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FIGURE 5 in Alpha-taxonomy and phylogeny of African Junoniini butterflies based on morphological data, with an emphasis on genitalia, and COI barcode (Lepidoptera Nymphalidae)
FIGURE 5. Male genitalia, lateral view, aedeagus extracted in dorsal view. A: Junonia westermanni, Kenya, Kakamega, prep. genit. 1426; B: Junonia sophia, Kenya, Kakamega, prep. genit. 1859; C: Junonia touhilimasa, Zaire, Kibomboma, prep. genit. 1861; D: Junonia hierta cebrene, Kenya, Kwale, prep. genit. 1781; E: Junonia oenone oenone, Uganda, Mubende, prep. genit. 1808: F: Junonia artaxia Zambia, Chingombe, prep. genit. 1853.
FIGURE 20 in Alpha-taxonomy and phylogeny of African Junoniini butterflies based on morphological data, with an emphasis on genitalia, and COI barcode (Lepidoptera Nymphalidae)
FIGURE 20. Maximum Parsimony tree based on morphological characters. Black and white circles indicate synapomorphies and homoplasies, respectively.
FIGURE 2 in Alpha-taxonomy and phylogeny of African Junoniini butterflies based on morphological data, with an emphasis on genitalia, and COI barcode (Lepidoptera Nymphalidae)
FIGURE 2. Male genitalia, lateral view, aedeagus extracted in dorsal view. A: Salamis amaniensis eileene stat. nov., Zimbabwe, prep. genit. 1821; B: Salamis augustina augustina, X: incurved base of the tegumen; Y: sacculus; Z: gnathos, Reunion, prep. genit. 1784; C: Salamis amaniensis humbloti stat. nov., Comoros, Anjouan, prep. genit. 1819; D: Salamis cacta Kibale, Uganda, prep. genit. 1424; E: Salamis amaniensis amanuensis stat. nov., Gongoni Forest, Kenya, prep. genit. 1785; F: Salamis anteva Madagascar, Sandrangato, prep. genit. 1428.
FIGURE 1 in Alpha-taxonomy and phylogeny of African Junoniini butterflies based on morphological data, with an emphasis on genitalia, and COI barcode (Lepidoptera Nymphalidae)
FIGURE 1. Male genitalia, lateral view, aedeagus extracted in dorsal view. A: Protogoniomorpha parhassus, X: apical process of the valva, Y: sacculus; Z: base of the valva adhered to vinculum, Nigeria, Ajebandele, prep. genit. 1407; B: Protogoniomorpha nebulosa, Kenya, Kaya Teleza, prep. genit. 1832; C: Protogoniomorpha anacardii, Ghana, Mt. Afajato, prep. genit. 1824; D: Protogoniomorpha duprei, Madagascar, prep. genit. 1447; E: Junonia temora, Kenya, Kakamega Forest, prep. genit. 1421; F: Junonia cytora, Ivory Coast, prep. genit. 1425.
FIGURE 3 in Alpha-taxonomy and phylogeny of African Junoniini butterflies based on morphological data, with an emphasis on genitalia, and COI barcode (Lepidoptera Nymphalidae)
FIGURE 3. Male genitalia, lateral view, aedeagus extracted in dorsal view. A: Junonia (Kamilla) cymodoce lugens, Sao Tome and Principe, Principe, prep. genit. 576; B: Junonia (Kamilla) ansorgei, X: tip of the valva; Y: harpe produced into a sharp tip; base of elontagted aedeagus, "Abessynia", prep. genit. 6015; C: Juonia (Kamilla) cymodoce lugens?, Cameroun, Bidjap, prep. genit. 01_16.11.2020; D: Junonia (Kamilla) ansorgei, Cameroun, Tabenken, prep. genit. 169SAFI; E: Junonia (Kamilla) cymodoce cymodoce, Nigeria, Ibadan, prep. genit. 02_16.11.2020; F: Junonia (Kamilla) agnesberenyiae, Guinea, Mont Nimba, prep. genit. 1809.
Parallel evolution of ancient, pleiotropic enhancers underlies butterfly wing pattern mimicry
Color pattern mimicry in Heliconius butterflies is a classic case study of complex trait adaptation via selection on a few large effect genes. Association studies have linked color pattern variation to a handful of noncoding regions, yet the presumptive cis-regulatory elements (CREs) that control color patterning remain unknown. Here we combine chromatin assays, DNA sequence associations, and genome editing to functionally characterize 5 cis-regulatory elements of the color pattern gene optix. We were surprised to find that the cis-regulatory architecture of optix is characterized by pleiotropy and regulatory fragility, where deletion of individual cis-regulatory elements has broad effects on both color pattern and wing vein development. Remarkably, we found orthologous cis-regulatory elements associate with wing pattern convergence of distantly related comimics, suggesting that parallel coevolution of ancestral elements facilitated pattern mimicry. Our results support a model of color pattern evolution in Heliconius where changes to ancient, multifunctional cis-regulatory elements underlie adaptive radiation.
