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Data from: Chromosome-level reference genome assembly and gene editing of the dead-leaf butterfly Kallima inachus
<p class="p">The leaf resemblance of <i><span>Kallima</span></i> (Nymphalidae) butterflies<i> </i>is an important ecological adaptive mechanism that increases survival. However, the genetic mechanism underlying ecological adaptation remains unclear owing to a dearth of genomic information. Herein, we revealed the karyotype (n = 31) of the dead-leaf butterfly <i><span>Kallima inach</span></i><i><span>us</span></i>, assembled its high-quality chromosome-level reference genome (568.92 Mb; contig N50: 19.20 Mb), and identified its Z and candidate W chromosomes. To our knowledge, this is the first study to report on these aspects of this species. In the assembled genome, 15,309 protein-coding genes and 49.86% repeat elements were annotated. Phylogenetic analysis showed that <i><span>K. inachus</span></i> diverged from <i><span>Melitaea cinxia </span></i>(no leaf resemblance), both of which are in Nymphalinae, around 40 million years ago. Demographic analysis indicated that the effective population size of <i><span>K. inach</span></i><i><span>us</span></i> decreased during the last interglacial period in the Pleistocene. The wings of adults with the pigmentary gene <i><span>ebony</span></i> knocked out using CRISPR/Cas9 showed phenotypes in which the orange dorsal region and entire ventral surface darkened, suggesting its vital role in the ecological adaption of dead-leaf butterflies. Our results provide important genome resources for investigating the genetic mechanism underlying protective resemblance in dead-leaf butterflies and insights into the molecular basis of protective coloration.</p>
Cuneiform: Dead Sheep (32)
Special Collections and University Archives - Colgate University Source: Objaverse 1.0 / Sketchfab
Dead Stone
Leaning tower of headstone. Over at one of the old local churches. Created with Polycam Source: Objaverse 1.0 / Sketchfab
Dead Man's Howe, Insch, University of Aberdeen
https://canmore.org.uk/site/18159/dead-mans-howe-wantonwells Source: Objaverse 1.0 / Sketchfab
FIGURES 8–18. Phlaeothripinae genera. 8–15 in Genera of fungivorous Phlaeothripinae (Thysanoptera) from dead branches and leaf-litter in Australia
FIGURES 8–18. Phlaeothripinae genera. 8–15 antennae: (8) Asemothrips picturatus; (9) Biconothrips reedi; (10) Holothrips oceanicus; (11) Plectrothrips australis; (12) Preeriella sp.; (13) Priesneria peronis; (14) Pygmaeothrips angusticeps; (15) Streptothrips tribulatius. (16) Deplorothrips sp.; (17–18) Bocathrips okajimai.
FIGURES 39–45 in Genera of fungivorous Phlaeothripinae (Thysanoptera) from dead branches and leaf-litter in Australia
FIGURES 39–45. Phlaeothripinae genera. (39) Stomothrips mouldeni; (40) Stephanothrips occidentalis; (41) Strepterothrips orientalis, male; (42) Plectrothrips australis, pelta & tergite II; (43) Stomothrips mouldeni, thoracic sternites; (44) Yarnkothrips kolourus; (45) Terthrothrips ananthakrishnani.
FIGURES 1–7 in Genera of fungivorous Phlaeothripinae (Thysanoptera) from dead branches and leaf-litter in Australia
FIGURES 1–7. Phlaeothripinae genera. (1) Adraneothrips sp.; (2) Adurothrips atopus; (3) Apelaunothrips tasmani; (4) Apostlethrips pygus; (5) Apostlethrips apostus; (6) Asemothrips picturatus; (7) Azaleothrips lepidus, metanotum.
FIGURES 32–38 in Genera of fungivorous Phlaeothripinae (Thysanoptera) from dead branches and leaf-litter in Australia
FIGURES 32–38. Phlaeothripinae genera. (32) Octurothrips pulcher, tergites IX–X; (33) Solomonthrips australiensis, tergites VII–IX; (34) Psalidothrips taylori, thoracic sternites—arrows indicate sterno-pleural sutures; (35) Psalidothrips sp., male; (36) Sophiothrips sp.; (37) Senithrips psomus, macroptera thoracic sternites; (38) Pygmaeothrips angusticeps, female microptera.
