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1,467 results for “Genetic structure”
Supplementary material 4 from: Aketarawong N, Isasawin S, Sojikul P, Thanaphum S (2015) Gene flow and genetic structure of Bactrocera carambolae (Diptera, Tephritidae) among geographical differences and sister species, B. dorsalis, inferred from microsatellite DNA data. In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 239-272. https://doi.org/10.3897/zookeys.540.10058
Comparisons among three different the individual admixture plots: Explanation note: Comparisons among the individual admixture plots of 289 individuals, for K = 3, considering correlated allele frequency, uncorrelated allele frequency, and missing data as recessive homozygotes for the null alleles, respectively.
Supplementary material 3 from: Aketarawong N, Isasawin S, Sojikul P, Thanaphum S (2015) Gene flow and genetic structure of Bactrocera carambolae (Diptera, Tephritidae) among geographical differences and sister species, B. dorsalis, inferred from microsatellite DNA data. In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 239-272. https://doi.org/10.3897/zookeys.540.10058
Component data at the four successive thresholds used to illustrate Figure 6: Explanation note: Component data are used to illustrate the structure of the subset of the Salaya5 strain and wild populations. The highest Betweenness-centrality is highlighted in blue.
Supplementary material 3 from: Astuti G, Roma-Marzio F, D'Antraccoli M, Bedini G, Carta A, Sebastiani F, Bruschi P, Peruzzi L (2017) Conservation biology of the last Italian population of Cistus laurifolius (Cistaceae): demographic structure, reproductive success and population genetics. Nature Conservation 22: 169-190. https://doi.org/10.3897/natureconservation.22.19809
Supplementary material 3 from: Astuti G, Roma-Marzio F, D'Antraccoli M, Bedini G, Carta A, Sebastiani F, Bruschi P, Peruzzi L (2017) Conservation biology of the last Italian population of Cistus laurifolius (Cistaceae): demographic structure, reproductive success and population genetics. Nature Conservation 22: 169-190. https://doi.org/10.3897/natureconservation.22.19809
Supplementary material 2 from: Astuti G, Roma-Marzio F, D'Antraccoli M, Bedini G, Carta A, Sebastiani F, Bruschi P, Peruzzi L (2017) Conservation biology of the last Italian population of Cistus laurifolius (Cistaceae): demographic structure, reproductive success and population genetics. Nature Conservation 22: 169-190. https://doi.org/10.3897/natureconservation.22.19809
Supplementary material 2 from: Astuti G, Roma-Marzio F, D'Antraccoli M, Bedini G, Carta A, Sebastiani F, Bruschi P, Peruzzi L (2017) Conservation biology of the last Italian population of Cistus laurifolius (Cistaceae): demographic structure, reproductive success and population genetics. Nature Conservation 22: 169-190. https://doi.org/10.3897/natureconservation.22.19809
Supplementary material 1 from: Astuti G, Roma-Marzio F, D'Antraccoli M, Bedini G, Carta A, Sebastiani F, Bruschi P, Peruzzi L (2017) Conservation biology of the last Italian population of Cistus laurifolius (Cistaceae): demographic structure, reproductive success and population genetics. Nature Conservation 22: 169-190. https://doi.org/10.3897/natureconservation.22.19809
Supplementary material 1 from: Astuti G, Roma-Marzio F, D'Antraccoli M, Bedini G, Carta A, Sebastiani F, Bruschi P, Peruzzi L (2017) Conservation biology of the last Italian population of Cistus laurifolius (Cistaceae): demographic structure, reproductive success and population genetics. Nature Conservation 22: 169-190. https://doi.org/10.3897/natureconservation.22.19809
Strong genetic structure and divergence of marginal populations of black poplar in Poland
<p><strong>The dataset comprises nuclear microsatellite data (PCR products lengths) used in the paper "Strong genetic structure and divergence of marginal populations of black poplar in Poland".</strong></p> <p>Abstract: Genetic diversity is crucial to secure the survival and sustainability of ecosystems. Given anthropogenic pressure, as well as the projected alterations connected with the level and circulation of water, riparian forests are of particular concern. In this paper, we assessed the genetic variation of black poplar – one of the keystone tree species of riverine forests. The natural habitats of black poplar have been severely transformed leading to a significant decline of its population size. Using a set of 18 nuclear microsatellites and geographic location data, we studied 26 remnant populations (1,261 trees) located along the biggest river valleys in Poland. Our main goal was to assess the overall genetic variation and to verify if range fragmentation and habitat transformation have disrupted gene exchange among populations. Genotyping revealed that 261 trees were clones. The level of clonality was generally higher in the two most transformed river valleys (the Oder and Warta). All populations have probably gone through a drastic genetic bottleneck in the distant past, and most of them have low effective population sizes. Still, the overall level of genetic variation remains high, but certain populations require attention