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1,598 results for “genetic diversity”
Figure 7 from: Boyd OF, Philips TK, Johnson JR, Nixon JJ (2020) Geographically structured genetic diversity in the cave beetle Darlingtonea kentuckensis Valentine, 1952 (Coleoptera, Carabidae, Trechini, Trechini). Subterranean Biology 34: 1-23. https://doi.org/10.3897/subtbiol.34.46348
Figure 7 Representative male genitalia from 17 of the sampled caves: 1 Wells Cave; 2 Pine Hill Cave; 3–5 Wind Cave; 6 Richardson's Cave; 7, 8 Lainhart #1 Cave; 9, 10 and 15, 16 Pourover Cave; 11, 12 John Griffin Cave; 13 Climax Cave; 14 Hicksey Cave; 17, 18 Stab Cave; 19, 20 Piney Grove Cave; 21, 22 Dykes Bridge Cave; 23 Great Saltpeter Cave; 24 Teamers Cave; 25 Mullins Spring Cave; 26 Jesse Cave; 27 Steel Hollow Cave. Note that Wells and Dykes Bridge Caves were not included in the genetic study.
Figure 1 from: Boyd OF, Philips TK, Johnson JR, Nixon JJ (2020) Geographically structured genetic diversity in the cave beetle Darlingtonea kentuckensis Valentine, 1952 (Coleoptera, Carabidae, Trechini, Trechini). Subterranean Biology 34: 1-23. https://doi.org/10.3897/subtbiol.34.46348
Figure 1 (Adapted from Barr 1985, Figure 3) Map showing the major geologic features important for cave development in the southeastern United States: MP-I and MP-II (green) are western and eastern bands of the Mississippian Plateau. Dots indicate collecting records (see Figure 3).
Figure 5 from: Boyd OF, Philips TK, Johnson JR, Nixon JJ (2020) Geographically structured genetic diversity in the cave beetle Darlingtonea kentuckensis Valentine, 1952 (Coleoptera, Carabidae, Trechini, Trechini). Subterranean Biology 34: 1-23. https://doi.org/10.3897/subtbiol.34.46348
Figure 5 Frequencies of COI haplotypes and their proportions, color coded for each hypothesis of structure; circle area corresponds to number of individuals assigned to each group. Overlain transparent dots show collecting localities. A Four faunal regions of hypothesis I (fifth region unsampled in this study: see discussion and Barr 1985, Kane et al. 1992) B ten minor watersheds of hypothesis II C five genetic clusters of hypothesis III.
Figure 4 from: Boyd OF, Philips TK, Johnson JR, Nixon JJ (2020) Geographically structured genetic diversity in the cave beetle Darlingtonea kentuckensis Valentine, 1952 (Coleoptera, Carabidae, Trechini, Trechini). Subterranean Biology 34: 1-23. https://doi.org/10.3897/subtbiol.34.46348
Figure 4 Distribution of cave collection sites and proportions of haplotypes from 27 populations of Darlingtonea kentuckensis in eastern Kentucky, USA. Circle area corresponds to number of individuals sampled per locality. Different colors indicate different haplotypes; similarity in hue qualitatively indicates sequence similarity. KR: Kentucky River; RR: Rockcastle River; CR: Cumberland River; MVF: Mount Vernon Fault; DD = drainage divide between Kentucky and Rockcastle rivers.
Figure 3 from: Boyd OF, Philips TK, Johnson JR, Nixon JJ (2020) Geographically structured genetic diversity in the cave beetle Darlingtonea kentuckensis Valentine, 1952 (Coleoptera, Carabidae, Trechini, Trechini). Subterranean Biology 34: 1-23. https://doi.org/10.3897/subtbiol.34.46348
Figure 3 Cave localities of currently known sites for Darlingtonea kentuckensis. White dots were the caves sampled for this study while black dots represent caves unsampled.
Figure 2 from: Boyd OF, Philips TK, Johnson JR, Nixon JJ (2020) Geographically structured genetic diversity in the cave beetle Darlingtonea kentuckensis Valentine, 1952 (Coleoptera, Carabidae, Trechini, Trechini). Subterranean Biology 34: 1-23. https://doi.org/10.3897/subtbiol.34.46348
Figure 2 Gravid female Darlingtonea kentuckensis photographed in Fletcher Spring Cave, Rockcastle County, Kentucky. Photo courtesy of Dr. Matthew Niemiller, University of Alabama, Huntsville.
