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173 results for “quantitative morphology”
FIGURE 1 in Qualitative and quantitative morphological evidence for recognition of a new species within Ceratozamia (Zamiaceae) from Mexico
FIGURE 1. (1.1) Linear Discriminant Analysis for variables show text (1.2–1.7). Average variation and standard deviation in characteres evaluated at species level. C. chamberlainii populations are represented by solid line and C. fuscoviridis populations with dotted line.
FIGURE 4 in Qualitative and quantitative morphological evidence for recognition of a new species within Ceratozamia (Zamiaceae) from Mexico
FIGURE 4. Distribution map of the species Ceratozamia chamberlainii and C. fuscoviridis; species records obtained from herbaria are represented by small solid black figures and sampled populations for this study with large figures.
FIGURE 6 in Qualitative and quantitative morphological evidence for recognition of a new species within Ceratozamia (Zamiaceae) from Mexico
FIGURE 6. Comparison between species: (6.1) Ceratozamia fuscoviridis with young leaves; (6.2) C. chamberlainii with young leaves; (6.3) vernation of C. chamberlainii; (6.4; 6.6) C. fuscoviridis, new leaves (vernation); (6.5) polymorphism of C. fuscoviridis, plant with green leaves and plant with brown leaves.
FIGURE 4 in Quantitative analysis of the morphological variation within the tiger beetle Calomera littoralis (Fabricius, 1787) (Coleoptera: Cicindelidae) in Mongolia
FIGURE 4. Association between the variables of longitude and body size. A) observed in female individuals (R2 = 0.28), B) observed in male individuals (R2 = 0.25). P<0.01 for both.
FIGURE 3 in Quantitative analysis of the morphological variation within the tiger beetle Calomera littoralis (Fabricius, 1787) (Coleoptera: Cicindelidae) in Mongolia
FIGURE 3. Association between the variables of longitude and the proportion of each C. littoralis population that contains a black dorsum. Expected distribution of phenotypic variation under models of A) random association, B) two distinct subspecies with a contact zone, C) clinal variation. D) Observed data based on 494 specimens collected from 34 populations (R2 = 0.63, P<0.01).
FIGURE 2 in Quantitative analysis of the morphological variation within the tiger beetle Calomera littoralis (Fabricius, 1787) (Coleoptera: Cicindelidae) in Mongolia
FIGURE 2. Map of Mongolia displaying the location of sampling sites used in this study. Red M's represent populations considered C. littoralis mongolensis and blue P's are C. littoralis peipingensis by Mandl's (1981) definitions. Purple I's correspond to intermediate populations that do not fit cleanly into either subspecies definition.
FIGURE 1 in Quantitative analysis of the morphological variation within the tiger beetle Calomera littoralis (Fabricius, 1787) (Coleoptera: Cicindelidae) in Mongolia
FIGURE 1. Left to right: dorsal habitus of C. littoralis peipingensis (Dornod Province, Mongolia); intermediate phenotype (Omnogovi Province, Mongolia) between both of Mandl's subspecies; and C. littoralis mongolensis (Khovd Province, Mongolia).
FIGURE 5 in Quantitative analysis of the morphological variation within the tiger beetle Calomera littoralis (Fabricius, 1787) (Coleoptera: Cicindelidae) in Mongolia
FIGURE 5. Non-metric Multidimensional Scaling analysis of individuals, nominally assigned (via Mandl 1981) to either C. l. peipingensis (blue "P"), C. l. mongolensis (red "M") or intergrades (purple "I"), based on 14 character states (see Introduction).
FIGURE 7. Variability range for the chosen quantitative characters. Points indicate a median value, boxes represent 5 and 95 in A revision of taxonomic relation between Oenothera royfraseri and O. turoviensis (sect. Oenothera, subsect. Oenothera; Onagraceae) based on multivariate analyses of morphological characters
FIGURE 7. Variability range for the chosen quantitative characters. Points indicate a median value, boxes represent 5 and 95 percentile, whiskers around the boxes refer to 1 and 99 percentile; B – O. biennis, P – O. perangusta, R – O. royfraseri (including the specimens labelled as O. turoviensis, except the original material of the latter), T – O. turoviensis (the original material only).
