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Supplementary material 6 from: García-Barros E (2015) Multivariate indices as estimates of dry body weight for comparative study of body size in Lepidoptera. Nota Lepidopterologica 38(1): 59-74. https://doi.org/10.3897/nl.38.8957
Alternative models.: Explanation note: Alternative or suboptimal regression models derived from the species means or from the independent contrasts.
Supplementary material 1 from: García-Barros E (2015) Multivariate indices as estimates of dry body weight for comparative study of body size in Lepidoptera. Nota Lepidopterologica 38(1): 59-74. https://doi.org/10.3897/nl.38.8957
Nexus format text.: Explanation note: Tree topology for the phylogenetic hypothesis adopted, to be used as input in applications reading nexus (requires some slight previous edition).
FIGURE 11. Multivariate PCA plot. Blue- L in Description of a new species of Liocheles Sundevall, 1833 (Hormuridae) from India
FIGURE 11. Multivariate PCA plot. Blue- L. nigripes, black- Liocheles schalleri sp. nov., male indicated by solid square and females by solid circles.
PLATE 1 in Taxonomic Studies Using Multivariate Analysis of Eriobotrya Based on Morphological Traits
PLATE 1. Leaf morphologies of different species in Eriobotrya. Note: the number sequence is consistent with Table 1.
FIGURE 5 in A multivariate analysis of the Pitcairnia palmeri group (Bromeliaceae: Pitcairnioideae)
FIGURE 5. Known distribution of Pitcairnia robert-downsii González-Rocha, Espejo, López-Ferr. & M. Castillo.
FIGURE 1 in A multivariate analysis of the Pitcairnia palmeri group (Bromeliaceae: Pitcairnioideae)
FIGURE 1. Herbarium specimens of A) Pitcairnia palmeri, B) Pitcairnia robert-downsii, C) P. colimensis, and D) P. compostelae.
FIGURE 4 in A multivariate analysis of the Pitcairnia palmeri group (Bromeliaceae: Pitcairnioideae)
FIGURE 4. Pitcairnia robert-downsii González-Rocha, Espejo, López-Ferr. & M. Castillo, nom. et stat. nov. A–B) Detail of the inflorescence, C) habitat and D) dissected flower.
FIGURE 3 in A multivariate analysis of the Pitcairnia palmeri group (Bromeliaceae: Pitcairnioideae)
FIGURE 3. Dendrogram of the cluster analysis applied to the Euclidean distance matrix (data transformed to log 2) of 12 morphological variables, grouped by the UPGMA fusion technique. The abbreviations correspond to: P. col: P. colimensis, P. com: P. compostelae, P. lon: Pitcarnia palmeri var. longebracteata and P. pal: P. palmeri var. palmeri. * Indicates the nomenclatural types.
FIGURE 2 in A multivariate analysis of the Pitcairnia palmeri group (Bromeliaceae: Pitcairnioideae)
FIGURE 2. Scatterplot between the first two axes (cumulative variance of 66.7 %) derived from the principal component analysis applied to 12 morphological variables transformed to log 2. The symbols correspond to Pitcairnia colimensis (♦), P. compostelae (▲), P. palmeri var. longebracteata (●) and P. palmeri var. palmeri (■).
FIGURE 8 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 8. Distribution of Oenothera royfraseri in Europe. Circles refer to the examined specimens, squares to localities cited in the literature. The question mark represents a stand given from France by Rostański (with no further details) (Rostański et al. 2010) which has not been traced.
FIGURE 9 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 9. Comparison of the arrangement of sepal tips at the base of the four examined taxa: a – O. biennis, b – O. parviflora, c – O. royfraseri (the lectotype), d – O. turoviensis (the holotype).
FIGURE 5. A 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 5. A scatterplot resulting from principal component analysis (PCA). Empty triangles refer to O. biennis, empty circles to O. parviflora and empty squares to the individuals labelled as O. royfraseri and/or O. turoviensis (except the original material). The lectotype of O. royfraseri and the holotype and isotypes of O. turovienisis are marked with full circle and full triangles, respectively.