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
Supplementary material 8 from: Zizka VMA, Weiss M, Leese F (2020) Can metabarcoding resolve intraspecific genetic diversity changes to environmental stressors? A test case using river macrozoobenthos. Metabarcoding and Metagenomics 4: e51925. https://doi.org/10.3897/mbmg.4.51925
Table S1. Number of macroinvertebrate individuals per sample and season
Minimum habitat thresholds required for conserving mountain lion genetic diversity
<p>Jointly considering the ecology (e.g., habitat use) and genetics (e.g., population genetic structure and diversity) of a species can increase understanding of current conservation status and inform future management practices. Previous analyses indicate that mountain lion (<i>Puma concolor</i>) populations in California are genetically structured and exhibit extreme variation in population genetic diversity. Although human development may have fragmented gene flow, we hypothesized the quantity and quality of remaining habitat available would affect the genetic viability of each population. Our results indicate that area of suitable habitat, determined via a resource selection function derived using 843,500 location fixes from 263 radio-collared mountain lions, is strongly and positively associated with population genetic diversity and viability metrics, particularly with effective population size. Our results suggested that contiguous habitat of ≥ 10,000 km<sup>2</sup> may be sufficient to alleviate the negative effects of genetic drift and inbreeding, allowing mountain lion populations to maintain suitable effective population sizes. Areas occupied by five of the nine geographic–genetic mountain lion populations in California fell below this habitat threshold, and two (Santa Monica Area and Santa Ana) of those five populations lack connectivity to nearby populations. Enhancing ecological conditions by protection of greater areas of suitable habitat and facilitating positive evolutionary processes by increasing connectivity (e.g., road crossing structures) might promote persistence of small or isolated populations. The conservation status of suitable habitat also appeared to influence genetic diversity of populations. Thus, our results demonstrate that both the area and status (i.e., protected or unprotected) of suitable habitat influence the genetic viability of mountain lion populations.</p>
Taxus genotype data for "Trunk perimeter correlates with genetic bottleneck intensity and the level of genetic diversity in populations of Taxus baccata L."
<p>The data set contains microsatellite genotypes (18 loci) of Taxus baccata trees, together with the information about sampling sites.</p>
Genetic diversity and structure of wild Vaccinium populations - V. myrtillus, V.vitis-idaea and V. uliginosum in the Baltic States
<p>V. myrtillus L., V. vitis-idaea L. and V. uliginosum L. belong to the genus Vaccinium. These wild species are widely distributed and ecologically important within the Baltic countries but they have not been extensively studied using molecular markers. EST-SSR and cpSSR markers were used to investigate the population structure and genetic diversity of these species to obtain information useful for the development of in situ conservation strategies for these species.</p> <p>Wild Vaccinium species populations are moderately genetically differentiated, with some populations more highly differentiated, but without higher order clustering of groups of populations, indicating that there are no dispersal barriers for these species within the Baltic countries. Genetic diversity of populations growing in protected areas, managed forests and intensively utilised public recreational areas is similar.</p>
Systems genetics in diversity outbred mice inform BMD GWAS and identify determinants of bone strength
<p>This data repository contains data in support of "Systems genetics in diversity outbred mice inform BMD GWAS and identify determinants of bone strength".</p> <p>The following data are included:</p> <ul> <li>Raw genotyping results using the GigaMUGA array for 619 Diversity Outbred mice (.txt files).</li> <li>Genotype probabilities as an Rdata file, calculated using R/qtl2 (pr_basic_cleaned.Rdata).</li> <li>Allele probabilities as an Rdata file, calculated using R/qtl2 (apr_basic_cleaned.Rdata).</li> <li>R/qtl2 cross files for QTL (cross_basic_cleaned.Rdata) and eQTL (cross_eqtl_REDO.Rdata) mapping. Cross files also include covariates (including PEER factors in cross_eqtl_REDO.Rdata).</li> </ul> <p>Supporting sequencing data can be found from the NCBI Gene Expression Omnibus database (GSE152708, GSE152806).</p> <p>For more information, please visit (https://github.com/basel-maher/DO_project).</p> <p>Contact: bma8ne AT virginia DOT edu</p> <p> </p>
Genetic and phenotypic diversity of guppy population pre- and post-flood disturbance
<p class="paragraph"><span>Rare extreme "black swan" disturbances can impact ecosystems in many ways, such as destroying habitats, depleting resources, and causing high mortality. In rivers, for instance, exceptional floods that occur infrequently (e.g., so-called "50-year floods") can strongly impact the abundance of fishes and other aquatic organisms. Beyond such ecological effects, these floods could also impact intraspecific diversity by elevating genetic drift or dispersal and by imposing strong selection, which could then influence the population's ability to recover from disturbance. And yet, natural systems might be resistant (show little change) or resilient (show rapid recovery) even to rare extreme events – perhaps as a result of selection due to past events. We considered these possibilities in two rivers where native guppies experienced two extreme floods - one in 2005 and another in 2016. For each river, we selected four sites and used archived "historical" samples to compare levels of genetic diversity and phenotypic mean traits before versus after floods. Genetic diversity was represented by 33 neutral microsatellite markers, and phenotypic diversity was represented by body length and male melanic (black) color. We found that genetic diversity and population structure was mostly <i>resistant</i> to even these extreme floods; whereas the larger impacts on phenotypic diversity were short-lived, suggesting additional <i>resilience</i>. We discuss the determinants of these two outcomes for guppies facing floods, and then consider the general implications for the resistance and resilience of intraspecific variation to black swan disturbances. </span></p>
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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.