Data from: Quantitative genetic inheritance of morphological divergence in a lake-stream stickleback ecotype pair: implications for reproductive isolation
Ecological selection against hybrids between populations occupying different habitats might be an important component of reproductive isolation during the initial stages of speciation. The strength and directionality of this barrier to gene flow depends on the genetic architecture underlying divergence in ecologically relevant phenotypes. We here present line cross analyses of inheritance for two key foraging-related morphological traits involved in adaptive divergence between stickleback ecotypes residing parapatrically in lake and stream habitats within the Misty Lake watershed (Vancouver Island, Canada). One main finding is striking genetic dominance of the lake phenotype for body depth. Selection associated with this phenotype against first and later generation hybrids should therefore be asymmetric, hindering introgression from the lake to the stream population but not vice versa. Another main finding is that divergence in gill raker number is inherited additively and should therefore contribute symmetrically to reproductive isolation. Our study suggests that traits involved in adaptation might contribute to reproductive isolation qualitatively differently, depending on their mode of inheritance.
FIGURE 3. A in A standardized and statistically defensible framework for quantitative morphological analyses in taxonomic studies
FIGURE 3. A visual description of a boxplot to illustrate how outliers are detected. Using this method, values above the maximum range or below the minimum range are considered outliers. The maximum range is defined as the upper quartile (Q3) plus 1.5 times the interquartile range (IQR), while the minimum range is defined as the lower quartile (Q1) minus 1.5 times the IQR.
FIGURE 2. The 5 in A standardized and statistically defensible framework for quantitative morphological analyses in taxonomic studies
FIGURE 2. The 5-step workflow for statistical hypothesis testing of morphological characters. Note that mensural and meristic data should be analyzed separately and that Step 4 should only be performed on mensural data. * = parametric tests; ** = nonparametric tests. The non-parametric tests shown here only serve as examples—the appropriate test should be selected based on which assumptions are violated. A companion R script that implements this workflow is provided in the Supplementary Material.
FIGURE 1 in A standardized and statistically defensible framework for quantitative morphological analyses in taxonomic studies
FIGURE 1. Results of the meta-analysis on taxonomic papers focused on amphibians (top) and reptiles (bottom) published in the journal ZooKeys from 2008–2020. The analysis evaluated whether body size corrections or statistical analyses were performed on morphological data.
Quantitative Evaluation of the Breast's Morphological Variations During Radiotherapy: MorphoBreast3D
ClinicalTrials.gov study NCT03801850. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Data from: Taxonomic revision of the Malagasy Camponotus grandidieri and niveosetosus species groups (Hymenoptera, Formicidae) using qualitative and quantitative morphology
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Data from: Quantitative genetic inheritance of morphological divergence in a lake-stream stickleback ecotype pair: implications for reproductive isolation
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Quantitative morphological data on Ramomarthamyces octomerus fungi (Ascomycota)
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Data from: The quantitative genetics of physiological and morphological traits in an invasive terrestrial snail: additive versus non-additive genetic variation
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Data from: Taxonomic revision of the Malagasy Camponotus subgenus Mayria (Hymenoptera, Formicidae) using qualitative and quantitative morphology
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Quantitative genetics of wing morphology in the parasitoid wasp Nasonia vitripennis: hosts increase sibling similarity
<p>The central aim of evolutionary biology is to understand patterns of genetic variation between species and within populations. To quantify the genetic variation underlying intraspecific differences, estimating quantitative genetic parameters of traits is essential. In Pterygota, wing morphology is an important trait affecting flight ability. Moreover, gregarious parasitoids such as Nasonia vitripennis oviposit multiple eggs in the same host, and siblings thus share a common environment during their development. Here we estimate the genetic parameters of wing morphology in the outbred HVRx population of N. vit-ripennis, using a sire-dam model adapted to haplodiploids and disentangled additive genetic effects and host effects. The results show that the wing size traits have low heritability (h2~0.1), while most wing shape traits have roughly twice the heritability compared to wing size traits. However, the estimates in-creased to h2~0.6 for wing size traits when omitt ing the host effect from the statistical model, while no meaningful increases were observed for wing shape traits. Overall, host effects contributed ~50% of the variation in wing size traits. This indicates that hosts have a large effect on wing size traits, about five-fold more than genetics. Moreover, bivariate analyses were conducted to derive the genetic relationships among traits. Overall, we demonstrate the evo-lutionary potential for morphological traits in the N. vitripennis HVRx outbred population and report the host effects on wing morphology. Our findings can contribute to a further dissection of the genetics underlying wing morphology in N. vitripennis, with relevance for gregarious parasitoids and possible other insects as well.</p>
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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)
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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
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.