The stinging wasps (Hymenoptera: Aculeata) are an extremely diverse lineage of hymenopteran insects, encompassing over 70,000 described species and a diversity of life history traits, including ectoparasitism, cleptoparasitism, predation, pollen feeding (bees [Anthophila] and Masarinae), and eusociality (social vespid wasps, ants, and some bees) [1]. The most well-studied lineages of Aculeata are the ants, which are ecologically dominant in most terrestrial ecosystems [2], and the bees, the most important lineage of angiosperm-pollinating insects [3]. Establishing the phylogenetic affinities of ants and bees helps us understand and reconstruct patterns of social evolution as well as fully appreciate the biological implications of the switch from carnivory to pollen feeding (pollenivory). Despite recent advancements in aculeate phylogeny [4-11], considerable uncertainty remains regarding higher-level relationships within Aculeata, including the phylogenetic affinities of ants and bees [5-7]. We used ultraconserved element (UCE) phylogenomics [7, 12] to resolve relationships among stinging-wasp families, gathering sequence data from >800 UCE loci and 187 samples, including 30 out of 31 aculeate families. We analyzed the 187-taxon dataset using multiple analytical approaches, and we evaluated several alternative taxon sets. We also tested alternative hypotheses for the phylogenetic positions of ants and bees. Our results present a highly supported phylogeny of the stinging wasps. Most importantly, we find unequivocal evidence that ants are the sister group to bees+apoid wasps (Apoidea) and that bees are nested within a paraphyletic Crabronidae. We also demonstrate that taxon choice can fundamentally impact tree topology and clade support in phylogenomic inference.

Targeted enrichment of conserved and ultraconserved genomic elements allows universal collection of phylogenomic data from thousands of species. Prior to downstream inference, data from these types of targeted enrichment studies must undergo pre-processing to assemble contigs from sequence data;; identify targeted, enriched loci from the off-target background data;; align enriched contigs representing conserved loci to one another;; and prepare and manipulate these alignments for subsequent phylogenomic inference. PHYLUCE is an efficient and easyto-install software package that accomplishes these tasks across hundreds of taxa and thousands of enriched loci. Availability and ImplementationPHYLUCE is written for Python 2.7. PHYLUCE is supported on OSX and Linux (RedHat/CentOS) operating systems. PHYLUCE source code is distributed under a BSD-style license from https://www.github.com/faircloth-lab/phyluce/. PHYLUCE is also available as a package (https://binstar.org/faircloth-lab/phyluce) for the Anaconda Python distribution that installs all dependencies, and users can request a PHYLUCE instance on iPlant Atmosphere (tag: phyluce-1.5). The software manual and a tutorial are available from

Summary1. Targeted enrichment of conserved genomic regions is a popular method for collecting large amounts of sequence data from non-model taxa for phylogenetic, phylogeographic and population genetic studies. For example, two available bait sets each allow enrichment of thousands of orthologous loci from >20 000 species (Faircloth et al. Systematic Biology, 61, 717-726, 2012; Molecular Ecology Resources, 15, 489-501, 2015).Unfortunately, few open-source workflows are available to identify conserved genomic elements shared among divergent taxa and to design enrichment baits targeting these regions. Those that do exist require extensive bioinformatics expertise and significant amounts of time to use. These shortcomings limit the application of targeted enrichment methods to additional organismal groups. 2. Here, I describe a universal workflow for identifying conserved genomic regions in available genomic data and for designing targeted enrichment baits to collect data from these conserved regions. These methods require less expertise, less time and better use commonly available information to identify conserved loci and design baits to capture them. 3. I apply this computational approach to the understudied arthropod groups Arachnida, Coleoptera, Diptera, Hemiptera or Lepidoptera to identify thousands of conserved loci in each group and design target enrichment baits to capture these loci. I then use in silico analyses to demonstrate that targeted enrichment of the conserved loci can be used to reconstruct the accepted relationships among genome sequences from the focal arthropod orders. 4. The software workflow I created allowed me to identify thousands of conserved loci in five diverse arthropod groups and design sequence capture baits to target them. This suite of capture bait designs should enable collection of phylogenomic data from >900 000 arthropod species. Although the examples in this manuscript focus on understudied arthropod groups, the approach I describe is applicable to all organismal groups having some form of pre-existing genomic information (e.g. other invertebrates, plants, fungi and microbes). Finally, the documentation, design steps, software code and bait sets developed here are available under an open-source license for restriction-free testing, use, and additional modification by any research group.