DOI: 10.5061/dryad.v41ns1s23
title: Ultraconserved elements support the elevation of a new avian family, Eurocephalidae, the white-crowned shrikes

description: In this study, we infer genus-level relationships within shrikes
 (Laniidae), crows (Corvidae), and their allies using ultraconserved
 elements (UCEs). We confirm previous results of the Crested Shrikejay
 (Platylophus galericulatus) as comprising its own taxonomic family and
 find strong support for its sister relationship to laniid shrikes. We also
 find strong support that the African-endemic genus Eurocephalus, which
 comprises two allopatric species (E. ruppelli and E. anguitimens), are not
 shrikes. We propose elevating the white-crowned shrikes to their own
 family, Eurocephalidae. 
description: Sampling and laboratory
 methods  Following
 the taxonomy of Gill et al. (2022), we collected genus-level UCE data for
 Corvidae, Laniidae, and their allies (Supplementary Material Table 1). In
 total, our dataset comprised 47 samples and every genus within Laniidae
 and Corvidae. We downloaded raw reads from recent, higher-level UCE
 studies of passerines (n=23; (Moyle et al. 2016, Oliveros et al. 2019,
 McCullough et al. 2022). For all other samples (n=24), we extracted DNA
 from ethanol- or frozen-preserved tissue samples loaned from natural
 history collections. However, for some rarely collected taxa, we relied on
 specimen toepad clippings (14% of samples, denoted in Supplementary
 Material Table 1). Because this source of genetic material is well known
 to be degraded, we treated samples derived from frozen or ethanol
 preserved samples differently than those derived from museum toepad
 clippings. For tissue-derived samples, we used the Qiagen DNeasy kit to
 extract genomic DNA and estimated fragment size using gel electrophoresis.
 For toepad-derived samples, we used a phenol-chloroform DNA extraction
 with gel phase-lock tubes. This method has been shown to produce higher
 yields of DNA than silica columns of Qiagen kits (Tsai et al. 2019). Prior
 to library prep, we quantified DNA concentrations with a Qubit 3.0
 Fluorometer (ThermoFisher Scientific) for all samples.
 We followed established protocols for library
 preparation and target capture of UCEs (Faircloth et al. 2012, McCormack
 et al. 2016). We made toepad-specific modifications to improve yields,
 following McCormack et al. (2016); these included increasing
 concentrations of AMPure bead (Beckman Coulter) cleanups to 3X, extending
 ligation times, and using Eppendorf Lo-Bind tubes to increase retention of
 DNA. We pooled tissue (8 libraries per pool) and toepad-derived (5–6 per
 pool) libraries separately for UCE enrichment. We used an updated version
 of the Arbor Biosciences MYbaits kit for Tetrapods UCE-5Kv2b probe set,
 which includes a larger number of baits per locus designed from both
 chicken and Zebra Finch genomes. We hybridized probes at either 65ºC
 (tissues) or 62ºC (toepads) for 24 hours. We sequenced samples either on
 an Illumina HiSeq 2500 System at the University of Kansas Genome
 Sequencing Core or an Illumina NovaSeq 6000 at the Oklahoma Medical
 Research Foundation (sequencing technology is denoted in Supplementary
 Material Table 1).  Data
 processing and phylogenetic analyses To process
 UCE data, we used the Phyluce v1.7.0 (Faircloth 2016); described in full
 at https://github.com/faircloth-lab/phyluce) Python package with the University of New Mexico’s Center for Advanced Computing (CARC) cluster. We trimmed adaptor sequences and low quality sites from demultiplexed raw reads with Illumiprocessor v2.1 (Faircloth 2013, Bolger et al. 2014), assembled clean reads into contigs with Spades v1.7 (Prjibelski et al. 2020), and extracted UCE loci with this updated probe set.  During initial phylogenetic exploration, we identified five toepad-derived (two <em>Temnurus </em>and three <em>Zavattariornis</em>) samples that had extraordinary long branches in concatenated RAxML analyses (see below for more depth on these phylogenetic methods and Supplementary Material Table 1 for these problematic samples). These long branches are biologically unlikely and this problem has been shown to be an artifact of poor trimming and ‘dirty ends’ of UCE loci from degraded toepad-sourced samples (Smith et al. 2020). To remove these problematic artifacts that contribute to artificially long branches, we followed the bioinformatic pipeline by Smith et al. (2020). In detail, we identified our reference