Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
4,479
datasets available to search
ShareScore release 0.7.1
Dataset results
4,479 results for “hybrids”
Goniopteris ×tico (Thelypteridaceae), a new hybrid fern from Costa Rica
<p><em>Goniopteris</em> ×<em>tico</em>, a new hybrid fern from La Selva Biological Station in Heredia Province, Costa Rica, is described based on morphology and analysis of target-capture DNA sequence data. The hybrid co-occurs with its two putative progenitors, <em>Goniopteris</em> <em>mollis</em> and <em>Goniopteris</em> <em>nicaraguensis</em>, and is readily recognizable by its intermediate leaf dissection and venation. It is also intermediate in pinnae size and shape and presents irregularly lobed pinnae. Despite the broad overlap in the geographic distribution of its parental taxa, <em>Goniopteris</em> ×<em>tico</em> is only known from two collections from a single area of the La Selva Biological Station, highlighting the importance of close observation of ferns from even well-collected areas.</p>
Genomic landscape of introgression from the ghost lineage in a gobiid fish uncovers the generality of forces shaping hybrid genomes
<p>Extinct lineages can leave legacies in the genomes of extant lineages through ancient introgressive hybridization. The patterns of genomic survival of these extinct lineages provide insight into the role of extinct lineages in current biodiversity. However, our understanding of the genomic landscape of introgression from extinct lineages remains limited due to challenges associated with locating the traces of unsampled "ghost" extinct lineages without ancient genomes. Herein, we conducted population genomic analyses on the East China Sea (ECS) lineage of <em>Chaenogobius annularis</em>, which was suspected to have originated from ghost introgression, with the aim of elucidating its genomic origins and characterizing its landscape of introgression. By combining phylogeographic analysis and demographic modeling, we demonstrated that the ECS lineage originated from ancient hybridization with an extinct ghost lineage. Forward simulations based on the estimated demography indicated that the statistic <em>γ</em> of the HyDe analysis can be used to distinguish the differences in local introgression rates in our data. Consistent with introgression between extant organisms, we found reduced introgression from extinct lineage in regions with low-recombination rates and with functional importance, thereby suggesting a role of linked selection that has eliminated the extinct lineage in shaping the hybrid genome. Moreover, we identified enrichment of repetitive elements in regions associated with ghost introgression, which was hitherto little-known but was also observed in the reanalysis of published data on introgression between extant organisms. Overall, our findings underscore the unexpected similarities in the characteristics of introgression landscapes across different taxa, even in cases of ghost introgression.</p>
Transition between distinct hybrid skyrmion textures through their hexagonal-to-square crystal transformation in a polar magnet
<p>The file Manuscript data files.7z contains the experimental data used for creating the figures in the manuscript entitled "Transition between distinct hybrid skyrmion textures through their hexagonal-to-square crystal transformation in a polar magnet" that appear in Nature Communications 14, 8050 (2023).</p><p>Paper abstract: Magnetic skyrmions, topological vortex-like spin textures, garner significant interest due to their unique properties and potential applications in nanotechnology. While they typically form a hexagonal crystal with distinct internal magnetisation textures known as Bloch- or Néel-type, recent theories suggest the possibility for direct transitions between skyrmion crystals of different lattice structures and internal textures. To date however, experimental evidence for these potentially useful phenomena have remained scarce. Here, we discover the polar tetragonal magnet EuNiGe3 to host two hybrid skyrmion phases, each with distinct internal textures characterised by anisotropic combinations of Bloch- and Néel-type windings. Variation of the magnetic field drives a direct transition between the two phases, with the modification of the hybrid texture concomitant with a hexagonal-to-square skyrmion crystal transformation. We explain these observations with a theory that includes the key ingredients of momentum-resolved Ruderman–Kittel–Kasuya–Yosida and Dzyaloshinskii-Moriya interactions that compete at the observed low symmetry magnetic skyrmion crystal wavevectors. Our findings underscore the potential of polar magnets with rich interaction schemes as promising for discovering new topological magnetic phases.</p>
