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51 results for “geometric morphometric analyses”
FIGURE 6. A in Distinguishing between three modern Ellobius species (Rodentia, Mammalia) and identification of fossil Ellobius from Kaldar Cave (Iran) using geometric morphometric analyses of the first lower molar
FIGURE 6. A) First two PCs from the Principal component analysis performed on the size and shape including the reference collection and Kaldar Cave material. B) Boxplot of the total length of Ellobius from the extant reference collections and Kaldar Cave.
FIGURE 4. Ellobius right lower m1. A in Distinguishing between three modern Ellobius species (Rodentia, Mammalia) and identification of fossil Ellobius from Kaldar Cave (Iran) using geometric morphometric analyses of the first lower molar
FIGURE 4. Ellobius right lower m1. A) 14 landmarks: Landmarks on the outermost turning point of buccal (2, 4, 6) and lingual (8, 10, 12, 14) salient angles, and on the innermost turning point of buccal (3, 5) and lingual (9, 11, 13) reentrant angle. B) 60 semi-landmarks on the anterior cap.
FIGURE 2. Ellobius lower m1s in Distinguishing between three modern Ellobius species (Rodentia, Mammalia) and identification of fossil Ellobius from Kaldar Cave (Iran) using geometric morphometric analyses of the first lower molar
FIGURE 2. Ellobius lower m1s (all figured as right ones) from the extant reference collections and Kaldar Cave. A) Ellobius fuscocapillus: A.1-Kaldar Cave, 2014/4/SL5II/E6/125-130, right lower m1, number 157. A.2-Kaldar Cave, 2014/4/SL5/E5/109-111, right lower m1, number 520. A.3-Kaldar Cave, 2014/5/SL7II/E7/170-180, right lower m1, number 104. A.4- Kaldar Cave, 2014/5/SL7II/F6/135-145, right lower m1, number 547.A.5-modern, NHM86101513, Afghanistan, right lower m1. A.6-modern, FM111846, Iran, right lower m1. A.7-modern, NHM86101512, Afghanistan, right lower m1; B) Ellobius lutescens: B.1-Kaldar Cave, 2014/5/SL7II/F6/130-140, right lower m1, number 319. B.2- Kaldar Cave, 2014/4/SL5II/F7/115-118, right lower m1, number 90. B.3- Kaldar Cave, 2014/4/SL5II/F7/115-118, right lower m1, number 91. B.4- Kaldar Cave, 2014/5/SL7II/E7/145-150, right lower m1, number 436. B.5-modern, NMH916416, Turkey, right lower m1. B.6-modern, NMH916414, Turkey, right lower m1. B.7-modern, NMH916412, Turkey, right lower m1; C) Ellobius talpinus: C.1-modern, NHM3421126, Russia, right lower m1. C.2-modern, FM103163, Afghanistan, right lower m1. C.3-modern, AMNH59797, Mongolia, right lower m1. Scale 1 mm.
FIGURE 1. A in Distinguishing between three modern Ellobius species (Rodentia, Mammalia) and identification of fossil Ellobius from Kaldar Cave (Iran) using geometric morphometric analyses of the first lower molar
FIGURE 1. A) Occlusal surface of Ellobius right lower m1: triangle (T); buccal re-entrant angle (BRA); lingual reentrant angle (LRA); anterior cap (AC); posterior lobe (PL); B) Lingual view of left lower m1.
FIGURE 5 in Distinguishing between three modern Ellobius species (Rodentia, Mammalia) and identification of fossil Ellobius from Kaldar Cave (Iran) using geometric morphometric analyses of the first lower molar
FIGURE 5. Principal component analysis on the normalized landmarks and sliding semilandmarks and shape configuration at the extreme ends of the two first PCs.
FIGURE 7 in Distinguishing between three modern Ellobius species (Rodentia, Mammalia) and identification of fossil Ellobius from Kaldar Cave (Iran) using geometric morphometric analyses of the first lower molar
FIGURE 7. Morphological differences between Ellobius fuscocapillus (left) and Ellobius lutescens (right). Arrows depict the displacements between corresponding landmarks in the reference (dots) and Ellobius lutescens as target specimens.
Fig. 7 in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part A): introduction and preliminary analyses
Fig. 7. Example of search for shape outliers (emphasized in red) in yellow-bellied marmots: (a) phenogram of shape; (b1-2) scatterplots of PC1 vs PC3 and PC4 vs PC9 (percentages of variance accounted for by each PC shown in parentheses); (c-d) visualization of individual 193 using displacement vectors for this specimen relative to the sample mean shape (c), as well as its original photograph (d).
