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Fig. 7 in Early summer aspect of butterflies (Lepidoptera: Papilionoidea) of Republic of Khakassia as examined in 2000, with some additional data
Fig. 7. Landscapes at the Terensug River headwaters Рис. 7. ΑанΔшафты в верховьях р. Теренсуг
Fig. 4. Larch parkland 11 in Early summer aspect of butterflies (Lepidoptera: Papilionoidea) of Republic of Khakassia as examined in 2000, with some additional data
Fig. 4. Larch parkland 11 km WNW of Shira Village Рис. 4. Парковый Λиственничник 11 км ЗСЗ с. Шира
Fig. 2. Hilly terrain 5 in Early summer aspect of butterflies (Lepidoptera: Papilionoidea) of Republic of Khakassia as examined in 2000, with some additional data
Fig. 2. Hilly terrain 5 km NNE of Parnaya Village Рис. 2. ХоΛмистая местность 5 км ССВ с. Парная
Evolutionary mechanisms of varying chromosome numbers in the radiation of Erebia butterflies
<p>This is the dataset for the study with the same title published at MDPI Genes (please see the paper for all details).</p> <p>To test for a phylogenetic signal of varying chromosome numbers in Erebia butterflies (Lucek submitted), I reconstructed a phylogeny using a subset of Peña et al. Biol J Linn Soc 2016 for which chromosome numbers were available. Data for an additional 5 species was taken from GenBank. Chromosome numbers used are included.<br> The final alignment comprised sequence data for four genes: 620 bp of the mitochondrial cytochrome oxidase subunit I (COI), 598 bp of the nuclear glyceraldehyde-3-phosphate dehydrogenase (GAPDH), 565 bp of the nuclear ribosomal protein S5 (RpS5) and 343 bp of the nuclear wingless gene.</p> <p>I used PartitionFinder 2 (Lanfear et al. Mol Biol Evol 2017) to infer the best partition scheme and associated substitution model for each codon position and gene. The output of PartitionFinder is provided in the data file. The resulting best partitioning scheme for the Bayesian inference is given in each nexus file. For the maximum likelihood (ML) based phylogeny I used the GTR model with invariant sites and gamma correction (GTR+I+G) in RAXML 8.2.8 (Stamatakis, Bioinformatics 2014) with the corresponding partition scheme from PartitionFinder. I further used 1000 bootstrap replicates to assess significance. I ran RAXML for the dataset comprising either all four genes, the mitochondrial COI gene only or the three nuclear genes. In the latter case, data was only available for 35 taxa. I conducted the Bayesian analysis in MrBayes 3.2.2 (Ronquist et al. Syst Biol 2012) for either dataset using in each case, 5’000’000 generations with four chains – three heated and one cold. Trees were sampled every 1’000 generations.<br> Provided are the input and output files of MrBayes and RAXML for all genes combined (subfolder all), the mitochondrial COI gene only (subfolder mtdna) or the three nuclear genes (subfolder nuclear).</p>
Data for 'Priors and Posteriors in Bayesian Timing of Divergence Analyses: the Age of Butterflies Revisited'
<p>Data and results for Chazot et al. (2018) 'Priors and Posteriors in Bayesian Timing of Divergence Analyses: the Age of Butterflies Revisited.'</p> <p>S1. List of taxa and Genbank accession codes.</p> <p>S2. Molecular matrix for the core analysis (S2a), the reduced dataset (S2b) and the dataset with a mitochondrial fragment (S2c).</p> <p>S3. RAxML topology (S3a) and time-calibrated tree (S3b) obtained from the core analysis. In S3a, numbers at the nodes indicate rapid-bootstrap support values. In S3b, node ages are the median of node age posterior distributions.</p> <p>S4. Tree obtained when using only deep-level fossil calibrations. Node ages are the median of node age posterior distributions.</p> <p>S5. Tree obtained when using only shallow-level fossil calibrations. Node ages are the median of node age posterior distributions.</p> <p>S6. Tree obtained from the reduced dataset. Node ages are the median of node age posterior distributions.</p> <p>S7. Tree obtained when using exponential fossil calibration priors. Node ages are the median of node age posterior distributions.</p> <p>S8. Tree obtained when adding a mitochondrial gene fragment. Node ages are the median of node age posterior distributions.</p> <p>S9. Tree obtained when using fossil information only modeled using lognormal priors. Node ages are the median of node age posterior distributions.</p> <p>S10. Tree obtained when using the host-plant ages obtained from Foster et al. (2017). In S10a node ages are the median of node age posterior distributions, while in S10b the node ages are the mode the mode of the kernel density estimate of the posterior distribution.</p> <p>S11. Tree obtained when using a Yule prior instead of Birth-Death tree prior. Node ages are the median of node age posterior distributions.</p>
Fig. 14 in Revision of the poorly known Neotropical butterfly genus Zischkaia Forster, 1964 (Lepidoptera, Nymphalidae, Satyrinae), with descriptions of nine new species
Fig. 14. Distribution maps for taxa of Zischkaia Forster, 1964.
