Arthropod Systematics & Phylogeny 35

64 (1) 35–44 © Museum für Tierkunde Dresden, ISSN 1863-7221, 30.10.2006

A Molecular Phylogeny of

KARL M. KJER 1, FRANK L. CARLE 2, JESSE LITMAN 2 & JESSICA WARE 2

1 Department of Ecology, Evolution and Natural Resources, Rutgers University, New Brunswick, NJ, 08901, USA [[email protected]] 2 Department of Entomology, Rutgers University, New Brunswick, NJ, 08901, USA [[email protected]; [email protected]; [email protected]]

Received 27.ii.2006, accepted 23.viii.2006. Available online at www.-systematics.de

> Abstract We present a supermatrix approach to the phylogeny of Insecta that stemmed from a talk given at the 2nd Dresden Meeting on Phylogeny (2005). The data included a fragment of the 28S (D1–D8) and complete sequences for the 18S, histone (H3), EF-1α, COI, COII, the 12S and 16S plus the intervening tRNA, and 170 morphological characters. Ribosomal RNA sequences were manually aligned to secondary structure. Two separate Bayesian likelihood analyses were performed, as well as a weighted parsimony analysis, on combined data. Partitioned datasets were also explored. Expected clades like Hexapoda, Insecta, Dicondylia, , Neoptera, , , and Endopterygota were consistently recovered. However, confl icting hypotheses from independent datasets, as well as a lack of quantitative support from the combined supermatrix, suggest that the elucidation of relationships between non-holometabolous neopteran orders is far from resolved. Substitution rate heterogeneity among lineages, missing intermediate taxa, near simultaneous divergences, fl awed phylogenetic models and nucleotide compositional bias are discussed as possible causes for unresolved interordinal relationships. The capacity of this dataset to convey information, its inherent limitations, and the role and responsibility of the systematist in interpreting data are explored.

> Key words Insecta, Hexapoda, phylogeny, ribosomal RNA, secondary structure alignment, supermatrix, Bayesian analysis, weighted parsimony, among site rate variation, compositional bias.

1. Introduction

Our understanding of hexapod phylogeny has been genetics (WHITING et al. 1997, 2003; WHEELER et al. dominated by two data sources; morphology, and 2001; WHITING 2002a,b; SVENSON & WHITING 2004; nuclear small subunit rRNA (18S). Recently, a large TERRY & WHITING 2005a,b) favor unadjusted computer fragment of the nuclear large subunit rRNA (28S) has alignments, and most recently, a direct optimization been added by M.F. Whiting and others, along with approach using a program called POY (WHEELER 1996; the nuclear protein coding gene, histone H3 (OGDEN GLADSTEIN & WHEELER 1997). While we credit these & WHITING 2003, 2005; WHITING et al. 2003; TERRY workers for their contribution to insect systematics, & WHITING 2005a). Several mitochondrial genes are we feel that this approach suffers from some serious also available for a large number of insect taxa, and drawbacks (see KJER et al. in press). We therefore recently, insect mitochondrial genomes have been felt it important to provide alternative conclusions to ex plored (CAMERON et al. 2004, 2006a,b). Kjer was those drawn from POY analyses of combined data. We in vited to present a combined data analysis of insect show qualitative support through corroboration with re lation ships, at the 2nd Dresden Meeting on Insect partitioned analyses, followed by quantitative support Phy logeny, which took place in September, 2005. In with combined data. We also show the robustness of preparing for this presentation, we combined sequence our conclusions to changes in analytical assumptions. data from GenBank with a new morphological matrix. These analyses are meant to provide the basis for a Analytical assumptions have a great infl uence on phy- discussion of the issues from an alternative perspective, lo genetic conclusions. The most infl uential and pro - rather than to provide the defi nitive phylogeny of ductive group of workers on insect molecular phylo- Insecta (which is not yet ready). 36 KJER et al.: Molecular phylogeny of Hexapoda

