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262 results for “Genetic variability”
Fig. 1 in Morphological and genetic variability of Cotesia tibialis species complex (Hymenoptera: Braconidae: Microgastrinae)
Fig. 1. Fore wing of C. tibialis. (A) Wing venation. Nomenclature follows Sharkey and Wharton (1997). Wing veins: C, costa; Cu, cubitus; M, media; SC, subcosta; R, radius (metacarpus); A, analis; m-cu, transverse medio cubital vein; cu-a, transverse cubital-anal vein; Cells: I – marginal, II – first submarginal, III – first discal, IV – basal, V – subbasal, VI first subdiscal, VII – second and third submarginal, VIII – second discal, IX – second subdiscal, X – anal; (B) Set of landmarks.
Fig. 6 in Morphological and genetic variability of Cotesia tibialis species complex (Hymenoptera: Braconidae: Microgastrinae)
Fig. 6. Phylogenetic tree of nine Cotesia tibialis morphotypes based on the COI gene obtained using the Maximum Likelihood (ML) method. Numbers at nodes represent bootstrap value (%); C. ofella and C. glomerata represent outgroups. The average genetic distance between the three main phylogenetic lines is shown as a percentage.
Fig. 5 in Morphological and genetic variability of Cotesia tibialis species complex (Hymenoptera: Braconidae: Microgastrinae)
Fig. 5. PCA plot of 14 C. tibialis morphotypes. Distribution of fore wings in the morphospace defined by the first two principal components. Ellipses account for a confidence interval of 0.7. The blue outline shows the fore wing shape for the maximum positive or negative score along the PC1 and PC2 axes, while the pink outline explains the average shape fore wing. All changes in wing shape were enlarged 2 times. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Genetic variability among and within domestic Old and New World Camels at the α-lactalbumin gene (LALBA) reveals new alleles and polymorphisms responsible for differential expression
<p><strong>Supplementary Figure 1</strong>. Schematic representation of the <em>LALBA</em> gene in camelids. Boxes indicate the exons (black for the un-translated, white for the signal peptide and grey for the translated). The line shows introns, 5’- and 3’-flanking regions.</p> <p><strong>Supplementary Figure 2</strong><strong>. </strong>Alignment of the α-LA calcium-binding site (red rectangle) of 23 terrestrial species available in the UniProt database (<a href="http://www.uniprot.org/uniprot/">http://www.uniprot.org/uniprot/</a>). The calcium-binding site in the <em>Camelus dromedarius</em> is indicated by a yellow rectangle.</p>
Stoneflies of Medvednica Nature Park: genetic diversity and morphological variability
<p>Stoneflies of Medvednica Nature Park: genetic diversity and morphological variability</p> <p>Version 2: corrected the sequence lengths</p>
Environmental and geographic variables are effective surrogates for genetic variation in conservation planning
<p><strong>Environmental and geographic variables are effective surrogates for genetic variation in conservation planning</strong></p> <p>Jeffrey O. Hanson, Jonathan R. Rhodes, Cynthia Riginos, Richard A. Fuller.</p> <p>Correspondence should be addressed to jeffrey.hanson@uqconnect.edu.au.</p> <p><em>Research summary</em></p> <p>Protected areas buffer species from anthropogenic threats and provide places for the processes that generate and maintain biodiversity to continue. However, genetic variation, the raw material for evolution, is difficult to capture in conservation planning, not least because genetic data require considerable resources to obtain and analyze. Here we show that freely available environmental and geographic distance variables can be highly effective surrogates in conservation planning for representing adaptive and neutral intra-specific genetic variation. We obtained occurrence and genetic data from the IntraBioDiv project for 27 plant species collected over the European Alps using a gridded sampling scheme. For each species, we identified loci that were potentially under selection using outlier loci methods, and mapped their main gradients of adaptive and neutral genetic variation across the grid cells. We then used the cells as planning units to prioritize protected area acquisitions. First, we verified that the spatial patterns of environmental and geographic variation were correlated, respectively, with adaptive and neutral genetic variation. Second, we showed that these surrogates can predict the proportion of genetic variation secured in randomly generated solutions. Finally, we discovered that solutions based only on surrogate information secured substantial amounts of adaptive and neutral genetic variation. Our work paves the way for widespread integration of surrogates for genetic variation into conservation</p> <p><em>Overview</em></p> <p>This repository contains the data and source code that underpins the findings in our manuscript "Environmental and geographic variables are effective surrogates for genetic variation in conservation planning".</p> <p>To rerun all computational analyses, run `make clean && make all`.</p> <ul> <li>article <ul> <li>manuscript main text, figures, and supporting information</li> </ul> </li> <li>code <ul> <li>R: scripts used to run the analysis</li> <li>parameters: files used to run analysis in TOML format</li> <li>rmarkdown: files used to compile them manuscript</li> </ul> </li> <li>data <ul> <li>raw: raw data used to run the analysis</li> <li>intermediate_ results generated during processing</li> <li>final: results used in the paper</li> </ul> </li> </ul> <p><em>Software required</em></p> <ul> <li>Operating system <ul> <li>Ubuntu (Trusty 14.04 LTS)</li> </ul> </li> <li>Programs <ul> <li>[R (version 3.3.2)](https://www.r-project.org)</li> <li>GNU make</li> <li>pandoc (version 1.16.0.2+)</li> <li>Gurobi (version 7.0.2; academic licenses are available for no cost)</li> <li>LaTeX</li> </ul> </li> </ul>
Genetic diversity of Avena ventricosa populations along an ecogeographical transect in Cyprus is correlated to environmental variables
<p>genetic data</p>
Interspecific gene exchange introduces high genetic variability in crop pathogen - dataset and scripts
<p>The files found in this repository are the one used for generating the analyses presented in the Feurtey et al. manuscript submission accepted in GBE.</p>
Widespread naturally variable human exons aid genetic interpretation (Preprint)
