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Precipitation and vegetation shape patterns of genomic and craniometric variation in the Central African rodent Praomys misonnei
<p><span><span>Predicting species capacity to respond to climate change is an essential first step in developing effective conservation strategies. However, conservation prioritization schemes rarely take evolutionary potential into account. Ecotones provide important opportunities for diversifying selection and may thus constitute important reservoirs of standing variation, increasing the capacity for future adaptation. Here we map patterns of environmentally-associated genomic and craniometric variation in the central African rodent <i>Praomys misonnei</i> to identify areas with the greatest turnover in genomic composition. We also project patterns of environmentally-associated genomic variation under future climate change scenarios to determine where populations may be under the greatest pressure to adapt. While precipitation gradients influence both genomic and craniometric variation, craniometric variation is also affected by changes in vegetation structure. Areas of elevated environmentally-associated genomic and craniometric variation overlap with zones of rapid ecological transition underlining their importance as reservoirs of evolutionary potential. We find that populations in the Sanaga river basin, central Cameroon and coastal Gabon are likely to be under the greatest pressure from climate change. Lastly, we make specific conservation recommendations on how to protect zones of high evolutionary potential and identify areas where populations will be the most susceptible to climate change. </span></span></p>
Figure 4 in Cryptic complexity in felid vertebral evolution: shape differentiation and allometry of the axial skeleton
Figure 4. Vertebral profile plots of locomotory groups (i.e. arboreal, terrestrial and scansorial species) showing variation in vertebral measurements along the vertebral column number. A, centrum length (CL); B, centrum height (CH); C, centrum width (CW); D, centrum shape (CS); E, lamina width (LW); F, neural spine lever arm (NSLA); J, transverse process dorsoventral angle (TPDV); K, transverse process anteroposterior angle (TPAP); L, interzygapophyseal length (IZL); M, accessory process distance (APD). Regular vertical bars mark the boundaries between vertebral regions (i.e. cervical, thoracic and lumbar regions) and the corresponding analytical bins, while dotted vertical lines mark boundaries only related to vertebral bins.
Figure 3 in Cryptic complexity in felid vertebral evolution: shape differentiation and allometry of the axial skeleton
Figure 3. PCA plots of PC1 X PC2 (A) and PC1 X PC3 (B) showing species distribution in vertebral morphospace. Species are grouped according to their locomotory mode (i.e. cross: arboreal species; triangle: scansorial species; squares: terrestrial species).
Figure 2 in Cryptic complexity in felid vertebral evolution: shape differentiation and allometry of the axial skeleton
Figure 2. Vertebral measurements: A–C, atlas; D and E, axis; F, C6; G–J, L2. Abbreviations: LDA, length of dorsal arch; Pre_Z-D, prezygapophyseal distance; Post_Z-D, postzygapophyseal distance; TPLA, transverse process lever arm; WDA, width of dorsal arch. B. LVA, length of ventral arch; WVA, width of ventral arch. C. HNC, height of the neural canal. D. DW, dens width. E. DA, dens angle; DL, dens length; NSL, neural spine anteroposterior length at tip. F. LIL, length of inferior lamella. G. CL, centrum length; IZL, interzygapophyseal length; NSL, neural anteroposterior length at tip; NSLA, neural spine lever arm. H. APD, accessory process distance; CH, centrum height; CW, centrum width; NSLA, neural spine lever arm. I. TPDV, transverse process dorsoventral angle; TPLA, transverse process lever arm. J. LW, lamina width; TPAP, transverse process anteroposterior angle. Vertebral images are from of a CT scan of Acinonyx jubatus (cheetah).
Figure 1. Felid phylogeny showing studied species, from a in Cryptic complexity in felid vertebral evolution: shape differentiation and allometry of the axial skeleton
Figure 1. Felid phylogeny showing studied species, from a subset of Nyakatura & Bininda-Emonds (2012), with felid lineage designation according to Johnson et al. (2006), and locomotory (A, S, and T) and prey size specialization (circles at tip of phylogeny) according to Meachen-Samuels & Van Valkenburgh (2009b). Abbreviations: arboreal (A), scansorial (S) and terrestrial (T). Prey size symbols: black circles – large prey specialist; dark grey circles – mixed prey specialist; and light grey with black rim circles – small prey specialist.
Figure 8. Phylogeny showing a in Evolution of molar shape in didelphid marsupials (Marsupialia: Didelphidae): analysis of the influence of ecological factors and phylogenetic legacy
Figure 8. Phylogeny showing a summary of the optimization for the third upper molar (M3). Numbers on the branches indicate node number. Taxon names and nodes in bold indicate the optimizations being shown. Deformation grids show the changes with respect to the previous node.
FIGURE 3. Senecio fernandinus. A. Habitat and habit. B. Capitulum. C. Corolla. D. Involucral bract. E. Leaf shape variability. Pictures A in Two new species of Senecio (Compositae, Senecioneae) from Peru
FIGURE 3. Senecio fernandinus. A. Habitat and habit. B. Capitulum. C. Corolla. D. Involucral bract. E. Leaf shape variability. Pictures A by Luis Mirano, B by Berni Britto; details C–E from P. Gonzáles & E. Becerra 4188 (USM) by H. Beltrán.
Figure 6 in Geometric morphometric analysis of shell shape variation in Conus (Gastropoda: Conidae)
Figure 6. Plot of the results of the principal components analysis (PCA) with the identified Conus specimens and specimens unidentified a priori. Labels indicate identification of specimens after the PCA. +, Conus consors;, Conus miles; ¥, Conus stercusmuscarum; O, Conus striatus; °, Conus textile; •, unidentified.
