DOI: 10.5061/dryad.dj31p
title: Data from: Addressing gene tree discordance and non-stationarity to resolve a multi-locus phylogeny of the flatfishes (Teleostei: Pleuronectiformes)

description: Non-homogeneous processes and, in particular, base compositional
 non-stationarity have long been recognized as a critical source of
 systematic error. But only a small fraction of current molecular
 systematic studies methodically examine and effectively account for the
 potentially confounding effect of non-stationarity. The problem is
 especially overlooked in multi-locus or phylogenomic scale analyses, in
 part because no efficient tools exist to accommodate base composition
 heterogeneity in large data sets. We present a detailed analysis of a data
 set with 20 genes and 214 taxa to study the phylogeny of flatfishes
 (Pleuronectiformes) and their position among percomorphs. Most genes vary
 significantly in base composition among taxa and fail to resolve flatfish
 monophyly and other emblematic groups, suggesting that non-stationarity
 may be causing systematic error. We show a strong association between base
 compositional bias and topological discordance among individual gene
 partitions and their inferred trees. Phylogenetic methods applying
 non-homogeneous models to accommodate non-stationarity have relatively
 minor effect to reduce gene tree discordance, suggesting that available
 computer programs applying these methods do not scale up efficiently to
 the data set of modest size analysed in this study. By comparing
 phylogenetic trees obtained with species tree (STAR) and concatenation
 approaches, we show that gene tree discordance in our data set is most
 likely due to base compositional biases than to incomplete lineage
 sorting. Multi-locus analyses suggest that the combined phylogenetic
 signal from all loci in a concatenated data set overcomes systematic
 biases induced by non-stationarity at each partition. Finally,
 relationships among flatfishes and their relatives are discussed in the
 light of these results. We find support for the monophyly of flatfishes
 and confirm findings from previous molecular phylogenetic studies
 suggesting their close affinity with several carangimorph groups (i.e.,
 jack and allies, barracuda, archerfish, billfish and swordfish, threadfin,
 moonfish, beach salmon, and snook and barramundi).
description: FIG_SUPP_1_DNA_AASupplementary Figure 1. RAxML tree estimated with the DNA-AA data set (214 taxa, 7428 sites). Nodal numbers indicate support values (≥ 75%), estimated with the rapid bootstrapping algorithm in RAxML.FIG_SUP_2_Subset_treesSupplementary Figure 2. Four representative multi-locus phylogenies based on the subset. Color branches represent flatfish lineages (cyan, Pleuronectoidei; orange, Psettodoidei).Supplementary_Table_1Supplementary Table 1. Material examined and GenBank Accession numbers.Supplementary_Table_2Supplementary Table 2. Primers used and optimized PCR conditions.Supplementary_Table_3Supplementary Table 3. Characterization of molecular markers, variation, distribution of present/missing sequences and taxa, and GenBank accession numbers (see also Supplementary Table 1).ALL_TAXA_20_GENESNexus dataset with the complete alignment (214 taxa and 20 gene fragments; 19461 sites). The alignment file is fully annotated for gene partitions, including the batch command in PAUP* to export individual gene files .SUBSET_18_GENESNexus alignment file for the subset (37 taxa and 18 gene fragments; 17616 sites; no missing data). The alignment file is fully annotated for gene partitions, including the batch command in PAUP* to export individual gene files.Supplementary Table4Likelihood (ln) scores for gene trees and AU test P values comparing gene tree topologies estimated with RAxML against the concatenation topology (from Fig. 3, pruned to only include taxa in the subset), calculated under homogeneous (RAxML) and non-homogeneous (nhPhyML) optimization. The nhPhyML model used unlimited base equilibrium frequencies. Values in bold highlight cases in which the concatenation topology resulted in either better or non-significantly worse likelihood scores.Supplementary_Table4.docx