2. Methods Cryptocercus (Blattaria), Liposcelis (Psocodea) and Boreus (), which were coded separately. We focused on the characters these authors have used The data included complete 18S (1882 nucleotides = in coming to confl icting conclusions regarding the nts), a fragment of 28S (D1–D8: 2214 nts), Histone H3 positions of the palaeopterous orders (Ephemeroptera (375 nts), the complete EF-1α (1243 nts, 414 amino and ), Dictyoptera, and Endopterygota. One acids = AAs), the complete COI (1551 nts, 518 AAs), hundred and seventy binary characters were used. the complete COII (684 nts, 228 AAs), complete 12S Other more recent work on morphology (KLASS 1997, and 16S plus the intervening tRNA (1435 nts), and 170 1998, 2003; WILLMANN 1997; GEREBEN-KRENN & PASS morphological characters taken from the literature. We 1999; BITSCH & BITSCH 2000; KOCH 2000; STANICZEK also examined the nuclear EF-2 gene (2188 nts, 729 2000; BEUTEL & GORB 2001; HÖRNSCHEMEYER 2002 are AAs), but did not include it in the combined data because only few examples), fossils (GRIMALDI 2001; ENGEL there were so few pterygote sequences available. In & GRIMALDI 2004; GRIMALDI & ENGEL 2005), sperm total, there were 14,209 characters in combinations structure (DALLAI et al. 2002), embryology (MACHIDA of nucleotides, amino acids, and morphology. This et al. 2004) and many other systems discussed in this represents 6 independent partitions: nuclear ribosomal symposium were not included because we did not have genes (18S+28S), Histone H3, EF-1α, mitochondrial the time to compile them for this largely molecular genes (COI, COII, 12S, 16S), and morphology. Data presentation. These are important works, which have were put together with a supermatrix approach with greatly expanded morphological knowledge and 137 taxa from 18S providing a core (all taxa had 18S massively revised previous interpretations, and should data). Other data were added to the 18S data, usually eventually be included. from the same . A few terminal taxa are chimeras combined from members of the same family. In a very few cases, when sampling within an order was limited to two taxa, we constructed a chimera of any two taxa 3. Results and discussion within the order (permissible because monophyletic nodes with two taxa can freely spin without affecting the tree). 3.1. Combined data Ribosomal RNAs were aligned manually with referen- ce to secondary structure, following KJER (2004). Two Results from the Bayesian analyses are shown in Bayesian likelihood analyses were perfor med using the Fig. 1. There is strong support for most relationships program MrBayes (HUELSENBECK & RONQUIST 2002), that would not surprise us; , Hexapoda, one partitioned by gene, using a GTR+I+G model for Insecta, Dicondylia, Pterygota, Neoptera, Dictyoptera, the nucleotides (YANG 1994; YANG et al. 1994; GU Paraneoptera, and Endopterygota are supported, along et al. 1995), and an MK model for the morphology with the monophyly of most orders. When we look (LEWIS 2001), and the other using a site-specifi c rate for further resolution of the nodes that are in confl ict model (SSR, described in KJER et al. 2001), with among insect systematists, we fi nd little information morphology excluded. Each analysis consisted of two from this huge dataset. Interestingly, however, separate runs of 750,000 iterations. After the “burnin” these analyses yield a clade Collembola + trees were discarded, trees from both analyses (all four + Diplura (i.e. the old “”), and a clade runs) were pooled into a single treefi le, from which a Odonata + Ephemeroptera (i.e., the strongly disputed majority-rule consensus tree was constructed. “”. A further interesting aspect is the Weighted parsimony was also employed with a group including Grylloblattodea, Mantophasmatodea, subset of the data we designated as “conservative”. , , and Dictyoptera. This These data included the nuclear and mitochondrial group was supported in 89% of the Bayesian trees, rRNAs, and amino acids from protein coding genes and also with the weighted parsimony analysis (Fig. (nucleotides excluded). We implemented pseudo- 2). However, when we look for stability of analytical replicate reweighting, as in KJER et al. (2001) on the assumptions in other nodes by examining the weighted “conservative dataset”. parsimony tree (Fig. 2), we see that what we might In order to explore whether or not the morphology fi nd most “interesting” in one analysis, are not found had a large effect on phylogenetic conclusions, a in the other. An exception to this is a strong resolution morphological matrix was constructed from the of a monophyletic Palaeoptera, from both Bayesian literature (KRISTENSEN 1975, 1981, 1991; BOUDREAUX analyses, (Fig. 1: despite the inclusion of morphological 1979; HENNIG 1981). For the most part, orders were used characters that favor a clade containing Odonata and as terminal taxa, except for Timema (Phasmatodea), Neoptera), and from the weighted parsimony analysis Arthropod Systematics & Phylogeny 64 (1) 37 of the altered “conservative” dataset (Fig. 2; amino + Mantodea, Paraneoptera, + Liposcelis acids replacing nucleotide characters). + Phthiraptera, Thysanoptera + , and Bayesian analyses have