Fig. 1 in 'Species' from two different butterfly genera combined into one: description of a new genus of Euptychiina (Nymphalidae: Satyrinae) with unusually variable wing pattern
Fig. 1. Adult male of Sepona punctata – Jaru, Rondônia, Brazil. Dorsal above, ventral below.
Fig. 5 in Stichelia pelotensis (Lepidoptera, Riodinidae): conservation, notes, and rediscovery of an endangered butterfly from southern Brazil
Fig. 5. Knowing distribution of Stichelia pelotensis in Rio Grande do Sul state, Brazil.
Figure 1a in Checklist of Butterflies (Lepidoptera: Rhopalocera) of Union Territory Jammu and Kashmir, India
Figure 1a. Map showing Union Territory of Jammu & Kashmir with 20 districts.
Figure 2 in Sighting of a rare species of butterfly, Tajuria maculata Hewitson, 1865 (Spotted Royal) from Darjeeling District, West Bengal, India
Figure 2. Records of previous and recent distribution of Tajuria maculata from India.
Figure 1 in Sighting of a rare species of butterfly, Tajuria maculata Hewitson, 1865 (Spotted Royal) from Darjeeling District, West Bengal, India
Figure 1. Spotted Royal Tajuria maculata (Hewitson, 1865) (Lepidoptera: Lycaenidae).
Figure 3 in Species richness and diversity of butterflies (Insecta: Lepidoptera) of Ganga Lake, Itanagar Wildlife Sanctuary, Arunachal Pradesh, India
Figure 3. Relative abundance of butterflies in Ganga Lake.
Figure 1 in Species richness and diversity of butterflies (Insecta: Lepidoptera) of Ganga Lake, Itanagar Wildlife Sanctuary, Arunachal Pradesh, India
Figure 1. Map of the survey area (source: Google Map).
Figure 4 in Diversity of butterflies (Lepidoptera: Rhopalocera) from cold desert of district Kargil in the union territory of Ladakh, India
Figure 4. Percentage composition of families of
Figure 5 in Diversity of butterflies (Lepidoptera: Rhopalocera) from cold desert of district Kargil in the union territory of Ladakh, India
Figure 5. Status-wise composition of butterfly species.
Figure 6 in Diversity of butterflies (Lepidoptera: Rhopalocera) from cold desert of district Kargil in the union territory of Ladakh, India
Figure 6. Percentage composition of families of butterflies.
Figure 3 in Butterflies of the family Pieridae (Lepidoptera: Papilionoidea) of the Frio river basin, northeastern Andes of Santander, Colombia
Figure 3. Altitudinal distribution of the genera of family Pieridae in the Frio river basin.
Figure 2 in Butterflies of the family Pieridae (Lepidoptera: Papilionoidea) of the Frio river basin, northeastern Andes of Santander, Colombia
Figure 2. Abundance and richness of species by sampling sites.
Figure 1 in Butterflies of the family Pieridae (Lepidoptera: Papilionoidea) of the Frio river basin, northeastern Andes of Santander, Colombia
Figure 1. Location of study zone and sampling places (modified from Google Earth Pro 2020).
Fig. 3 in Heteroptera attracted to butterfly traps baited with fish or shrimp carrion
Fig. 3. Melucha phyllocnemis (Burmeister) on the side of a trap and Leptoglossus sp. on the base.
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