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Figure 8 in Eigensurface analysis, ecology, and modelling of morphological adaptation in the falconiform humerus (Falconiformes: Aves)

Figure 8. Ordination of the falconiform species for which accurate flight speed information was available (47 species) within the space of the first two canonical variates axes of 21 eigensurface shape variables. Based on this result 97.9% of the training set species were assigned to their correct flight-speed groups on the basis of proximity of their projected positions to the group means. Shape models below the ordination plot represent along-axis coordinate locations through the canonical variates space for CV-1 calculated using the method of MacLeod (2007). Major differences can be seen in the size of the deltoid crest (DC) and the bicipital crest (BC), and in species with lower scores the bicipital surface (BS) is larger and more rounded, forming a dome. Note the CV-2 axis contains no information with regard to group discrimination and should not be interpreted. Species abbreviations are listed in Appendix 1.

Figure 6 in Eigensurface analysis, ecology, and modelling of morphological adaptation in the falconiform humerus (Falconiformes: Aves)

Figure 6. Models of the first three eigensurface (E-Surf) axes that describe the distribution of morphology among the 50 humerus specimens. Eigensurface axis 1 describes 41.8% of observed shape variation and shows clear differences in the shapes of the deltoid and bicipital crests between high and low scoring specimens. Eigensurface axis 2 accounts for 12.8% of morphological variation and describes more subtle variation in the shape of the deltoid crest. Eigensurface axis 3 includes 7.78% of surface shape variation and exhibits differences in the shape and position of the deltoid and bicipital crests between specimens. See text for further discussion of the shape changes and interpretation of the morphological variation described by these model axes.

Figure 9 in Eigensurface analysis, ecology, and modelling of morphological adaptation in the falconiform humerus (Falconiformes: Aves)

Figure 9. Ordination of the falconiform species for which foraging flight-style information was available (42 species) within the space of the four canonical variates axes established in a CVA using 13 eigensurface shape variables. The distribution of humerus shapes on the first two CV axes (above) together accounted for 73.7% of between-groups humeral shape variation, while CV-3 and CV-4 (below) together represent the remaining 26.3% of the variation used to distinguish between the five flight-style groups: perch-hunting, chasing, hovering, soaring, and low-flight. Based on this result 88.1% of training set species were assigned to their correct flight-style groups on the basis of proximity to the group means. Shape models representing along-axis coordinate positions through the canonical variates space are shown in Figure 10, highlighting morphological features that were particularly important in distinguishing between high- and low-scoring species on each CV axis. Species abbreviations are listed in Appendix 1.

Figure 4 in Eigensurface analysis, ecology, and modelling of morphological adaptation in the falconiform humerus (Falconiformes: Aves)

Figure 4. Steps in the definition of eigensurface sampling grids as exemplified by the three scans of the proximal–dorsal surfaces of the humeri illustrated in Figure 3. The upper row shows the region selected for analysis. Point clouds of approximately 2000–3000 points representing this region were saved as ASCII text files along with separate files recording the boundary outline coordinates, points along a midline, and the coordinate positions of the two landmarks selected to orient the grid. Lower row: eigensurface form sampling grids. Each eigensurface grid is calculated by selecting a grid resolution (e.g. 10, 15, 20) and then interpolating that number of equally spaced semilandmark points along each half-outline (black) and along the surface trace of the chord joining the orientation landmarks (white). In this analysis a grid resolution of 10 was selected and the landmark chord oriented so that it traced the approximate position of the mid-line running from the humerus head down the centre of the shaft. Lateral or 'rib' chords are then drawn along the surface of the form such that each mid-line node is joined to a boundary outline node on either side. Equally spaced rib semilandmarks (grey) were then located along each of these rib chords with the number of semilandmarks used to quantify the shape of the chord being set iteratively as the number required to represent 95% of the length of the most contorted corresponding chord across the entire sample. This iterative procedure is identical to that used by MacLeod (1999) to sample boundary outline form in extended eigenshape analysis. The resulting grids quantify the geometric form of the surface of the humerus using an equal number of semilandmark points for each specimen in the sample and with each point being located in a position that corresponds topologically (relative to the grid point set as a whole) to all other points across the sample.