samples by expanding fastas (with phyluce_assembly_explode_get_fastas_file) and chose a closely related, tissue-derived sample. We chose<em> Platysmurus atterimus</em> (LSU B58660) as a reference for <em>Temnurus </em>samples and <em>Podoces hendersoni </em>(KU 20444) for <em>Zavattarriornis </em>samples based on an initial concatenated RAxML analyses (Kozlov et al. 2019)<em>. </em>With bwa, SAMtools, and GNU parallel (Li and Durbin 2009, Li et al. 2009, Tange 2021), we indexed the reference samples (UCE contigs) and aligned cleaned reads of the problematic toepad-derived samples to these references. To remove the low quality data in the flanking regions that were contributing to spurious inferences, we dropped sites with less than 5x coverage and quality scores &lt; 20. We incorporated these cleaned samples back into the pipeline with the other samples by manually adding 1) the nucleotide data into the combined, unaligned fasta file, 2) the names of samples into the .conf file, and 3) adding the list of loci for each sample into the incomplete matrix conf file; all these files are originally produced from the phyluce_assembly_get_fastas_from_match_counts phyluce script. Together with these cleaned toepad-based samples, we aligned all 47 samples with MAFFT (Katoh and Standley 2013) without initially trimming. Instead, we used TrimAl v1.4.rev15 (Capella-Gutiérrez et al. 2009) to trim UCE loci with the “-automated1” flag. Finally, we produced a 100%-complete matrix, in which all samples are present at each UCE locus.  We implemented both concatenated maximum likelihood (ML) and species tree methods. We estimated the maximum likelihood tree with RAxML-ng v 1.0.1 (Kozlov et al. 2019) and evaluated support with bootstrap replicates with the autoMRE function (set to 100 BS). We accounted for gene tree heterogeneity with SVDquartets (Chifman and Kubatko 2014) implemented in Paup*v4.0a166 (Swofford 2003). SVDquartets is a concatenated quartet method that does not rely on individual gene trees and has recently been shown to perform better for large multilocus datasets than other coalescent based tree-building programs (Wascher and Kubatko 2021). We analyzed all possible quartets (n=169,661 quartets) and performed 100 bootstrap replicates to assess nodal support.  Time calibration To infer a time-calibrated tree, we used BEAST v2.6.7 (Bouckaert et al. 2014). We created six randomized subsets of 50 UCE loci each without replacement (a total of 300 loci) from our complete matrix (Supplementary Material Table 2). We ran two independent MCMC chains per dataset for 10 million generations, sampling every 5,000 generations. We used a relaxed log normal clock, a birth-death tree prior, and assigned the HKY+G sequence model to each UCE locus. We constrained the BEAST topology to the RAxML-inferred topology using a multi-monophyletic constraint prior. We used two secondary calibrations from Oliveros et al. (2019) to date the phylogeny. This comprehensive phylogeny used 13 fossil calibrations to date a family-level UCE tree of all songbirds. For this study, we assigned a normal distribution for the split between <em>Rhipidura</em> and the rest of our sampled taxa with a mean date of 18.93 Ma (confidence interval = 22.0–15.9, standard deviation (sigma value) of 1.5). The second calibration point was assigned to the split between <em>Dicrurus </em>and all other taxa with a normal distribution and a mean date of 18.46 Ma (CI = 21.4–15.6, sigma value of 1.5). We visualized posterior estimates in Tracer v 1.7.1 (Rambaut et al. 2018) to assess convergence of chains and that ESS values were &gt;200. We discarded the first 25% of trees as burn-in and first combined the two runs from each dataset, then we combined those six tree files into one final maximum clade credibility (MCC) tree using TreeAnnotator v 2.6.7 (Bouckaert et al. 2014).  ND2 P-distances To assess relative divergences between major clades within our dataset, we compared uncorrected p-distances in the mitochondrial ND2 gene for representative taxa. We used Mitofinder v 1.4 (Allio et al. 2020) to extract mitochondrial genomes from cleaned UCE reads using a complete mitochondrial genome of <em>Corvus corax</em> as a reference (PRJNA321255; Johnsen et al. 2017). Next, we extracted ND2 from these mitochondrial genomes and used PAUP* v 4a168 (Swofford 2003) to generate P-distances.
description: fasta and Nexus files can be opened in a text editor, like
 BBedit. ".tre" files can be viewed in FigTree.