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309 results for “phenotypic variation”
FIGURE 2 in Phenotypical variation and taxonomic correlates of five closely related Andean species of Poa (Poaceae) along geographic and climatic gradients
FIGURE 2. Plots of PC1 × PC2 and PC1 × PC3 from principal components analysis (PCA) of all specimens in the study. ANFA: P. anfamensis; JUJ: P. jujuyensis; LILL: P. lilloi; PARV: P. parviceps; SCAB: P. scaberula.
FIGURE 1 in Phenotypical variation and taxonomic correlates of five closely related Andean species of Poa (Poaceae) along geographic and climatic gradients
FIGURE 1. DIVA-GIS map of environmental variables and 150 collection sites of Poa specimens from the Andes in South American. A. Elevation. B. Annual mean precipitation. C. Annual mean temperature. D. Annual maximum temperature. E. Annual minimum temperature. Symbols for the Poa species are described in E.
FIGURE 4 in Phenotypical variation and taxonomic correlates of five closely related Andean species of Poa (Poaceae) along geographic and climatic gradients
FIGURE 4. Box plots representing the mean, median, interquartile range, adjacent values (lines), and outliers (dots) of quantitative characters in P. lilloi and P. scaberula.
FIGURE 3 in Phenotypical variation and taxonomic correlates of five closely related Andean species of Poa (Poaceae) along geographic and climatic gradients
FIGURE 3. Plot of discriminant analysis (DA) along the first two discriminant axes obtained from all specimens pertaining to a priori defined species. ANFA: P. anfamensis; JUJ: P. jujuyensis; LILL: P. lilloi; PARV: P. parviceps; SCAB: P. scaberula.
Figure 3 in Isolation by geographical distance after release from Pleistocene refugia explains genetic and phenotypic variation in Xylotrupes siamensis (Coleoptera: Scarabaeidae)
Figure 3. Phylogenetic networks of the three analysed loci and three historical scenarios explaining the origin of the CLAOS population tested using the DIYABC program. The colour of each individual shown in the network corresponds to the taxonomic assignment colour in the DIYABC analysis. Scenario 2 was the most likely model selected by the program (~60% posterior support), and scenario 1 also received moderate support (~30% posterior support). Results of posterior support among scenarios and model checking, in addition to the estimated parameter values (population sizes and divergence times), can be found in the Supporting Information (Figs S3–S8).
Figure 1 in Isolation by geographical distance after release from Pleistocene refugia explains genetic and phenotypic variation in Xylotrupes siamensis (Coleoptera: Scarabaeidae)
Figure 1. Different male horn phenotypes in Xylotrupes siamensis. Left panels show exemplars of males of the Tonkinensis (short horn) phenotypes and right panels the Siamensis (long horn with a cephalic horn denticle) phenotype. Samples from the CLAOS population (for details, see Table 1; Fig. 2) exhibit intermediate horn length, and the major males have the cephalic horn denticle.
Figure 4 in Isolation by geographical distance after release from Pleistocene refugia explains genetic and phenotypic variation in Xylotrupes siamensis (Coleoptera: Scarabaeidae)
Figure 4. Associations between three biogeographical distance measures and genetic differentiation (FST), measured at three loci (CO1, ITS2 and H3), for populations of Xylotrupes siamensis. Mantel test statistics (r) indicate the strength and significance of correlations between the genetic distance and biogeographical distance. Distance calculations are detailed in the Material and Methods section. Least cost distances were subject to rescaling and are thus unitless.
Figure 2. A in Isolation by geographical distance after release from Pleistocene refugia explains genetic and phenotypic variation in Xylotrupes siamensis (Coleoptera: Scarabaeidae)
Figure 2. A, the sampling sites of Xylotrupes siamensis populations in this study. White circles indicate samples of the Tonkinensis form and grey circles the Siamensis form. The Mekong River is indicated using a thick grey line; elevational differences are shown via a coloured scale, where a lighter colour depicts higher elevation (maximum = 4000 m a.s.l.). B, location of the study area within the Asia–Pacific region. C–E, species distribution model predictions based on the Last Interglacial (C), the Last Glacial Maximum (D) and current climatic conditions (E) are shown, where darker blue indicates higher predicted climatic suitability.