Data from: Rapid diversification associated with ecological specialization in Neotropical Adelpha butterflies
Rapid diversification is often associated with morphological or ecological adaptations that allow organisms to radiate into novel niches. Neotropical Adelpha butterflies, which comprise over 200 species and subspecies, are characterized by extraordinary breadth in host plant use and wing colour patterns compared to their closest relatives. To examine the relationship between phenotypic and species diversification, we reconstructed the phylogenetic history of Adelpha and its temperate sister genus Limenitis using genomewide restriction-site-associated DNA (RAD) sequencing. Despite a declining fraction of shared markers with increasing evolutionary distance, the RAD-Seq data consistently generated well-supported trees using a variety of phylogenetic methods. These well-resolved phylogenies allow the identification of an ecologically important relationship with a toxic host plant family, as well as the confirmation of widespread, convergent wing pattern mimicry throughout the genus. Taken together, our results support the hypothesis that evolutionary innovations in both larvae and adults have permitted the colonization of novel host plants and fuelled adaptive diversification within this large butterfly radiation.
Community science butterfly data Northwest Arkansas
<p>This data set contains the butterfly behavior observations of community members who visited the Botanical Gardens of the Ozarks, as well as students in the Principles of Zoology and Animal Behavior courses at the University of Arkansas from spring 2017 to fall 2020. This data was used to assess the relationship between butterfly color and butterfly flower color choice, as well as the relationship between butterfly color, behavior, abundance, and cloud cover in Northwest Arkansas. It corresponds to "Engaging the community in pollinator research: the effect of wing pattern and weather on butterfly behavior" in Integrative and Comparative Biology. This data set includes the original data reported by community observers as well as the cleaned values used in the manuscript.</p>
FIGURE 1 in The butterflies (Lepidoptera: Papilionoidea and Hesperioidea) of the Catimbau National Park, Pernambuco, Brazil
FIGURE 1. Aspects of the Catimbau National Park. a–b: Panoramic views, showing typical rocky formations c: characteristic shrub-like vegetation of the Park.
Data from: A comprehensive and dated phylogenomic analysis of butterflies
Butterflies (Papilionoidea), with over 18,000 described species [1], have captivated naturalists and scientists for centuries. They play a central role in the study of speciation, community ecology, biogeography, climate change, and plant-insect interactions and include many model organisms and pest species [2, 3]. However, a robust higher-level phylogenetic framework is lacking. To fill this gap, we inferred a dated phylogeny by analyzing the first phylogenomic dataset, including 352 loci (> 150,000 bp) from 207 species representing 98% of tribes, a 35-fold increase in gene sampling and 3-fold increase in taxon sampling over previous studies [4]. Most data were generated with a new anchored hybrid enrichment (AHE) [5] gene kit (BUTTERFLY1.0) that includes both new and frequently used (e.g., [6]) informative loci, enabling direct comparison and future dataset merging with previous studies. Butterflies originated around 119 million years ago (mya) in the late Cretaceous, but most extant lineages diverged after the Cretaceous-Paleogene (K-Pg) mass-extinction 65 mya. Our analyses support swallowtails (Papilionidae) as sister to all other butterflies, followed by skippers (Hesperiidae) + the nocturnal butterflies (Hedylidae) as sister to the remainder, indicating a secondary reversal from diurnality to nocturnality. The whites (Pieridae) were strongly supported as sister to brush-footed butterflies (Nymphalidae) and blues + metalmarks (Lycaenidae and Riodinidae). Ant association independently evolved once in Lycaenidae and twice in Riodinidae. This study overturns prior notions of the taxon's evolutionary history, as many long-recognized subfamilies and tribes are para- or polyphyletic. It also provides a much-needed backbone for a revised classification of butterflies and for future comparative studies including genome evolution and ecology.
FIGURE 4 in The thorny subject of insular endemic taxonomy: morphometrics reveal no evidence of speciation between Coenonympha corinna and Coenonympha elbana butterflies (Lepidoptera: Nymphalidae)
FIGURE 4. Graphical representation of the first (RW1) and of the second (RW2) relative warps of the tegumen+uncus analysis. Variations in shape along both axes are shown in thin-plate spline deformation grides. Open circles, Uccellina; open squares, Giannutri; open triangles, Elba; black circles, Corsica; black squares, Sardinia; black triangles, Capraia.
FIGURE 3 in The thorny subject of insular endemic taxonomy: morphometrics reveal no evidence of speciation between Coenonympha corinna and Coenonympha elbana butterflies (Lepidoptera: Nymphalidae)
FIGURE 3. Discriminant scores from the functions separating Corsican from Tuscan specimens successively applied to the remnant populations (a, spot dimension; b, traditional genitalia morphometrics, c and d, tegumen+uncus and valva geometric morphometrics, respectively). Uc, Uccellina; Ucc, Uccellina cross-validation sample; Gi, Giannutri island; El, Elba island; Ca, Capraia island, Sa, Sardinia island; Co, Corsica island). Black squares indicate individuals predicted as C. corinna by DA, while white squares indicate individuals predicted as C. elbana.
