Molecular Phylogenetics and Evolution 85 (2015) 230–237

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Molecular Phylogenetics and Evolution

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A mitochondrial genome phylogeny of owlet moths (Lepidoptera:Noctuoidea), and examination of the utility of mitochondrial genomesfor lepidopteran phylogenetics

http://dx.doi.org/10.1016/j.ympev.2015.02.0051055-7903/� 2015 Elsevier Inc. All rights reserved.

⇑ Corresponding author.E-mail address: hanhx@ioz.ac.cn (H. Han).

1 These authors contributed equally to this work.

Xiushuai Yang a,1, Stephen L. Cameron b,1, David C. Lees c,1, Dayong Xue a, Hongxiang Han a,⇑a Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, Chinab Earth, Environmental & Biological Sciences School, Science & Engineering Faculty, Queensland University of Technology, GPO Box 2434, Brisbane, QLD 4001, Australiac Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, United Kingdom

a r t i c l e i n f o

Article history:Received 15 April 2014Revised 27 January 2015Accepted 6 February 2015Available online 17 February 2015

Keywords:NoctuoideaMitochondrial genomePhylogenyMAFFTGblocksGaps

a b s t r a c t

A phylogenetic hypothesis for the lepidopteran superfamily Noctuoidea was inferred based on the com-plete mitochondrial (mt) genomes of 12 species (six newly sequenced). The monophyly of each noctuoidfamily in the latest classification was well supported. Novel and robust relationships were recovered atthe family level, in contrast to previous analyses using nuclear genes. Erebidae was recovered as sister to(Nolidae + (Euteliidae + Noctuidae)), while Notodontidae was sister to all these taxa (the putativelybasalmost lineage Oenosandridae was not included). In order to improve phylogenetic resolution usingmt genomes, various analytical approaches were tested: Bayesian inference (BI) vs. maximum likelihood(ML), excluding vs. including RNA genes (rRNA or tRNA), and Gblocks treatment. The evolutionary signalwithin mt genomes had low sensitivity to analytical changes. Inference methods had the most significantinfluence. Inclusion of tRNAs positively increased the congruence of topologies, while inclusion of rRNAsresulted in a range of phylogenetic relationships varying depending on other analytical factors. The twoGblocks parameter settings had opposite effects on nodal support between the two inference methods.The relaxed parameter (GBRA) resulted in higher support values in BI analyses, while the strict parameter(GBDH) resulted in higher support values in ML analyses.

� 2015 Elsevier Inc. All rights reserved.

1. Introduction

The application of molecular markers in phylogenetics hasmade a tremendous contribution to our knowledge of the evolu-tion of life on Earth (Delsuc et al., 2005; Wheat and Wahlberg,2013). Inference of phylogenetic relationships within Lepidoptera(butterflies and moths), as well their macroevolutionary history(Wahlberg et al., 2013), have been greatly improved by a seriesof studies based on an ever-increasing numbers of molecularmarkers, largely nuclear protein-coding genes (PCGs) (Mutanenet al., 2010; Regier et al., 2013). For example, a comprehensive phy-logenetic study of Bombycoidea (silkworms and hawkmoths)based on a combination of up to 25 nuclear genes (19.3 kb total),the largest to date for a lepidopteran superfamily, found strongernodal support for many more subclades (families and subfamilies)than either a parallel 5-gene analyses or previous studies (Regier

et al., 2008; Zwick et al., 2011). Surprisingly, the relationships ofBombycidae, Saturniidae, and Sphingidae are still contentious(e.g. Timmermans et al., 2014), although Breinholt and Kawahara(2013) found phylogenomic evidence for a sister relationship ofthe last two. The most recent transcriptomic analysis using 46 lepi-dopteran taxa recovered a novel placement of butterflies andestablished a preliminary framework to understand the evolutionof butterflies and moths (Kawahara and Breinholt, 2014).

Noctuoidea is the biggest superfamily of Lepidoptera, number-ing over 42,400 species (Nieukerken et al., 2011). Many speciesare pests of considerable importance in agriculture and forestry,such as armyworms (Spodoptera spp.), bollworms (Helicoverpaspp.) and cutworms (Agrotis spp.) (Zhang, 1994). The superfamilyalso includes the tiger moths (Arctiinae), one of the more attractivegroups of day-flying moths. The taxonomic history of Noctuoidea iscomplicated, but at present six families are recognized: Oenosan-dridae, Notodontidae, Erebidae, Euteliidae, Nolidae and Noctuidae(Nieukerken et al., 2011; Zahiri et al., 2011). Several other well-known noctuoid groups, including the Lymantriinae and Arctiinae,previously accorded family rank are now treated as erebid sub-families (Zahiri et al., 2012).