Measured data of article "Superconducting NbN–Al hybrid technology for quantum devices"
<p>The folder contains raw data of figure 3 & 4 as well as a preprint of the article:</p> <p><em>Superconducting NbN–Al hybrid technology for quantum devices</em></p> <p>Authors: E. Mutsenik, S. Linzen, E. Il’ichev, M. Schmelz, M. Ziegler, V. Ripka, B. Steinbach, G. Oelsner, U. Hübner, and R. Stolz</p> <p>Journal: Low Temperature Physics/Fizyka Nyzkykh Temperatur, 2023, Vol. 49, No. 1, pp. 98–101</p>
Probabilistic genetic identification of wild boar hybridization to support control of invasive wild pigs (Sus scrofa)
<p>The rapid expansion of wild pigs (<em>Sus scrofa</em>) throughout the United States (US) has been fueled by unlawful introductions, with invasive populations causing extensive crop losses, damaging native ecosystems, and serving as a reservoir for disease. Multiple states have passed laws prohibiting the possession or transport of wild pigs. However, genetic and phenotypic similarities between domestic pigs and invasive wild pigs – which overwhelmingly represent domestic pig-wild boar hybrids – pose a challenge for the enforcement of such regulations. We sought to exploit wild boar ancestry as a common attribute among the vast majority of invasive wild pigs as a means of genetically differentiating wild pigs from breeds of domestic pigs found within the US. We organized reference high-density single nucleotide polymorphism genotypes (1,039 samples from 33 domestic breeds and 382 samples from 16 wild boar populations) into five genetically cohesive reference groups: mixed-commercial breeds, Durocs, heritage breeds, primitive breeds, and wild boar. Building upon well-established genetic clustering approaches, we structured the test statistic to describe the difference in the likelihood of a given genotype's ancestry vectors (<em>sensu</em> genetic clustering analysis) if derived strictly from the four described domestic pig reference groups versus allowing for admixture from the wild boar group. By fitting statistical distributions to test statistics of reference domestic pigs, we characterized the distribution of the null hypothesis – that a given genotype descends strictly from domestic pig reference groups. We tested the approach with simulated genotypes and empirical data from an additional 29 breeds of domestic pig represented by 435 unique genotypes; all associated test statistics for simulated and empirical domestic pig challenge sets fell within the distribution of reference domestic pigs. We then evaluated 6,566 invasive wild pigs sampled across the contiguous United States, of which 63% exceeded the maximum threshold for domestic pigs and could be statistically classified as possessing wild boar ancestry. This approach provides a scientific foundation to enforce regulations prohibiting the possession of this destructive invasive species. Further, this computationally efficient and generalizable approach could be readily adapted to quantify gene flow among ecological systems of conservation or management concern.</p>
Identifying climatic drivers of hybridization with a new ancestral niche reconstruction method
<p>Applications of molecular phylogenetic approaches have uncovered evidence of hybridization across numerous clades of life, yet the environmental factors responsible for driving opportunities for hybridization remain obscure. Verbal models implicating geographic range shifts that brought species together during the Pleistocene have often been invoked, but quantitative tests using paleoclimatic data are needed to validate these models. Here, we produce a phylogeny for Heuchereae, a clade of 15 genera and 83 species in Saxifragaceae, with complete sampling of recognized species, using 277 nuclear loci and nearly complete chloroplast genomes. We then employ an improved framework with a coalescent simulation approach to test and confirm previous hybridization hypotheses and identify one new intergeneric hybridization event. Focusing on the North American distribution of Heuchereae, we