Assassin bugs (Hemiptera: Reduviidae Latreille) comprise not only one of the largest radiations of predatory animals (22 subfamilies; >6,800 spp.) but also include the medically important kissing bugs (Triatominae Jeannel). Reduviidae are morphologically diverse, engage in an astounding array of predatory strategies and have evolved some of the most unique anti‐predator and stealth techniques in the animal kingdom. While significant progress has been made to reveal the evolutionary history of assassin bugs and revise their taxonomy, the non‐monophyly of the second largest assassin bug subfamily, Reduviinae Latreille, remains to be addressed. Leveraging phylogenomic data (2,291 loci) and 112 morphological characters, we performed the first data‐ and taxon‐rich (195 reduvioid taxa) combined phylogenetic analysis across Reduvioidea and reconstructed morphological diagnostic features for major lineages. We corroborated the rampant polyphyly of Reduviinae that demands substantial revisions to the subfamilial and tribal classification of assassin bugs. Our new classification for Reduviidae reduces the number of subfamilies to 19 and recognizes 40 tribes. We describe three new subfamilies to accommodate distantly related taxa previously classified as Reduviinae (Heteropinae subfam . nov ., Nanokeralinae subfam . nov ., and Pasirinae subfam . nov .). Triatominae sensu nov . are expanded to include closely related predatory reduviine genera. Cetherinae Jeannel, Chryxinae Champion, Pseudocetherinae Villiers, Salyavatinae Amyot & Serville and Sphaeridopinae Amyot & Serville are treated as junior synonyms of Reduviinae sensu nov . Epiroderinae Distant are synonymized with Phimophorinae Handlirsch sensu nov . and Bactrodini Stål stat . nov . are reclassified as a tribe of Harpactorinae Amyot & Serville. Psophidinae Distant is treated as a valid subfamily. This new classification represents a robust framework for future taxonomic and evolutionary research on assassin bugs. Zoobank Registration: http://zoobank.org/urn:lsid:zoobank.org:pub:2310C9AA-0D53-4EF6-9E75-C2B3A98CE096

Despite many bioinformatic solutions for analyzing sequencing data, few options exist for targeted sequence retrieval from whole genomic sequencing (WGS) data with the ultimate goal of generating a phylogeny. Available tools especially struggle at deep phylogenetic levels and necessitate amino-acid space searches, which may increase rates of false positive results. Many tools are also difficult to install and may lack adequate user resources. Here, we describe a program that uses freely available similarity search tools to find homologs in assembled WGS data with unparalleled freedom to modify parameters. We evaluate its performance compared to other commonly used bioinformatics tools on two divergent insect species (>200 My) for which annotated genomes exist, and on one large set each of highly conserved and more variable loci. Our software is capable of retrieving orthologs from well-curated or unannotated, low or high depth shotgun, and target capture assemblies as well or better than other software as assessed by recovering the most genes with maximal coverage and with a low rate of false positives throughout all datasets. When assessing this combination of criteria, ALiBaSeq is frequently the best evaluated tool for gathering the most comprehensive and accurate phylogenetic alignments on all types of data tested. The software (implemented in Python), tutorials, and manual are freely available at https://github.com/AlexKnyshov/alibaseq.

Though the phylogenetic signal of loci on sex chromosomes can differ from those on autosomes, chromosomal-level genome assemblies for nonvertebrates are still relatively scarce and conservation of chromosomal gene content across deep phylogenetic scales has therefore remained largely unexplored. We here assemble a uniquely large and diverse set of samples (17 anchored hybrid enrichment, 24 RNA-seq, and 70 whole-genome sequencing samples of variable depth) for the medically important assassin bugs (Reduvioidea). We assess the performance of genes based on multiple features (e.g., nucleotide vs. amino acid, nuclear vs. mitochondrial, and autosomal vs. X chromosomal) and employ different methods (concatenation and coalescence analyses) to reconstruct the unresolved phylogeny of this diverse (∼7,000 spp.) and old (>180 Ma) group. Our results show that genes on the X chromosome are more likely to have discordant phylogenies than those on autosomes. We find that the X chromosome conflict is driven by high gene substitution rates that impact the accuracy of phylogenetic inference. However, gene tree clustering showed strong conflict even after discounting variable third codon positions. Alternative topologies were not particularly enriched for sex chromosome loci, but spread across the genome. We conclude that binning genes to autosomal or sex chromosomes may result in a more accurate picture of the complex evolutionary history of a clade.

Automated insect identification systems have been explored for more than two decades but have only recently started to take advantage of powerful and versatile convolutional neural networks (CNNs). While typical CNN applications still require large training image datasets with hundreds of images per taxon, pretrained CNNs recently have been shown to be highly accurate, while being trained on much smaller datasets. We here evaluate the performance of CNN-based machine learning approaches in identifying three curated species-level dorsal habitus datasets for Miridae, the plant bugs. Miridae are of economic importance, but species-level identifications are challenging and typically rely on information other than dorsal habitus (e.g., host plants, locality, genitalic structures). Each dataset contained 2–6 species and 126–246 images in total, with a mean of only 32 images per species for the most difficult dataset. We find that closely related species of plant bugs can be identified with 80–90% accuracy based on their dorsal habitus alone. The pretrained CNN performed 10–20% better than a taxon expert who had access to the same dorsal habitus images. We find that feature extraction protocols (selection and combination of blocks of CNN layers) impact identification accuracy much more than the classifying mechanism (support vector machine and deep neural network classifiers). While our network has much lower accuracy on photographs of live insects (62%), overall results confirm that a pretrained CNN can be straightforwardly adapted to collection-based images for a new taxonomic group and successfully extract relevant features to classify insect species.