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X. Yang et al. / Molecular Phylogenetics and Evolution 85 (2015) 230–237 231

Phylogenetic studies of Noctuoidea have followed the trend ofincreased reliance on molecular markers over morphological char-acters during the past few decades. The monophyly of Noctuoidea(sensu lato) is supported by the presence of a metathoracic tympa-nal organ, which is regarded as uniquely gained apomorphic char-acter (Miller, 1991). Speidel et al. (1996) conducted an analysis of30 subfamilies based on 10 morphological characters and con-firmed the monophyly of Noctuidae, but found poor resolutionamong subordinate groups. Kitching et al. (1998) extendedSpeidel’s (1996) dataset with larval characters for resolving themonophyly of the families Arctiidae, Nolidae, Lymantriidae andPantheidae, as well as their interrelationships. Lafontaine andFibiger found further support for the respective monophyly ofthe ‘trifid’ noctuoids (Oenosandridae and Notodontidae, with anapparently 3-branching forewing cubital vein) and ‘quadrifids’(with a 4-branching forewing cubital vein) based on adult andlarval characters (Fig. 1B and C) (Fibiger and Lafontaine, 2005;Lafontaine and Fibiger, 2006). In their system, all quadrifid moths,including the families Arctiidae, Lymantriidae and Aganaidae, wereredefined as subfamilies of Noctuidae, and therefore Noctuidae(sensu lato) became the largest (�38,000 of 42,000 species) andmost complex noctuoid family (48 subfamilies and 79 tribes).

Molecular studies of Noctuoidea were initially based on one ortwo genes, and limited taxon sampling (three families, 29–49 spe-cies) (Fang et al., 2000; Mitchell et al., 1997, 2000; Weller et al.,1994). Mitchell et al. (2000, 2006) initially conducted comprehen-sive systematic analysis of Noctuidae based on two nuclear genesand a wide range of taxon sampling. Besides finding strong supportfor the monophyly of its subfamilies, the ‘‘LAQ’’ clade was proposedfor the first time. Lymantriidae and Arctiidae became subordinatesubfamilies within the quadrifid noctuids (Fig. 1D). Mitchell’sresearch, in concert with that of Lafontaine and Fibiger (Fibigerand Lafontaine, 2005), has greatly clarified several hitherto elusiverelationships within Noctuoidea. However, the support valuesabove the subfamily level were low, possibly resulting from a sam-pling biased towards ‘trifine’ species (with an apparently 3-branch-ing hindwing cubital vein), with relatively few ‘quadrifines’ (with a4-branching hindwing cubital vein), and/or a result of the limits ofphylogenetic resolution from the two nuclear genes sequenced.Recently however, Zahiri et al. (2011) conducted the most exten-sive molecular phylogenetic analyses of Noctuoidea to date, basedboth on combined sequences of eight PCGs (nuclear and mitochon-drial) and on comprehensive sampling of 152 species, which cov-ered nearly all of the subfamilies recognized within Noctuoidea.Zahiri et al.’s (2011) results differed significantly from all previousstudies, both morphological and molecular ones (Fig. 1E). The tra-ditional group of quadrifid noctuids was broken up, and thefamilies Nolidae and Erebidae (the last family newly defined toencompass a still vast clade of some 18 subfamilies and about24,500 species) were reconstituted and Euteliidae was newly rec-ognized (previously a noctuid subfamily) (Zahiri et al., 2012). Inso doing, they proposed a novel six-family system that has current-ly been accepted within synoptic classifications of Lepidoptera(Nieukerken et al., 2011). While the datasets of Zahiri et al.(2011, 2012, 2013a,b) provide strong support for the monophylyof the six families and most of the subfamilies, nodal support forErebidae + Nolidae and most inter-subfamily relationships wasnot significant. This is despite a sizeable molecular dataset(6407 bp) including most of the nuclear genes shown to be mosteffective at deep phylogenetic levels within Lepidoptera(Mutanen et al., 2010; Wahlberg et al., 2009a); further resolutionof noctuoid relationships thus requires novel data sources. Anadditional outcome of Zahiri et al. (2011, 2012, 2013a,b) was thatmorphological and molecular studies were contradictory, with nomorphological support for Erebidae. In practice, the Erebidae isimpossible to recognize without comprehensive faunal knowledge

(Barbut and Lalanne-Cassou, 2011; Holloway, 2011). Althoughsome potential autapomorphies were proposed in a recent study,such as the loss of a muscle in the male genitalia, none was indis-putable and they were absent in some subfamilies or some taxa(Minet et al., 2012). Despite rapid acceptance by a range of lepi-dopteran systematists, the higher-level phylogenetic relationshipsof Noctuoidea were once again potentially contentious.

Complete mitochondrial (mt) genomes have been increasinglyused to address phylogenetic questions where multi-gene analyseshave been either unresolved or poorly supported. They have beenwidely used in invertebrates in general and insects in particular(Cameron, 2014). Within Lepidoptera, multiple studies have usedmt genomes to address relationships between superfamilies andwithin major families such as Nymphalidae (Tian et al., 2012;Yang et al., 2009). These studies have demonstrated significantand novel findings, including the first strong evidence for thenon-monophyly of Macrolepidoptera, primarily due to an earlierdiverging position of the butterflies (Papilionoidea) than tradition-ally thought (Yang et al., 2009), a position subsequently confirmedby analyses of nuclear PCGs (Mutanen et al., 2010; Regier et al.,2009). Most recently, a comprehensive phylogenetic study of Lepi-doptera was conducted using mt genomes of 106 species from 24subfamilies (Timmermans et al., 2014). The topologies are highlycongruent with prior nuclear and/or morphological studies.