<p>Supplemental files for Widespread naturally variable human exons aid genetic interpretation (<a href="https://www.biorxiv.org/content/10.1101/2024.09.09.612029v1">Preprint link</a>)</p> <p> </p> <p>For newest version, click here: <a href="https://zenodo.org/records/15790343">https://zenodo.org/records/15790343</a></p> <p> </p>
Fig. 4 A in DNA barcoding and genetic variability of earthworms (Clitellata: Oligochaeta) with new records from Mizoram, India
Fig. 4 A MP tree showing 145 COI barcodes with Drawida japon- ▸ ica as out-group. Figure 4B BI tree with 145 COI barcodes with D. japonica as out-group
Fig. 3 in DNA barcoding and genetic variability of earthworms (Clitellata: Oligochaeta) with new records from Mizoram, India
Fig. 3 Barcode gap results: a ABGD analysis showing percentage of intra and interspecific divergence with 10–12% barcode gap; b BGA analysis shows percentage of interspecific divergence
Fig. 5 in DNA barcoding and genetic variability of earthworms (Clitellata: Oligochaeta) with new records from Mizoram, India
Fig. 5 Haplotype networking of 20 earthworm species of Mizoram, NER. In the network, each haplotype is represented by a circle, and the size of the circle is directly proportional to the number of homozygous haplotypes. The undetected haplotypes are indicated in small red circles, while the different colors indicate different haplotypes of 20 earthworm species. The out-group D. japonica is represented in a black circle (for more information, see Table 4)
Data from: Non-equilibrium conditions explain spatial variability in genetic structuring of little penguin (Eudyptula minor)
Factors responsible for spatial structuring of population genetic variation are varied, and in many instances there may be no obvious explanations for genetic structuring observed, or those invoked may reflect spurious correlations. A study of little penguins (Eudyptula minor) in southeast Australia documented low spatial structuring of genetic variation with the exception of colonies at the western limit of sampling, and this distinction was attributed to an intervening oceanographic feature (Bonney Upwelling), differences in breeding phenology, or sea level change. Here, we conducted sampling across the entire Australian range, employing additional markers (12 microsatellites and mitochondrial DNA, 697 individuals, 17 colonies). The zone of elevated genetic structuring previously observed actually represents the eastern half of a genetic cline, within which structuring exists over much shorter spatial scales than elsewhere. Colonies separated by as little as 27 km in the zone are genetically distinguishable, while outside the zone, homogeneity cannot be rejected at scales of up to 1400 km. Given a lack of additional physical or environmental barriers to gene flow, the zone of elevated genetic structuring may reflect secondary contact of lineages (with or without selection against interbreeding), or recent colonization and expansion from this region. This study highlights the importance of sampling scale to reveal the cause of genetic structuring.
Data from: Structure and genetic variability of golden mussel (Limnoperna fortunei) populations from Brazilian reservoirs
The golden mussel, Limnoperna fortunei a highly invasive species in Brazil, has generated productive, economical, and biological impacts. To evaluate genetic structure and variability of L. fortunei populations present in fish farms in the reservoirs of Canoas I (CANFF), Rosana (ROSFF), and Capivara (CAPFF) (Paranapanema river, Paraná, Brazil), eight microsatellite loci were amplified. Five of those eight loci resulted in 38 alleles. The observed heterozygosity (Ho) was lower than the expected heterozygosity (He) in all populations, with a deviation from the Hardy-Weinberg equilibrium (HWE). The average value for the inbreeding coefficient (Fis) was positive and significative for all populations. There was higher genetic variability within populations than among them. The fixation index (Fst) showed a small genetic variability among these populations. The occurrence of gene flow was identified in all populations, along with the lack of a recent bottleneck effect. The clustering analysis yielded K = 2, with genetic similarity between the three populations. The results demonstrate low genetic structure and suggest a founding population with greater genetic variability (ROSFF). Our data point to the possible dispersal of L. fortunei aided by anthropic factors in the upstream direction. It was concluded that the three populations presented a unique genetic pool for Paranapanema river, with occurrence of gene flow.
Data from: The ghost of introduction past: spatial and temporal variability in the genetic diversity of invasive smallmouth bass
Understanding the demographic history of introduced populations is essential for unravelling their invasive potential and adaptability to a novel environment. To this end, levels of genetic diversity within the native and invasive range of a species are often compared. Most studies, however, focus solely on contemporary samples, relying heavily on the premise that the historic population structure within the native range has been maintained over time. Here, we assess this assumption by conducting a three-way comparison of the genetic diversity of native (historic and contemporary) and invasive (contemporary) smallmouth bass (Micropterus dolomieu) populations. Analyses of a total of 572 M. dolomieu samples, representing the contemporary invasive South African range, contemporary and historical native USA range (dating back to the 1930s when these fish were first introduced into South Africa), revealed that the historical native range had higher genetic diversity levels when compared to both contemporary native and invasive ranges. These results suggest that both contemporary populations experienced a recent genetic bottleneck. Furthermore, the invasive range displayed significant population structure, whereas both historical and contemporary native USA populations revealed higher levels of admixture. Comparison of contemporary and historical samples showed both a historic introduction of M. dolomieu, as well as a more recent introduction, thereby demonstrating that undocumented introductions of this species have occurred. Although multiple introductions might have contributed to the high levels of genetic diversity in the invaded range, we discuss alternative factors that may have been responsible for the elevated levels of genetic diversity and highlight the importance of incorporating historic specimens into demographic analyses.
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.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.