central, eastern and southeastern Europe
Figure 3. Genetic relationship between locations calculated using Cavalli-Sforza and Edwards Dc distance (Cavalli-Sforza and Edwards, 1967) using the software POPULATIONS 1.2.28 (Langella, 1999). The distances were calculated with 5 microsatellite markers and branches with bootstrap support>40 were indicated.
Figure 2 in Well-known species, unexpected results: high genetic diversity in declining Vipera ursinii in central, eastern and southeastern Europe
Figure 2. Maximum-likelihood tree from combined data (Cytochrome b and ND4, totalling 1920 bp) for different subspecies of Vipera ursinii. Values of bootstrap support for maximum likelihood (first) maximum parsimony (middle) are shown for nodes found in more than 50% of 1000 trees, as well as posterior probability from Bayesian inference (right). The population number (see fig. 1 and supplementary table S1) where the haplotypes have been found are added to the haplotype label. Drawing of Vipera ursinii rakosiensis courtesy of Márton Zsoldos.
Figure 1 in Well-known species, unexpected results: high genetic diversity in declining Vipera ursinii in central, eastern and southeastern Europe
Figure 1. Location of the samples used in the study: squares represent mtDNA data, round symbols represent microsatellites data. The size of the round symbols is proportional to the number of samples used. Locality numbers correspond with supplementary table S1 (in black when microsatellite data are available; in white when mtDNA data). The colours of the marks are different between subspecies: green: V. ursinii rakosiensis, yellow: V. u. moldavica, blue: V. u. macrops, grey: V. u. macrops from Bistra Mt., red: V. renardi. White striped grids show distribution of each subspecies/species on a 100x100 UTM grid resolution (after Sillero et al., 2014). Distribution area of V. greaca (from IUCN red list, Mizsei et al., 2018) is colored in pink. On the top left, insert A shows a zoom in the V. ursinii macrops region, while insert B illustrates the location of study area within Europe.
Figure 4. Comparative phylogenetic relationship between the 11 in Well-known species, unexpected results: high genetic diversity in declining Vipera ursinii in central, eastern and southeastern Europe
Figure 4. Comparative phylogenetic relationship between the 11 regions with both mtDNA (left) and nDNA (right). left: Mitochondrial DNA tree based on the genetic distances of the different haplotypes (combining cytochrome b and ND4; 1920 bp) within each region. right: Nuclear tree based on Cavalli-Sforza and Edwards Dc distances (Cavalli-Sforza and Edwards, 1967) calculated with the software POPULATIONS 1.2.28 (Langella, 1999) based on 5 microsatellites markers. Dashed branches correspond to discrepancies between both phylogenetic reconstructions. Both trees were not rooted. The colours are different between subspecies: green: V. ursinii rakosiensis, yellow: V. u. moldavica, blue: V. u. macrops, grey: V. u. macrops from Bistra Mt., red: V. renardi.
Supplementary material 2 from: Nazari V, Lukhtanov VA, Naderi A, Fric ZF, Dincă V, Vila R (2023) More hidden diversity in a cryptic species complex: a new subspecies of Leptidea sinapis (Lepidoptera, Pieridae) from Northern Iran. Comparative Cytogenetics 17: 113-128. https://doi.org/10.3897/compcytogen.17.102830
Length of male genitalia components (phallus, saccus, vinculum) and ratios (PL/VW, SL/VW) among Leptidea specimens measured in this study
Predicting species richness and diversity using satellite remote sensing and random forest machine learning algorithm
<p><strong>Aims</strong>: Remote sensing approaches could be beneficial for monitoring and compiling essential biodiversity data because it is cost-effective and allows for coverage of large areas over a short period. This study investigated the relationship between multispectral remote sensing data from Landsat 8 and Sentinel 2 and species richness and diversity in mountainous and protected grasslands.