Various analytical approaches using insect mt genomes toresolve phylogeny have been tested (Cameron, 2014); however,optimal approaches appear to vary between insect lineages. Forexample, the inclusion of third codon positions positively affectedphylogenetic resolution in Orthoptera, Lepidoptera and Diptera(Cameron et al., 2007; Fenn et al., 2008; Yang et al., 2013), whilethe exclusion of third codon positions performed better in Dicty-optera and Hymenoptera (Cameron et al., 2012; Dowton et al.,2009). So reporting sensitivity tests for partitioning is of interestfor different lineages and at different taxonomic scales. In thisstudy, we present six new noctuoid mt genomes, including repre-sentatives of two newly sequenced families (Euteliidae and Noli-dae), and three additional erebid subfamilies. We conductedphylogenetic analyses of mt genomes from 57 lepidopteran taxa.Our aim was to address the infraordinal relationships within Lepi-doptera, particularly the family-level relationships within Noc-tuoidea and subfamily relationships within Erebidae (the largestnoctuoid family) using mt genome data. In addition, we employ arange of analytical approaches to explore the phylogenetic utilityof mt genomes.

2. Materials and methods

2.1. Samples and sequencing

Complete mt genomes were sequenced for six noctuoid species:Asota plana lacteata (Erebidae, Aganainae), Agylla virilis (Erebidae,Arctiinae), Catocala deuteronympha (Erebidae, Erebinae), Euteliaadulatricoides (Euteliidae, Euteliinae), Gabala argentata (Nolidae,Chloephorinae), and Risoba prominens (Nolidae, Risobinae). Threelegs from each specimen were removed and preserved in 100%ethanol during collection. Legs were stored at �20 �C until DNAextraction, and voucher specimens were deposited at the Museumof the Institute of Zoology, Chinese Academy of Sciences. Totalgenomic DNA was extracted from the leg muscle tissue with theDNeasy Blood and Tissue Kit (QIAGEN, USA). Mt genomes wereamplified and sequenced as described in our previous study(Yang et al., 2013). Generally, primer walking was employed forlong fragments; fragments containing the control region werecloned using TOPO� TA Cloning� Kit (Invitrogen, USA), and theresultant plasmid DNA was isolated using the TIANpure Midi Plas-mid Kit (Tiangen, China). The sequence data have been deposited in

Fig. 1. Previous hypotheses of phylogenetic relationships within Noctuoidea: (A) Speidel et al. (1996); note: Noctuidae in this study includes the quadrifine groups; (B)Fibiger and Lafontaine (2005); (C) Lafontaine and Fibiger (2006); (D) Mitchell et al. (2006); (E) Zahiri et al. (2011) and (F) Zahiri et al. (2013b). Family and subfamily names areas Zahiri et al. (2011). Terminals were changed from the original publication to track Zahiri et al. (2011) and allow easy comparison between studies.

232 X. Yang et al. / Molecular Phylogenetics and Evolution 85 (2015) 230–237

GenBank under accession numbers KJ173908 (A. plana lacteata),KJ185131 (E. adulatricoides), KJ396197 (R. prominens), KJ410747(G. argentata), KJ364659 (A. virilis), and KJ432280 (C. deuteronympha).

2.2. Annotation and alignment

Sequence assembly and mt genome annotation followed ourprevious study (Yang et al., 2013). Alignments of individual geneswere performed using MAFFT (Katoh et al., 2002). Q-INS-i, theRNA structural alignment method implemented in MAFFT, wasexplored for rRNA and tRNA alignments and PCGs were aligned withthe default settings (Katoh and Toh, 2008). Three different datasetswere generated to test the effect of data inclusion/exclusion ontopology and nodal support: (1) 13PCG123, complete 13 PCGs (allRNA genes omitted); (2) 13PCG123tRNA, 13PCG123 + tRNA genes(rRNA genes omitted); (3) 13PCG123LRSR, 13PCG123 + rRNA genes(tRNA genes omitted).

In order to remove unreliably aligned regions within the data-sets, we used Gblocks (Castresana, 2000) to identify the conservedregions with the two following parameter settings: GBDH, defaultparameter settings except allowing gaps at a given position in upto half of the aligned species; GBRA, relaxed parameter settings(Minimum Number Of Sequence For A Conserved Position: mini-mum; Minimum Number Of Sequence For A Flank Position: mini-mum; Maximum Number Of Contiguous Nonconserved Positions:default; Minimum Length Of A Block: minimum; Allowed GapPositions: all). Additionally, datasets analyzed without Gblockstreatments were adjusted manually to remove the non-homologous regions at the beginning and the end of each alignedgene.

