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Figure 1 in Molecular-genetic-based contribution to the taxonomy of the Acanthocyclops robustus group
Figure 1. Acanthocyclops vernalis (Fisher) female (A–D) and male (E) from the type locality, Peterhof, St Petersburg, Russia. (A) Abdomen ventral view; (B) antennal basipod frontal side; (C) antennal basipod caudal side; (D) Leg 4 with coxa and intercoxal sclerite; (E) furca, and Legs 5 and 6. Notes: D and E are from the same samples used for molecular genetics, registered in Table 1.
Figure 9 in Molecular-genetic-based contribution to the taxonomy of the Acanthocyclops robustus group
Figure 9. Phylogenetic relationships based on maximum-likelihood analysis of mitochondrial 12S rRNA sequences. Numbers beside nodes indicate bootstrap support values>70%. Individuals for which nucleotide sequences are determined in this work are shown in bold; they are identified by locality and the corresponding COI haplotype name registered in Table 1 and shown abbreviated in Figure 7. The set of added existing 12S sequences for comparative purposes are identified by country, locality (when coincident with our sequences), GenBank accession number and assigned taxonomcal nomenclature; they are all taken from Bláha et al. (2010).
Figure 4 in Six degrees of separation in barnacles? Assessing genetic variability in the sea-turtle epibiont Stomatolepas elegans (Costa) among turtles, beaches and oceans
Figure 4. Map of Teopa Beach and vicinity, Jalisco, Mexico (García and Ceballos 1994, p. 109). Teopa Beach is adjacent to the Chamela-Cuixmala Biosphere Reserve.
Figure 5. Minimum spanning haplotype network derived from a 658 base-pair cytochrome c oxidase subunit I in Six degrees of separation in barnacles? Assessing genetic variability in the sea-turtle epibiont Stomatolepas elegans (Costa) among turtles, beaches and oceans
Figure 5. Minimum spanning haplotype network derived from a 658 base-pair cytochrome c oxidase subunit I (COI) fragment from 57 Stomatolepas elegans collected from nine different Lepidochelys olivacea nesting on Playa Teopa, Jalisco, Mexico, six S. elegans from Caretta caretta from the western Atlantic, and six S. praegustator from C. caretta from the western Atlantic. Circle sizes are proportional to the frequency of each haplotype, with haplotype 1 being most common. Coloured pie slices are also proportional, and represent the number of S. elegans from each turtle characterized by the respective haplotype. Colours represent the nine Mexican turtles randomly sampled for S. elegans populations. Open circles with numbers indicate Atlantic haplotypes. Solid black circles designate hypothetical missing haplotypes. The network includes S. elegans haplotypes 1–21, and S. praegustator haplotypes 19, 26–30. Haplotypes 1–17, shown in colour, represent Jalisco, Mexico specimens collected from nine different turtles in the Pacific, and haplotypes 18–21 and 26–30, shown as unshaded circles, represent southeastern United States Atlantic specimens collected from six different C. caretta (see Table 1).
Figure 3 in Six degrees of separation in barnacles? Assessing genetic variability in the sea-turtle epibiont Stomatolepas elegans (Costa) among turtles, beaches and oceans
Figure 3. Lateral view of Stomatolepas elegans (Costa) (YPM IZ 41655), from external neck skin of an olive ridley turtle, Teopa Beach, Careyes, Jalisco, Mexico. Diameter 8.5 mm.
Figure 2 in Six degrees of separation in barnacles? Assessing genetic variability in the sea-turtle epibiont Stomatolepas elegans (Costa) among turtles, beaches and oceans
Figure 2. Lateral view of neotype of Stomatolepas elegans (Costa) (YPM IZ 42775), from external neck skin of a loggerhead turtle Nova Scotia, Canada. Diameter 6.64 mm.
Figure 1 in Six degrees of separation in barnacles? Assessing genetic variability in the sea-turtle epibiont Stomatolepas elegans (Costa) among turtles, beaches and oceans
Figure 1. Lateral view of Stomatolepas praegustator Pilsbry (YPM IZ 47956), from inside the gullet of a loggerhead turtle, Wassaw Island, Georgia, USA. Diameter 7.36 mm.
Figure 2 in Effects of fragmentation on genetic variation in populations of the terrestrial earthworm Drawida japonica Michaelsen, 1892 (Oligochaeta, Moniligastridae) in Shandong and Liaodong peninsulas, China
Figure 2. Maximum-parsimony (MP) tree of the unique haplotypes. Numbers at nodes represent bootstrap values (> 50) as a measure of support.
Figure 4 in Effects of fragmentation on genetic variation in populations of the terrestrial earthworm Drawida japonica Michaelsen, 1892 (Oligochaeta, Moniligastridae) in Shandong and Liaodong peninsulas, China
Figure 4. Mismatch curve of nucleotide pairwise based on 16S sequences. Exp, expected value; Obs, observed value.
Figure 3 in Effects of fragmentation on genetic variation in populations of the terrestrial earthworm Drawida japonica Michaelsen, 1892 (Oligochaeta, Moniligastridae) in Shandong and Liaodong peninsulas, China
Figure 3. Maximum-likelihood (ML) tree of the unique haplotypes. Bootstrap values (> 50) are shown on the nodes; Bayesian inference (BI) tree based on the analysis of the unique haplotypes. Posterior probability values (> 0.5) are shown.
