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Fig. 12 in Revisiting the Andean butterfly Eryphanis zolvizora group (Lepidoptera, Nymphalidae): one or several species?
Fig. 12. Male genitalia of various specimens of Eryphanis zolvizora chachapoya ssp. nov. A. PT from San José de Molinopampa, Amazonas, Peru (MNHN, PBGL 190). B. PT from Alto Nieva, Amazonas, Peru (MNHN, PBB 2157). C. Specimen from San Augustín, San Martín, Peru (MNHN, PBGL 521). D. PT from Cumpang, La Libertad, Peru (MJP). E. HT from Huamanpata, Amazonas, Peru (MJP). F. Specimen from Carpish, Huánuco, Peru (MNHN, PBB 2311). G. Specimen from San Francisco, Chanchamayo, Junin, Peru (MNHN, PBB 1407). H. PT from La Suiza, Pasco, Peru (UFPC). I. PT from Huancabamba, Pasco, Peru (BMNH 8224). J. PT from Cushi, Pasco, Peru (BMNH 8225).
Fig. 11 in Revisiting the Andean butterfly Eryphanis zolvizora group (Lepidoptera, Nymphalidae): one or several species?
Fig. 11. Gnathos and valva extremity in various specimens of Eryphanis zolvizora inca ssp. nov. A. PT from Santo Domingo, Puno, Peru (BMNH 8223). B. PT from San Lorenzo, Cusco, Peru (MNHN, PPB 2115). C. HT from Aguas Calientes, Cusco, Peru (MJP). D. PT from Alfamayo, Cusco, Peru (MNHN, PBB 2184). E. PT from Calabaza, Junín, Peru (MNHN, PBB 2308). F. Specimen from Oxapampa, Pasco, Peru (MJP).
Fig. 10. Male genitalia. A in Revisiting the Andean butterfly Eryphanis zolvizora group (Lepidoptera, Nymphalidae): one or several species?
Fig. 10. Male genitalia. A. Eryphanis zolvizora zolvizora (Caranavi, Bolivia; MNHN, PBB 2286). B. Eryphanis zolvizora inca ssp. nov., PT (Llactohuaman, Cusco, Peru; MJP).
Fig. 9 in Revisiting the Andean butterfly Eryphanis zolvizora group (Lepidoptera, Nymphalidae): one or several species?
Fig. 9. Inter- and intra-individual variations of the extremities of valvae in a population of E. zolvizora chachapoya ssp. nov. from San José de Molinopampa, Amazonas, Peru (MNHN).
Fig. 1 in Revisiting the Andean butterfly Eryphanis zolvizora group (Lepidoptera, Nymphalidae): one or several species?
Fig. 1. Characters studied on the habitus (recto) and their major variations. a. HW projection, estimated by P = LCu1-((LM3+LCu2)/2) in milimetres). b. Orange mark between veins R5-M1. c. Size and shape of the orange marks. d. Violet-blue iridescence. e. Male androconial patch.
Fig. 2 in Revisiting the Andean butterfly Eryphanis zolvizora group (Lepidoptera, Nymphalidae): one or several species?
Fig. 2. Characters studied on the habitus (verso), and their major variations. f. Ground colour of the ventral surface and of the median area. g. Isolated white spot on FW cell. h. White spot at the angle formed by the cubital vein of the FW cell and Cu2. i. 'Bridge' between white stripes in FW cell Cu2-2A. j. Length on HW of posterior extension of the white stripes. k. Black designs in the HW cell, and black curved line anterior to the Cu1-Cu2 ocellus. l. Ring around the HW costal ocellus. m. Widest diameter Φ of the HW Cu1-Cu2 ocellus.
Fig. 8 in Revisiting the Andean butterfly Eryphanis zolvizora group (Lepidoptera, Nymphalidae): one or several species?
Fig. 8. Habitus of females. Yellow spot: paratype (PT). A. Eryphanis zolvizora reyi ssp. nov., PT (La Chimenea, Barinas, Colombia; MIZA). B. Eryphanis zolvizora isabelae ssp. nov., PT (Colonia Tovar, Aragua, Venezuela; MCC).
Fig. 4 in Revisiting the Andean butterfly Eryphanis zolvizora group (Lepidoptera, Nymphalidae): one or several species?
Fig. 4. Habitus of males. Red spot: holotype (HT) or lectotype (LT). Yellow spot: syntype (ST) or paratype (PT). A. Eryphanis zolvizora zolvizora (Hewitson, 1877), LT (Bolivia; BMNH). B. The southernmost known specimen of E. z. zolvizora (Manchones, Santa Cruz, Bolivia; MHNC). C. Eryphanis zolvizora inca ssp. nov., HT (Aguas Calientes, Cuzco, Peru; MJP). D. Eryphanis zolvizora chachapoya ssp. nov., HT (Huamanpata, Amazonas, Peru; MJP). E. Eryphanis z. chachapoya ssp. nov., ST of Eryphanis opimus Staudinger, 1887, from Chanchamayo, Junín, Peru (BMNH). F. PT of E. z. chachapoya ssp. nov. with small spots (Alto Nieva, Amazonas, Peru; MNHN, PBGL 706).