2.3. Phylogenetic analyses

Each dataset was analyzed using two inference methods, Baye-sian inference (BI) and maximum likelihood (ML). Analyses wereperformed using MrBayes v 3.2.2 (Ronquist et al., 2012) andRAxML-HPC2 v 7.6.3 (Stamatakis, 2006) on XSEDE via the Cyberin-frastructure for Phylogenetic Research (CIPRES) Science gatewayportal (Miller et al., 2010). Two rRNA, 22 tRNA genes and 13 PCGswere treated as 4 partitions, lrRNA, srRNA, a combined partition ofthe 22 tRNA genes, and a combined partition of 13 PCGs. Model

selection was via Akaike Information Criterion implemented inMrModeltest 2.3 (Nylander, 2004). For BI analyses, Ahamus yunna-nensis (Hepialidae) was selected as the outgroup (Lee et al., 2006).Four independent runs were conducted for 1,000,000 generations,and each was sampled every 1000 generations. Convergence wasdetermined using Tracer ver. 1.5 (Rambaut and Drummond,2007) and all analyses had converged within 1,000,000 gen-erations. Posterior probabilities over 0.95 were interpreted asstrongly-supported (Erixon et al., 2003). For ML analyses, twoHepialidae species Ahamus yunnanensis and Thitarodes renzhiensiswere selected as outgroups, and 1000 replicate bootstraps wereconducted under GTRCAT. Nodes with a bootstrap percentage(BP) of at least 70% were considered well-supported in the ML ana-lyses (Hillis and Bull, 1993).

3. Results

3.1. Gene order and variation

The six noctuoid species were all completely sequenced, and allof them possessed the typical metazoan mt genome composition of13 PCGs, 22 transfer RNAs, two ribosomal RNAs and a single A + T-rich region (putative control region) (Fig. 2) (Boore, 1999). TheA + T rich regions of noctuoid species were routinely short with arange of 311–541 bp, as well as a high AT content of 90.6–96.1%.The mt gene order was identical to other noctuoid species previ-ously published, which possessed the gene rearrangement oftRNAMet found in many lepidopterans. This character, once sug-gested to be synapomorphic for Lepidoptera (Gong et al., 2012),has recently been found to be restricted to the more derived cladeDitrysia (Cao et al., 2012) and their close relatives Tischerioideaand Palaephatoidea (Timmermans et al., 2014).

3.2. Problematic mt genomes within the first dataset

A range of preliminary analyses were conducted using the sixnewly sequenced mt genomes in this study plus all complete mtgenomes of Lepidoptera in GenBank (61 species by March 2013).However, some taxa were obviously misplaced in all analyses.For example, in no case was a monophyletic Hesperiidae (Ochlodesvenata and Ctenoptilum vasava) recovered. Instead, all datasets

Fig. 2. Mt genome organization and mt genome sizes for the six newly sequenced noctuoid species.

X. Yang et al. / Molecular Phylogenetics and Evolution 85 (2015) 230–237 233

supported the grouping of O. venata and three Lycaenidae species(Fig. S1). Sequence blasting of the 50 COI of the O. venata mtgenome published on GenBank does not closely match other sam-ples identified to this species or genus on BOLD (http://www.boldsystems.org) (Ratnasingham and Hebert, 2007). Themonophyly of Hesperiidae is not in dispute (Heikkilä et al., 2012;Warren et al., 2008, 2009). Similarly, one of the lycaenid species(Celastrina hersilia) grouped with Papilionidae. BLAST identificationof this sequence suggested it was from another genus (Cyaniris).The controversial placements of O. venata and C. hersilia needfurther investigation (see below) to determine whether theyare caused by contamination during sequencing, or specimenmisidentification.

Finally, we found that the divergence between two mt genomesof Parnassius bremeri (GenBank accession nos. HM243588 andNC_014053) reached 4.0%, which was quite unexpected for con-specific taxa. Further alignment analysis showed that the regionfrom the tRNA cluster ARNSEF to lrRNA of NC_014053 was themost divergent region (8.2%), while the rest of the mt genomewas only 0.5% divergent. Further blasting analysis showed thatthe divergent region of NC_014053 had 94% identity to C. hersilia,while only as much as 87% to other species of Papilionidae. Thissuggests that the sequence deposited on GenBank might be achimera, due to a contig formed of long PCR resulting from morethan one species.

In the preliminary analyses, most of the problems were withinthe unpublished data deposited in GenBank, so we conductedanalyses (57 taxa) excluding the unpublished data in this study(Table S1). We recommend careful checking and revision beforeconducting the phylogenetic analyses with lepidopteran mtgenomes from GenBank, especially those associated with unpub-lished studies.

3.3. Methodological effects of various approaches

Fourteen independent phylogenetic analyses were performed totest sensitivity to inference methods, data treatment approaches,and variable region exclusion with Gblocks. A total of eightdifferent tree topologies were recovered (Figs. 3, S2 and S3). A briefsummary of the datasets is displayed in Table 1.