been widely embraced in Endopterygota. Within Endopterygota, relationships systematics, not because systematists are naturally were (Coleoptera + ( + ( + Bayesians, but rather, because these analyses permit (Diptera + (Mecoptera + ((Boreus + Siphonaptera) iterative refi nement of multiple parameters, and also + ())))))). Except for those noted provide branch support that would be impossible above, orders were monophyletic (but this would be under traditional likelihood analysis in a reasonable expected, since for the most part, orders were coded amount of time. (In other words, Bayesian analyses as terminal taxa, and this provides a strong constraint are fast and effi cient.) However, we have been on combined analyses, both here, and in other studies concerned about relatively high support, in the form that use this approach). There was no resolution of Bayesian posterior probabilities, for the same short among polyneopteran orders other than listed above. internodes that have low bootstrap support when The point of our morphological analysis was not analyzed under parsimony and likelihood. LEWIS et to provide anything new, but rather, to see how the al. (2005) demonstrate this problem clearly, and we traditional morphological characters would infl uence regard Bayesian branch support from short internodes a combined analysis. Our morphological matrix was with suspicion. P. Lewis and coworkers are working substandard in many ways. First, we omitted much on software solutions for this problem, but none of the recent work. Second, in our opinion, mole- were available for these analyses. Bayesian posterior cular systematists producing “groundplan” coded cha- probabilities are interpreted as “the probability that a racters from the literature, without actually looking particular node is true…given the model and the data”. at specimens, is better than nothing…but just barely. It is important not to overemphasize the fi rst part of What is desperately needed is a morphology matrix, the statement as evidence for truth, because we know put together by a morphologist, collaborating with a that all models are simplifi cations, and the data may be molecular systematist with coordinated taxa. In the biased. Since we are most interested in phylogeny, and future, we hope to collaborate with morphologists who since we believe that phylogenetic hypotheses without wish to add a molecular component to their work. indications of branch support are nearly meaningless, Histone H3. This nuclear protein-coding gene with we feel that the problems with posterior probabilities extreme conservation in amino acid structure, has are serious. In order to interpret branch support (before been sequenced for a large number of . The there is a remedy the problem of high support for only ordinal level node (and above) we recovered in a hard polytomies), we devised a temporary solution: parsimony analysis of H3 was Mantodea. Every other alter both the model and the data, so that these less taxon found in the combined analysis, at the level interesting parts of the defi nition play a less important of order or above, was polyphyletic or paraphyletic. role in the estimates of branch support. This is what Model-based analyses may correct for homoplasy, we did here, by combining two Bayesian analyses; one so we ran a GTR+I+G Bayesian likelihood analysis with a partitioned GTR+I+G model, with morphology of the H3 dataset. This analysis added Zygoptera (under an MK model), and another with a site-specifi c and Crustacea to the clades found in the parsimony rate model without morphology (Fig. 1). These are analysis, but clearly, the H3 by itself does not provide very different treatments of the data, but none-the- information in estimating relationships among insect less, recover remarkably similar phylogenies. orders. The reason for this is similar to problems encountered with the EF-1α, as discussed in KJER et al. (2001). As is the case with proteins that are so tightly 3.2. Partitioned data conserved, the signal is dominated by silent 3rd codon sites, and is highly homoplastic, even among closely While quantitative support should come from com- related taxa. Conservative “looking” protein-coding bined data (Figs. 1 and 2), qualitative support (in- genes (with invariant fi rst and second codon positions) fl uen cing what we believe to be accurate, or at least are inappropriate for deep-level phylogenetics unless corroborated) can come from partitioned data. Is there are suffi cient amino acid changes to provide some there any corroboration for relationships among poly- nonhomoplastic signal. We examined amino acids in neopteran orders that we can deduce from independent H3 as a potential source of conservative signal. There datasets? are 4 parsimony informative amino acid positions Morphology. A parsimony analysis of the mor- among (three of them within a span of phology supported the clades Insecta, Dicondylia, four codons). All 4 shared the same pattern, group- Pterygota, Odonata + Neoptera, Neoptera, Timema ing Negha (Rhaphidioptera), Dolichopeza (Diptera), + Euphasmida + Embioptera, Dictyoptera, Blattaria and Letothorax (Hymenoptera) together, and apart 38 KJER et al.: