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713 results for “population genetic structure”
Figure 3 in Genetic diversity, population structure and demographic history of Dugesia japonica in Taihang Mountains
Figure 3. Maximum likelihood (ML) and Bayesian inference (BI) phylogentic trees based on mitochondrial gene COI. Dugesia ryukyuensis (Genbank accession no. AB618488) serves as the outgroup. The broken lines denote inconsistent branches. Bootstrap percentages (BP,>50 only) of ML analysis and posterior probabilities (PP,>0.50 only) of Bayesian inference are shown above and below the branch, respectively. HG—haplogroup.
Fig. 2 in Microsatellite variation and population genetic structure of a neotropical endangered Bryconinae species Brycon insignis Steindachner, 1877: implications for its conservation and sustainable management
Fig. 2. UPGMA clustering of the Nei's genetic distance (1972) of the Brycon insignis sampling locations based on six microsatellite loci. Bootstrap values above 50% are shown above branches indicating percentage support in 5000 permutations. Power Company Hatchery (PCH), São João River (SJR), Paraíba do Sul River (PSR), Imbé River (IMR), Muriaé River (MUR) and Itabapoana River (ITR).
Data for: Amazonian birds in more dynamic habitats have less population genetic structure and higher gene flow
<p>Understanding the factors that govern variation in genetic structure across species is key to the study of speciation and population genetics. Genetic structure has been linked to several aspects of life history, such as foraging strategy, habitat association, migration distance, and dispersal ability, all of which might influence dispersal and gene flow. Comparative studies of population genetic data from species with differing life histories provide opportunities to tease apart the role of dispersal in shaping gene flow and population genetic structure. Here, we examine population genetic data from sets of bird species specialized on a series of Amazonian habitat types hypothesized to filter for species with dramatically different dispersal abilities: stable upland forest, dynamic floodplain forest, and highly dynamic riverine islands. Using genome-wide markers, we show that habitat type has a significant effect on population genetic structure, with species in upland forest, floodplain forest, and riverine islands exhibiting progressively lower levels of structure. Although morphological traits used as proxies for individual-level dispersal ability did not explain this pattern, population genetic measures of gene flow are elevated in species from more dynamic riverine habitats. Our results suggest that the habitat in which a species occurs drives the degree of population genetic structuring via its impact on long-term fluctuations in levels of gene flow, with species in highly dynamic habitats having particularly elevated gene flow. These differences in genetic variation across taxa specialized in distinct habitats may lead to disparate responses to environmental change or habitat-specific diversification dynamics over evolutionary time scales.</p>
Temporal population structure, a genetic dating method for ancient Eurasian genomes from the past 10,000 years
<p>Radiocarbon dating is the gold standard in archeology to estimate the age of skeletons, a key to studying their origins. Many published ancient genomes lack reliable and direct dates, which results in obscure and contradictory reports. Here, we developed the Temporal Population Structure (TPS), the first DNA-based dating method for ancient genomes ranging from the Late Mesolithic to modern-days, and applied it to 3,591 ancient and 1,307 modern Eurasians. We show that TPS predictions align with their known dates and correctly account for kin relationships. TPS dating of poorly dated Eurasian samples resolved conflicting reports in the literature, as illustrated by one test case. We demonstrated how TPS improved the ability to study phenotypic traits over time.</p>
Figure 5 Nav kdr 1016 and 1534 in Genetic study in Aedes (Stegomyia) aegypti (Linnaeus, 1762) from Londrina (Paraná State, Brazil): an approach to population structure and pyrethroid resistance
Figure 5 Nav kdr 1016 and 1534 site allele frequencies in UEL. Collection site locations were distributed in the three regions of the campus.
Figure 4 in Genetic study in Aedes (Stegomyia) aegypti (Linnaeus, 1762) from Londrina (Paraná State, Brazil): an approach to population structure and pyrethroid resistance
Figure 4 Allele frequencies of Nav kdr 1016 and 1534 genotyping distributed in the five different regions of Londrina.
Figure 3 in Genetic study in Aedes (Stegomyia) aegypti (Linnaeus, 1762) from Londrina (Paraná State, Brazil): an approach to population structure and pyrethroid resistance
Figure 3 Haplotypic network obtained through specimens collected in UEL. The circles are proportional to the number of specimens observed in each haplotype.
Figure 2 in Genetic study in Aedes (Stegomyia) aegypti (Linnaeus, 1762) from Londrina (Paraná State, Brazil): an approach to population structure and pyrethroid resistance
Figure 2 Haplotype network observed in five regions of Londrina. The circles are proportional to the number of specimens observed in each haplotype. The haplotypes observed are in bold. The numbers represent nucleotide change positions.