Fig. 5 in Revisiting the Andean butterfly Eryphanis zolvizora group (Lepidoptera, Nymphalidae): one or several species?
Fig. 5. Habitus of males. Red spot: holotype (HT) or lectotype (LT). A. Eryphanis zolvizora greeneyi Penz & DeVries, 2008, stat. rev., HT (Yanayacu, Napo, Ecuador; BMNH). B. E. z. greeneyi, specimen with increased forewing spots (Baeza, Napo, Ecuador, Pinas). C. Eryphanis zolvizora casagrande ssp. nov., HT (Altaquer, Nariño, Colombia; ICNUN). D. E. z. casagrande from Western Ecuador (Las Gralarias, Pichincha; FLMNH). E. Eryphanis zolvizora opimus (Staudinger, 1887), LT (Manizales, Colombia; ZMHB). F. E. z. opimus, specimen with reduced spots (Cauca, Colombia; MNHN, CG).
Fig. 6 in Revisiting the Andean butterfly Eryphanis zolvizora group (Lepidoptera, Nymphalidae): one or several species?
Fig. 6. Habitus of males. Red spot: holotype (HT). Yellow spot: paratype (PT). A. Eryphanis zolvizora reyi ssp. nov., HT (La Chimenea, Barinas, Venezuela; MIZA). B. E. z. reyi ssp. nov., PT (Charalá, Colombia; MNHN, PBB 2321). C. Eryphanis zolvizora isabelae ssp. nov., HT (Choroní, Aragua, Venezuela; R, to be donated to MIZA). D. E. z. isabelae ssp. nov., PT; specimen with reduced forewing spots (Choroní, Aragua, Venezuela; R).
Is sexual conflict a driver of speciation? a case study with a tribe of brush-footed butterflies
Understanding the evolutionary mechanisms governing the uneven distribution of species richness across the tree of life is a great challenge in biology. Scientists have long argued that sexual conflict is a key driver of speciation. This hypothesis, however, has been highly debated in light of empirical evidence. Recent advances in the study of macroevolution make it possible to test this hypothesis with more data and increased accuracy. In the present study, we use phylogenomics combined with four different diversification rate analytical approaches to test whether sexual conflict is a driver of speciation in brush-footed butterflies of the tribe Acraeini. The presence of sphragis, an external mating plug found in most species among Acraeini, was used as a proxy for sexual conflict. Diversification analyses statistically reject the hypothesis that sexual conflict is associated with shifts in diversification rates in Acraeini. This result contrasts with earlier studies and suggests that the underlying mechanisms driving diversification are more complex than previously considered. In the case of butterflies, natural history traits acting in concert with abiotic factors possibly play a stronger role in triggering speciation than does sexual conflict.
Sun compass neurons are tuned to migratory orientation in monarch butterflies
Every autumn, monarch butterflies migrate from North America to their overwintering sites in Central Mexico. To maintain their southward direction, these butterflies rely on celestial cues as orientation references. The position of the sun combined with additional skylight cues are integrated in the central complex, a region in the butterfly's brain that acts as an internal compass. However, the central complex does not solely guide the butterflies on their migration but helps monarchs in their non-migratory form manoeuvre on foraging trips through their habitat. By comparing the activity of input neurons of the central complex between migratory and non-migratory butterflies, we investigated how a different lifestyle affects the coding of orientation information in the brain. During recording, we presented the animals with different simulated celestial cues and found that the encoding of the sun was narrower in migratory compared to non-migratory butterflies. This feature might reflect the need of the migratory monarchs to rely on a precise sun compass to keep their direction during their journey. Taken together, our study sheds light on the neural coding of celestial cues and provides insights into how a compass is adapted in migratory animals to successfully steer them to their destination.
Figs 1–6 in Nosema pieriae sp. n. (Microsporida, Nosematidae): A New Microsporidian Pathogen of the Cabbage Butterfly Pieris brassicae L. (Lepidoptera: Pieridae)
Figs 1–6. Light micrographs of the microsporidian pathogen infecting P. brassicae. 1 – intestine which is heavily infected with microsporidian spores; 2–3 – microsporidian spores in fresh smears, note that meront and sporoblast stages are easily seen and marked by arrows; 4 – tetranucleate spherical meront (schizont); 5 – binucleate oval meront; 6 – diplokaryotic sporoblast. Scale bars: 30 µm (1), 15–10 µm (2–3), 3 μm (4), 2 μm (5), 4 μm (4).
Fig. 11 in Nosema pieriae sp. n. (Microsporida, Nosematidae): A New Microsporidian Pathogen of the Cabbage Butterfly Pieris brassicae L. (Lepidoptera: Pieridae)
Fig. 11. The phylogenetic analysis was carried out by Maximum Likelihood (ML) using an HKY85 substitution model of PAUP 4.0b10 software. The topology of the consensus tree was constructed and evaluated by 1000 bootstrap replications. The branches with lower than 50% confidence values were ignored.