Molecular phylogeny of Hexapoda from Anopheles (Diptera), Sialis (), and molecular markers put Ephemeroptera with Neoptera. Hemerobius (Neuroptera). There are apparently two Just as morphologists have argued for their beliefs in permissible conformations; change one amino acid, the face of confl icting morphological evidence, when and the other three amino acids change. This pattern the evidence is weak, molecular systematists may must either represent convergence, or the polyphyly argue their beliefs in the face of confl icting molecular of both fl ies and neuropteroids. One may keep the H3 evidence. This confl ict illustrates the problem we all in, or throw it out, it doesnʼt matter. But the insect face between the philosophy of science and the utility phylogenetics community should stop sequencing this of phylogenetic hypotheses. We can never prove a uninformative marker for deep-level phylogenetics, phylogenetic hypothesis is true, and such an attempt and spend its resources on collecting appropriate may be counterscientifi c. However, as working data. systematists, we prefer “accurate” hypotheses, and Elongation factor EF-2. Examining the EF-2, the pursuit of accuracy should not be discouraged as we conclude that there are not enough pterygotan meaningless, even under a “falsifi cation” paradigm. sequences to be useful for estimating polyneopteran Trees that are “wrong” are worse than worthless, relationships. Among pterygotes, only Ephemeroptera, because they actively promote confusion. Things like Dermaptera, Blattaria, Hemiptera, and Diptera were bootstrap values, or congruence do not demonstrate represented. However, a separate analysis of the EF- truth, but they can infl uence our beliefs. Here we 2 did recover Pancrustacea, adding to the number present our results, but also provide our interpretation of molecular markers that consistently recover this of those results. group. The mitochondrial data, and the nuclear rRNA Confounding factors in insect phylogenetics. data also supported Pancrustacea. Fig. 3 is presented to show the root of the problem Nuclear rRNAs. The combined results are still with estimating relationships among the principal dominated by nuclear rRNAs. This is probably a good lineages of Neoptera. These lineages diverged from thing, as these markers possess meaningful characters one-another hundreds of millions of years ago, and for a wide range of divergence times. Characters in the fossil evidence indicates that this divergence rRNA stems are also less prone to convergence, was nearly simultaneous (LABANDEIRA & SEPKOSKI because the nucleotide state that is coded in the 1993; GRIMALDI & ENGEL 2005). The lengths of the datamatrix is not under direct selection. Instead, the internodes separating neopteran lineages in Fig. 3 are structure is selected, and any of the 4 nucleotide states virtually zero. Adding additional data that is noisy or may function structurally. Fig. 3 represents the single essentially randomized will never get us any closer tree with the best likelihood score from a GTR+I+G to answering these diffi cult questions, even though Bayesian analysis of the nuclear rRNAs (remember, it may provide “strong support” based on bias in the the “majority rule” consensus, from which posterior data due to branch length heterogeneity or nucleotide probabilities are calculated, is different from this composition. tree). Interestingly, the nuclear rRNAs support Another striking feature that can be seen in Fig. 3 Ephemeroptera + Neoptera. This is the same result as is the excessive rate heterogeneity among orders. in the analysis of the 18S alone in KJER (2004). Thus Diptera and Diplura evolve at rates that are orders the addition of the 28S data to the 18S data does not of magnitude higher than the norm. alter the positions of the two palaeopterous orders. has similarly accelerated rates, as does A partitioned analysis of the EF-1α also recovered (see phylogram in YOSHIZAWA & JOHNSON 2005), yet Ephemeroptera + Neoptera. However, Palaeoptera is Odonata and Mantodea seem virtually frozen. While monophyletic in the combined analyses (Figs. 1 and at least with Diptera, we can “believe” that it is 2) as well as in a partitioned analysis of mitochondrial endopterygotan, and not a crustacean, there is little data (not shown). This result is coming from a few to guide us with Zoraptera. YOSHIZAWA & JOHNSON mitochondrial characters that confl ict with other more (2005) place Zoraptera with the Dictyoptera. TERRY conservative markers. & WHITING (2005a) place Zoraptera with Dermaptera. We chose not to include Zoraptera in this analysis because of its excessive substitution rate acceleration, 3.3 Interpretation of phylogenetic results and associated alignment diffi culties (similar to our decision to exclude Strepsiptera). By sequencing Support, accuracy, and error. We may believe that additional zorapterans, YOSHIZAWA & JOHNSON (2005) Palaeoptera is monophyletic, because of the combined confi rmed that the zorapteran sequence from WHEELER analysis, or because of their wings. We may believe et al. (2001) was not a contaminant (as mistakenly that Odonata is plesiotypic because it lacks direct suggested as a possibility