introduce and implement a newly developed approach to reconstruct potential past distributions for ancestral lineages across all species in the clade and across a paleoclimatic record extending from the late Pliocene. Time calibration based on both nuclear and chloroplast trees recovers a mid- to late-Pleistocene date for most inferred hybridization events, a timeframe concomitant with repeated geographic range restriction into overlapping refugia. Our results indicate an important role for past episodes of climate change, and the contrasting responses of species with differing ecological strategies, in generating novel patterns of range contact among plant communities and therefore new opportunities for hybridization. The new ancestral niche method flexibly models the shape of niche while incorporating diverse sources of uncertainty and will be an important addition to the current comparative methods toolkit.</p>
Determinants of microbiome composition: Insights from free-ranging hybrid zebras (Equus quagga × grevyi)
<p>The composition of mammalian gut microbiomes is highly conserved within species, yet the mechanisms by which microbiome composition is transmitted and maintained within lineages of wild animals remain unclear. Mutually compatible hypotheses exist, including that microbiome fidelity results from inherited dietary habits, shared environmental exposure, morphophysiological filtering, and/or maternal effects. Interspecific hybrids are a promising system in which to interrogate the determinants of microbiome composition because hybrids can decouple traits and processes that are otherwise co-inherited in their parent species. We used a population of free-living hybrid zebras (<em>Equus quagga</em> × <em>grevyi</em>) in Kenya to evaluate the roles of these four mechanisms in regulating microbiome composition. We analyzed fecal DNA for both the <em>trn</em>L-P6 and the 16S rRNA V4 region to characterize the diets and microbiomes of the hybrid zebra and of their parent species, plains zebra (<em>E. quagga</em>) and Grevy's zebra (<em>E. grevyi</em>). We found that both diet and microbiome composition clustered by species, and that hybrid diets and microbiomes were largely nested within those of the maternal species, plains zebra. Hybrid microbiomes were less variable than those of either parent species where they co-occurred. Diet and microbiome composition were strongly correlated, although the strength of this correlation varied between species. These patterns are most consistent with the maternal-effects hypothesis, somewhat consistent with the diet hypothesis, and largely inconsistent with the environmental-sourcing and morphophysiological-filtering hypotheses. Maternal transmittance likely operates in conjunction with inherited feeding habits to conserve microbiome composition within species.</p>
Datasets for polygenic mechanisms of hybrid incompatibility in butterflies
<p><strong>Version 1.2 includes data that are missing in the previous versions.</strong></p> <p> </p> <p>Note: relevant scripts can also be found at</p> <p>https://github.com/tzxiong/2022_Papilio_HybridIncompatibilityMapping</p> <p>======================================================<br>Description of source data and scripts for all figures<br>======================================================</p> <p>==== MAIN FIGURES ====</p> <p>Fig. 1</p> <p> - Panel A<br> * Schematic figure, no source data are provided</p> <p> - Panel B<br> * Schematic figure, no source data are provided</p> <p> - Panel C<br> * Source data folder(s):<br> SourceData/Fig1/Fig1C<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 5</p> <p> - Panel D<br> * Source data folder(s):<br> SourceData/Fig1/Fig1D<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 5</p> <p> - Panel E<br> * Source data folder(s):<br> SourceData/Fig1/Fig1E<br> * Source code:<br> SourceData/Code.JupyterLab/Code01_SequencingData.ipynb: Section 2</p> <p> - Panel F<br> * Source data folder(s):<br> SourceData/Fig1/Fig1F<br> * Source code:<br> SourceData/Code.JupyterLab/Code01_SequencingData.ipynb: Section 2</p> <p>Fig. 2</p> <p> - Panels A-G<br> * Source data folder(s): <br> SourceData/Fig2+S1toS2 <br> * The "Raw" folder contains unedited images.<br> * Two edited images used in Fig2 is also included for each subfigure.</p> <p> - Panels H-L<br> * Source data folder(s): <br> SourceData/Fig2+S1toS2 <br> * The "Raw" folder (unzipped) contains unedited confocal data in .czi format.