Figs 7–10 in Nosema pieriae sp. n. (Microsporida, Nosematidae): A New Microsporidian Pathogen of the Cabbage Butterfly Pieris brassicae L. (Lepidoptera: Pieridae)
Figs 7–10. Transmission electron micrographs of microsporidian spores infecting P. brassicae. 7 – longitudinal (a) and transversal (b) sections of diplokaryotic spores, polar filament (pf), posterior vacuole (pv) and nuclei (n) are easily seen; 8 – spherical nuclei (n); 9 – polaroplast (pp) and anchoring disc (ad) structures; pp thin lamellar type polaroplast, pp thick lamellar type polaroplast; 10 – cross section of 1, 2, polar filaments; exospore (ex), endospore (en), plasmalemma (p) and polar filament (pf). Scale bars: 800 nm (7), 250 nm (8), 200 nm (9, 10).
Regional differences in thermoregulation between two European butterfly communities
<p>Understanding how different organisms cope with changing temperatures is vital for predicting future species' distributions and highlighting those at risk from climate change. As ectotherms, butterflies are sensitive to temperature changes, but the factors affecting butterfly thermoregulation are not fully understood.</p> <p>We investigated which factors influence thermoregulatory ability in a subset of a Mediterranean butterfly community. We measured adult thoracic temperature and environmental temperature (787 butterflies; 23 species) and compared buffering ability (defined as the ability to maintain a consistent body temperature across a range of air temperatures) and buffering mechanisms to previously published results from Great Britain. Finally, we tested whether thermoregulatory ability could explain species' demographic trends in Catalonia.</p> <p>The sampled sites in each region differ climatically, with higher temperatures and solar radiation but lower wind speeds in the Catalan sites. Both butterfly communities show nonlinear responses to temperature, suggesting a change in behaviour, from heat-seeking to heat avoidance, at approximately 22 °C. However, the communities differ in the use of buffering mechanisms, with British populations depending more on microclimates for thermoregulation compared to Catalan populations.</p> <p>Contrary to the results from British populations, we did not find a relationship between region-wide demographic trends and butterfly thermoregulation, which may be due to the interplay between thermoregulation and the habitat changes occurring in each region. Thus, although Catalan butterfly populations seem to be able to thermoregulate successfully at present, evidence of heat avoidance suggests this situation may change in the future.</p>
Data from: Extreme heat reduces host and parasite performance in a butterfly-parasite interaction
<p>Environmental temperature fundamentally shapes insect physiology, fitness, and interactions with parasites. Differential climate warming effects on host versus parasite biology could exacerbate or inhibit parasite transmission, with far-reaching implications for pollination services, biocontrol, and human health. Here, we experimentally test how controlled temperatures influence multiple components of host and parasite fitness in monarch butterflies (<em>Danaus plexippus</em>) and their protozoan parasites <em>Ophryocystis elektroscirrha</em>. Using five constant temperature treatments spanning 18-34°C, we measured monarch development, survival, size, immune function, and parasite infection status and intensity. Monarch size and survival declined sharply at 34°C, as did infection probability, suggesting that hot temperatures decrease both host and parasite performance. The lack of infection at 34°C was not due to greater host immunity or faster larval development but could instead reflect the thermal limits of parasite invasion and within-host replication. In the context of ongoing climate change, our experiment suggests that temperature increases above the upper thermal range will reduce the fitness of both monarchs and their parasites, with lower infection rates potentially mitigating the impact of extreme heat on future monarch abundance and distribution.</p>
Bees and Butterflies Monitoring
<p>Biodiversity monitoring surveys of selected pollinator species (bees and butterflies) performed according to specific protocols adapted to NBS and observers, along fixed transects, once a week during the pollinators’ season, for 3 years.</p> <p>Reference methods: Underwood, Darwin, Gerritsen, (2017), Pollinator initiatives in EU Member States: Success factors and gaps. Report for European Commission under contract for provision of technical support related to Target of the EU Biodiversity Strategy to 2020 – maintaining and restoring ecosystems and their services. ENV.B.2/SER/2016/0018. Institute for European Environmental Policy, Brussels; Potts et al. (2020), Proposal for an EU Pollinator Monitoring Scheme, EUR 30416 EN, Publications Office of the European Union, Luxembourg.</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 1 in Mitochondrial genomes of four pierid butterfly species (Lepidoptera: Pieridae) with assessments about Pieridae phylogeny upon multiple mitogenomic datasets
Figure 1. Circular map of Baltia butleri, Talbotia naganum, Pontia callidice, Pontia daplidice mitochondrial genome. COI, COII, and COIII refer to the cytochrome oxidase subunits; CytB refers to cytochrome B; ATP6 and ATP8 refer to subunits 6 and 8 of F0 ATPase; ND1-6 refers to the components of NADH dehydrogenase. The tRNAs locations are marked by the color blocks and labeled by the IUPAC-IUB single letter amino acid code. L1, L2, S1, and S2 denote tRNALeu (CUN), tRNALeu (UUR), tRNASer (AGN), and tRNASer (UCN), respectively. The non-underlined genes are transcribed on the majority strand whereas the underlined genes are transcribed on the minority strand.
ScienceDex guides
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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.