due to their lower genetic variation, higher clonality and strong spatial genetic structure. Genetic differentiation was low, yet Bayesian clustering supported the existence of 11 separate gene pools. According to the results, the intensity of gene exchange is very low and limited to adjacent stands. Relatively free gene flow occurs only along the Vistula, particularly in its middle section which is characterized by the highest genetic variation. The greatest genetic structuring was observed along the Oder. Populations located at the range margin had unique gene pools and showed signs of genetic divergence and reduction of variation caused by genetic drift. We conclude that human activities have seriously impacted the gene pool of black poplar in Poland by disrupting landscape connectivity and preventing the species from generative reproduction. The study provides practical guidelines on how to develop and implement the conservation program for the gene pool of black poplar in Poland.</p>
Data For: City divided: Unveiling family ties and genetic structuring of coyotes in Seattle
<p>Included in the zip file:</p> <ol> <li>ReadMe file</li> <li>R Project</li> <li>3 R Markdown Files</li> <li>All necessary scripts and data files needed to replicate analyses</li> </ol>
Role of environmental factors in the genetic structure of a highly mobile seabird
<p><strong><span>Aim:</span></strong><span> Environmental features can act as selection pressures and barriers to gene flow between populations. The genetic structuring of highly mobile but philopatric seabirds creates a paradox, and the role of oceanographic and geographic variables is still poorly understood. In this study, we investigate the influence of environmental and geographic variables in the genetic and phenotypic diversity of a pantropical seabird breeding in islands and archipelagos separated by different geographic distances, up to thousand kilometers, and which differ in environmental characteristics.</span></p> <p><strong><span>Location:</span></strong><span> Islands and archipelagos in the southwestern Atlantic Ocean.</span></p> <p><strong><span>Taxon:</span></strong><span> <em>Sula dactylatra</em><span>, Lesson, 1831 (masked booby)</span><em>.</em></span></p> <p><strong><span>Methods:</span></strong><span> </span><span>The population structure of the species was accessed through mitochondrial and nuclear DNA. To test Isolation by Environment (IBE) <em>vs</em>. by Distance (IBD), sea surface temperature, primary productivity, and salinity, as well as isotopic niche based on carbon and nitrogen, and distances between colonies and from the continent, were used. We also tested the correlation between the genetic structure and the morphometry of individuals in each colony.</span></p> <p><strong><span>Results:</span></strong><span> We identified the presence of low genetic structure between populations. Nevertheless, differences were identified between inshore and offshore colonies, with the influence of landscape characteristics of these two types of environment. The morphometric and isotopic niche variations are consistent with this segregation. </span></p> <p><strong><span>Main conclusions:</span></strong><span> Environmental variables of coastal and oceanic environments seem to influence the genetic structure of masked boobies, </span><span>even though it is low in the SW Atlantic Ocean</span><span>, highlighting the role of environmental heterogeneity in shaping biodiversity.</span></p>
FIGURE 9 in Phylogeography, genetic structure and wing pattern variation of Erebia pronoe (Esper, 1780) (Lepidoptera: Nymphalidae) in Europe
FIGURE 9. Scatterplot of Canonical variate analysis. Morphometric overlap in hindwings of E. pronoe structure according to canonical variates CV1 and CV2. IT: Italy; SL: Slovenia; SP: Spain; SR: Slovakia.
FIGURE 7 in Phylogeography, genetic structure and wing pattern variation of Erebia pronoe (Esper, 1780) (Lepidoptera: Nymphalidae) in Europe
FIGURE 7. PCA analysis. Scatterplot of the 'Eyespot dataset' showing group differences. IT: Italy; SL: Slovenia; SP: Spain; SR: Slovakia.
FIGURE 6 in Phylogeography, genetic structure and wing pattern variation of Erebia pronoe (Esper, 1780) (Lepidoptera: Nymphalidae) in Europe
FIGURE 6. The results of the permutation test in CVA between the studied populations represented by "Country" variable.
FIGURE 4. A in Phylogeography, genetic structure and wing pattern variation of Erebia pronoe (Esper, 1780) (Lepidoptera: Nymphalidae) in Europe
FIGURE 4. A Maximum Likelihood (ML) tree of Erebia pronoe based on mitochondrial data of COI genes. Bootstrap support values are shown above the branches. A and B phylogroups are indicated on the right. The ML pattern is congruent with the statistical parsimony pattern of the sampled individuals. Sequences comprising E. pronoe populations from isolated clades are shown in grey shading. COI sequences of E. euryale (GU669656) and E. pandrose (KP870341) were used as outgroups. (a) The ML tree of E. pronoe based on mitochondrial data of CR markers. Bootstrap support values are shown above the branches. The ML pattern of CR markers is congruent with the ML pattern of COI genes.