<br> * Edited images are included with both monochrome and merged versions.</p> <p>Fig. 3</p> <p> - Panel A<br> * Source data folder(s):<br> SourceData/Fig3+S8toS10/Fig3A_3B_3C_3D_3E<br> * Source code:<br> D(DB): SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 3.1<br> B(BD): SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 2.3</p> <p> - Panel B<br> * Source data folder(s): <br> SourceData/Fig3+S8toS10/Fig3A_3B_3C_3D_3E<br> * Source code:<br> D(DB): SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 4.1<br> B(BD): SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 4.2</p> <p> - Panel C<br> * Source data folder(s): <br> SourceData/Fig3+S8toS10/Fig3A_3B_3C_3D_3E<br> * Source code:<br> D(DB): SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 4.1<br> B(BD): SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 4.2</p> <p> - Panel D<br> * Source data folder(s): <br> SourceData/Fig3+S8toS10/Fig3A_3B_3C_3D_3E<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Run all of Sections 4.1 and 4.2</p> <p> - Panel E<br> * Source data folder(s): <br> SourceData/Fig3+S8toS10/Fig3A_3B_3C_3D_3E<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Run all of Sections 4.1 and 4.2</p> <p>Fig. 4</p> <p> - Note 1: For Heliconius analysis, all data are from SourceData/Fig4-Heliconius+S11C/dat.4.qtl.lumped.csv. This file contains Heliconius ovary dysgenesis data from https://doi.org/10.1111/mec.16272</p> <p> - Panel A (Heliconius)<br> * Source data folder(s): <br> SourceData/Fig4-Heliconius+S11C<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 6.1<br> <br> - Panel A (Papilio) <br> * Source data folder(s): <br> SourceData/Fig4-Papilio+S13<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 3.2</p> <p> - Panel B (Heliconius)<br> * Source data folder(s): <br> SourceData/Fig4-Heliconius+S11C<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 6.1<br> <br> - Panel B (Papilio)<br> * Source data folder(s): <br> SourceData/Fig4-Papilio+S13<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 3.2</p> <p> - Panel C (Heliconius) <br> * Source data folder(s): <br> SourceData/Fig4-Heliconius+S11C<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 6.2<br> <br> - Panel C (Papilio) <br> * Source data folder(s):<br> SourceData/Fig4-Papilio+S13<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 3.3</p> <p> - Panel D<br> * Schematic figure, no source data are provided<br> <br> - Panel E (Heliconius) <br> * Source data folder(s): <br> SourceData/Fig4-Heliconius+S11C<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 6.3<br> <br> - Panel E (Papilio) <br> * Source data folder(s): <br> SourceData/Fig4-Papilio+S13<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 3.3<br> <br> - Panel F (Heliconius)<br> * Source data folder(s): <br> SourceData/Fig4-Heliconius+S11C<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 6.3<br> <br> - Panel F (Papilio) <br> * Source data folder(s): <br> SourceData/Fig4-Papilio+S13<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 3.3<br> <br> <br> <br>==== SUPPLEMENTARY FIGURES ====</p> <p>Fig. S1-S2</p> <p> * Source data folder(s): <br> SourceData/Fig2+S1toS2 <br> * The "Raw" folder (unzipped) contains unedited confocal data in .czi format.<br> * Edited images are included with both monochrome and merged versions.</p> <p>Fig. S3</p> <p> * Source data folder(s): <br> SourceData/FigS3<br> * Source code:<br> SourceData/Code.JupyterLab/Code01_SequencingData.ipynb: Section 1<br> * Note: Source data file 04.0_IBD.NgsRelate.zip contains results from the NGSRelate software.<br> </p> <p>Fig. S4</p> <p> * Source data folder(s): <br> SourceData/FigS4<br> * Source code:<br> SourceData/Code.JupyterLab/Code01_SequencingData.ipynb: Section 2<br> * Note 1: Source data file CorrectedReferenceGenome.zip is the corrected reference genome used for all analyses. It is in .fasta format.<br> * Note 2: Source data file DenovoMarkerOrder_on_CorrectedRefGenome.zip contains all outputs from the LepMap3/OrderMarkers2 module that uses genotype likelihoods and pedigree information to generate a new marker order. Use script "OrderMarkers2_ReOrder.sh" from the script repo.