The two inference methods (BI and ML) resulted in differenttree topologies for the same dataset. The monophyly of Yponomeu-toidea was supported by all the BI analyses (although not sig-nificantly in 13PCG123maGBDH and 13PCG123LRSRmaGBDH)whereas as it was never significantly supported in the ML analysesand the superfamily did not emerge as monophyletic (Plutellaxylostella was sister to Apoditrysia) in 13PCG123tRNAmaGBDHand 13PCG123LRSRmaGBDH. Relationships within Papilionoidea

also varied between inference methods. For the M13PCG123maand 13PCG123tRNAGBRA datasets, Pieridae and Lycaenidae wererecovered as a sister group (the B2-type relationship is presentedin Fig. 3) in ML analyses, whereas BI analyses recovered Nym-phalidae and Lycaenidae (the B1-type relationship is presented inFig. 3) as sister groups (these relationships, however, also varybetween datasets for the same inference methods, see below, andnote due to our cutoff for public sequences we did not include arecently published sequence for Lycaenidae’s widely consideredsister group, Riodinidae). Relationships within families showedlittle sensitivity to inference method, however the relationshipswithin Olethreutinae (Tortricidae) and the placement of Libytheaceltis within Nymphalidae were divergent in the two inferencemethods, while the placement of Noctuidae within Noctuoideawas another example in BI analyses (Fig. 3).

For the different data treatments, we compared the effect ofinclusion of rRNA or tRNA genes in combined analyses ofPCGs + rRNA and PCGs + tRNA against PCGs alone. Inclusion of tRNAgenes had no effect on tree topology and slight effect on nodalsupport when the datasets including third codon positions wereanalyzed using BI (13PCG123tRNAmaGBDH vs. 13PCG123maGBDHand 13PCG123tRNAmaGBRA vs. 13PCG123maGBRA). However,different relationships were recovered within Noctuoidea, Nym-phalidae and Tortricidae when rRNA genes were included(13PCG123LRSRmaGBDH and 13PCG123LRSRmaGBRA). In BI ana-lyses, Euteliidae and Nolidae were recovered as sister groups (theD2-type relationship is presented in Fig. 3) in 13PCG123LRSR-maGBDH and 13PCG123LRSRmaGBRA, while Noctuidae and Nolidaewere recovered as a sister group (the D1-type relationship is pre-sented in Fig. 3) from the other datasets (PCGs + tRNA and PCGsalone). Diverse interfamily and intrafamily relationships wererecovered across the different data treatments using ML analyses.The B2-type relationship (Lycaenidae + (Pieridae + Nymphalidae))was recovered from PCGs + tRNA (13PCG123tRNAmaGBDH and13PCG123tRNAmaGBRA), while the B1-type relationship((Lycaenidae +Pieridae) + Nymphalidae) was recovered fromPCGs + rRNA (13PCG123LRSRmaGBDH and 13PCG123LRSR-maGBRA). More diverse relationships were recovered within Nym-phalidae in ML analyses. 13PCG123tRNAmaGBDH recoveredL. celtis to be the sister group of the remaining nymphalids (exclud-ing Euploea mulciber) (the C1-type relationship is presented inFig. 3), while 13PCG123maGBDH and 13PCG123LRSRmaGBDHrecovered L. celtis as the sister group of a clade consisting of Calinagadavidis plus Hipparchia autonoe (the C3-type relationship ispresented in Fig. 3).

Using Gblocks with two different parameter settings appearedto have limited effect on phylogenetic reconstruction under theBayesian framework. The only difference found was between PCGs

http://www.boldsystems.orghttp://www.boldsystems.org

Fig. 3. Summary of different phylogenetic relationships recovered in this study. The entire tree was inferred from the Bayesian analysis of the 13PCG123maGBRA dataset, anddifferent types of relationships recovered in the Bayesian and maximum likelihood analyses from the other datasets are displayed on the left.

Table 1Brief summary of the datasets established from the mt genomes of 57 species.

Dataset name Dataset size (bp)

M13PCG123ma 11,26313PCG123maGBDH 10,95913PCG123maGBRA 11,25813PCG123tRNAmaGBDH 12,15913PCG123tRNAmaGBRA 12,94713PCG123LRSRmaGBDH 12,55813PCG123LRSRmaGBRA 13,604

Abbreviation and explanation: ‘M’, manually (manually adjusted the genes afteralignment); ‘ma’, MAFFT (genes were aligned by MAFFT); ‘GBDH’, Gblocks maskingwith the strict parameter settings; ‘GBRA’, Gblocks masking with the relaxedparameter settings; ‘LR’, large subunit ribosomal RNA; ‘SR’, small subunit ribosomalRNA.