FIGURE 5 in Phylogeography, genetic structure and wing pattern variation of Erebia pronoe (Esper, 1780) (Lepidoptera: Nymphalidae) in Europe
FIGURE 5. Scatterplot of canonical variate analysis. Morphometric overlap in Erebia pronoe forewings structure according to canonical variates CV1 and CV2. IT: Italy; SL: Slovenia; SP: Spain; SR: Slovakia.
FIGURE 3 in Phylogeography, genetic structure and wing pattern variation of Erebia pronoe (Esper, 1780) (Lepidoptera: Nymphalidae) in Europe
FIGURE 3. Statistical parsimony network of Erebia pronoe based on the COI mitochondrial dataset and statistical parsimony as implemented in TCS v1.21. Each haplotype is labelled by a number. Unsampled and extinct haplotypes are indicated by small circles. Division of the E. pronoe populations into seven haplogroups was detected, described in the Results section. These groups are illustrated with different symbols. Collection sites are indicated in the legend.
FIGURE 1 in Phylogeography, genetic structure and wing pattern variation of Erebia pronoe (Esper, 1780) (Lepidoptera: Nymphalidae) in Europe
FIGURE 1. Map of collection sites of Erebia pronoe in European mountain ranges and possible re-colonization paths of E. pronoe after the retreat of glacial stages during the Pleistocene. Grey shading and symbols represent different collection sites (by states); dashed arrows illustrate hypothesized paths of re-colonization from the refugia in the Pyrenees and Alps/Central Europe; triangles illustrate specimens collected from the European mountain ranges; circles illustrate specimens mined from the GenBank and BOLD databases. The number of analysed specimens is given in Table 1.
FIGURE 3 in Geographically structured genetic variation in Ptychozoon lionotum (Squamata: Gekkonidae) and a new species from an isolated volcano in Myanmar
FIGURE 3. Deciduous dipterocap forest habitat of Ptychozoon popaense sp. nov. on Mt. Popa, Mandalay Region, Myanmar.
FIGURE 1 in Geographically structured genetic variation in Ptychozoon lionotum (Squamata: Gekkonidae) and a new species from an isolated volcano in Myanmar
FIGURE 1. Left: distribution of the samples of the Ptychozoon lionotum Group used in the molecular phylogenetic analysis. Right: Maximum likelihood consensus tree topology of Ptychozoon. Black dots denote nodes with BPP ¿ 0.95, UFB ¿ 95, and LRT ¿ 0.80. The grey dot denotes a node with BPP ¿ 0.95, UFB ± 95, and LRT ¿ 0.80. The open dot denotes a node with no significant support.
FIGURE 2 in Geographically structured genetic variation in Ptychozoon lionotum (Squamata: Gekkonidae) and a new species from an isolated volcano in Myanmar
FIGURE 2. Adult male holotype of Ptychozoon popaense sp. nov. (LSUHC 13508) from Yay Su camp, 16 km northeast of Kyauk-pa-taung Town, Kyauk-pa-taung Township, Mt. Popa, Mandalay Region, Myanmar.
FIGURE 15 in Geographic structure of genetic variation in the Parachute Gecko Ptychozoon lionotum Annandale, 1905 across Indochina and Sundaland with descriptions of three new species
FIGURE 15. Forest habitat of Ptychozoon lionotum at Tad Kuang Si, Luang Prabang Province, Laos. Photograph by B. L. Stuart.
FIGURE 16 in Geographic structure of genetic variation in the Parachute Gecko Ptychozoon lionotum Annandale, 1905 across Indochina and Sundaland with descriptions of three new species
FIGURE 16. Locality data (map) and new distributional records for Ptychozoon kaengkrachaense (Sumontha et al. 2012) and P. trionotaterra. Thailand: 1. Type locality in Nakhon Ratchasima Province (Brown 1999); 2. Ubon Ratchathani Province (Kunya et al. 2011). Cambodia: 3. Siem Reap Province (Hartmann et al. 2014); 4. Keo Seima, Mondulkiri Province (LSUDPC 10961) newly reported here. 5. Ratanakiri Province (LSUDPC 10962) newly reported here. Vietnam: 6. Dong Nai province (Nguyen et al. 2009); 7. Dak Lak Province (Brown 1999); 8. Gia Lai Province (Brown 1999). Laos: 9. Nakai-Nam Theun, Khammouan Province (NCSM 77915) newly reported here.
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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.