<br> </p> <p>Fig. S5-S7</p> <p> * Source data folder(s): <br> SourceData/FigS5toS7<br> * Source code:<br> SourceData/Code.JupyterLab/Code01_SequencingData.ipynb: Section 2<br> * Note: The source data file PedigreeAncestryInGrandparentalPhase.zip contains all outputs from the LepMap3/OrderMarkers2 module that uses genotype likelihoods and pedigree information to impute ancestry at each marker. Ancestry is phased according to the sex of grandparents. Use script "OrderMarkers2.sh" from the GitHub repo.</p> <p>Fig. S8</p> <p> - Panel A <br> * Source data folder(s): <br> SourceData/Fig3+S8toS10<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 3.1</p> <p> - Panel B <br> * Source data folder(s): <br> SourceData/Fig3+S8toS10<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 2.3</p> <p> - Panel C<br> * Source data folder(s): <br> SourceData/Fig3+S8toS10<br> * Source code:<br> See previous two panels</p> <p>Fig. S9</p> <p> - Panel A<br> * Source data folder(s): <br> SourceData/Fig3+S8toS10<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 4.1</p> <p> - Panel B<br> * Source data folder(s): <br> SourceData/Fig3+S8toS10<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 4.2</p> <p>Fig. S10</p> <p> - Panel A<br> * Source data folder(s):<br> SourceData/Fig3+S8toS10<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 4.1</p> <p> - Panel B<br> * Source data folder(s): <br> SourceData/Fig3+S8toS10<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 4.2</p> <p>Fig. S11</p> <p> - Panel A<br> * Source data folder(s): <br> SourceData/FigS11AB<br> * Source code:<br> Data in SpeciesAncestry.B0D1.zip can be directly visualized to get the figure</p> <p> - Panel B<br> * Source data folder(s): <br> SourceData/FigS11AB<br> * Source code:<br> Data in SpeciesAncestry.B0D1.zip can be directly visualized to get the figure<br> * Note: This file contains ancestry at each marker phased according to species (bianor=0, dehaanii=1). These data are directly transformed from files in SourceData/FigS5toS7/PedigreeAncestryInGrandparentalPhase.zip.</p> <p> - Panel C<br> * Source data folder(s): <br> SourceData/Fig4-Heliconius+S11C<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 6.4</p> <p>Fig. S12</p> <p> * Source data folder(s): <br> No source data are needed<br> * Source code:<br> SourceData/Code03_PolygenicGhostQTL.ipynb</p> <p>Fig. S13</p> <p> - Panel A<br> * Source data folder(s): <br> SourceData/Fig4-Papilio+S13<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 3.3</p> <p> - Panel B<br> * Source data folder(s): <br> SourceData/Fig4-Papilio+S13<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 3.2</p> <p>Fig. S14</p> <p> * Source data folder(s): <br> No source data are needed<br> * Source code:<br> SourceData/Code03_PolygenicGhostQTL.ipynb</p> <p>Fig. S15</p> <p> - Panel A<br> * Source data folder(s): <br> SourceData/FigS15/FigS15A<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 2.1</p> <p> - Panel B<br> * Source data folder(s): <br> SourceData/FigS15/FigS15B<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 2.2</p> <p>Fig. S16</p> <p> * Source data folder(s): <br> SourceData/FigS16<br> * Source code:<br> SourceData/Code.JupyterLab/Code01_SequencingData.ipynb: Section 3<br> </p> <p>==== OTHER SOURCE DATA & SUMMARY OF SOURCE CODE FOLDERS====</p> <p>LepMap3-SourceData</p> <p> * LepMap3_SourceData-Family_Info_Finalized_withPseudoGrandParents_transposed.txt<br> <br> This file is the pedigree file ready-to-use in LepMap3. Note that it contains pseudo grandparents for families missing grandparents in sequencing. Pseudo grandparents are simply created from fixed SNPs in all existing grandparents and adding them to the original vcf files containing genotype likelihoods.</p> <p> * LepMap3_SourceData-vcf_files.zip<br> <br> The vcf files containing genotype likelihoods for LepMap3 to use. Note that it contains the aforementioned pseudo grandparents.</p> <p><br>Code.NGSRelate</p> <p> * Code used for inferring kinship from low-coverage sequencing data</p> <p>Code.LepMap3<br> <br> * Code used for all LepMap3 analysis</p> <p>Code.JupyterLab</p> <p> * Code used for all Julia and R analysis in .ipynb format</p> <p><br> </p>
FIGURE 2 in Environmental correlates of the European common toad hybrid zone
FIGURE 2 Two-species 'global' distribution model for Bufo toads in western Europe. The colour scale runs from deep red for B. spinosus (Pb is zero) to deep blue for B. bufo (Pb at unity). The interrupted black line represents the center of the species' hybrid zone from molecular data, as in fig. 1. Outlined circular windows are those for which model fit is less than good (AUC <0.8, windows 1, 6–8 and 14–16).
