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101 results for “character variations”
Figure 9 in Geometric and traditional morphometrics for the assessment of character state identity: multivariate statistical analyses of character variation in the genus Arrenurus (Acari, Hydrachnidia, Arrenuridae)
Figure 9. Shape variation of dorsoglandularia D4 in dorsal view (data set 4). (A) scatter plot of canonical variate scores (root 1 vs. root 2). (B) overall pattern of shape similarity among 11 Megaluracarus species and two Dadayella species based on Mahalanobis distances computed from the canonical variate analysis. This UPGMA phenogram (unweightedpair grouping method using averages) groups the three character states discovered in the dorsoglandularia. Branches are labelled according to discrimination order defined by the canonical variates. Symbols in the plot and in the phenogram are as listed in Fig. 8.
Figure 10 in Geometric and traditional morphometrics for the assessment of character state identity: multivariate statistical analyses of character variation in the genus Arrenurus (Acari, Hydrachnidia, Arrenuridae)
Figure 10. Shape variation of ventroglandularia V1, V2 and V3 (data set 5). (A) scatter plot of canonical variate scores (root 1 vs. root 2); shape changes relative to the mean shape are shown for both roots. (B) overall pattern of shape similarity among 11 Megaluracarus species and two Dadayella species based on Mahalanobis distances computed from the canonical variate analysis. This UPGMA phenogram (unweighted-pair grouping method using averages) groups the nine character states discovered in the distribution patterns of ventroglandularia. Branches are labelled according to discrimination order defined by the canonical variates. Symbols in the plot and in the phenogram are as follows: solid green triangles, Arrenurus (Dadayella) adrianae; solid red rhombuses, Dadayella aztecus; lilac en-dashes, Arrenurus (Megaluracarus) anae; open blue circles, Megaluracarus anitahoffmannae; horizontal blue lines, Megaluracarus catoi; open red squares, Megaluracarus colitus; solid grey squares, Megaluracarus costeroae; open green rhombuses, Megaluracarus maya; solid black circles, Megaluracarus neoexpansus; blue asterisks, Megaluracarus olmeca; green hyphens, Megaluracarus tabascoensis; open pink triangles, Megaluracarus urbanus; purple plus signs, Megaluracarus zitavus.
Figure 3 in Geometric and traditional morphometrics for the assessment of character state identity: multivariate statistical analyses of character variation in the genus Arrenurus (Acari, Hydrachnidia, Arrenuridae)
Figure 3. Two Arrenurus (Dadayella) species included for comparison with the 11 species of Arrenurus (Megaluracarus) in the morphometric analyses: (A, B) Dadayella adrianae in dorsal and posterior view, respectively; (C, D) Dadayella aztecus in dorsal and posterior view, respectively. The arrows in (C) and (D) are as described in Figs 1F and 2F, respectively. Scale bars: 100 µm.
FIGURE1. Diagnostic characters of Platynaspis spp.: a. head; b. antenna; c. labium; d. maxilla; e. abdomen; f, g. abdominal postcoxal line, variations; h. fore leg; i. hind leg; j. female genitalia; k, l. spermatheca. in A review of Platynaspini (Coleoptera: Coccinellidae) of the Indian subcontinent, including description of a new genus from north-eastern India and Bangladesh
FIGURE1. Diagnostic characters of Platynaspis spp.: a. head; b. antenna; c. labium; d. maxilla; e. abdomen; f, g. abdominal postcoxal line, variations; h. fore leg; i. hind leg; j. female genitalia; k, l. spermatheca.
Data from: Digest: trait variation in Mimulus provides new evidence for the joint action of ecological sorting and character displacement
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Data from: The large X-effect on secondary sexual characters and the genetics of variation in sex comb tooth number in Drosophila subobscura
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Figures 2-3 from: Lemos TH, da Cunha Tavares V, Moras LM (2020) Character variation and taxonomy of short-tailed fruit bats from Carollia in Brazil. Zoologia 37: 1-7. https://doi.org/10.3897/zoologia.37.e34587
Figures 2-3 Scatter plot of the three first principal component scores of a principal component analysis based on 17 cranial and external measurements from C. benkeithi (black triangles), C. brevicauda (gray squares) and C. perspicillata (black dots): (2) PC 1 x PC 2; (3) PC2 x PC 3.
Figure 1 from: Lemos TH, da Cunha Tavares V, Moras LM (2020) Character variation and taxonomy of short-tailed fruit bats from Carollia in Brazil. Zoologia 37: 1-7. https://doi.org/10.3897/zoologia.37.e34587
Figure 1 Distribution map of C. perspicillata (black dots), C. brevicauda (blue diamonds), C. brevicauda and C. perspicillata in sympatry (gray dots and red cross). C. benkeithi and C. perspicillata in sympatry (red pentagon), and C. benkeithi, C. brevicauda and C. perspicillata in sympatry (red star). Both localities of C. benkeithi represent new records for the species, and the red cross is a new record for C. brevicauda.