Fig. 4 in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part A): introduction and preliminary analyses
Fig. 4. Graphical examination of replicability in shape using the reduced 12 landmarks configuration. a. PCA scatterplot (in parentheses the variance accounted for by each PC) with convex hulls for the first (grey) and second (red) duplicate. b. Example of phenogram used to count 'sister duplicates' in the whole sample (the inset zooms in the phenogram to exemplify how duplicates 1 and 2 of each individual, e.g., number 97, a female, or number 81, a male, etc., should cluster in pairs, if ME is smaller than inter-individual differences).
Fig. 1 in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part A): introduction and preliminary analyses
Fig. 1. Study flowchart for both preliminary (A) and main (B) analyses. The flowchart can be used as a reminder for the main analytical steps in a taxonomic study using GMM. To the same aim, at the end of part B, I added a checklist (Appendix B). In the flowchart, I have included the power analysis and few other analyses, which are optional (dotted lines). The power analysis is shown here connected to both the preliminary steps and the group comparisons, because it can be either prospective or retrospective. The sensitivity of results to the inclusion or exclusion of the smallest samples is also connected to both preliminary and main analyses, because it can be used at any step in the analysis. Sometimes (e.g., in the discriminant analysis (DA) of shape), if p is large relative to N and dimensionality reduction is needed, one could also assess the sensitivity of results to the inclusions of different number of PCs. I stress that, as discussed in the main text of both parts A and B, if a taxon is known to have a large SDM, which may vary in pattern depending on the species or subspecies, even preliminary analyses (such as those for ME or outlier detection) should probably be run with separate sexes.
Fig. 3. a in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part A): introduction and preliminary analyses
Fig. 3. a. Final configuration, reduced to 12 landmarks after excluding low precision landmarks. b. Graphical examination of size replicability (12 landmarks configuration) using a plot of CS in the second duplicate against CS in the first duplicate.
Fig. 2. Initial configuration with 15 in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part A): introduction and preliminary analyses
Fig. 2. Initial configuration with 15 landmarks (a) and analysis of absolute per-landmark imprecision (b, c). Figure 2b shows the profile plot for the summary statistics of per-landmark variance in the two digitizations. Figure 2c shows the scatter of landmarks purely due to digitization error (red landmarks mark the mean form, to which the differences between the first and second digitization were added).
Fig. A1 in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part A): introduction and preliminary analyses
Fig. A1. Box and jitter plot of shape SDM estimated using randomized subsampling experiments in yellow-bellied marmots (whiskers in this figure mark the range from minimum to maximum, with no assessment of outliers, which are irrelevant in the context of this didactic example). The total female sample (F) is split in progressively smaller, mutually exclusive, random subsamples and the same is done for males (M). The first subsample of 70 individuals per sex is almost the same of the total samples; the second consists of two female and two male subsamples with 35 individuals each; the third set of subsamples is made of 17–18 individuals per sex and the fourth and fifth include only 10 or 5 individuals respectively. The vertical axis shows the Procrustes distance between means of females and means of males of each set of subsamples. The red circles show the observed mean female to male Procrustes distance in VIM, Alaskan (bro) and Olympic marmots (oly); they are added to the box and jitter plots of yellow-bellied marmot subsamples whose N is closer to the observed N is these three species.
Fig. 5 in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part A): introduction and preliminary analyses
Fig. 5 (next page). Examples of ME. a. Suspected bias due to a time lag in the data collection. The scatterplots (a2-a3) are ordinations of 3D cranial shapes in a large sample of African adult men; above the ordinations (a1), the mean shape differences between green (first round of data collection, N = 377) and blue (second round, N = 161) groups are shown (magnified five times) in dorsal, side and frontal views. In the PCA (a2) there is a small amount of separation between 'green' and 'blue' on PC2 (variance explained in parentheses). In the DA (a3), the 'green-blue' separation on the horizontal axis (DF1, with, in parentheses, the between group variance explained) is almost perfect despite the fact that groups, whose differences are being maximized in this plot, are in fact the real 32 geographic populations (shown using convex hulls - see main text). b. PC1-2 scatterplots of adult 3D craniofacial shapes in a European sample of adult women (N = 351, shown in pink) and men (N = 380, in blue). The configuration is smaller and different from the one in (a). Above the scatterplots, the shape corresponding to the positive extreme of PC1 (variance explained in parentheses) is shown, in dorsal view, using displacement vectors (b1-b2). The scatterplot to the left (b3) is the full landmark configuration, whereas the one to the right (b4) has euryon removed. Euryon dominates PC1 differences (b3) in the full configuration (b1). After removing it (b2), not only there is no landmark that dominates variation on PC1 (b4), but also the separation of females and males disappears and (see main text) the shape variance accounted for by sex drops from 6% to 3%. c. PC1-2 scatterplot (variance explained in parentheses) of marmot mandibular shape using the reduced landmark configuration in real (c1) and simulated (c2) samples of hoary marmots and woodchucks. The simulated data are obtained by adding to the original shape coordinates large random gaussian noise (SD = 0.05) before Procrustes re-superimposing the data. Random noise (c2) completely obliterates the real differences (c1) and brings the F ratio and Rsq from F = 38 and Rsq = 20%, in the real data, to F = 2 and Rsq = 0.8% in the simulated ones. In both datasets, the tangent space approximation (assessed in TPSSmall) in the real data (c1) produces a correlation of one between shape distances; however, whereas the slope of the least square regression (tangent space Euclidean shape distances onto Procrustes shape distances) is one in the real data, it is 0.97 in the simulated ones, which suggests an almost problematically large amount of shape variance in this second dataset.
