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Hydaticus dhofarensis Pederzani, 2003, the only endemic diving beetle known from Dhofar Region is newly recorded from Masirah Island, central Oman. Analysis of 3ʹ region of the mitochondrial cytoch...

Cydistinae are a rare monogeneric beetle lineage from Asia with a convoluted history of classification, historically placed in various groups within the series Elateriformia. However, their position has never been rigorously tested. To resolve this long-standing puzzle, we are the first to present sequences of two nuclear and two mitochondrial markers for four species of Cydistinae to determine their phylogenetic position. We included these sequences in two rounds of analyses: one including a broad Elateriformia dataset to test placement at the superfamily/family level, and a second, including a richer, targeted sampling of presumed close relatives. Our results strongly support Cydistinae as sister to Phengodidae in a clade with Rhagophthalmidae. Based on our molecular phylogenetic results and examination of morphological characters, we hereby transfer the formerly unplaced Cydistinae into Phengodidae and provide diagnoses for the newly circumscribed Phengodidae, Cydistinae and Cydistus. Since both Phengodidae and Rhagophthalmidae have bioluminescent larvae and strongly neotenic females, similar features can be hypothesized for Cydistinae. Additionally, Cydistus minor is transferred to the new genus Microcydistus.

The available data on the biology of the water scavenger beetle genus Protosternum Sharp, 1890 (Hydrophilidae: Sphaeridiinae: Protosternini) are summarized, indicating an obligate association of Protosternum beetles with banana plants (Musaceae): they inhabit decaying tissues of old banana stems in which adult feeding, larval development and pupation takes place. Second and third instar larvae and pupa of the Taiwanese-endemic P. abnormale (Orchymont, 1913) are described in detail, based on field-collected specimens associated with co-occurring adults by cox1 DNA barcodes. Larvae are unique in the Hydrophiloidea by having strongly asymmetrical mouthparts with a large basal bifid tooth on the right mandible, indicating a specialized prey preference. Putative larval synapomorphies of the Protosternini are revealed, based on the comparison of the Protosternum larva with that of Sphaerocetum. Taxonomic status of P. abnormale is revealed to be confused by previous authors due to the absence of male specimens and is clarified here. Two new species are described based on specimens previously misidentified as P. abnormale: P. malayanum sp. nov. from Sumatra and Peninsular Malaysia, and P. hainanense sp. nov. from Hainan.

Entomological collections are the target of various insect pests, e.g. carpet beetles (Dermestidae) and booklice (Psocoptera) which can damage and completely destroy dry specimens in a relatively short time. Collections in the National Museum, Czech Republic (NMP) including the entomological ones are protected by fumigation using commercially available smoke shells ‘Cytrol Super SG’; fumigation is performed twice a year. The active insecticidal substance of these smoke shells is cypermethrin (6.25%). We tested whether the repeated cypermethrin fumigation of the NMP entomological collections negatively affects the quality of mitochondrial DNA in dry specimens and prevents the subsequent use of these samples for molecular analyses required for identification, taxonomy, systematics, and phylogenetic studies. We used 32 freshly fixed specimens of the flower chafer Oxythyrea funesta (Poda von Neuhaus, 1761) and 32 freshly fixed specimens of the brown-tailed cockroach Supella longipalpa (Fabricius, 1798). One half of specimens of both species was stored outside NMP and not fumigated (negative control), and the other half was deposited in collection hall with the NMP insect collection and directly exposed to the fumigation. Subsequently, all specimens were processed in a molecular laboratory under a standardized protocol using one leg as the source tissue after each fumigation, and the 658 bp long barcoding region of the cytochrome oxidase I (cox1) as the testing gene fragment. Results of the PCR product electrophoresis and the sequences acquired confirmed that the repeated fumigation had no negative effect on tested samples.