Data from: A comment on the use of stochastic character maps to estimate evolutionary rate variation in a continuously valued trait
Phylogenetic comparative biology has progressed considerably in recent years. One of the most important developments has been the application of likelihood-based methods to fit alternative models for trait evolution in a phylogenetic tree with branch lengths proportional to time. An important example of this type of method is O'Meara et al.'s (2006) "noncensored" test for variation in the evolutionary rate for a continuously valued character trait through time or across the branches of a phylogenetic tree. According to this method, we first hypothesize evolutionary rate regimes on the tree (called "painting" in Butler and King, 2004); and then we fit an evolutionary model, specifically the popular Brownian model, in which the instantaneous variance of the Brownian random diffusion process has different values in different parts of the phylogeny. The authors suggest that to test a hypothesis that the state of a discrete character influenced the rate of a continuous character, one could use the approach of Neilsen (2002) to first stochastically map the discretely valued trait, and then "test to see whether the portions of the tree with one state for the discrete character have a different rate of evolution for the continuous character than portions of the tree to which the other discrete state has been mapped" (O'Meara et al., 2006, p. 931). Indeed, this has become common practice for this and other closely related methods. Here, I examine this practice. In particular, I show that evolutionary rates estimated this way (i.e., by using maximum likelihood [ML] to fit a multirate model on each stochastically mapped tree; and then averaging across trees) are systematically biased to be more similar to each other than are the underlying generating parameters. My analysis also reveals that this effect is dependent on the rate of evolution for the discrete trait. Specifically, if the rate of evolution for the discrete character is low then the difference between the true history and any stochastically mapped 1 history is generally small. This results in evolutionary rates for the continuous trait that are estimated with little bias. Conversely, if the rate of evolution for the discrete character is very high, then the true and hypothesized character histories are often extremely dissimilar, evolutionary rate estimates are biased to be more similar to each other than their underlying generating values, and we lose power to distinguish evolutionary rates on the tree.
Data from: Among-character rate variation distributions in phylogenetic analysis of discrete morphological characters
Likelihood-based methods are commonplace in phylogenetic systematics. Although much effort has been directed toward likelihood-based models for molecular data, comparatively less work has addressed models for discrete morphological character data. Among-character rate variation may confound phylogenetic analysis, but there have been few analyses of the magnitude and distribution of rate heterogeneity among discrete morphological characters. Using seventy-six data sets covering a range of plants, invertebrate, and vertebrate animals, we used a modified version of MrBayes to test equal, gamma-distributed and lognormally-distributed models of among-character rate variation, integrating across phylogenetic uncertainty using Bayesian model selection. We found that in approximately 80% of data sets, unequal-rates models outperformed equal-rates models, especially among larger data sets. Moreover, although most data sets were equivocal, more data sets favored the lognormal rate distribution relative to the gamma rate distribution, lending some support for more complex character correlations than in molecular data. Parsimony estimation of the underlying rate distributions in several data sets suggests that the lognormal distribution is preferred when there are many slowly evolving characters and fewer quickly evolving characters. The commonly adopted four rate category discrete approximation used for molecular data was found to be sufficient to approximate a gamma rate distribution with discrete characters. However, among the two data sets tested that favored a lognormal rate distribution, the continuous distribution was better approximated with at least eight discrete rate categories. Although the effect of rate model on the estimation of topology was difficult to assess across all data sets, it appeared relatively minor between the unequal-rates models for the one data set examined carefully. As in molecular analyses, we argue that researchers should test and adopt the most appropriate model of rate variation for the data set in question. As discrete characters are increasingly used in more sophisticated likelihood-based phylogenetic analyses, it is important that these studies be built on the most appropriate and carefully selected underlying models of evolution.
FIGURE 1 in Well, what about intraspecific variation? Taxonomic and phylogenetic characters in the genus Synoeca de Saussure (Hymenoptera, Vespidae)
FIGURE 1. Cladogram after Andena et al. (2009).
Figure 7 from: Szczepańska K, Guzow-Krzemińska B, Urbaniak J (2021) Infraspecific variation of some brown Parmeliae (in Poland) – a comparison of ITS rDNA and non-molecular characters. MycoKeys 85: 127-160. https://doi.org/10.3897/mycokeys.85.70552
Figure 7 Haplotype network, based on ITS rDNA sequences from specimens of Montanelia sorediata. Newly-generated sequences are described with isolate numbers preceding the species names. Sequences downloaded from GenBank are described with their accession numbers. Mutational changes are presented as numbers in brackets near lines between haplotypes.