234 X. Yang et al. / Molecular Phylogenetics and Evolution 85 (2015) 230–237

and PCGs + tRNA. Datasets with the strict parameter settings(13PCG123maGBDH and 13PCG123tRNAmaGBDH) recovered theC2-type relationship within Nymphalidae (C. davidis plus H. auto-noe sister to all Nymphalidae except E. mulciber), while datasetswith the relaxed parameter settings (13PCG123maGBRA and13PCG123tRNAmaGBRA) recovered the C1-type relationship (seeabove). For the three PCGs datasets, the dataset generated byGblocks with the relaxed parameter settings (13PCG123maGBRA)did not influence topology but generally increased nodal supportvalues when compared with the dataset manually adjusted(M13PCG123ma). In ML analyses, Gblocks also had limited influ-ence among the three PCGs datasets. However, the PCGs datasetwith the strict parameter settings of Gblocks (13PCG123maGBDH)inferred a tree with less weakly supported nodes than the one withthe relaxed parameter settings (13PCG123maGBRA), and the samepattern was found for the other datasets (13PCG123tRNAmaGBDH

vs. 13PCG123tRNAmaGBRA and 13PCG123LRSRmaGBDH vs.13PCG123LRSRmaGBRA). In addition, Gblocks treatments moder-ately affected topology when tRNA or rRNA genes were included.The strict parameter Gblocks (GBDH) ML analyses failed to recoverthe monophyly of Yponomeutoidea from PCGs + tRNA andPCGs + rRNA datasets (Fig. S3). These results indicate that MLanalysis is more sensitive to gaps than BI analysis.

3.4. Phylogeny

Phylogenetic analyses were performed using 57 complete mtgenomes, representing eight lepidopteran superfamilies. Themonophyly of each superfamily was generally well supported,except for two ML analyses, in which Yponomeutoidea becameparaphyletic, with Leucoptera malifoliella sister to Plutella xylostel-la + other six ingroup superfamilies. Relationships inferred at thesuperfamily level were highly consistent among all analyses. How-ever, we do not expect a robust phylogenetic relationship at thesuperfamily level due to only a small proportion of the lepidopter-an superfamilies being represented in this study. The monophylyof Yponomeutoidea is well supported after the removal of certainproblematic taxa. Relationships within Bombycoidea, Geometroi-dea and Pyraloidea were almost identical to our previous study(Yang et al., 2013). Relationships within Olethreutinae (Tortricidae,Tortricoidea) were sensitive to different analytical approaches(Fig. 3, A1–A4). However, we found some novel relationships with-in Noctuoidea and Papilionoidea.

Novel relationships were found for the Noctuoidea families(Fig. 3) compared to previous molecular phylogenies of this super-family. The redefined family Noctuidae grouped with the recentlyrecognized family Euteliidae. Noctuidae + Euteliidae was sister tothe reconstituted family Nolidae, and the recently reconstituted

X. Yang et al. / Molecular Phylogenetics and Evolution 85 (2015) 230–237 235

family Erebidae was sister to the clade of (Nolidae + (Euteli-idae + Noctuidae)). Notodontidae was the sister group of the otherfour noctuoid families. Within Erebidae, only four subfamilies wereanalyzed, and their relationships were as follows: (Arctiinae + (Ere-binae + (Lymantriinae + Aganainae))). Phylogenetic relationshipswithin Noctuoidea were consistent among most of the datasetsexcept those including rRNA genes (13PCG123LRSRmaGBDH and13PCG123LRSRmaGBRA) in BI analyses (Euteliidae was sister toNolidae, the D2-type relationship Fig. 3), and received strong sup-port from all PCG123 datasets in the BI analyses and in most of MLanalyses (Figs. 3 and S2).

In the present analyses, a total of 21 Papilionoidea species wereincluded representing five families and 12 subfamilies. However,two controversial nodes within Papilionoidea that have been foundin previous analyses of mt genomes still remained (Yang et al., 2013).In particular, the position of L. celtis was unstable with respect toDanainae (represented by E. mulciber) and Calinaginae + Satyrinae(represented by C. davidis and H. autonoe respectively), and three dif-ferent possible relationships were inferred (Fig. 3). The C1-type rela-tionship (Danainae + (Libytheinae + remaining Nymphalidae))received the highest support from both BI and ML analyses. Theother unstable relationships were among Pieridae, Lycaenidae andNymphalidae. The B1-type, more morphologically intuitiverelationship (Pieridae + (Lycaenidae + Nymphalidae)) was well sup-ported in most of the BI analyses (Fig. S2). However, in ML analyses,the B1-type relationship received strong support just in13PCG123LRSRmaGBRA, and the B2-type relationship ((Pieridae +Lycaenidae) + Nymphalidae)) was well supported in three datasets(M13PCG123ma, 13PCG123maGBDH, and 13PCG123tRNA-maGBDH) (Fig. S3).