</p> <p><strong>Locations</strong>: Golden Gate Highlands National Park, Free State, South Africa. </p> <p><strong>Methods</strong>: In-situ data of plant species composition and cover from 142 plots with 16 releves each were distributed across the study site and used to calculate species richness and Shannon-wiener species diversity index (species diversity. We used a machine-learning random forest algorithm to optimise the prediction of species richness and diversity. The algorithm was used to identify the optimal spectral bands and vegetation indices for estimating species richness and diversity. Subsequently, the selected bands and vegetation indices were used to estimate species richness through random forest regression. </p> <p><strong>Results</strong>: This research found weak relationships between remote sensing vegetation indices and the diversity metrics, but significant relationships were found between some spectral bands and diversity metrics. Moreover, using machine learning random forest, the multispectral datasets exhibited strong predictive powers. In this investigation, for both sensors, near-infrared (NIR) seemed to be the most selected band to explain species diversity in mountainous grasslands.</p> <p><strong>Main</strong> <strong>conclusions</strong>: This finding further ascertains the efficiency of using NIR in vegetation mapping. This research shows that NIR, SAVI and EVI are the most adequate for predicting species richness and diversity in mountainous grasslands with relatively good accuracies.</p>
Data for Selfing species has greater genetic diversity and less structure than related outcrossing species due to seed dispersal and population history in Roscoea (Zingiberaceae)
<p>Data matrix of two species with nexus format.</p>
Supplementary material 1 from: Lee W, Kim JS, Seo CW, Lee JW, Kim SH, Cho Y, Lim YW (2023) Diversity of Cladosporium (Cladosporiales, Cladosporiaceae) species in marine environments and report on five new species. MycoKeys 98: 87-111. https://doi.org/10.3897/mycokeys.98.101918
Strains of Cladosporium isolated in this study with detailed information on habitats and regions and GenBank accession numbers
FIGURE 4 in Relative Genetic Homogeneity within a Phenotypically Diverse group: The Case of Lake Tana Labeobarbus (Cyprinidae) Species Flock, Ethiopia
FIGURE 4. An unrooted Neighbor Joining tree of Lake Tana Labeobarbus species constructed by APE (an R-package) using pair-wise genetic distances among individuals based on analysis of 10 microsatellite loci. Bootstrap percentage values shown at nodes are from 100 pseudo-replicates and indicate the level of support for each group; only bootstrap values higher than 50% are indicated.
FIGURE 3 in Relative Genetic Homogeneity within a Phenotypically Diverse group: The Case of Lake Tana Labeobarbus (Cyprinidae) Species Flock, Ethiopia
FIGURE 3. Scatter plot from Discriminant analysis of Principal Components (DPC; Jombart et al. 2010) of microsatellite data from 161 Lake Tana Labeobarbus individuals. The 95% confidence ellipses of each group based on the common covariance matrix assumption for the multivariate normal distribution are presented. Points represent individual genotypes and symbols represent species (A) or spawning populations (B). A. Using species as grouping factor with asterisks representing the estimated means for each population: Pop A = L. acutirostris, Pop B = L. brevicephalus, Pop C = L. crassibarbus, Pop D = L. gorgorensis, Pop E = L. gorguari, Pop F = L. intermedius, Pop G = L. longissimus, Pop H = L. macrophtalmus, Pop I = L. megastoma, Pop J = L. nedgia, Pop K = L. platydorsus, Pop L = L. surkis, Pop M = L. tsanensis, Pop N = L. truttiformis. B. Using spawning strategy as grouping criteria: ▲riverine spawning population, ●lacustrine spawning population.
FIGURE 2 in Relative Genetic Homogeneity within a Phenotypically Diverse group: The Case of Lake Tana Labeobarbus (Cyprinidae) Species Flock, Ethiopia
FIGURE 2. Bayesian clustering assignment of individuals based on Structure analysis of 10 microsatellite loci of Labeobarbus populations from Lake Tana and adjacent water bodies. A. Analysis of whole dataset without a priori population definitions (K=1–15). B. Lake Tana Labeobarbus populations only with K = 2 inferred clusters. 1. L. acitirostris, 2. L. brevicephalus, 3. L. crassibarbus, 4. L. gorgorensis, 5. L. gorguari, 6. L. longissimus, 7. L. macrophtalmus, 8. L. megastoma, 9. L. nedgia, 10. L. platydorsus, 11. L. surkis, 12. L. truttiformis, 13. L. tsanensis, 14. L. intermedius, 15. L. beso.