FIGURES 46–49 in Genera of fungivorous Phlaeothripinae (Thysanoptera) from dead branches and leaf-litter in Australia
FIGURES 46–49. Phlaeothripinae genera. (46) Tylothrips sp.—arrows indicate metathoracic ventro-lateral setae; (47) Zemiathrips trisetis; (48) Hoplandrothrips flavipes, male; (49) Azaleothrips lepidus.
FIGURES 26–31 in Genera of fungivorous Phlaeothripinae (Thysanoptera) from dead branches and leaf-litter in Australia
FIGURES 26–31. Phlaeothripinae genera. (26) Jacotia glyptus; (27) Macrophthalmothrips argus; (28) Majerthrips barrowi; (29) Malacothrips sp.; (30) Mystrothrips sp.; (31) Octurothrips pulcher.
FIGURES 19–24 in Genera of fungivorous Phlaeothripinae (Thysanoptera) from dead branches and leaf-litter in Australia
FIGURES 19–24. Phlaeothripinae genera. (19) Baenothrips moundi, tergites IX–X; (20) Senithrips psomus, antenna; (21) Ecacanthothrips tibialis, small male; (22) Habrothrips curious; (23) Hoplothrips orientalis small male; (24) Horistothrips australis; (25) Holothrips oceanicus.
FIGURE 2 in Greeneria saprophytica sp. nov. on dead leaves of Syzygium cumini from Chiang Rai, Thailand
FIGURE 2. Greeneria saprophytica (MFLUCC 12-0298, holotype). A. Specimen on dead leaf of Syzygium cumini. B. Conidiomata on the host surface. C. L.S. of a conidioma. D–H. Phialidic conidiogenous cells with developing conidia; in G. note proliferating conidiogenous cell. I–L. Conidia. M. Germinating conidium. N–O. Colonies on PDA; N. From top, O. From reverse. Scale bars: C = 50 μm, D–M = 10 μm.
FIGURE 1 in Greeneria saprophytica sp. nov. on dead leaves of Syzygium cumini from Chiang Rai, Thailand
FIGURE 1. Maximum likelihood (ML) majority rule 28S nuclear large subunit (nuLSU) consensus tree for Greeneria saprophytica, G. uvicola and other representatives in order Diaporthales and genera incertae sedis. RAxML bootstrap support values are given at the nodes. The tree is rooted to Coniochaeta velutina (Coniochaetales).
Fig. 3 in Dead-twig discrimination for oviposition in a cicada, Cryptotympana facialis (Hemiptera: Cicadidae)
Fig. 3 Kinematic diagrams for oviposition-related behaviors. Sequential relationships between behaviors, which occurred successively within 60 s, are linked with lines for females hung on a wood stick (a, from 25 females), a dead twig (b, from 24 females), or a live twig (c, from 25 females). The number in a box is a cumulative total number of observations of each behavior. The number associated with a line is the number of observations of a flow, and the thickness of a line indicates its proportion relative to the total number of sequences, except that flows constituting less than 10 % are depicted as broken lines. T-shaped lines indicate the end of a sequence that did not lead to oviposition. For the definition of each action, see Table 1
Fig. 2 in Dead-twig discrimination for oviposition in a cicada, Cryptotympana facialis (Hemiptera: Cicadidae)
Fig. 2 Effects of oviposition substrates on induction and success of oviposition. An adult female was caged with one of the three oviposition substrates, and its behavior was observed for 90 min. a The number of ovipositions completed per female. b The number of oviposition trials, that is, initiation of boring, per female. c Completion rates of oviposition trials. Data are shown by Tukey box plots; they indicate the median (bold line), the 25th and 75th percentiles (box edges),
Fig. 4 in Dead-twig discrimination for oviposition in a cicada, Cryptotympana facialis (Hemiptera: Cicadidae)
Fig. 4 Frequency and duration of probing behavior of Cryptotympana facialis females on different oviposition substrates. a The number of probing behaviors per female during a 90-min observation period. b The mean duration of a single probing behavior. The number of females is indicated at the top of the graph. Groups marked with the same alphabetical letter were not significantly different (a likelihood-ratio test of GLM and post hoc multiple comparisons, p> 0.05). For details of Tukey box plots, see Fig. 2
Ecological significance of marcescence in Himalayan plants: Why is standing dead phytomass more important in demanding, resource-limited environments?