4. Discussion

4.1. Methodological effects of various approaches

In our previous study (Yang et al., 2013), we have tested theeffect of inference and alignment methods on phylogenetic recon-struction, including or excluding the 3rd codon positions fromPCGs. Those results showed that inference using BI and ML, align-ment using Muscle and MAFFT, and including third codon positionsall performed better than maximum parsimony analysis, Clustalalignment and excluding third codon positions, respectively. Themost recent research on Noctuidae based on COI and seven nucleargenes arrived at a similar conclusion that removing the third codonpositions resulted in unstable phylogeny and weakened supportvalues (Zahiri et al., 2013b). In the present study, we extendedour exploration of mt genome phylogenetic utility by testing twoinference methods (BI and ML), the inclusion of rRNA or tRNAgenes and Gblocks removal of variable regions. Overall, lepidopter-an mt genomes have low sensitivity to these various analyticalapproaches, and thus are of high utility in resolving higher-levelrelationships between and within the lepidopteran superfamilies(Timmermans et al., 2014; Yang et al., 2013). We found that infer-ence method was the most significant factor, which is consistentwith a previous study of neuropteridan mt genomes (Cameronet al., 2009). In all instances, BI analyses recovered a more consis-tent tree topology across different datasets, and nodal support wasimpressively high for the majority of nodes. However, it has beensuggested in several recent studies that posterior probabilitiesare generally too liberal (Cummings et al., 2003; Simmons et al.,2004). Thus, caution should be used if BI analysis is solely applied.In contrast, ML analyses revealed many more topological differ-ences among varying datasets. Additionally, for none of the sevendatasets was the same topology recovered between the two infer-ence methods. This is different from our previous study where thetwo inference methods recovered identical tree topologies for

some datasets, indicating that this incongruence is the result ofmore comprehensive taxon sampling. Therefore, it will be impor-tant to employ different inference methods to evaluate the phylo-genetic signal as ever larger taxon datasets are analyzed.

Whether to use gene or codon exclusion is an ongoing debate inrelation to the accuracy of phylogenetic analyses using mt genomes.Our previous study indicated that it was not essential to excludethird codon positions in reconstructing lepidopteran phylogeny(Yang et al., 2013). In this study, we further tested the effect of geneexclusion by comparing the combined analyses of PCGs + rRNA/tRNA with analyses of PCGs alone. rRNAs and tRNAs are oftenexcluded in phylogenetic analyses of insects. However, analyses ofdipteran mt genomes suggested including rRNAs and tRNAsimproved resolution and nodal support (Cameron et al., 2007). Simi-lar conclusions were also obtained by other studies of Orthopteraand Neuropterida (Cameron et al., 2009; Fenn et al., 2008). In ouranalyses, the inclusion of tRNAs positively increased the congruenceof topologies, indicating tRNAs contribute signal to phylogeneticanalyses. By contrast, inclusion of rRNAs resulted in more diverseand poorly supported phylogenetic relationships compared withother datasets. Three candidate explanations might be responsiblefor noisy signals in rRNAs. First, rRNAs have long been a challengeto align accurately. Although incorporating structural informationinto alignment promises to improve the accuracy, this is difficultto apply for more distantly related taxa (Cameron et al., 2009).Another possible reason is that rRNA genes have much higher levelsof homoplasy than either PCGs or tRNA genes (Cameron et al., 2007).A further reason is that compensatory substitutions in pairedregions of RNA genes are not directly accounted for by the phyloge-netic inference methods unless explicitly modeled, such as the dou-blet model (Schöniger and Von Haeseler, 1994) used by Letsch et al.(2010). Therefore, substitutions at each site are assumed to be inde-pendent of one another possibly resulting in conflicting signal to thePCGs partition (Hudelot et al., 2003).

Gblocks was used to improve signal-to-noise ratio by removinghighly variable regions, caused by poor alignment or inaccuratelydefined gene boundaries. For example, most CO1 genes in Lepi-doptera have been predicted to start with the nonstandard startcodon-CGA, while others were predicted to start with the standardstart codon-ATN (Fig. S4). Therefore, the relatively arbitrary defini-tion of a gene boundary might or might not include variableregions at the gene’s 50 end. Similar problems exist in many othergenes, such as ND2, ND4, ND4L, and ND5. However, recent com-parative analyses suggest that removing variable regions has anegative impact on phylogenetic accuracy (Dessimoz and Gil,2010). We employed two different parameter settings of Gblocksin our analyses. The results showed that the relaxed parameters,GBRA, resulted in variant relationships and poor support valuesin ML analyses, while the strict parameter, GBDH, had a beneficialeffect, increasing nodal support. By contrast, the two parametersettings both resulted in more variable topologies and poorer nodalsupport in BI analyses. A similar conclusion was drawn by Dwivediand Gadagkar (2009), and they suggested that the possible reasonfor the opposite effects was due to the different criteria for treatinggaps as missing data in the different inference methods.

