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1,659 results for “structured population”
Microcoleus (Cyanobacteria) form watershed-wide populations without strong gradients in population structure
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Assessment of genetic diversity and population structure of Eulaema nigrita (Hymenoptera: Apidae: Euglossini) as a factor of habitat type in Brazilian Atlantic forest fragments
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Understanding the Early Evolutionary Stages of a Tandem Drosophila melanogaster - Specific Gene Family: A Structural and Functional Population Study
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Nuclear genetic markers used to infer population genetic structure among breeding Adelie penguins from four regional rookeries along the western Antarctic Peninsula, 2008-2011.
We used nuclear and mitochondrial DNA (mtDNA, data archived in GenBank) markers to better understand historical population genetic structure and gene flow given relatively recent and ongoing reductions in sea ice habitats and changes in numbers of breeding adult Adelie penguins at regional rookeries along the western Antarctic Peninsula. Study nests near Anvers Island, where pairs of adults were present, were individually marked and chosen before the onset of egg-laying, and consistently monitored each season (2008-2009). When study nests were found at the one-egg stage, both adults were captured to obtain blood samples used for population genetic analyses. At the time of capture, each adult penguin was quickly blood sampled (~1 ml) from the brachial vein using a and non-heparinized, sterile 3 ml syringe infusion needle. After handling, individuals at study nests were further monitored to ensure the pair reached clutch completion, i.e., two eggs. Adélie penguin chicks at Avian Island were sampled over two years (2009-2010), Adelie chicks as Prospect Point were sampled during one year (2011), while chicks at Charcot Island were sampled during two seasons (2010-2011). Blood samples from crèched chicks (~1 ml) were taken from the brachial vein using a sterile 3 ml syringe and infusion needle following sampling procedures used for adult penguins. Genetic analyses were conducted at the wildlife genetics laboratory, Alaska Science Center - USGS, under the supervision of geneticist Dr. S.L. Talbot. Data presented here are raw data only and do not include any derived data products. For any meta-analyses with other microsatellite data, proper calibration across labs must be completed. Data were produced at the Alaska Science Center, USGS wildlife genetics laboratory under the supervision of Dr. Sandra Talbot (stalbot@usgs.gov). Questions regarding data or any laboratory cross-validation should be directed to Dr. Kristen Gorman (kgorman@sfu.ca).
Fig. 4. Mismatch distribution for mitochondrial haplotypes for 92 in Genetic diversity and population structure of Brycon nattereri (Characiformes: Bryconidae): a Neotropical fish under threat of extinction
Fig. 4. Mismatch distribution for mitochondrial haplotypes for 92 individuals of Brycon nattereri from the Laranjinha River.
Fig. 2 in Genetic diversity and population structure of Brycon nattereri (Characiformes: Bryconidae): a Neotropical fish under threat of extinction
Fig. 2. Bayesian analysis results (Structure). a. Values of K obtained based on ΔK. b. Values of K obtained based on mean likelihood Ln (K). c. Graphic representation of K = 2. Each column represents a different individual and the colors denote the probable ancestry coefficient of the individual and each genetic cluster.
Fig. 1 in Genetic diversity and population structure of Brycon nattereri (Characiformes: Bryconidae): a Neotropical fish under threat of extinction
Fig. 1. Brycon nattereri sampling sites along the Laranjinha River (A, B, C and D). Also shown are the sampling sites used in a previous study (D, E, F, G, H, I and J) and the number of individuals of B. nattereri collected at each site (in parentheses). On the South America map, numbers and arrows indicate the Paraná, Tocantins, and São Francisco basins and the asterisks indicate the areas with records of Brycon nattereri (Rosa, Lima, 2008; Viana et al., 2013; Frota et al., 2016).