Figure 6 in Subspecies at crossroads: the evolutionary significance of genomic and phenotypic variation in a wide-ranging Australian lizard (Ctenotus pantherinus)
Figure 6. Genotypic clustering and admixture in Ctenotus pantherinus. Bars depict the relative proportion of alleles in each individual corresponding to the inferred genotypic clusters (i.e. ancestry proportions of individuals). Pie charts on the map indicate the average ancestry proportions corresponding to each cluster at each site (based on all individuals at that site). The clusters closely match the sample composition and geographical distribution of major clades from the nuclear phylogenetic analysis (Fig. 5). Multicoloured pies indicate admixture among clusters.
Figure 2 in Subspecies at crossroads: the evolutionary significance of genomic and phenotypic variation in a wide-ranging Australian lizard (Ctenotus pantherinus)
Figure 2. Selected phenotypic characters examined in museum specimens of Ctenotus pantherinus and their variation. A, dorsal coloration (from left to right): typical ocellated pattern, vertebral stripe, longitudinal lines, hiatus of ocelli on the vertebral region and ocelli with thick dark borders. B, condition of the plantar scales: smooth, pyramidal and with a spiny projection. C, condition of the subdigital lamellae (yellow arrows): single broad keel, single fine keel and fine medial keel flanked by two smaller parallel keels. Variation among the character states shown was often near-continuous and difficult to categorize.
Figure 1. A in Subspecies at crossroads: the evolutionary significance of genomic and phenotypic variation in a wide-ranging Australian lizard (Ctenotus pantherinus)
Figure 1. A, Illustrative picture of C. pantherinus in life (subspecies C. p. ocellifer), courtesy of Eric Vanderduys. B, C, distributions of currently recognized Ctenotus pantherinus subspecies. B, presumed distributions of C. pantherinus subspecies as typically presented in field guides and taxonomic compendiums (based on Ehmann & Strahan, 1992; Storr et al., 1999). Subspecies are as follows: C. p. acripes (a), C. p. calx (c), C. p. ocellifer (o) and C. p. pantherinus (p). Note the disjunct distribution of C. p. acripes, whose type locality is on a Western Australian island (Barrow Island; arrow). C, sampling localities of 1464 voucher specimens split by subspecies assignment as in the original museum records (for details on how we compiled these data, see the Material and methods section). Note that subspecies ranges as commonly understood (B) often disagree with those suggested by museum records.
Data from: Intra-specific variation in responses to habitat restoration: Could artificial reefs increase spatiotemporal segregation between migratory phenotypes of lake sturgeon?
