DOI: 10.5061/dryad.59j3k8p
title: Phylogenomics indicates Amazonia as the major source of Neotropical swarm-founding social wasp diversity

description: The Neotropical realm harbors unparalleled species richness and hence has
 challenged biologists to explain the cause of its high biotic diversity.
 Empirical studies to shed light on the processes underlying biological
 diversification in the Neotropics are focused mainly on vertebrates and
 plants, with little attention to the hyperdiverse insect fauna. Here, we
 use phylogenomic data from ultraconserved element (UCE) loci to
 reconstruct for the first time the evolutionary history of Neotropical
 swarm-founding social wasps (Hymenoptera, Vespidae, Epiponini). Using
 maximum likelihood, Bayesian, and species tree approaches we recovered a
 highly resolved phylogeny for epiponine wasps. Additionally, we estimated
 divergence dates, diversification rates, and the biogeographic history for
 these insects in order to test whether the group followed a “museum”
 (speciation events occurred gradually over many millions of years) or
 “cradle” (lineages evolved rapidly over a short time period) model of
 diversification. The origin of many genera and all sampled extant
 Epiponini species occurred during the Miocene and Plio-Pleistocene.
 Moreover, we detected no major shifts in the estimated diversification
 rate during the evolutionary history of Epiponini, suggesting a relatively
 gradual accumulation of lineages with low extinction rates. Several lines
 of evidence suggest that the Amazonian region played a major role in the
 evolution of Epiponini wasps. This spatio-temporal diversification
 pattern, most likely concurrent with climatic and landscape changes in the
 Neotropics during the Miocene and Pliocene, establishes the Amazonian
 region as the major source of Neotropical swarm-founding social wasp
 diversity.
description: Matrix:
 Epiponini-102T-200L-F50Matrix of UCE data used
 in the phylogenetic analysesmafft-nexus-min-50per-taxa.phylipBEAST data filesBEAST XML file and DNA matrix created using 100 selected UCE loci used in divergence dating analyze.Beast files.zip
description: <i>Library
 preparation, UCE enrichment, and
 sequencing</i>             The
 library preparation and target enrichment steps were performed following
 Faircloth et al. (2015). For all samples we ran 10μL of each DNA extract
 for 60 min at 100 volt on 1.5% agarose SB (sodium borate) gels, to
 estimate size of the genomic DNA. Moreover, we measured DNA concentration
 with a Qubit 2.0 fluorometer (High sensitivity or Broad range kit; Life
 Technologies Inc., Carlsbad, CA) and 2-500 ng DNA was sheared for 15-60
 secs (amp = 25, pulse = 10) to an average fragment size of 500-600 base
 pair (bp) (as verified on an agarose gel) by sonication using a Qsonica
 Q800R sonicator (Qsonica LLC, Newton, CT). Degraded DNA from all pinned
 museum specimens were not sheared. The
 sheared DNA was used as input for a modified genomic DNA library
 preparation protocol (Kapa Hyper Prep Library Kit, Kapa Biosystems),
 incorporating “with-bead” cleanup steps (Fisher et al., 2011) and a
 generic SPRI substitute [(Rohland et al. 2012), “speedbeads” hereafter],
 as described by Faircloth et al. (2015). For adapter ligation, we used
 TruSeq-style adapters (Faircloth et al., 2012) and PCR amplified 50% of
 the resulting library volume (15 μL) with a reaction mix of 25 μL HiFi
 HotStart polymerase (Kapa Biosystems), 2.5 μL each of Illumina
 TruSeq-style i5 and i7 primers (5 μM each), and 5 μL double-distilled
 water (ddH20). We used the following thermal protocol: 98°C for 45 s; 13
 cycles of 98°C for 15 s, 65°C for 30 s, 72°C for 60 s, and final extension
 at 72°C for 5 m. After rehydrating (in 23 μL pH 8 Elution Buffer (EB
 hereafter) and purifying reactions using 1.0X speedbeads, 8–10 libraries
 were combined at equimolar ratios into enrichment pools with final
 concentrations of 139–168 ng/μL. We
 enriched each pool using the updated bait design Hymenoptera-V2 contained
 a total of 2590-targeted UCE loci for Hymenoptera (see Branstetter et al.,
 2017) synthesized by MYcroarray (MYcroarray, Ann Arbor, MI). We followed
 library enrichment procedures for the MYcroarray MYBaits kit (Blumenstiel
 et al., 2010), except we used a 0.1X concentration of the standard MYBaits
 concentration and added 0.7 μL of 500 μM custom blocking oligos designed
 against our custom sequence tags. We ran the hybridization reaction for 24
 h at 65°C, subsequently bound all pools to streptavidin beads (MyOne C1;
 Life Technologies), and washed bound libraries according to a standard
 target enrichment protocol (Blumenstiel et al., 2010). We used the
 with-bead approach for PCR recovery of enriched libraries, as described by
 Faircloth et al. (2015). We combined 15 μL of streptavidin bead-bound,
 enriched library with 25 μL HiFi HotStart Taq (Kapa Biosystems), 5 μL of
 Illumina TruSeq primer mix (5 μM forward and reverse primers) and 5 μL of
 ddH2O. We ran post-enrichment PCR using the following thermal conditions:
 98°C for 45 s; 18 cycles of 98°C for 15 s, 60°C for 30 s, 72°C for 60 s;
 and a final extension of 72°C for 5 m. We purified resulting reactions
 using 1.0X speedbeads, and we rehydrated the enriched pools in 22 μL EB.