FIGURE 1. A in Relative Genetic Homogeneity within a Phenotypically Diverse group: The Case of Lake Tana Labeobarbus (Cyprinidae) Species Flock, Ethiopia
FIGURE 1. A. Map showing major river drainages of Ethiopia; B. Map of Lake Tana. All samples of Lake Tana were drawn from sites (area lying within the red rectangle) located in the Bahir Dar Gulf.
FIGURE 7 in The genus Paraxantia Liu & Kang (Orthoptera: Tettigoniidae: Phaneropterinae Vosiini) in Himalaya-Hengduan Mountains, revealing high diversity of species
FIGURE 7. Distribution map of the Paraxantia spp. in Himalaya-Hengduan Mountains. ■: P. tibetensis; ●: P. angustipennis sp. nov.; ◆: P. nujiangensis sp. nov.; ▲: P. rubripes; ★: P. kaquewa.
FIGURE 4 in The genus Paraxantia Liu & Kang (Orthoptera: Tettigoniidae: Phaneropterinae Vosiini) in Himalaya-Hengduan Mountains, revealing high diversity of species
FIGURE 4. Male right side of the cerci and external genitalia of Paraxantia spp.. A–C: cercus; D–F: male external genitalia; G– J: titillator. A, D, G: P. tibetensis; B, E, H: P. angustipennis sp. nov.; C, F, I: P. nujiangensis sp. nov.; J: P. daweishanensis.
FIGURE 5 in The genus Paraxantia Liu & Kang (Orthoptera: Tettigoniidae: Phaneropterinae Vosiini) in Himalaya-Hengduan Mountains, revealing high diversity of species
FIGURE 5. Male fore tibiae and female ovipositor of Paraxantia spp.. A–E: fore tibia; F, G: ovipositor; A: P. rubripes; B: P. kaquewa; C: P. tibetensis; D, F, G: P. angustipennis sp. nov.; E: P. nujiangensis sp. nov..
Figure 10 in A review of Augochlora (Oxystoglossella) bees from South America: unexpected Amazonian diversity and assessment of vulnerable species
Figure 10. Augochlora phoenicis (Vachal, 1911): (A) small female, frontal view of head; (B) mediumsized female, frontal view of head; (C) large, macrocephalic female, frontal view of head; (D) male, frontal view of head; € male, lateral view of mesosoma; (F) male, dorsal view of metasoma. Scale bar: 1.0 mm, all at same scale.
Figure 5 in A review of Augochlora (Oxystoglossella) bees from South America: unexpected Amazonian diversity and assessment of vulnerable species
Figure 5. Augochlora lamellata sp. nov.: (A–C) holotype female; (D–F) paratype male, (A) Female, oblique view of head, arrow: lamellate preoccipital carina; (B) female, dorsal view of mesosoma; (C) female, dorsal view of metasoma; (D) male, lateral view of head, arrow: lamellate preoccipital carina; (E) male, dorsal view of mesosoma; (F) male, dorsal view of metasoma. Scale bar: 1.0 mm, all at same scale.
Figure 4 in A review of Augochlora (Oxystoglossella) bees from South America: unexpected Amazonian diversity and assessment of vulnerable species
Figure 4. Augochlora laevinota sp. nov.: (A–C) holotype female; (D–F) paratype male. (A) Female, frontal view of head; (B) female, dorsal view of mesosoma; (C) female, dorsal view of metasoma; (D) male, frontal view of head; (E) male, dorsal view of mesosoma; (F) male, dorsal view of metasoma. Scale bar: 1.0 mm, all at same scale.
Figure 2 in A review of Augochlora (Oxystoglossella) bees from South America: unexpected Amazonian diversity and assessment of vulnerable species
Figure 2. Theholotype female of Augochlora brevipilosa sp.nov., (A) frontal view of head; (B) dorsal view of mesosoma; (C) obliqueview of habitus, red arrows showing S4–5 short pubescence. Scale bar: 1.0 mm, all at same scale.
Figure 7 in A review of Augochlora (Oxystoglossella) bees from South America: unexpected Amazonian diversity and assessment of vulnerable species
Figure 7. The holotype female of Augochlora meloi sp. nov.: (A) frontal view of head; (B) dorsal view of mesosoma; (C) dorsal view of metasoma. Scale bar: 1.0 mm, all at same scale.
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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.