Figure 3 from: Szczepańska K, Guzow-Krzemińska B, Urbaniak J (2021) Infraspecific variation of some brown Parmeliae (in Poland) – a comparison of ITS rDNA and non-molecular characters. MycoKeys 85: 127-160. https://doi.org/10.3897/mycokeys.85.70552
Figure 3 Haplotype network, based on ITS rDNA sequences from specimens of Melanelia agnata. Newly-generated sequences are described with isolate numbers preceding the species names. Sequences downloaded from GenBank are described with their accession numbers. Mutational changes are presented as numbers in brackets near lines between haplotypes.
Figure 6 from: Szczepańska K, Guzow-Krzemińska B, Urbaniak J (2021) Infraspecific variation of some brown Parmeliae (in Poland) – a comparison of ITS rDNA and non-molecular characters. MycoKeys 85: 127-160. https://doi.org/10.3897/mycokeys.85.70552
Figure 6 Haplotype network, based on ITS rDNA sequences from specimens of Montanelia disjuncta. Newly-generated sequences are described with isolate numbers preceding the species names. Sequences downloaded from GenBank are described with their accession numbers. Mutational changes are presented as numbers in brackets near lines between haplotypes.
Figure 2 from: Szczepańska K, Guzow-Krzemińska B, Urbaniak J (2021) Infraspecific variation of some brown Parmeliae (in Poland) – a comparison of ITS rDNA and non-molecular characters. MycoKeys 85: 127-160. https://doi.org/10.3897/mycokeys.85.70552
Figure 2 Haplotype network, based on ITS rDNA sequences from specimens of Cetraria commixta. Newly-generated sequences are described with isolate numbers preceding the species names. Sequences downloaded from GenBank are described with their accession numbers. Mutational changes are presented as numbers in brackets near lines between haplotypes.
Supplementary material 1 from: Szczepańska K, Guzow-Krzemińska B, Urbaniak J (2021) Infraspecific variation of some brown Parmeliae (in Poland) – a comparison of ITS rDNA and non-molecular characters. MycoKeys 85: 127-160. https://doi.org/10.3897/mycokeys.85.70552
Figure S1
Figure 5 from: Szczepańska K, Guzow-Krzemińska B, Urbaniak J (2021) Infraspecific variation of some brown Parmeliae (in Poland) – a comparison of ITS rDNA and non-molecular characters. MycoKeys 85: 127-160. https://doi.org/10.3897/mycokeys.85.70552
Figure 5 Haplotype network, based on ITS rDNA sequences from specimens of Melanelia stygia. Newly-generated sequences are described with isolate numbers preceding the species names. Sequences downloaded from GenBank are described with their accession numbers. Mutational changes are presented as numbers in brackets near lines between haplotypes.
Figure 9 from: Szczepańska K, Guzow-Krzemińska B, Urbaniak J (2021) Infraspecific variation of some brown Parmeliae (in Poland) – a comparison of ITS rDNA and non-molecular characters. MycoKeys 85: 127-160. https://doi.org/10.3897/mycokeys.85.70552
Figure 9 Melanelia stygia specimens treated AM. stygia, AMNH 28243 (Iceland) BM. stygia, AMNH 16894 (Iceland) CM. stygia, C 19893 (Greenland) DM. stygia, C 19893 (Greenland) EM. stygia, Szczepańska 1160, WRSL (Poland) FM. stygia, Szczepańska 737, WRSL (Austria). Scale bars: 0.5 cm (A, C, E);1 mm (B, D); 0.5 mm (F).
Figure 4 from: Szczepańska K, Guzow-Krzemińska B, Urbaniak J (2021) Infraspecific variation of some brown Parmeliae (in Poland) – a comparison of ITS rDNA and non-molecular characters. MycoKeys 85: 127-160. https://doi.org/10.3897/mycokeys.85.70552
Figure 4 Haplotype network, based on ITS rDNA sequences from specimens of Melanelia hepatizon. Newly-generated sequences are described with isolate numbers preceding the species names. Sequences downloaded from GenBank are described with their accession numbers. Mutational changes are presented as numbers in brackets near lines between haplotypes.
Figure 8 from: Szczepańska K, Guzow-Krzemińska B, Urbaniak J (2021) Infraspecific variation of some brown Parmeliae (in Poland) – a comparison of ITS rDNA and non-molecular characters. MycoKeys 85: 127-160. https://doi.org/10.3897/mycokeys.85.70552
Figure 8 Melanelia agnata specimens treated AMelanelia agnata H-NYL 36086 (holotype) BMelanelia agnata, H-NYL 36086 (holotype) CM. agnata, AMNH 27562 (Iceland) DM. agnata, AMNH 30974 (Iceland) EM. agnata, C 19019 (Greenland) FM. agnata, C 19019 (Greenland) GM. agnata, Szczepańska 1050, WRSL (Poland) HM. agnata, Szczepańska 1050, WRSL (Poland). Scale bars: 0.5 cm (A, C, E, G); 0.5 mm (B, D, F); 1 mm (H).
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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.