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Figure 8 in Genetic and morphological studies of species status for poorly known endemic Trochulus phorochaetius (Bourguignat, 1864) (Gastropoda: Pulmonata: Hygromiidae), and its comparison with closely related taxa
Figure 8. Canonical discriminant analysis based on genital measurements of Trochulus hispidus, Trochulus plebeius, Trochulus phorochaetius, and the Coiserette specimens. Wilks' lambda = 0.10933, F21,115 = 6.3833, P <0.00001.
Figure 1 in Genetic and morphological studies of species status for poorly known endemic Trochulus phorochaetius (Bourguignat, 1864) (Gastropoda: Pulmonata: Hygromiidae), and its comparison with closely related taxa
Figure 1. Geographical locations of the populations sampled (*) in France (A), Poland (B), Great Britain (C), and Germany (D); , main cities. See Table 1 for abbreviations.
Figure 7 in Genetic and morphological studies of species status for poorly known endemic Trochulus phorochaetius (Bourguignat, 1864) (Gastropoda: Pulmonata: Hygromiidae), and its comparison with closely related taxa
Figure 7. Correspondence analysis of qualitative data in Tochulus taxa and the Coiserette specimens.
Figure 3 in Genetic and morphological studies of species status for poorly known endemic Trochulus phorochaetius (Bourguignat, 1864) (Gastropoda: Pulmonata: Hygromiidae), and its comparison with closely related taxa
Figure 3. Shell traits: A, internal rib (ir) in Trochulus plebeius specimen from Zieleniec; B, band (b) in Trochulus hispidus specimen from Échallon; C, short hairs in T. plebeius specimen from Coiserette; D, long hairs in Trochulus phorochaetius specimen from Le Pont du Lac; E, very long hairs in Trochulus villosulus specimen from Wojtkowa, south-east Poland; F, very long hairs in Trochulus villosus specimen from Fützen, south Germany; G, short hairs in T. hispidus specimen from Buchenbach. Scale bars: A, B, 2.5 mm; C–G, 1 mm.
Figure 5. Maximum likelihood trees constructed from mitochondrial cytochrome c oxidase subunit I in Cryptic diversity in coastal Australasia: a morphological and mitonuclear genetic analysis of habitat-forming sibling species
Figure 5. Maximum likelihood trees constructed from mitochondrial cytochrome c oxidase subunit I (left; log likelihood: -399.5730) and nuclear adenine nucleotide transporter intron (right; log likelihood: -12170.8682) sequences of Pyura praeputialis and Pyura doppelgangera sp. nov. Nodal support from 1000 bootstrap replications (> 75%) is indicated next to some branches. Circles indicate regions in which a particular allele was present. For simplicity, allele frequencies are not indicated.
Figure 2 in Cryptic diversity in coastal Australasia: a morphological and mitonuclear genetic analysis of habitat-forming sibling species
Figure 2. Pyura doppelgangera sp. nov. A, anal border (view from above); B, anal border (lateral view); C, gonoduct left side; D, an individual specimen collected in Tasmania. Photograph: Carmen Primo; E, the same individual without tunic. Photograph: Carmen Primo; F, dorsal tubercle and lamina with languets; G, internal structure detailing the position of the hepatic gland, dorsal tubercle, gonad on the right side of the body and the gut and gonad on the left side. Scale bars: A-C = 1 mm; D, E = 20 mm; F = 2 mm; G = 10 mm.
Figure 1 in Cryptic diversity in coastal Australasia: a morphological and mitonuclear genetic analysis of habitat-forming sibling species
Figure 1. Scanning electron microscope photographs of the siphonal spines of the species comprising the Pyura stolonifera species complex. A, Pyura doppelgangera sp. nov., B, Pyura praeputialis, C, Pyura dalbyi, D, Pyura herdmani, E, P. stolonifera. Scale bars: A = 100 Mm; B, D = 40 Mm; C = 50 Mm; E = 10 Mm.