4.2. Phylogeny

The phylogeny and classification of Noctuoidea has experienceda chaotic history, changing rapidly with the implementation ofmolecular markers and the discovery of morphological synapo-morphies. The status of Noctuidae (sensu stricto), including all tri-fine noctuids and some ‘pseudoquadrifine’ noctuids, tends to beconsistent with recent morphological and molecular researches(Fibiger and Lafontaine, 2005; Lafontaine and Fibiger, 2006;Mitchell et al., 2006; Zahiri et al., 2011, 2013b). Noctuoidea with

236 X. Yang et al. / Molecular Phylogenetics and Evolution 85 (2015) 230–237

a quadrifid forewing and a quadrifine hindwing, consisting of themost species-rich groups such as Lymantriinae and Arctiinae, arealso the lineages most often subject to change between differentclassification schemes. In addition, various noctuoid classificationschemes have not tended towards a consensus of relationships atfamily or subfamily levels. To date, the most comprehensive mole-cular phylogenetic analyses of Noctuoidea were conducted byZahiri et al. (2011), from which a novel classification system forthis giant superfamily was proposed. Support values for each ofthe six major clades were strong, although least so (0.96 posteriorprobability) for Erebidae, and monophyly of each erebid subfamilywas moderately to strongly supported (Zahiri et al., 2011). Zahiriet al.’s (2011) family system was however well supported by ourmitochondrial phylogeny, despite our limited taxon sampling.Novel and robust relationships were, however, recovered at bothfamily and subfamily levels that are in conflict with Zahiri et al.(2011). In our analyses, the position of Notodontidae as sister tothe remaining noctuoid families and the sister grouping of Noctu-idae plus Euteliidae were congruent with the ML analyses in Zahiriet al. (2011). In contrast, Nolidae was found to have a closer rela-tionship with the clade (Noctuidae + Euteliidae), rather than withErebidae as suggested by the BI analyses of Zahiri et al. (2013b).A different relationship between Noctuidae and quadrifine noctu-ids was recovered in our BI analyses of datasets 13PCG123tRNA-maGBRA and 13PCG123LRSRmaGBRA, with Noctuidae recoveredas the sister group to the clade (Euteliidae + Nolidae). In the pre-sent study, Arctiinae was recovered as the sister of the remainingerebid subfamilies, which is consistent with the recent analysesbased on 19 nuclear PCGs in Regier et al. (2013). Interestingly, Aga-nainae and Lymantriinae were here grouped together for the firsttime, whereas Aganainae was found to have a closer relationshipwith Arctiinae in recent molecular and morphological studies(Figs. 1 and 3) (Fibiger and Lafontaine, 2005; Zahiri et al., 2012).Zahiri et al. (2011) proposed that trifine noctuids evolved from quad-rifines multiple times, due to hindwing vein reduction in some quad-rifines and the position of quadrifine species within some trifinesubfamilies. This proposal is also supported by our mt genome phy-logenies as the trifine groups Arctiinae and Nolidae never formed amonophyletic group; however, the multiple origins of these wingvenation patterns in Noctuidae still needs more research. Thesequestions can probably be resolved by extending both taxon sam-pling (especially additional representatives of subfamilies/tribeswithin the massive groups Erebidae and Noctuidae families) andthe number of molecular markers. The combination of mt genomeswith nuclear genes (at least when a limited proportion of the nucleargenome or transcriptome is used) could be an effective way to refinethe classification of noctuoid families and test the robustness ofinterfamily relationships. A stable and informative classification ofNoctuoidea will ultimately help interpret and/or discover relevantmorphological autapomorphies, especially for Erebidae and Nolidae.

Many previous studies have demonstrated great differences inphylogenetic signal between mitochondrial and nuclear genes(Lin and Danforth, 2004; Rubinoff and Holland, 2005; Shaw,2002; Wahlberg et al., 2009b). However, the mitochondrial phy-logeny of Noctuoidea in the present study shows promising con-gruence with the previous, almost entirely nuclear PCG-basedstudies (Zahiri et al., 2011), suggesting that greater integration ofnuclear and mt genomes will indeed lead to an improvement inour knowledge of insect evolution and phylogeny, although theimportance of growing amounts of nuclear data (e.g. Kawaharaand Breinholt, 2014) cannot be understated.

Acknowledgments

This study was supported by the grants from the National Nat-ural Science Foundation of China (Nos. 31272288, 31071952, and

31372176) and the Key Laboratory of the Zoological Systematicsand Evolution of the Chinese Academy of Sciences (No.O529YX5105). David Lees was funded by ERC Grant #250325 (Bel-gium) during the writing of this study. Stephen Cameron was fund-ed by an Australian Research Council Future Fellowship(FT120100746).

Appendix A. Supplementary material

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.ympev.2015.02.005.

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A mitochondrial genome phylogeny of owlet moths (Lepidoptera: Noctuoidea), and examination of the utility of mitochondrial genomes for lepidopteran phylogenetics1 Introduction2 Materials and methods2.1 Samples and sequencing2.2 Annotation and alignment2.3 Phylogenetic analyses

3 Results3.1 Gene order and variation3.2 Problematic mt genomes within the first dataset3.3 Methodological effects of various approaches3.4 Phylogeny

4 Discussion4.1 Methodological effects of various approaches4.2 Phylogeny

AcknowledgmentsAppendix A Supplementary materialReferences