 We quantified 2 μL of each enriched pool using a Qubit 2.0 fluorometer
 (broad sensitivity kit). We
 quantified post-enrichment library concentration with qPCR using a SYBR®
 FAST qPCR kit (Kapa Biosystems) on a ViiA™ 7 (Life Technologies), and
 based on the size-adjusted concentrations estimated by qPCR, we pooled
 libraries at equimolar concentrations and size-selected for 250–800 bp
 with a BluePippin (SageScience). We sent size-selected pools to the
 University of Utah’s High Throughput Genomics Core Facility for sequencing
 as single lane of a 125-bp paired-end Illumina HiSeq
 2500. <i>Bioinformatics and matrix preparation </i> We performed all bioinformatics steps, including read cleaning, assembly, and alignment, using the Phyluce v1.5 software package. The cleaned and trimmed process of raw reads was performed using Illumiprocessor (Faircloth, 2013), based on the package Trimmomatic (Bolger et al., 2014). The cleaned reads were assembled using a Phyluce script (assemblo_trinity.py) around Trinity (Grabherr et al., 2011). After assembly we calculated n50’s for contigs using a script (fasta_stats.pl) and mapped contigs to UCE loci (match_contigs_to_probes.py), with the min-coverage = 50 and min-identity = 80. Next, we used two scripts (get_match_counts.py and get_fastas_from_match_counts.py) to create a fasta file containing all taxa and UCE loci. During these steps we added contigs previously published from nine taxa (<i>Rhopalosoma nearcticum</i>, <i>Pachodynerus alayoi</i>, <i>Parancistrocerus bacu</i>, <i>Pseudomasaris vespoides</i>, <i>Metapolybia cingulata</i>, <i>Mischocyttarus flavitarsis</i>, <i>Mischocyttarus mexicanus</i>, <i>Polistes poeyi</i>, and <i>Vespa</i> sp.) to get UCE data using the Hymenoptera-V2 bait set. We aligned each locus using a script (align_seqcap_align.py) (min-length = 20, no-trim) using the program MAFFT v7.221 (Katoh et al., 2013). After alignment, we trimmed all alignments using a specific script (align_get_gblocks_trimmed_alignments_from_untrimmed.py) for Gblocks (Castresana, 2000) with the following settings: b1 = 0.5, b2 = 0.5, b3 = 12, and b4 = 7. Next, considering amount of missing data, we performed several rounds of matrix filtering followed by phylogenetic inference and assessment. To do this we performed two strategies: first, considering only taxa with a minimum number of UCE loci (&gt;10 loci; &gt;200 loci; and &gt;700 loci) because for some pinned museum specimens we recovered a low number of loci, and second, we used a script (get_only_loci_with_min_taxa.py) that filters loci for varying amounts of minimum taxon occupancy (% samples required to be present in each locus). We used the script to filter all loci for 90, 80, 70, 50, 25%, and non-filtering of minimum taxon occupancy. For each data set we generated alignment stats using two scripts (get_align_summary_data.py and get_informative_sites.py) and generated a concatenated matrix for each data set (format_nexus_files_for_raxml.py). Finally, we analyzed all concatenated matrices using RAxML v8.2.11 (Stamatakis, 2014).