Genetic diversity and population structure of two endangered neotropical parrots inform In Situ and Ex Situ conservation strategies
<p></p><p>A key aspect in the conservation of endangered populations is understanding patterns of genetic variation and structure, which can provide managers with critical information to support evidence-based status assessments and management strategies. This is especially important for species with small wild and larger captive populations, as found in many endangered parrots. We used genotypic data to assess genetic variation and structure in wild and captive populations of two endangered parrots, the blue-throated macaw, Ara glaucogularis, of Bolivia, and the thick-billed parrot, Rhynchopsitta pachyrhyncha, of Mexico. In the blue-throated macaw, we found evidence of weak genetic differentiation between wild northern and southern subpopulations, and between wild and captive populations. In the thick-billed parrot we found no signal of differentiation between the Madera and Tutuaca breeding colonies or between wild and captive populations. Similar levels of genetic diversity were detected in the wild and captive populations of both species, with private alleles detected in captivity in both, and in the wild in the thick-billed parrot. We found genetic signatures of a bottleneck in the northern blue-throated macaw subpopulation, but no such signal was identified in any other subpopulation of either species. Our results suggest both species could potentially benefit from reintroduction of genetic variation found in captivity, and emphasize the need for genetic management of captive populations.</p><p></p>
Figure 8 in A morphometric and genetic framework for the genus Gazella de Blainville, 1816 (Ruminantia: Bovidae) with special focus on Arabian and Levantine mountain gazelles
Figure 8. Phylogeny of the genus Gazella. Consensus tree of 5000 post burn-in trees from Bayesian analysis of mitochondrial control region, cytochrome b and 12S rRNA gene sequences. Branch labels are posterior probabilities. Museum specimens labelled 'G. arabica' are highlighted with grey background. For GenBank accession numbers see Appendix S2. Photographs of living animals courtesy of S. Hammer (http://awwp.alwabra.com, photo gallery).
Nonrandom missing data can bias PCA inference of population genetic structure
<p>Population genetic studies in non-model systems increasingly use next-generation sequencing to obtain more loci, but such methods also generate more missing data that may affect downstream analyses. Here we focus on the Principal Component Analysis (PCA) which has been widely used to explore and visualize population structure with mean-imputed missing data. We simulated data of different population models with various total missingness (1%, 10%, 20%) introduced either randomly or biased among individuals or populations. We found that individuals biased with missing data would be dragged away from their real population clusters to the origin of PCA plots, making them indistinguishable from true admixed individuals and potentially leading to misinterpreted population structure. We also generated empirical data of the big brown bat (<i>Eptesicus fuscus</i>) using restriction site-associated DNA sequencing (RADseq). We filtered three data sets with 19.12%, 9.87%, and 1.35% total missingness, all showing nonrandom missing data with biased individuals dragged towards the PCA origin, consistent with results from simulations. We highlight the importance of considering missing data effects on PCA in non-model systems where nonrandom missing data are common due to varying sample quality. To help detect missing data effects, we suggest to 1) plot PCA with a color gradient showing per sample missingness, 2) interpret samples close to the PCA origin with extra caution, 3) explore filtering parameters with and without the missingness-biased samples, and 4) use complementary analyses (e.g., model-based methods) to cross-validate PCA results and help interpret population structure.</p>
Figure 2 in Historical demography and spatial genetic structure of the subterranean rodent Ctenomys magellanicus in Tierra del Fuego (Argentina)
Figure 2. Bayesian inference trees of Ctenomys genus. A, tree derived from the D-loop marker. B, tree derived from Cyt b. Numbers next to branches are bootstrap support values and Bayesian posterior probabilities, respectively.
Figure 1 in Historical demography and spatial genetic structure of the subterranean rodent Ctenomys magellanicus in Tierra del Fuego (Argentina)
Figure 1. Geographical distribution of Ctenomys magellanicus sampling sites along the study area. Squares show the two regions: north (steppe, chromosome form 2n = 34) and south (ecotone, chromosome form 2n = 36). Each region was subdivided into subpopulations: two for the north (subpopulations A and B) and four for the south (subpopulations C–F).