FIGURE 3 in Environmental correlates of the European common toad hybrid zone
FIGURE 3 Two regions in the common–spined toad hybrid zone where the mutual species border appears to coincide with rivers. Coloured dots indicate toad populations with nuclear genetic species identifications as Bufo bufo (Q = Pb> 0.5, blue symbols) and B. spinosus (Q = Pb <0.5, red symbols). Open dots have Q-values in the 0.2–0.8 range. For numerical detail see supplementary table S1. Base map figure credits as in fig. 1. A) central France where the species border appears to coincide with the northern- most sections of the Loire (windows 5 and 6) and the upper stretches of the Cher (windows 7 and 8). B) southeastern France where the species border coincides with the Rhône and the lower Isère river at window 13. Note the paucity of data for the high Alps at windows 15 and 16 (see also Lescure and de Downloaded from Brill.com 12/12/2023 03:07:30PM Massary, 2012; Arntzen et al., via2017Open). Access. This is an open access article distributed under the terms of the CC-BY 4.0 License. https://creativecommons.org/licenses/by/4.0/
FIGURE 1 in Environmental correlates of the European common toad hybrid zone
FIGURE 1 Western Europe with France and adjacent countries in Mercator projection. Colours from green to brown indicate increasing altitudes. The Bufo bufo versus B. spinosus mutual range delineation is based upon molecular genetic data, in which the smooth interrupted line is derived by linear interpolation whereas the more angular line is based upon Dirichlet cells (for details see text). The small bodied common toad B. bufo occurs to the northeast and the large bodied spined toad B. spinosus to the southwest of the mutual range border. Environmental data were gathered for 17 overlapping and adjoining circular windows positioned over the mutual range border. Here shown are window 1 in the northwest of France, window 17 in the northwest of Italy and windows 5, 9 and 13 in between. The two boxed areas are highlighted in fig. 3. The base map was downloaded from MapsLand at https://www.mapsland.com, under a Creative Commons Attribution-ShareAlike 3.0 Licence. The animal drawings are by Bas Blankevoort, Naturalis Biodiversity Center.
FIGURE 4 Average values for eight environmental variables over 17 windows that follow the Bufo bufo – B in Environmental correlates of the European common toad hybrid zone
FIGURE 4 Average values for eight environmental variables over 17 windows that follow the Bufo bufo – B. spinosus hybrid zone from the Atlantic coast (window 1) to the Mediterranean (window 17). Variables shown are those selected by a logistic regression analysis, with 'species' as dependent variable and explanatory variables available for selection as in table 1. Units are as in table 1; see also Hijmans et al. (2005). Values for B. bufo and B. spinosus are shown by small and large dots, respectively. Grey areas indicate that values for B. bufo are lower than for B. spinosus. The graph at the top left provides AUC model fit values along with major topographical references. Rectangles indicate stretches of the species contact for which the environmental models have good fit (AUC> 0.8), with consistent results indicated by green shadings. For the other windows with less than good model fit, signals are likely to be absent or void, either from poor sampling (window 1), the presence of rivers (windows 5–9, 13–14), or a thin or absent species' contact (windows 16–17) (see fig. 3).