Figure 1 in Genetic study in Aedes (Stegomyia) aegypti (Linnaeus, 1762) from Londrina (Paraná State, Brazil): an approach to population structure and pyrethroid resistance
Figure 1 Collection sites in Londrina. The red line is a boundary between the five regions of the city. The yellow ones represent the streets and avenues of the city. Only the urban area was evaluated in the study.
Data and codes from "How does dispersal shape the genetic structure of animal populations in European cities? A simulation approach"
<p>Codes and data used for "Savary et al. How does dispersal shape the genetic structure of animal populations in European cities? A simulation approach".</p> <p> </p>
Population genomic evidence that stream networks structure genetic diversity in the narrowly endemic patch-nosed salamander (Urspelerpes brucei)
<p>Described in 2009, the Patch-nosed Salamander (<em>Urspelerpes brucei</em>) is a miniature species of lungless salamander with a geographic range of only ~45 km<sup>2</sup>. This species is endemic to the foothills of the Appalachian Mountains in extreme northeastern Georgia and northwestern South Carolina. The Tugaloo River—a waterway of some 50 m in width that forms the political boundary between the two states—bisects the tiny range of <em>U. brucei</em> and likely acts as a barrier to gene flow. Using RADcap data and a suite of complementary population genomic analyses, we evaluated the role that this river and its tributaries may play in enabling and/or interrupting gene flow among populations of <em>U. brucei</em>, and we investigated patterns of within-population and between-population genetic variation. Our results revealed a general pattern of isolation-by-stream distance and indicated that a population separated by the Tugaloo River is moderately more differentiated than what is explainable by stream distance alone. Unique in both its physiography and geologic history, this region in which <em>U. brucei</em> lives also harbors more than a dozen other species of lungless salamanders. Therefore, the genetic patterns that we have elucidated may have larger implications for differentiation among populations of other species with similar dispersal abilities.</p>
figure 1 in Spatial genetic structure in the Eurasian otter (Lutra lutra) meta-population from its core range in Italy
figure 1 The area surveyed for collection of otter samples (40° 40' N, 39° 37' N). Red spots indicate the location of the collected samples. The blue lines highlight the main rivers (order 1) and their tributaries (order 2, 3 and 4 according to waterway hierarchy). The continuous red lines represent regional boundaries. In the inset, the current otter distribution (inferred from Balestrieri et al., 2016, modified) is reported in orange and the study area is defined by the black bold square.
figure 4 in Spatial genetic structure in the Eurasian otter (Lutra lutra) meta-population from its core range in Italy
figure 4 Principal Component Analysis (pca) performed on microsatellite genotypes (dots). Circles show the well-defined spatial groups. A) pca according to the belonging of genotypes to the six river basins: the Cilento basin (green dots); the Agri basin (pink dots); the Sinni basin (blue dots); the Lao basin (red dots); the Basento basin (orange dots); the Abatemarco basin (violet dots); black dots indicate the samples outside of the main river basins. Dashed line indicates geographically contiguous but genetically different genotypes. B) pca according to clusters inferred by STRUCTURE: genotypes assigned unambiguously to K2 (green dots), to K3 (yellow dots), to K5 (violet dots). Grey dots represent samples with mixed genotypes assignable to K1 and K4.
figure 3 in Spatial genetic structure in the Eurasian otter (Lutra lutra) meta-population from its core range in Italy
figure 3 Genetic structure and distribution of the Italian otter genotypes in the study area. A) Estimated population structure based on the analysis of 11 microsatellite loci according to STRUCTURE (K = 5). Each bar represents a sample analysed. B) Geographic visualisation of genotypes in the study area performed using QGIS 3.4.1 software with base layers acquired from http://www.pnc.miniambiente. it/. Each circle represents a sample analysed. The colours indicate the percentage of assignment of an individual to each cluster: in blue, K1; in green, K2; in orange, K3; in red, K4; in violet, K5. The bold blue lines highlight the main rivers, while the tiny blue lines show all other waterways.
figure 6 Mantel test for A in Spatial genetic structure in the Eurasian otter (Lutra lutra) meta-population from its core range in Italy
figure 6 Mantel test for A) the correlation between geographic distance (GGDsq) and genetic distance (LinGD) (Rxy = 0.264, P = 0.0001) and for B) the correlation between resistance distance (a measure of ecological distance) (ECO500) and LinGD (Rxy = 0.217, P = 0.0001).
Structural genomic variation in the inbred Scandinavian wolf population contributes to the realized genetic load but is positively affected by immigration
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Simulating genetic mixing in strongly structured populations of the threatened southern brown bandicoot (Isoodon obesulus)
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Data from: Different genetic structures revealed resident populations of a specialist parasitoid wasp in contrast to its migratory host
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Population genomic evidence that stream networks structure genetic diversity in the narrowly endemic patch-nosed salamander (Urspelerpes brucei)
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Data and code from: Evaluating genomic offset predictions in a forest tree with high population genetic structure
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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