Evolution of green lacewings (Neuroptera: Chrysopidae): an anchored phylogenomics approach

Table S1. Taxa used in this study, including SRA accession numbers.Table S2. Divergence time estimates (mean ages and ranges) and branch support values for nodes in Figs 2 and S1. PP, posterior probability.Figure S1. Chronogram node numbers and fossils.Figure S2. Maximum likelihood phylogeny of Chrysopidae using AHE data. Bootstrap support values are indicated on nodes and grouped by colour according to value.Figure S3. Nucleotide Astral tree.Figure S4. BAMM plot showing the two most common shift configurations in the credible set. The ‘f’ number corresponds to the proportion of the posterior samples in which this configuration is present.Figure S5. Macroevolutionary cohort matrix for diversifica-tion. Each cell in the matrix is coded by a colour denoting the pairwise probability that two species share a common macroevolutionary rate regime. The maximum clade credi-bility tree is shown for reference in the left and upper margins of each cohort matrix.Figure S6. BAMM rate shift tree showing the overall best fit configuration. Red circles signify placement of shifts.File S1. Chrysopidae Anchored hybrid enrichment alignment. (https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1111%2Fsyen.12347&file=syen12347-sup-0001-FileS1.txt)File S2. Chrysopidae anchored hybrid enrichment, partition datasets. (https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1111%2Fsyen.12347&file=syen12347-sup-0002-FileS2.txt)A phylogeny of green lacewings (Neuroptera: Chrysopidae) using anchored hybrid enrichment data is presented. Using this phylogenomic approach, we analysed 137 kb of sequence data (with < 10% missing) for 82 species in 50 genera of Chrysopidae under Bayesian and maximum likelihood criteria. We recovered a strongly supported tree topologically congruent with recently published phylogenies, especially relationships amongst higher‐level groups. The subfamily Nothochrysinae was recovered as paraphyletic, with one clade sister to the rest of Chrysopidae, and the second clade containing the nominal genus (Nothochrysa Navás) as sister to the subfamily Apochrysinae. Chrysopinae was recovered as a monophyletic with the monobasic Nothancylini tribe n. sister to the rest of the subfamily. Leucochrysini was recovered sister to Belonopterygini, and Chrysopini was rendered paraphyletic with respect to Ankylopterygini. Divergence times and diversification estimates indicate a major shift in rate in ancestral Chrysopini at the end of the Cretaceous, and the extensive radiation of Chrysopinae, the numerically dominant clade of green lacewings, began in the Mid‐Paleogene (c. 45 Ma).Brazilian National Council for Scientific and Technological Development (209447/2013–3, to JPG), the US National Science Foundation (DEB-1144119, to SLW; DEB-1144162, to MSE; and DEB-0933588, to JDO) and the Beijing Natural Science Foundation (5162016) (to XL).https://onlinelibrary.wiley.com/journal/136531132020-07-01hj2019Zoology and Entomolog

The strength in numbers: comprehensive characterization of house dust using complementary mass spectrometric techniques

Untargeted analysis of a composite house dust sample has been performed as part of a collaborative effort to evaluate the progress in the field of suspect and nontarget screening and build an extensive database of organic indoor environment contaminants. Twenty-one participants reported results that were curated by the organizers of the collaborative trial. In total, nearly 2350 compounds were identified (18%) or tentatively identified (25% at confidence level 2 and 58% at confidence level 3), making the collaborative trial a success. However, a relatively small share (37%) of all compounds were reported by more than one participant, which shows that there is plenty of room for improvement in the field of suspect and nontarget screening. An even a smaller share (5%) of the total number of compounds were detected using both liquid chromatography–mass spectrometry (LC-MS) and gas chromatography–mass spectrometry (GC-MS). Thus, the two MS techniques are highly complementary. Most of the compounds were detected using LC with electrospray ionization (ESI) MS and comprehensive 2D GC (GC×GC) with atmospheric pressure chemical ionization (APCI) and electron ionization (EI), respectively. Collectively, the three techniques accounted for more than 75% of the reported compounds. Glycols, pharmaceuticals, pesticides, and various biogenic compounds dominated among the compounds reported by LC-MS participants, while hydrocarbons, hydrocarbon derivatives, and chlorinated paraffins and chlorinated biphenyls were primarily reported by GC-MS participants. Plastics additives, flavor and fragrances, and personal care products were reported by both LC-MS and GC-MS participants. It was concluded that the use of multiple analytical techniques was required for a comprehensive characterization of house dust contaminants. Further, several recommendations are given for improved suspect and nontarget screening of house dust and other indoor environment samples, including the use of open-source data processing tools. One of the tools allowed provisional identification of almost 500 compounds that had not been reported by participants. [Figure not available: see fulltext.]. © 2019, The Author(s)