F I G U R E 8 in Genetic characteristics and growth patterns of the hybrid grouper derived from the hybridization of Epinephelus fuscoguttatus (female) Epinephelus polyphekadion (male)
F I G U R E 8 Allometric growth: regression curves for total length and body mass for the hybrid grouper in 5 months old (a), 6 months old (b) and 8 months old (c)
F I G U R E 7 in Genetic characteristics and growth patterns of the hybrid grouper derived from the hybridization of Epinephelus fuscoguttatus (female) Epinephelus polyphekadion (male)
F I G U R E 7 Correlation coefficients among morphological traits of the hybrid grouper in 5 months old (a), 6 months old (b) and 8 months old (c)
F I G U R E 5 Phylogenetic study using the 5S in Genetic characteristics and growth patterns of the hybrid grouper derived from the hybridization of Epinephelus fuscoguttatus (female) Epinephelus polyphekadion (male)
F I G U R E 5 Phylogenetic study using the 5S rDNA sequences from Epinephelus fuscoguttatus, Epinephelus polyphekadion and the hybrid grouper
F I G U R E 6 in Genetic characteristics and growth patterns of the hybrid grouper derived from the hybridization of Epinephelus fuscoguttatus (female) Epinephelus polyphekadion (male)
F I G U R E 6 Proportion of the total length and head length of the hybrid grouper, shown as percentages of body length
F I G U R E 3 in Genetic characteristics and growth patterns of the hybrid grouper derived from the hybridization of Epinephelus fuscoguttatus (female) Epinephelus polyphekadion (male)
F I G U R E 3 Representative sequences of 5S rDNA. (a) Complete 5S coding regions from Epinephelus fuscoguttatus, Epinephelus polyphekadion and the hybrid grouper. Internal control regions of the coding region are shaded. (b) Comparison of the nontranscribed spacer (NTS) sequences from Epinephelus fuscoguttatus, Epinephelus polyphekadion and the hybrid grouper. The NTS upstream TATA elements are shaded and asterisks mark variable sites in the NTS
F I G U R E 1 in Genetic characteristics and growth patterns of the hybrid grouper derived from the hybridization of Epinephelus fuscoguttatus (female) Epinephelus polyphekadion (male)
F I G U R E 1 DNA content of (a) Epinephelus fuscoguttatus, (b) Epinephelus polyphekadion and (c) the hybrid grouper
F I G U R E 2 in Genetic characteristics and growth patterns of the hybrid grouper derived from the hybridization of Epinephelus fuscoguttatus (female) Epinephelus polyphekadion (male)
F I G U R E 2 Chromosome spreads at metaphase in Epinephelus fuscoguttatus, Epinephelus polyphekadion and the hybrid grouper. (a) The 48 chromosomes of Epinephelus fuscoguttatus. (b) The 48 chromosomes of Epinephelus polyphekadion. (c) The 48 chromosomes of the hybrid grouper
Fig. 4 in Altai Mountains - cradle of hybrids and introgressants: A case study in Veronica subg. Pseudolysimachium (Plantaginaceae)
Fig. 4. STRUCTURE results showing the probability of ancestry of each individual (horizontal axis) to each of K = 2 populations (vertical axis) in all the five scenarios. A, Veronica spicata × V. pinnata; B, V. incana and V. longifolia; C, V. longifolia and V. porphyriana; D & E, V. pinnata and V. porphyriana involving putative hybrids of V. ×schmakovii and V. ×sessiliflora. Details of the exact posterior probabilities of each putative hybrid individual and their corresponding parents are given in suppl. Table S3.
ScienceDex guides
Understand access before you commit
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.