Figure 2 in Eigensurface analysis, ecology, and modelling of morphological adaptation in the falconiform humerus (Falconiformes: Aves)

Figure 2. Phylogeny of Falconiformes based on Griffiths et al. (2004), Lerner & Mindell (2005), Lerner et al. (2008) and Griffiths et al. (2007). Species are grouped by their ecological or vernacular names. Size of the wedges indicates species richness. Genus or species names of taxa, along with the number of species from each genus included in the study dataset, are indicated after each clade name. Specimens were selected to be representative of overall morphological, ecological, and behavioural diversity within their group. Lineages shown in grey were not included in this investigation.

Figure 3 in Eigensurface analysis, ecology, and modelling of morphological adaptation in the falconiform humerus (Falconiformes: Aves)

Figure 3. Creation of 3D humerus proximal–dorsal surface sampling grids. Upper row: original clouds of scanned points located in xyz space for Falco sparverius (American kestrel, left), an open habitat species that forages using hovering flight; Accipiter cooperii (Cooper's hawk, centre), a forest habitat perch-hunter; and Gyps africanus (white-backed vulture, right), a woodland-dwelling, soaring scavenger. Middle row: adaptive mesh representations of the original point clouds interpolated to 2000–3000 mesh vertices. Lower row: filled and smoothed mesh illustrating the degree of surface detail captured by the surface sampling procedure. Note this is not the level of detail captured and employed in the eigensurface analysis procedure (see Fig. 4). Rather, the lower row figures represent the morphological starting points for eigensurface analysis.

Figure 5 in Eigensurface analysis, ecology, and modelling of morphological adaptation in the falconiform humerus (Falconiformes: Aves)

Figure 5. Ordination of the 50 falconiform humerus surfaces in the space formed by the first three eigensurface axes. When taken together this three-dimensional space accounts for 62.4% of observed surface shape variation. Species abbreviations are given in Appendix 1. Note the strong clustering of taxa into non-phylogenetic groups in the subspace, circled in the figure. The locations of these same specimens in the space formed by the first 21 eigensurface (E-Surf) axes (accounting for 95.4% of observed surface shape variation) were used as a summary of biologically important shape variation for all subsequent analyses. See text for discussion.

Figure 11 in Eigensurface analysis, ecology, and modelling of morphological adaptation in the falconiform humerus (Falconiformes: Aves)

Figure 11. Ordination of the falconiform species for which foraging habitat information was available (34 species) within the space of the first two canonical variates axes of 18 eigensurface shape variables. Based on this result 91.9% of training set species were assigned to foraging habitat groups correctly on the basis of proximity to the group means; however, a likelihood ratio test showed that there was a 15.6% chance that this result could have been achieved using a dataset that lacked subgroup structure, and a cross-validation test could only accurately assign 55.6% of species to the correct habitat group. Rows of shape models below the ordination plot represent along-axis coordinates through the canonical variates space for CV-1 calculated using the method of MacLeod (2007). Although differences between the models were extremely subtle, in higher scoring specimens the bicipital crest (BC) extends slightly further distally towards the shaft and the bicipital surface is slightly flatter. Note the CV-2 axis contains no information with regard to group discrimination and should not be interpreted. Species abbreviations are listed in Appendix 1.

Figure 10 in Eigensurface analysis, ecology, and modelling of morphological adaptation in the falconiform humerus (Falconiformes: Aves)

Figure 10. Shape models representing along-axis trajectories through the canonical variates space for the CVA of flight style [CV-1 (first row), CV-2 (second row), CV-3 (third row) and CV-4 (fourth row)]. Models were calculated using the method of MacLeod (2007). Detailed inspection of the CV-1 models revealed that low scoring hovering taxa had humeri with larger, more pointed deltoid crests (DC), more rounded bicipital crests (BC) and more distinct bicipital furrows (BF); in higher scoring soaring taxa the humerus is more slender with a flatter appearance, the humeral head (H) is more domed and the external tuberosity (ET) is in a more proximal position. The CV-2 axis represented a shift between chase-hunting, perch-hunting, and low-flying species. From the CV-2 models it is clear that higher scoring taxa have more robust humeri with a thicker shaft (S), larger DC and BC. In specimens that have a lower score on CV-3 (which tended to be perch-hunters), the distal portion of the deltoid crest (DC distal) is larger than in higher scoring specimens, which have smaller DCs and less defined BFs. A high score on CV-4 is indicative of a flatter bicipital surface and a smaller DC (particularly the proximal portion) that extends further down the shaft.