Figure 2 from: Chanthran SSD, Lim P-E, Li Y, Liao T-Y, Poong S-W, Du J, Hussein MAS, Sade A, Rumpet R, Loh K-H (2020) Genetic diversity and population structure of Terapon jarbua (Forskål, 1775) (Teleostei, Terapontidae) in Malaysian waters. ZooKeys 911: 139-160. https://doi.org/10.3897/zookeys.911.39222
Figure 2 Maximum likelihood haplotype tree reconstructed based on the concatenated mtDNA dataset. The bootstrap values higher than 50% are shown near the nodes.
Figure 4 from: Chanthran SSD, Lim P-E, Li Y, Liao T-Y, Poong S-W, Du J, Hussein MAS, Sade A, Rumpet R, Loh K-H (2020) Genetic diversity and population structure of Terapon jarbua (Forskål, 1775) (Teleostei, Terapontidae) in Malaysian waters. ZooKeys 911: 139-160. https://doi.org/10.3897/zookeys.911.39222
Figure 4 Pairwise number of difference (mismatch distribution) analysis was conducted using the constant population size model to observe the population size changes. The observed frequencies were represented by red dotted line. The frequency expected under the hypothesis of population expansion model was depicted by continuous green line. a Kuala Selangor b Kuantan c Mukah d Sandakan e Tawau f all populations.
Figure 1 from: Chanthran SSD, Lim P-E, Li Y, Liao T-Y, Poong S-W, Du J, Hussein MAS, Sade A, Rumpet R, Loh K-H (2020) Genetic diversity and population structure of Terapon jarbua (Forskål, 1775) (Teleostei, Terapontidae) in Malaysian waters. ZooKeys 911: 139-160. https://doi.org/10.3897/zookeys.911.39222
Figure 1 Sampling localities from East (Sandakan and Tawau, Sabah & Mukah, Sarawak) and West (Peninsula) Malaysia (Kuala Selangor, Selangor and Kuantan, Pahang).
Figure 3 from: Chanthran SSD, Lim P-E, Li Y, Liao T-Y, Poong S-W, Du J, Hussein MAS, Sade A, Rumpet R, Loh K-H (2020) Genetic diversity and population structure of Terapon jarbua (Forskål, 1775) (Teleostei, Terapontidae) in Malaysian waters. ZooKeys 911: 139-160. https://doi.org/10.3897/zookeys.911.39222
Figure 3 Haplotypes median-joining network corresponding to the ML tree with three observed clusters. The star-like profile observed in cluster III indicates the presence of sudden expansion.
Supplementary material 2 from: Grabowska J, Kvach Yu, Rewicz T, Pupins M, Kutsokon I, Dykyy I, Antal L, Zięba G, Rakauskas V, Trichkova T, Čeirāns A, Grabowski M (2020) First insights into the molecular population structure and origins of the invasive Chinese sleeper, Perccottus glenii, in Europe. NeoBiota 57: 87-107. https://doi.org/10.3897/neobiota.57.48958
Table S2. Values of FST population pairwise.
Figure 7 from: Ortíz-Gamino D, Gregorio J, Cunha L, Martínez-Romero E, Fragoso C, Ortíz-Ceballos ÁI (2020) Population genetics and diversity structure of an invasive earthworm in tropical and temperate pastures from Veracruz, Mexico. ZooKeys 941: 49-69. https://doi.org/10.3897/zookeys.941.49319
Figure 7 Classification of Pontoscolex corethrurus individuals according to a Bayesian assignment algorithm implemented in NEWHYBRIDS (Anderson and Thompson 2002) to detect gene flow. Each unit represents an individual corresponding to parental lineages (Lineage A and Lineage B), F1 generation, F2 (F1 x F1) and later generation or introgressive hybrids B1 (Lineage A x F1) and B2 (e.g., Lineage B x F1).
Figure 6 from: Ortíz-Gamino D, Gregorio J, Cunha L, Martínez-Romero E, Fragoso C, Ortíz-Ceballos ÁI (2020) Population genetics and diversity structure of an invasive earthworm in tropical and temperate pastures from Veracruz, Mexico. ZooKeys 941: 49-69. https://doi.org/10.3897/zookeys.941.49319
Figure 6 Genetic structure using ISSR data for 35 Pontoscolex corethrurus individuals based on discriminant analysis of principal components (DAPC). Proportion of eigenvalues in discriminant analysis (bottom left plot) and PCA eigenvalues (bottom right), with the first 12 significant principal components highlighted in black.