Genomic time-series data show that gene flow maintains high genetic diversity despite substantial genetic drift in a butterfly species
<p>Effective population size affects the efficacy of selection, rate of evolution by drift, and neutral diversity levels. When species are subdivided into multiple populations connected by gene flow, evolutionary processes can depend on global or local effective population sizes. Theory predicts that high levels of diversity might be maintained by gene flow, even very low levels of gene flow, consistent with species long-term effective population size, but tests of this idea are mostly lacking. Here, we show that Lycaeides butterfly populations maintain low contemporary (variance) effective population sizes (e.g., ~200 individuals) and thus evolve rapidly by genetic drift. In contrast, populations harbored high levels of genetic diversity consistent with an effective population size several orders of magnitude larger. We hypothesized that the differences in the magnitude and variability of contemporary versus long-term effective population sizes were caused by gene flow of sufficient magnitude to maintain diversity but only subtly affect evolution on generational time scales. Consistent with this hypothesis, we detected low but non-trivial gene flow among populations. Furthermore, using short-term population-genomic time-series data, we documented patterns consistent with predictions from this hypothesis, including a weak but detectable excess of evolutionary change in the direction of the mean (migrant gene pool) allele frequencies across populations, and consistency in the direction of allele frequency change over time. The documented decoupling of diversity levels and short-term change by drift in Lycaeides has implications for our understanding of contemporary evolution and the maintenance of genetic variation in the wild.</p>
Beneficial wake-capture effect for forward propulsion with a restrained wing-pitch motion of a butterfly
Unlike other insects, a butterfly uses a small amplitude of the wing-pitch motion for flight. From an analysis of the dynamics of real flying butterflies, we show that the restrained amplitude of the wing-pitch motion enhances the wake-capture effect so as to enhance forward propulsion. A numerical simulation refined with experimental data shows that, for a small amplitude of the wing-pitch motion, the shed vortex generated in the downstroke induces air in the wake region to flow towards the wings, which enables a butterfly to capture this induced flow and to acquire an additional forward propulsion. When the amplitude of the wing-pitch motion exceeds 45<sup>o</sup>, the flow induced by the shed vortex drifts away from the wings; it attenuates the wake-capture effect and causes the butterfly to lose a part of its forward propulsion. Our results provide a physical elucidation for a butterfly adopting a small amplitude of the wing-pitch motion to enhance the wake-capture effect and forward propulsion. This work clarifies the variation of the flow field correlated with the wing-pitch motion, which is useful in the design of a micro-aerial vehicle.
Figure 10 in A long-term survey of spring monarch butterflies in north-central Florida
Figure 10. The yearly decline in oviposition rate at Cross Creek from 1994 to 2017, recorded as average eggs/stem (500 plant minimum each year) for the four weeks from 22 March to 18 April. (F = 11.501, p = 0.003, R2 = 0.390). Excluding the extraordinary high value in 1995, the decline by 1,18 linear regression is about 78%. Based on a total of 3545 eggs.
Figure 2 in A long-term survey of spring monarch butterflies in north-central Florida
Figure 2. View across Hogan's pasture showing numerous flowering Asclepias humistrata plants. 28 April 2010. Photo by K. Sims Dunford.
Figure 6 in A long-term survey of spring monarch butterflies in north-central Florida
Figure 6. Appearance of adults at Cross Creek calculated as the number captured per hour, averaged for each week from 1994 through 2017 (excluding 1997, 2000–2001, 2003). The earliest arrival was 14 March (week of 8–14 March). Not all weeks were surveyed in each year. Means are shown with 95% CI. Based on observations of a total of 865 adults.
Figure 9 in A long-term survey of spring monarch butterflies in north-central Florida
Figure 9. Egg abundance through the spring, recorded as mean eggs/stem for each week from 1994 through 2017 (50 plants per sample, minimum of 500 plants examined each year; excludes 1997, 2001, and 2004). Means are shown with 95% CI calculated from 3545 eggs found during 44,753 surveys of stems.
Figure 5 in A long-term survey of spring monarch butterflies in north-central Florida
Figure 5. Variation in flowering phenology of Asclepias humistrata at Cross Creek across three years; 2002 was a typical year, 2008 was an early year, and 2010 was a late year.
Figure 11 in A long-term survey of spring monarch butterflies in north-central Florida
Figure 11. Phenology of immatures at Cross Creek, totalled for 1994–2017. Larvae are divided into 1st through 3rd and 4th and 5th instar. Based on totals of 3444 eggs and 1295 larvae.
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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.