Figure 7 in Eigensurface analysis, ecology, and modelling of morphological adaptation in the falconiform humerus (Falconiformes: Aves)

Figure 7. Results of a CVA for the original 20 eigensurface variables (A and B), and a projection of the original data into a space formed by the eigenvector decomposition of the covariance matrix of the standardized contrasts between internal nodes of the cladogram in Figure 2 as modelled by the phylogenetically independent contrasts method (C and D, see text for discussion). Note the well-defined lineage-group separation achieved by both analyses and the overall similarities in lineage-group placements relative to one another. The fact that highly structured lineage-specific differences in shape variation remain part of the system even after phylogenetic contrasts have been removed strongly suggests that the dominant shape variation factor(s) being expressed in the eigensurface space cannot be accounted for under a simple model of phylogenetic covariation. See text for discussion.

Figure 12 in Eigensurface analysis, ecology, and modelling of morphological adaptation in the falconiform humerus (Falconiformes: Aves)

Figure 12. Ordination of the falconiform species for which migratory behaviour information was available (32 species) within the space of the first two canonical variates axes of ten eigensurface shape variables. Based on this result 90.6% of training set species were assigned to migratory behaviour groups correctly on the basis of proximity to the group means. Rows of shape models below the ordination plot represent along-axis coordinates through the canonical variates space for CV-1, calculated using the method of MacLeod (2007). In high-scoring taxa the external tuberosity (ET) is more prominent and other protrusions may be noted around the humeral head (H), while in lower scoring taxa the humeral head has a smoother, more rounded appearance. See text for further discussion. Note the CV-2 axis contains no information with regard to group discrimination and should not be interpreted. Species abbreviations are listed in Appendix 1.

Figure 8 in Tracking parallel adaptation of shell morphology through geological times in the land snail genus Pupilla (Gastropoda: Stylommatophora: Pupillidae)

Figure 8. Phylogenetic tree based on COI. 50% majority rule consensus tree from Bayesian analysis with posterior probabilities/bootstrap support values (from maximum likelihood analysis). Sample abbreviations and groups of samples are given in Tables 1 and 3.

Figure 11 in Tracking parallel adaptation of shell morphology through geological times in the land snail genus Pupilla (Gastropoda: Stylommatophora: Pupillidae)

Figure 11. Pupa muscorum var. pratensis. Series of four shells from type locality Dinkelscherben near Augsburg (Germany) in frontal and lateral view. A, neotype SMNS-ZI0138339. B–D, SMNS-ZI0138341.

Figure 10 in Tracking parallel adaptation of shell morphology through geological times in the land snail genus Pupilla (Gastropoda: Stylommatophora: Pupillidae)

Figure 10. Pupilla loessica. Series of four shells from type locality Předmostí at Přerov (Czech Republic, fossil from Saalian loess) in frontal and lateral view. A is the designated neotype. All deposited at the National Museum of Prague.

Figure 7 in Tracking parallel adaptation of shell morphology through geological times in the land snail genus Pupilla (Gastropoda: Stylommatophora: Pupillidae)

Figure 7. Shell microsculpture (SEM micrographs). A–T, P. loessica. U–Z, P. alpicola. a–d, P. alpicola (morphogroup P. m. densegyrata). e–h, P. alpicola (lowland populations "P. pratensis"). i–j, P. muscorum. A–B, M_4559, Altai, Saylyugem (Russia). C–D, M_4575, Altai, Saylyugem (Russia). E–F, H_MC409, Altai, Dzhazator (Russia). G–H, M_3970, Khatgal, shore of Lake Khövsgöl Nuur (northern Mongolia). I–J, M_2523_2, Yelantsy near Lake Baikal (Russia). K–L, M_2523_1, Yelantsy near Lake Baikal (Russia). M–N, M_994, Karsdorf (Saxony Anhalt, Germany), fossil from Early Saalian. O–P, M_459, Zeuchfeld (Saxony