Figure 4 from: Ortíz-Gamino D, Gregorio J, Cunha L, Martínez-Romero E, Fragoso C, Ortíz-Ceballos ÁI (2020) Population genetics and diversity structure of an invasive earthworm in tropical and temperate pastures from Veracruz, Mexico. ZooKeys 941: 49-69. https://doi.org/10.3897/zookeys.941.49319
Figure 4 UPGMA dendrogram of genetic distance between MGLs (A) and between populations (B) observed in the distinct populations of Pontoscolex corethrurus collected in central Veracruz State, Mexico. Only bootstrap values higher than or equal to 70% are shown.
Figure 2 from: Ortíz-Gamino D, Gregorio J, Cunha L, Martínez-Romero E, Fragoso C, Ortíz-Ceballos ÁI (2020) Population genetics and diversity structure of an invasive earthworm in tropical and temperate pastures from Veracruz, Mexico. ZooKeys 941: 49-69. https://doi.org/10.3897/zookeys.941.49319
Figure 2 Rarefaction curve of expected number of MLGs captured per earthworm of Pontoscolex corethrurus sampled (A), and a MLG accumulation curve according to the number of loci sampled (B).
Figure 3 from: Ortíz-Gamino D, Gregorio J, Cunha L, Martínez-Romero E, Fragoso C, Ortíz-Ceballos ÁI (2020) Population genetics and diversity structure of an invasive earthworm in tropical and temperate pastures from Veracruz, Mexico. ZooKeys 941: 49-69. https://doi.org/10.3897/zookeys.941.49319
Figure 3 A Principal Components Analysis, where colors indicate specimens of the population (A) and a Minimum Spanning Network where each node denotes a different MLG, with size matching the number of individuals. Edge thickness and color are proportional to absolute genetic distance. Edge lengths are arbitrary (B). Both analyses show the relationship between multilocus genotypes (MLGs) for four different earthworm populations of Pontoscolex corethrurus living in central Veracruz State, Mexico.
Figure 1 from: Ortíz-Gamino D, Gregorio J, Cunha L, Martínez-Romero E, Fragoso C, Ortíz-Ceballos ÁI (2020) Population genetics and diversity structure of an invasive earthworm in tropical and temperate pastures from Veracruz, Mexico. ZooKeys 941: 49-69. https://doi.org/10.3897/zookeys.941.49319
Figure 1 Pastures sampled in the central region of Veracruz State, Mexico. LV, Laguna verde; AC, Actopan; LC, La Concepción; NA, Naolinco. The digital elevation model was created using data provided by Instituto Nacional de Estadística y Geografía, México.
Figure 5 from: Ortíz-Gamino D, Gregorio J, Cunha L, Martínez-Romero E, Fragoso C, Ortíz-Ceballos ÁI (2020) Population genetics and diversity structure of an invasive earthworm in tropical and temperate pastures from Veracruz, Mexico. ZooKeys 941: 49-69. https://doi.org/10.3897/zookeys.941.49319
Figure 5 Estimated population genetic structure with a summary plot of Q estimates based on the ISSR data observed for four populations of Pontoscolex corethrurus in central Veracruz State, Mexico. Each individual is shown by a vertical line, which is partitioned into colored segments representing the fraction of the number of members in cluster K (%).
Supplementary material 1 from: Ortíz-Gamino D, Gregorio J, Cunha L, Martínez-Romero E, Fragoso C, Ortíz-Ceballos ÁI (2020) Population genetics and diversity structure of an invasive earthworm in tropical and temperate pastures from Veracruz, Mexico. ZooKeys 941: 49-69. https://doi.org/10.3897/zookeys.941.49319
Figure S1
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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.