Fig. 7. Geometric morphometric analyses. A. Principal Component Analysis. B in Early steps in the radiation of notoungulate mammals in southern South America: A new henricosborniid from the Eocene of Patagonia
Fig. 7. Geometric morphometric analyses. A. Principal Component Analysis. B. Canonical Variate Analysis.
FIGURE 3. A in Distinguishing between three modern Ellobius species (Rodentia, Mammalia) and identification of fossil Ellobius from Kaldar Cave (Iran) using geometric morphometric analyses of the first lower molar
FIGURE 3. A) Kaldar Cave location. B) Entrance from the south of Kaldar Cave.
Are geometric morphometric analyses replicable? Evaluating landmark measurement error and its impact on extant and fossil Microtus classification
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Data from: Geometric morphometric analyses of worn cheek teeth help identify extant and extinct gophers (Rodentia: Geomyidae)
Studies of the biostratigraphy and palaeoecology of fossil vertebrate assemblages require large samples of accurately identified specimens. Such analyses can be hampered by the inability to assign isolated and worn remains to specific taxa. Entoptychine gophers are a diverse group of burrowing rodents found in Oligo-Miocene deposits of the western United States. In both entoptychines and their extant relatives the geomyines, diagnostic characters of the occlusal surface of the teeth are modified with wear, making difficult the identification of many isolated fossil teeth. We use geometric morphometrics to test the hypothesis that tooth shape informs taxonomic affinities and expected levels of morphological variation across gopher taxa. We also incorporate data from microcomputer tomography to investigate changes in occlusal surface shape through wear within individuals. Our analyses demonstrate the usefulness of our approach in identifying extant geomyines to the genus, subgenus and species levels, and fossil entoptychines to the genus and, in some cases, the species level. Our results cast doubt on the validity of some species within Entoptychus and suggest future revisions to entoptychine taxonomy. The amounts of morphological divergence observed among fossil and extant genera are similar. Fossil species do not differ greatly from extant ones in that regard either. Further work evaluating the morphological variation within and across entoptychine species, including unworn teeth and osteological material, will allow revised analyses of the biostratigraphy and palaeoecology of important Oligo-Miocene mammalian assemblages of the western United States and help to infer the phylogenetic relationships and evolution of gophers.
Figure 4 in Geometric morphometric and phylogenetic analyses of Arizona Sky Island populations of Scaphinotus petersi Roeschke (Coleoptera: Carabidae)
Figure 4. ANOVA of male length and width trait measurements by mountain range and subspecies: A, male head width; B, male body length; C, male leg length; D, male head length; E, female head width; F, female body length; G, female leg length; H, female head length. Black string, median; open box, first interquartile; bar, second interquartile.
Figure 3 in Geometric morphometric and phylogenetic analyses of Arizona Sky Island populations of Scaphinotus petersi Roeschke (Coleoptera: Carabidae)
Figure 3. Maximum-likelihood tree of Scaphinotus petersi populations from combined 28S rDNA, COI, and ND1 + mtRNA data. The out-group, Sphaeroderus lecontei, is removed to show greater detail. Specimen numbers are removed, but the subspecies and mountain range from which they were collected is indicated. Specimens from all subspecies in Table 1 are represented in the molecular phylogeny. Support for major branches is indicated by Bayesian posterior probability/ maximum likelihood bootstrap values. *Bayesian posterior probability greater than 95%. Scale bar units are substitutions per site.
Figure 1. A in Geometric morphometric and phylogenetic analyses of Arizona Sky Island populations of Scaphinotus petersi Roeschke (Coleoptera: Carabidae)
Figure 1. A, study location; distribution area of Scaphinotus petersi is circled. Habitats above 1830 m a.s.l. are shown in black, and habitats between 1500 and 1830 m a.s.l. are shown in grey. B, shaded relief map of study area. Black dots denote the sampling localities of S. petersi used in this study (see Table 1), abbreviated as follows: C, Chiricahua Mountains; H, Huachuca Mountains; P, Pinal Mountains; PN, Pinaleño Mountains; R, Rincon Mountains; SA, Sierra Ancha Mountains; SC, Santa Catalina Mountains; SR, Santa Rita Mountains; WM, White Mountains. Figure modified from Ober et al. (2011).
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International Brain Laboratory public data
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OpenNeuro
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