Figure 1 in Geometric morphometric analysis of shell shape variation in Conus (Gastropoda: Conidae)
Figure 1. Conus species used in this study. A, Conus consors Sowerby ii, 1833; B, Conus miles Linnaeus, 1758; C, Conus stercusmuscarum Linnaeus, 1758; D, Conus striatus Linnaeus, 1758; E, Conus textile Linnaeus, 1758.
Figure 9 in Geometric morphometric analysis of shell shape variation in Conus (Gastropoda: Conidae)
Figure 9. Plot of the results of the principal components analysis of relative warp 1 and relative warp 2. +, Conus consors;, Conus miles; ¥, Conus stercusmuscarum; O, Conus striatus; °, Conus textile.
Figure 2 in Geometric morphometric analysis of shell shape variation in Conus (Gastropoda: Conidae)
Figure 2. Landmarks (LM) on a Conus specimen. LM1 – apex of the shell; LM2–5 – sutures between major whorls on right profile; LM6 – junction between end of suture and apertural lip; LM7 – outermost curve of aperture; LM8 – lowest point of aperture at base; LM9 – lowest point of last whorl at base; LM10 – most external point on left profile of last whorl; LM11 – shoulder on left profile, where last whorl curves; LM12 – point opposite to LM5 on left profile; LM13 – point opposite LM4 on left profile; LM14 – point opposite LM3 on left profile; LM15 – point opposite LM2 on left profile; LM16 – most external point on right profile of last whorl.
Figure 5 in Geometric morphometric analysis of shell shape variation in Conus (Gastropoda: Conidae)
Figure 5. Phylogram depicting relationships amongst the five Conus species based on morphological distances (between landmark coordinates), generated by the NEIGHBOR program of PHYLIP 3.69.
Figure 4 in Geometric morphometric analysis of shell shape variation in Conus (Gastropoda: Conidae)
Figure 4. Plot of the results of the principal components analysis of the 32 coordinates of 16 landmarks on Conus specimens grouped by dietary requirements. +, piscivores;, vermivores; °, molluscivores.
Figure 8 in Geometric morphometric analysis of shell shape variation in Conus (Gastropoda: Conidae)
Figure 8. Thin-plate spline grids; warps in reference to mean shape. Numbers indicate area expansion or compression factors (i.e. degree of local growth). Green represents expansion, purple compression. A, Conus consors; B, Conus miles; C, Conus stercusmuscarum; D, Conus striatus; E, Conus textile.
Figure 3 in Geometric morphometric analysis of shell shape variation in Conus (Gastropoda: Conidae)
Figure 3. Plot of the results of the principal components analysis of the 32 coordinates of 16 landmarks on Conus specimens. +, Conus consors;, Conus miles; ¥, Conus stercusmuscarum; O, Conus striatus; °, Conus textile.
Figure 3 in Mandible shape in marsupial and placental carnivorous mammals: a morphological comparative study using geometric morphometrics
Figure 3. Mandible shape variation along the first three relative warps (RW). A, relative warp 1 versus 3 showing the distribution of diet classes; B, relative warp 2 versus 3 showing the distribution of diet classes; C, relative warp 1 versus 3, showing the distribution of taxonomic groups; D, relative warp 2 versus 3, showing the distribution of taxonomic groups. Shape reconstructions show the extreme shape of each RW in black lines against the consensus shape in grey lines.
Figure 2 in Mandible shape in marsupial and placental carnivorous mammals: a morphological comparative study using geometric morphometrics
Figure 2. Mandible shape variation along the first four relative warps (RW). A, relative warp 1 versus 2, showing the distribution of diet classes; B, relative warp 3 versus 4, showing the distribution of diet classes; C, relative warp 1 versus 2, showing the distribution of taxonomic groups; D, relative warp 3 versus 4, showing the distribution of taxonomic groups. Shape reconstructions show the extreme shape of each RW in black lines against the consensus shape in grey lines.
Figure 1 in Mandible shape in marsupial and placental carnivorous mammals: a morphological comparative study using geometric morphometrics
Figure 1. Lateral view of a Canis lupus mandible showing the landmarks and semilandmarks used. Squares, landmarks; X, semilandmarks; 1, caudal extreme of the condyle; 2, most concave point of the mandibular notch; 3, dorso-caudal angle of the coronoid process; 4–11, semilandmarks; 12, distal extreme of the lower carnassial; 13, distal border of the protoconid projected to the base of the crown; 14, mesial border of the lower carnassial; 15, distal extreme of the c1; 16, mesial extreme of the c1; 17–28, semilandmarks; 29, anterior border of the masseteric fosa.
Figure 5 in Mandible shape in marsupial and placental carnivorous mammals: a morphological comparative study using geometric morphometrics
Figure 5. Canonical analysis of variance of taxonomic groups and diet classes. A, dietary discrimination in marsupials (factor 1 versus 2); B, discrimination of main Carnivora clades (factor 1 versus 2); C, discrimination of main Caniformia clades (factor 1 versus 2); D, discrimination of main Feliformia clades (factor 1 versus 2). Shape reconstructions show the extreme shape of each RW in black lines against the consensus shape in grey lines.
Figure 7 in Mandible shape in marsupial and placental carnivorous mammals: a morphological comparative study using geometric morphometrics
Figure 7. Allometric relationship between mandible shape and size. Consensus configuration is in the middle, shape of the largest species (Ursus arctos) to the left, and shape of the smallest species (Planigale maculate) to the right.
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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.