Figure 5 in Effects of fragmentation on genetic variation in populations of the terrestrial earthworm Drawida japonica Michaelsen, 1892 (Oligochaeta, Moniligastridae) in Shandong and Liaodong peninsulas, China
Figure 5. Mismatch curve of nucleotide pairwise based on 28S sequences. Exp, expected value; Obs, observed value.
Figure 2 in Genetic differentiation and gene flow of Porcellionides pruinosus (Crustacea: Isopoda: Oniscidea) in Tunisia
Figure 2. Unweighted pair group method with arithmetic averages (UPGMA) dendrogram based on genetic distance values D (Nei 1978) between populations of Porcellionides pruinosus.
Figure 1 in Genetic differentiation and gene flow of Porcellionides pruinosus (Crustacea: Isopoda: Oniscidea) in Tunisia
Figure 1. Geographic localizations of the nine study populations of Porcellionides pruinosus from Tunisia: 1. Tabarka, 2. Korba, 3. Sahline, 4. Raccada, 5. Garat Nâam, 6. Gafsa, 7. Rdayef, 8. Gabes, 9. Tamerza.
Agriculture creates subtle genetic structure among migratory and non-migratory populations of burrowing owls throughout North America
Population structure across a species distribution primarily reflects historical, ecological and evolutionary processes. However, large-scale contemporaneous changes in land use have the potential to create changes in habitat quality and thereby cause changes in gene flow, population structure, and distributions. As such, land-use changes in one portion of a species range may explain declines in other portions of their range. For example, many burrowing owl populations have declined or become extirpated near the northern edge of the species' breeding distribution during the second half of the 20th century. In the same period, large extensions of thornscrub were converted to irrigated agriculture in northwestern Mexico. These irrigated areas may now support the highest densities of burrowing owls in North America. We tested the hypothesis that burrowing owls that colonized this recently created owl habitat in northwestern Mexico originated from declining migratory populations from the northern portion of the species' range (migration-driven breeding dispersal whereby long-distance migrants from Canada and the U.S. became year-round residents in the newly created irrigated agriculture areas in Mexico). We used 10 novel microsatellite markers to genotype 1,560 owls from 36 study locations in Canada, Mexico, and the United States. We found that burrowing owl populations are practically panmictic throughout the entire North American breeding range. However, an analysis of molecular variance provided some evidence that burrowing owl populations in northwestern Mexico and Canada together are more genetically differentiated from the rest of the populations in the breeding range, lending some support to our migration-driven breeding dispersal hypothesis. We found evidence of subtle genetic differentiation associated with irrigated agricultural areas in southern Sonora and Sinaloa in northwestern Mexico. Our results suggest that land-use can produce location-specific population dynamics leading to subtle genetic structure even in the absence of dispersal barriers.
Figure 2 in Complete mitochondrial genome of Tetraophasis szechenyii Madarász, 1885 (Aves: Galliformes: Phasianidae), and its genetic variation as inferred from the mitochondrial DNA Control Region
Figure 2. Median-joining network of all the control region haplotypes found in Tetraophasis szechenyii. Notes: Missing haplotypes in the network are represented by black dots; circle sizes are proportional to the number of individuals sharing the same haplotypes (n); each mutation step is shown as a short line connecting neighbouring haplotypes; numbers of mutations between haplotypes are indicated near branches if greater than 1.
Figure 1 in Complete mitochondrial genome of Tetraophasis szechenyii Madarász, 1885 (Aves: Galliformes: Phasianidae), and its genetic variation as inferred from the mitochondrial DNA Control Region
Figure 1. Molecular phylogenetic tree derived from the complete DNA sequences of 12 mitochondrial protein-coding genes using Bayesian inference and maximum likelihood analyses. Notes:The numbers beside the nodes are Bayesian posterior probabilities (≥ 0.9 retained) and bootstrap proportions of maximum likelihood analyses calculated with 100 replicates (≥ 50% retained); Anas platyrhynchos and Alectura lathami were set as outgroups; *clades not supported by Bayesian inference.
Figure 3. Ninety-five per cent maximum parsimony networks obtained for the 12 in Mitochondrial DNA genetic variation and phylogeography of the recently described vole species Proedromys liangshanensis Liu, Sun, Zeng and Zhao, 2007 (Rodentia: Arvicolinae)
Figure 3. Ninety-five per cent maximum parsimony networks obtained for the 12 haplotypes in Proedromys liangshanensis. Circle sizes are proportional to haplotype frequencies.
Figure 2 in Mitochondrial DNA genetic variation and phylogeography of the recently described vole species Proedromys liangshanensis Liu, Sun, Zeng and Zhao, 2007 (Rodentia: Arvicolinae)
Figure 2. Bayesian inference tree using the TrN+I model depicting the relationship of Proedromys liangshanensis. The phylogenetic tree was rooted using Microtus kikuchii. Numbers represent node supports inferred from Bayesian posterior probabilities, neighbour joining and maximum parsimony bootstrap analyses.
Figure 4 in Mitochondrial DNA genetic variation and phylogeography of the recently described vole species Proedromys liangshanensis Liu, Sun, Zeng and Zhao, 2007 (Rodentia: Arvicolinae)
Figure 4. Expected (solid line) and observed (broken lines) mismatch distribution of Proedromys liangshanensis.
Figure 1 in Mitochondrial DNA genetic variation and phylogeography of the recently described vole species Proedromys liangshanensis Liu, Sun, Zeng and Zhao, 2007 (Rodentia: Arvicolinae)
Figure 1. Sampling localities of Proedromys liangshanensis. The filled circles represent the 12 haplotypes.
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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.