by KJER 2004), and also sperm transfer, or because the two most conservative confi rmed the bizarre nature of zorapteran rRNA. One Arthropod Systematics & Phylogeny 64 (1) 39 could legitimately criticize us for omitting Zoraptera all the taxa for which there was mitochondrial data, and and Strepsiptera, but not for the concept that excessive the other “clade” included all the taxa for which there substitution rate heterogeneity makes phylogenetics was 18S data, we could say with some confi dence that problematic, whether through long branch attraction the supermatrix failed, because it was grouping taxa in parsimony, or inadequate models in likelihood according to the presence or absence of data. If one (YANG 1996; BUCKLEY et al. 2001). The diplurans could judge our analyses by the recovery of expected always grouped with the proturans. The fl ies were relationships, the supermatrix approach worked. never with Antliophora, but the fact that they always Despite the fact that many of the data cells are missing came out within Endopterygota gave us some degree in these analyses, orders grouped together, without of reassurance. No such reassurance would be possible constraints, with and without morphology. Once an with Strepsiptera or Zoraptera, regardless of their order is grouped together, the missing data is simply placement. Could our placement of Diptera and Di- fi lled in by the algorithm, such that it is assumed that plura have been affected by their elevated substitution other taxa in the order fi t the “groundplan” estimated rates? Maybe. It would be good to examine this from the data cells that are fi lled. possibility in the future. Effect of adding taxa. Another point of discussion that is very clear from Fig. 3 is whether or not 3.4. Conclusions taxon sampling will help solidify our beliefs about relationships among the major lineages of Neoptera. Even with a data matrix in excess of 10,000 characters, We think not. If one samples two widely separated we fi nd little or no support for relationships among taxa within an order (a cricket, and a grasshopper for polyneopteran orders, except for Dictyoptera. We get example), then the branch leading to these taxa cannot phasmids with the Embioptera. The nuclear rRNAs be further subdivided unless some proto-orthopteran place Mantophasmatodea with the Grylloblattodea, is included. The discovery of unexpected diversity is while the mitochondrial data place them with the not impossible, as we have seen with the discovery Phasmatodea (see also results in TERRY & WHITING of Mantophasmatodea (KLASS et al. 2002; PICKER 2005 and CAMERON et al. 2006a,b). et al. 2002), but at the level of orders, it would be Our approach was to address insect phylogenetics to very diffi cult to truncate the branches leading to any a group of trained insect systematists by talking about particular order shown in Fig. 3, except for the branches the data and its properties. We look for corroboration leading to Grylloblattodea, and Mantophasmatodea, among independent datasets (and fi nd little), and we which are lightly sampled in these analyses. Taxon evaluate quantitative support from combined data sampling will certainly help with the resolution within (again, we fi nd little). This approach is different from orders, but when looking for relationships among the direct optimization (POY) of combined data. This orders, the sampling plan used herein is adequate. is not to say that our approach is the “correct” one. The situation is similar to morphology, where we Rather, we think that looking at the data and discussing fi nd a wealth of characters that defi ne, for example, it as if we were interested in “truth”, even if it can or Coleoptera. The diffi culty is fi nding never be achieved, is useful in infl uencing what we synapomorphies that group the orders to one-another. believe to be supported by the data. Looking at the short internodes among the principal neopteran lineages, there was probably very little time in which to accumulate synapomorphies that link one order to another, as is indicated by the lack of space to 4. Acknowledgements accommodate additional taxa on the internodes of Fig. 3. What we need is more quality in the molecular data, We thank Klaus-Dieter Klass for inviting us to participate in care in its analysis, and a morphological data matrix this forum and for his help in facilitating peer review for this to go with it. The addition of fossil data, even though work. We thank Kenneth (Tripp) MacDonald, Klaus-Dieter extremely fragmentary, may help (WIENS 2005). Klass and an anonymous reviewer for helpful comments on Missing data. These can cause problems, and these the manuscript. Financial support from NSF DEB 0423834, problems are diffi cult to spot if one has no phylogenetic and the New Jersey Agricultural Experiment Station, is expectations. However, if we expect the recovery of appreciated. some minimal corroborated phylogeny, such as the monophyly of most insect orders, then we can assess how the supermatrix approach worked. For example, if one were to see Ephemeroptera come out in two places on the tree, and one of these “clades” included 40 KJER et al.: Molecular phylogeny of Hexapoda