<ol> <li>Understanding mechanisms allowing plants to thrive in challenging conditions is critical for predicting their responses to global environmental change. An often overlooked ecological adaptation is marcescence, where leaves and stems are retained beyond their typical shedding time, with implications for nutrient recycling and carbon sequestration. Marcescence may be common in plants with conservative resource-use strategies, especially in environments with limited resources and marked seasonality, such as deserts and alpine regions. However, the extent to which marcescence occurs in different taxa across different habitats and its relationship to seasonality and plant functioning as reflected in ecophysiological traits remains understudied.</li> <li>We studied 600 individuals across 40 Himalayan herb species found in desert, steppe, alpine, and subnival habitats, spanning 3000 to 5400 m elevation. Marcescence was observed in 37 out of 40 species evaluated, with 57% of the sampled individuals displaying dead phytomass, indicating the widespread nature of this phenomenon in Himalayan ecosystems.</li> <li>Environment and plant traits emerged as significant factors influencing the amount of dead-standing biomass. These findings remained robust after accounting for intraspecific variation and phylogenetic inertia. Desert and steppe species exhibited greater marcescent biomass, characterized by tenfold more dead stem biomass than alpine and subnival plants. In contrast, the proportion of dead phytomass increased with elevation due to increased dead leaf fraction.</li> <li>Tall desert species with heavily lignified stems and limited nitrogen and phosphorus content had high levels of stem marcescence, which may aid litter photodegradation and nutrient recycling but also deter herbivory and promote seed dispersal (tumbleweed). Conversely, slow-growing alpine and subnival plants with higher water-use efficiency, foliar C:N ratios, and parenchymatic storage tissue rich in mobile carbohydrates had a high proportion of marcescent leaves, which may aid survival in these resource-limited environments by providing delayed nutrients through moisture-conserving mulch that also provides insulation and protection from frost.</li> <li>This study provides new insights into the complex interplay of habitat, seasonality, and plant traits in dead phytomass retention in Himalayan plants. Marcescence is prevalent in resource-conservative species in highly seasonal, resource-limited habitats and may significantly shape the functioning and biodiversity of Himalayan ecosystems.</li> </ol>
FIGURE. Conlarium sichuanense (HKAS 113024, holotype) a–c Colonies on dead branches. d–k Conidiogenous cells and conidia. l Germinated conidium. m, n Colony on PDA from surface and reverse. Scale bars: d-k = 10 μm, l = 20 μm. in Conlarium sichuanense sp. nov., on Ficus virens from Sichuan Province, China
FIGURE. Conlarium sichuanense (HKAS 113024, holotype) a–c Colonies on dead branches. d–k Conidiogenous cells and conidia. l Germinated conidium. m, n Colony on PDA from surface and reverse. Scale bars: d-k = 10 μm, l = 20 μm.
Blast output from: Lost in dead wood? Environmental DNA sequencing from dead wood shows little signs of saproxylic beetles
<p>eDNA metabarcoding has become a standard method for assessing wood-inhabiting fungi and bacteria, yet determination of dead-wood-inhabiting beetles still relies on time-consuming collection of beetle specimens. We thus tested whether beetle species can be identified by eDNA sequencing of wood in a mesocosm experiment that manipulated species assemblages. Dead wood samples were taken at exit holes of beetles and DNA was extracted and analyzed using two comparative methods: (i) metabarcoding with standard arthropod primers (421 bp) and (ii) using short species-specific primers (120-264 bp) with Sanger sequencing. Results showed that beetle DNA was amplified by each of the two approaches, however, with (i) we detected only one non-target saproxylic beetle species. In addition, we identified 80 different OTUs with four non-targeted species of arthropods. For (ii) we detected the targeted species in two fresh beetle exit holes out of 20 samples. We suggest that, in contrast to fungi and bacteria, this eDNA metabarcoding approach is not able to reliably detect saproxylic beetles from wood samples, likely due to rapid degradation of their target DNA. Adapting such an approach for large scale analyses thus requires a better knowledge of degradation processes affecting DNA quality and quantity in wood.</p>
FIGURE 2 in Crassiparies yunnanensis sp. nov. (Neohendersoniaceae, Pleosporales) from dead twigs of Coffea arabica in China
FIGURE 2. Crassiparies yunnanensis (HKAS 121977, holotype). a, b. Ascomata on coffee twig surface (arrow indicates orange secretion). c, d. Vertical section of ascomata. e. Peridium. f. Hamathecium. g–k. Asci. l–q. Ascospores. r. Germinating ascospore. s. Colony on PDA medium. Scale bars: c = 100 µm, d = 50 µm, e, r = 20 µm, f = 50 µm, g–k = 30 µm, l–q = 10 µm.
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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.