FIGURE 4. A 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 4. A chart presenting factor loadings of principal component analysis (PCA). For characters abbreviations see Table 1.
FIGURE 3 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 3. Two-dimensional ordination diagram of correspondence analysis (CA) along CA1 and CA2, based on all qualitative characters. Empty triangles refer to O. biennis, empty circles to O. parviflora and empty squares to the individuals labelled as O. royfraseri and/or O. turoviensis (except the original material). The lectotype of O. royfraseri and the holotype and isotypes of O. turovienisis are marked with full circle and full triangles, respectively. The numbers indicate the number of specimens to which a particular point refers to (only if more than one). For characters abbreviations see Table 1.
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).
FIGURE 6. A 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 6. A scatter diagram showing results of discriminant analysis (DA). Empty triangles refer to O. biennis, empty circles to O. parviflora and empty squares to the individuals labelled as O. royfraseri and/or O. turoviensis (except the original material). The lectotype of O. royfraseri and the holotype and isotypes of O. turovienisis are marked with full circle and full triangles, respectively.
Data from: Genetic basis of adult migration timing in anadromous steelhead discovered through multivariate association testing
Migration traits are presumed to be complex and to involve interaction among multiple genes, thus we employed both univariate analyses and multivariate Random Forest (RF) machine learning algorithm to conduct association mapping of 15,239 single nucleotide polymorphisms (SNPs) for adult migration-timing phenotype in steelhead (Oncorhynchus mykiss). Our study focused on a model natural population of steelhead that exhibits two distinct migration-timing life histories with high levels of admixture in nature. Neutral divergence was limited between fish exhibiting summer- and winter-run migration owing to high levels of interbreeding, but a univariate mixed linear model found three SNPs from a major effect gene to be significantly associated with migration-timing (p < 0.000005) that explained 46% of trait variation. Alignment to the annotated S. salar genome provided evidence that all three SNPs localize within a 46 kb region overlapping GREB1-like (an estrogen target gene) on chromosome Ssa03. Additionally, multivariate analyses with RF identified that these 3 SNPs plus 15 additional SNPs explained up to 60% of trait variation. These candidate SNPs may provide the ability to predict adult migration-timing of steelhead to facilitate conservation management of this species and this study demonstrates the benefit of multivariate analyses for association studies.
FIGURE 1 in Revision of the Calceolaria tripartita s. l. species complex (Calceolariaceae) using multivariate analyses of morphological characters
FIGURE 1. Bivariate scatter plot formed by the two first components of the Principal Component Analysis (PCA) of 14 characters (A) and 9 floral characters (B) from 190 specimens representing all taxa included in the Calceolaria tripartita complex and C. aquatica. Each point represents one specimen; filled symbols indicate specimens with only one anther theca fertile, empty symbols indicate specimens with both thecae fertile. Ellipses indicate species hypothesis. The different stamen morphologies found in each group are also shown in B.
FIGURE 4 in Revision of the Calceolaria tripartita s. l. species complex (Calceolariaceae) using multivariate analyses of morphological characters
FIGURE 4. Calceolaria aquatica (A) showing stamen morphology (B) and node of leaf petiole without wing (C). Calceolaria rivularis (D) showing stamen morphology (E) and winged node of the petiole (F). Calceolaria tripartita (G) showing two of the most common stamen morphologies (H, I), black arrow indicates connective's protuberance. All photos were taken by the author.
FIGURE 2 in Revision of the Calceolaria tripartita s. l. species complex (Calceolariaceae) using multivariate analyses of morphological characters
FIGURE 2. Bivariate scatter graph of connective length (x) vs. node wing width (y) of 84 specimens assigned to Calceolaria tripartita s.s. and C. chelidonioides. Arrows on the drawings indicate how these two characters were measured.
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Allen Brain Atlas
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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.