Limulus 100 Orthoporus Proteroiulus 100 Pagurus Artemia 100 Lepidocyrtus 100 Onychiurus 99 Neanura Pancrustacea 100 Podura Collembola 94 Hypogastrura 100 100 Neocondeellum 99 Acerentulus 100 Protura 100 Catajapyx 100 Campodea Parajapyx Diplura Hexapoda 98 Trigoniopthalmus 100 100 Dilta 100 Petrobius Allomachilis 100 Tricholepidion 100 Lepisma Thermobia 100 Lestes 100 Argia 94 Epiophlebia 100 66 Aeshna Insecta 100 Ophiogomphus Odonata Palaeoptera 100 100 Somatochlora Sympetrum 100 Baetis 100 Centroptilum 76 Anthopotamus 100 Hexagenia Ephemeroptera 61 Caenis 100 Ephemerella 100 Paraleptophlebia Nemoura Dicondylia Scopura 57 Aphanicerca Capnia Oemopteryx Paraleuctra 63 Tallaperla 100 87 Pteronarcys 100 Calineuria 83 Isoperla Plumiperla 100 Trinotoperla 75 100 Austroperla 100 Stenoperla 100 Diamphipnoa 100 Dendroiketes Pterygota 93 Tagalina 55 Auchenomus 100 Chelisoches Dermaptera 100 Forficula Labidura 64 Cylindraustralia 100 Morsea 100 Acrida Oxya 75 97 Cyphoderris 98 Tettigonia 99 Hemideina Orthoptera 71 Camptonotus 70 Ceuthophilus 74 Gryllus 72 Myrmecophila Gryllotalpidae 78 Grylloblatta Grylloblattodea 100 Lobophasma Tyrannophasma Sclerophasma Mantophasmatodea Timema 100 Phyllium 100 100 Carausius Phasmatodea Neoptera Antipaluria 100 100 Oligotoma 100 Teratembia 89 100 Diradius Embioptera 64 Australembia 100 Brasilembia Biguembia 94 Paraoxypilus 100 Kongobatha 94 Tenodera 100 Archimantis Mantodea 98 Creobroter 100 Gongylus 92 Polyphaga 100 Supella 55 Gromphadorhina 100 Blattella Periplaneta "Blattaria" 98 Cryptocercus 100 Mastotermes Dictyoptera 100 100 Coptotermes 64 Serritermes 100 Termitidae Isoptera 99 Microhodotermes 97 Hodotermopsis 57 Hodotermopsis Neotermes 100 Philaenus 100 Okanagana 50 Ranatra 100 Corythucha Hesperocorixa 100 100 Graphosoma Hemiptera 100 Rhaphigaster Paraneoptera 100 Lygus 100 Phymata 100 Frankliniella Idolothrips Thysanoptera 100 Ectopsocidae 100 Valenzuela 99 Liposcelis "Psocoptera" 100 Heterodoxus 100 Neophilopterus 100 Pediculus "Phthiraptera" Haematomyzus 100 Distocupes 100 Dynastes Coleoptera 99 Phaeostigma 94 Sialis Hemerobius Neuropteroidea 100 Polistes Endopterygota Athalia Hymenoptera 100 Tipula 100 Chrysops Diptera 88 Archaeopsylla 100 Boreus Siphonaptera 100 Merope 94 Panorpa "Mecoptera" 100 Micropterix 100 Galleria 100 Wormaldia Pycnopsyche Trichoptera