Figure 5 in A morphometric and genetic framework for the genus Gazella de Blainville, 1816 (Ruminantia: Bovidae) with special focus on Arabian and Levantine mountain gazelles
Figure 5. Bi-variate plots of specimen scores for discriminant functions. A, males, DA 2; B, females, DA 6.
Figure 6 in A morphometric and genetic framework for the genus Gazella de Blainville, 1816 (Ruminantia: Bovidae) with special focus on Arabian and Levantine mountain gazelles
Figure 6. Classification of the male Gazella arabica type skull ZMB_MAM_2115. A, B, bi-variate plots of specimen scores for main components in PCA 8; C, D, bi-variate plots of specimen scores for discriminant functions in DA 4.
Figure 1 in A morphometric and genetic framework for the genus Gazella de Blainville, 1816 (Ruminantia: Bovidae) with special focus on Arabian and Levantine mountain gazelles
Figure 1. Geographical origin of gazelle specimens included in this study. Black icons, specimens sampled for morphometrical data; white icons, specimens sampled for DNA data; grey icons, specimens sampled for morphometrical and DNA data. The geographical origins of specimens sampled for DNA data were taken from the original publications of the sequences (Hammond et al., 2001; Wronski et al., 2010; Zachos et al., 2010; Lerp et al., 2011; Wacher et al., 2011) or the specimen information on GenBank. The geographical range of G. subgutturosa extends further to the east into China and Mongolia.
Figure 2 in A morphometric and genetic framework for the genus Gazella de Blainville, 1816 (Ruminantia: Bovidae) with special focus on Arabian and Levantine mountain gazelles
Figure 2. Skull measurements used in this study. Abbreviations correlate with descriptions in Table 3.
Investigating the shared genetic architecture between multiple sclerosis and inflammatory bowel diseases
<p>This directory contains custom codes and supplementary datasets generated for the study "Investigating the shared genetic architecture between multiple sclerosis and inflammatory bowel diseases".<br> </p>
Figure 3 in Isolation by geographical distance after release from Pleistocene refugia explains genetic and phenotypic variation in Xylotrupes siamensis (Coleoptera: Scarabaeidae)
Figure 3. Phylogenetic networks of the three analysed loci and three historical scenarios explaining the origin of the CLAOS population tested using the DIYABC program. The colour of each individual shown in the network corresponds to the taxonomic assignment colour in the DIYABC analysis. Scenario 2 was the most likely model selected by the program (~60% posterior support), and scenario 1 also received moderate support (~30% posterior support). Results of posterior support among scenarios and model checking, in addition to the estimated parameter values (population sizes and divergence times), can be found in the Supporting Information (Figs S3–S8).
Figure 1 in Isolation by geographical distance after release from Pleistocene refugia explains genetic and phenotypic variation in Xylotrupes siamensis (Coleoptera: Scarabaeidae)
Figure 1. Different male horn phenotypes in Xylotrupes siamensis. Left panels show exemplars of males of the Tonkinensis (short horn) phenotypes and right panels the Siamensis (long horn with a cephalic horn denticle) phenotype. Samples from the CLAOS population (for details, see Table 1; Fig. 2) exhibit intermediate horn length, and the major males have the cephalic horn denticle.
Figure 4 in Isolation by geographical distance after release from Pleistocene refugia explains genetic and phenotypic variation in Xylotrupes siamensis (Coleoptera: Scarabaeidae)
Figure 4. Associations between three biogeographical distance measures and genetic differentiation (FST), measured at three loci (CO1, ITS2 and H3), for populations of Xylotrupes siamensis. Mantel test statistics (r) indicate the strength and significance of correlations between the genetic distance and biogeographical distance. Distance calculations are detailed in the Material and Methods section. Least cost distances were subject to rescaling and are thus unitless.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.