DOI: 10.5061/dryad.t358vv1
title: Data from: Impacts of inference method and dataset filtering on phylogenomic resolution in a rapid radiation of ground squirrels (Xerinae: Marmotini)

description: Phylogenomic datasets are illuminating many areas of the Tree of Life.
 However, the large size of these datasets alone may be insufficient to
 resolve problematic nodes in the most rapid evolutionary radiations,
 because inferences in zones of extraordinarily low phylogenetic signal can
 be sensitive to the model and method of inference, as well as the
 information content of loci employed. We used a dataset of &gt;3,950
 ultraconserved element (UCE) loci from a classic mammalian radiation,
 ground-dwelling squirrels of the tribe Marmotini (Sciuridae: Xerinae), to
 assess sensitivity of phylogenetic estimates to varying per-locus
 information content across 4 different inference methods (RAxML, ASTRAL,
 NJst, SVDquartets). Persistent discordance was found in topology and
 bootstrap support between concatenation- and coalescent-based inferences;
 among methods within the coalescent framework; and within all methods in
 response to different filtering scenarios. Contrary to some recent
 empirical UCE-based studies, filtering by information content did not
 promote complete among-method concordance. Nevertheless, filtering did
 improve concordance relative to randomly selected locus sets, largely via
 improved consistency of two-step summary methods (particularly NJst) under
 conditions of higher average per-locus variation (and thus increasing gene
 tree precision). The benefits of dataset filtering are notably variable
 among classes of inference methods and across different evolutionary
 scenarios, reiterating the complexities of resolving rapid radiations,
 even with robust taxon and character sampling.
description: READMESupplementary Appendix
 S1.csv file containing
 taxonomic identities, institutional IDs, collection year, and number of
 UCE contigs assembled for all samples sequenced in this study data fields
 are synonymized with standard Darwin Core terms
 (https://github.com/tdwg/dwc) where possibleSupplementary Fig.
 S1Barplots displaying
 changes across empirically filtered (‘Information-Filtered’) and randomly
 selected (‘Randomly Selected 1-5’) datasets in 3 different metrics; a)
 total numbers of sites analyzed, b) total numbers of variable sites
 analyzed, and c) total numbers of parsimony-informative sites analyzed.
 The grey box at far left represents the baseline UCE dataset. Boxes of the
 same color contain identical numbers of loci. Cumulatively, the plots show
 that changes in our metric of information content (proportion of variable
 sites) due to changes in absolute numbers of variable sites (i.e., panel
 b) and not change in locus length or other properties.Supplementary Fig.
 S2Relationships of UCE
 variability (proportion of variable sites per locus (i.e., our metric))
 with alternative metrics of information content: a) the proportion of
 parsimony sites per locus, b) the mean bootstrap support found in
 corresponding gene trees, and c) the proportion of gene tree nodes
 resolved with &gt;70% bootstrap support.Supplementary Fig.
 S3Pairwise changes in
 Robinson-Foulds distances among 4 inference methods across the filtering
 regimes employed in this study. Base = the original baseline UCE
 dataset.UCE alignments -
 baseline datasetTrimmed UCE alignments
 in fasta format. This is the “baseline” dataset as described in the text.
 These same data are available on GenBank.UCE_alignments_baseline_dataset.zipraxml_empirical_treesRAxML trees from empirically filtered datasets with support values from 100 bootstrap replicates and rooted on Aplodontia rufa. Trees are named with the filtering level used (e.g., 5var = at least 5 percent variable sites per locus) followed by the number of resulting UCE loci in each datasetastral_empirical_treesASTRAL species trees from empirically filtered datasets with support values from 100 bootstrap replicates and rooted on Aplodontia rufa. Trees are named with the filtering level used (e.g., 5var = at least 5 percent variable sites per locus) followed by the number of resulting UCE loci in each datasetnjst_empirical_treesNJst species trees from empirically filtered datasets labeled with support values from 100 bootstrap replicates and rooted on Aplodontia rufa. Trees are named with the filtering level used (e.g., 5var = at least 5 percent variable sites per locus) followed by the number of resulting UCE loci in each datasetsvdquartets_empirical_treesSVDQuartets species trees from empirically filtered datasets with support values from 100 bootstrap replicates and rooted on Aplodontia rufa. Trees are named with the filtering level used (e.g., 5var = at least 5 percent variable sites per locus) followed by the number of resulting UCE loci in each dataset