Senodoniini is a small lineage of click beetles currently comprising 21 species in two genera, distributed in the Himalayas and East and Southeast Asia. The definition and limits of this group have changed considerably during its history. Recent authors treat Senodoniini as a tribe within Dendrometrinae, usually close to Dimini, but this placement has never been rigorously tested. Here, we shed new light on the systematic position and limits of Senodoniini by performing a combined phylogenetic analysis of two nuclear and two mitochondrial molecular markers. Our results recovered Senodoniini not monophyletic, and placed them into the Lissominae complex, where they formed a clade with Austrelater Calder &amp, Lawrence (Protelaterini). Molecular phylogeny is in agreement with the adult morphology. Additionally, we examined the morphology of a monotypic genus Rostricephalus Fleutiaux from Southeast Asia, which was previously classified in various Elateridae groups including Senodoniini, and its position was always uncertain. This genus shares morphological characters with Protelaterini. We provide morphological redescriptions as well as the figures of main diagnostic characters for Senodonia Laporte, Sossor Cand&egrave, ze, and Rostricephalus. Based on our results, we place these genera to Lissominae: Protelaterini, and hence synonymize Senodoniini Schenkling with Protelaterini Schwarz.

Platynectes (Gueorguievtes) davidorum sp. nov. is described, including all larval instars, from hygropetric habitats in Fujian and Zhejiang provinces, eastern China. The new species can be easily distinguished from all other Asia Platynectes Régimbart, 1879 with dorsally strongly convex habitus, distinctly impressed double reticulation of elytra, short appendages and the shape of median lobe of aedeagus. Collected larvae were successfully associated with adults using a molecular approach and the barcode for the new species is provided as well as the phylogenetic position based on the mitochondrial cytochrome oxidase I gene; they are described and illustrated, with detailed morphometric and chaetotaxic analyses of the cephalic capsule, head appendages, legs, last abdominal segment, and urogomphi. The classification of the new species within the subgenus Gueorguievtes Vazirani, 1976, and its adaptation to hygropetric habitats are briefly discussed.

Rapid and reliable identification of insects is important in many contexts, from the detection of disease vectors and invasive species to the sorting of material from biodiversity inventories. Because of the shortage of adequate expertise, there has long been an interest in developing automated systems for this task. Previous attempts have been based on laborious and complex handcrafted extraction of image features, but in recent years it has been shown that sophisticated convolutional neural networks (CNNs) can learn to extract relevant features automatically, without human intervention. Unfortunately, reaching expert-level accuracy in CNN identifications requires substantial computational power and huge training data sets, which are often not available for taxonomic tasks. This can be addressed using feature transfer: a CNN that has been pretrained on a generic image classification task is exposed to the taxonomic images of interest, and information about its perception of those images is used in training a simpler, dedicated identification system. Here, we develop an effective method of CNN feature transfer, which achieves expert-level accuracy in taxonomic identification of insects with training sets of 100 images or less per category, depending on the nature of data set. Specifically, we extract rich representations of intermediate to high-level image features from the CNN architecture VGG16 pretrained on the ImageNet data set. This information is submitted to a linear support vector machine classifier, which is trained on the target problem. We tested the performance of our approach on two types of challenging taxonomic tasks: 1) identifying insects to higher groups when they are likely to belong to subgroups that have not been seen previously and 2) identifying visually similar species that are difficult to separate even for experts. For the first task, our approach reached \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$CDATA[$CDATA[$>$$\end{document}92% accuracy on one data set (884 face images of 11 families of Diptera, all specimens representing unique species), and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$CDATA[$CDATA[$>$$\end{document}96% accuracy on another (2936 dorsal habitus images of 14 families of Coleoptera, over 90% of specimens belonging to unique species). For the second task, our approach outperformed a leading taxonomic expert on one data set (339 images of three species of the Coleoptera genus Oxythyrea; 97% accuracy), and both humans and traditional automated identification systems on another data set (3845 images of nine species of Plecoptera larvae; 98.6 % accuracy). Reanalyzing several biological image identification tasks studied in the recent literature, we show that our approach is broadly applicable and provides significant improvements over previous methods, whether based on dedicated CNNs, CNN feature transfer, or more traditional techniques. Thus, our method, which is easy to apply, can be highly successful in developing automated taxonomic identification systems even when training data sets are small and computational budgets limited. We conclude by briefly discussing some promising CNN-based research directions in morphological systematics opened up by the success of these techniques in providing accurate diagnostic tools.