Figure 9 in Tracking parallel adaptation of shell morphology through geological times in the land snail genus Pupilla (Gastropoda: Stylommatophora: Pupillidae)

Figure 9. Phylogenetic tree based on ITS2. 50% majority rule consensus tree from Bayesian analysis with posterior probabilities/bootstrap support values (from maximum likelihood analysis). Sample abbreviations and groups of samples are given in Tables 1 and 3.

Figure 8. Hearing and diving can shape the ear structures. A in The shape of water: adaptations of cochlea morphology in seals and oưers

Figure 8. Hearing and diving can shape the ear structures. A, there is a slight correlation (P = 0.021) for terrestrial species of the bandwidth, number of octaves, and the length of the bony meatus. B, linear regression for the ratio of the average cross-section area size of the cochlea and low frequency cut-off in-air are highly significant for all specimens. C, cochlear height is a relevant factor for the high-frequency cut-off in aquatic animals. D, underwater low-frequency cut-off reveals correlation to the area size of the round window. Legend: filled circles = Pinnipedia + Ursidae, unfilled circles = Musteloidea + Canidae; green = terrestrial species, blue = aquatic + semi-aquatic species * = P ≤ 0.05, **** = P ≤ 0.0001.

Figure 2 in The shape of water: adaptations of cochlea morphology in seals and oưers

Figure 2. PCA of cochlea shape in Caniformia. The shape analysis revealed a paưern of clustering along the axes, with highly terrestrial animals (mustelids, dogs, and foxes) primarily on the upper right quadrant and highly aquatic (Pinnipedia) on the leħ one. The two first PCs explain about 82% of the cochlea shape (PC1 = 53%, PC2 = 29%). The four phylogenetic groups of Pinnipedia + Ursidae and Lutrinae + Mustelinae are highlighted by shaded areas. For beưer visualization, thumbnails of animals with audiogram data have been placed at the respective position of the data point. For each investigated sample, data from the right cochlea was used for PC analysis (N = 52). Blue = aquatic and semi-aquatic animals, green = terrestrial animals; full circle = Pinnipedia and Ursidae, empty circle = Musteloidea and Lutrinae.

Figure 1 in The shape of water: adaptations of cochlea morphology in seals and oưers

Figure 1. Phylogenetic tree of the analysed taxa of Caniformia (Mammalia: Carnivora). The circles illustrate how the taxa are grouped for their habitat: green = terrestrial, blue = semi-aquatic, and their phylogenetic affiliation: full circle = Pinnipedia/Ursidae, empty circle = Musteloidea/ Lutrinae + Canidae in this study. The headphone sign indicates the presence of hearing information via audiograms from the literature (see details in the Supporting Information, Table S1). The discussed origin of pinnipeds is marked by question marks and the presumed origin of a secondary return to the habitat of water with water wave symbols.

Figure 5 in The shape of water: adaptations of cochlea morphology in seals and oưers

Figure 5. Differences of morphological traits between Lutrinae, Pinnipedia, and terrestrial Caniformia are highlighted by side-to-side comparison. A, the ratio of tympanic membrane area to oval window area is drastically altered in aquatic specimens when compared to their terrestrial relatives. The ratio of seals is significantly smaller, but the same effect is visible in oưers albeit to a smaller degree. B, the distance between the tympanic sulcus and the oval window is drastically altered in aquatic species. There is however liưle difference between oưers (Lutrinae) and seals (Pinnipedia). C, comparison of the area size of the opening of the ECF/canaliculus cochleae, normed for body-length of all specimens with sufficient scan resolution (N = 35). Direct comparison shows clear differentiation between pinnipeds and the other groups, displaying an increase in area size of the opening. * = P ≤ 0.05, ** = P ≤ 0.01, ***= P ≤ 0.001, **** = P ≤ 0.0001.