<p>Habitat restoration is an important tool used to conserve biodiversity and restore species, but its effects are notoriously difficult to predict. Although outcomes of restoration projects are usually assessed using indices of species abundance and diversity, phenotypic differences among individuals within species are likely associated with differing responses to restored habitats. Here, we use lake sturgeon (<span>Acipenser fulvescens</span>) as a case study to illustrate how responses to habitat restoration can differ between phenotypes and potentially lead to unanticipated effects on populations. North America<span>'</span>s St. Clair River supports one of the largest remaining populations of lake sturgeon but has lost much spawning habitat due to its role as a major industrial corridor between the Laurentian Great Lakes Erie and Huron. Two artificial reefs were recently built in the lower and middle segments of the river to increase the available sturgeon spawning habitat. Interestingly, lake sturgeon in the St. Clair River express different migratory phenotypes that may be associated with different likelihoods of colonizing artificial reefs. Acoustic telemetry revealed that artificial reefs were more likely to be used by sturgeon that migrated downstream to overwinter in Lake St. Clair than those that migrated upstream to overwinter in Lake Huron. Furthermore, increasing time spent at the artificial reefs by Lake St. Clair migrants was associated with later arrival to and shorter occupancy of the river<span>'</span>s only natural spawning site, the primary location where the two phenotypes have opportunity to interbreed. Additional research is necessary to determine the ultimate impacts of the artificial reefs on lake sturgeon populations; nevertheless, our study showed phenotype-specific opportunity to colonize restored habitat and a mechanism through which this could lead to changes in gene flow. Our results illustrate the importance of considering intra-specific diversity when planning restoration projects and assessing the effects on populations.</p>
Data for: Phenotypic outcomes of predator-prey coevolution are predicted by landscape variation in climate and community composition
<ol> <li>Landscape patterns of phenotypic coevolution are determined by variation in the outcome of predator-prey interactions. These outcomes may depend not only on the functional phenotypes that mediate species interactions but also on aspects of the environment that enable encounters between coevolutionary partners.</li> <li>Exploring the relationship between coevolutionary traits and the environment requires extensive sampling across the range of the interaction to determine the relationship between local ecological variation and coevolution.</li> <li>In this study, we synthesized >30 years of data on predator-prey interactions between toxic newts (<em>Taricha</em> <em>granulosa</em>) and their snake predators (<em>Thamnophis</em> <em>sirtalis</em>) to explore the environmental predictors of arms race escalation.</li> <li>We found that geographic variation in phenotypes at the interface of coevolution was best predicted by a combination of community and climatic variation. Coevolutionary phenotypes were greatest in environments with climate favorable for newt-snake overlap. We found prey toxicity was elevated in regions with more predator species, and predator resistance was higher in regions with more prey species.</li> <li>Our results suggest specific environmental conditions reinforce the process of coevolution, signifying the phenotypic outcomes of coevolutionary arms races are sensitive to local ecological contexts that vary across the landscape. </li> </ol>
FIGURE 5. Tillandsia alcatrazensis phenotypical variation. A–C in Tillandsia alcatrazensis (Bromeliaceae), a new endemic species from Alcatrazes Island in southeastern Brazil
FIGURE 5. Tillandsia alcatrazensis phenotypical variation. A–C. Front view of the inflorescences. D–F. Lateral view of the inflorescences. G–I. Habit. Bars: A–F = 1 cm; G–I = 2 cm. Photos by G.P. Sabino based on G.P. Sabino et al. 772 (A,D,G), GPS et al. 773 (B,E,H) and GPS et al. 774 (C,F,I).
Supplementary material 1 from: Byerly AR, Jenck C, Goetz ARB, Weissman DB, Gray DA, Ross CL, Maroja LS, Larson EL (2023) Geographic variation in phenotypic divergence between two hybridizing field cricket species. Journal of Orthoptera Research 32(2): 189-200. https://doi.org/10.3897/jor.32.90713
Supplementary material 1 from: Byerly AR, Jenck C, Goetz ARB, Weissman DB, Gray DA, Ross CL, Maroja LS, Larson EL (2023) Geographic variation in phenotypic divergence between two hybridizing field cricket species. Journal of Orthoptera Research 32(2): 189-200. https://doi.org/10.3897/jor.32.90713
Rare Variation and Remote Gene Regulation of Osteoporosis Related Phenotypes in Han Chinesse
ClinicalTrials.gov study NCT04129671. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Human Genomic Population Structure and Phenotype-genotype Variation in ADME Genes in Four Populations
ClinicalTrials.gov study NCT02789527. IPD Sharing: UNDECIDED. Countries: 4. Publications: 1.
Phenotypic and Genotypic Variations of Enterococcus Spp
ClinicalTrials.gov study NCT05751317. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Genotype x environment interaction obscures genetic sources of variation in seed size in Dithyrea californica but provides the opportunity for selection on phenotypic plasticity
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Data from: Phenotypic, ecological and genomic variation in common bully (Gobiomorphus cotidianus) populations along depth gradients in New Zealand's Southern Great Lakes
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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
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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
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