Fig. 1. Results from Bayesian analyses. This is a majority-rule consensus tree from two separate analyses; one using a GTR+I+G for the nucleotides, with an MK model for the morphology, the other with a SSR model, with morphology excluded. Numerals above the nodes represent the percentage that these nodes were present among the pool of most likely trees (from all analyses) after the likelihood scores had stabilized. Arthropod Systematics & Phylogeny 64 (1) 41

Limulus 100 Orthoporus Proteroiulus 97 Pagurus Artemia 100 Lepidocyrtus 90 Onychiurus Neanura Pancrustacea 71 37 Collembola 76 Podura 100 Hypogastrura 100 Neocondeellum 71 Acerentulus 100 Baculentulus Protura 100 Catajapyx Hexapoda 100 Campodea Parajapyx Diplura 50 100 Trigoniopthalmus 91 Petrobius 38 Allomachilis Archaeognatha Dilta 92 Tricholepidion 100 Lepisma Thermobia Zygentoma 56 Lestes 100 100 Argia Insecta 45 Aeshna 86 Epiophlebia Odonata 85 Ophiogomphus Palaeoptera 97 100 Somatochlora Sympetrum 100 Baetis 100 Centroptilum 98 100 Ephemerella Ephemeroptera Dicondylia 73 Paraleptophlebia 24 Anthopotamus 44 Caenis Hexagenia Nemoura 100 Tallaperla 70 82 Pteronarcys 99 Calineuria 61 Isoperla 69 Plumiperla 21 Oemopteryx 100 Paraleuctra Pterygota 36 76 Scopura Plecoptera 87 57 Aphanicerca 47 Capnia 48 Trinotoperla 67 Austroperla 57 Stenoperla Diamphipnoa 47 Dendroiketes 70 Auchenomus 99 Tagalina 70 Chelisoches Dermaptera 100 Forficula Labidura Ceuthophilus 43 Cylindraustralia 100 93 94 Morsea Neoptera 100 Acrida 54 Oxya 11 Hemideina Orthoptera 92 Cyphoderris 16 Tettigonia 34 Camptonotus 85 Myrmecophila Gryllotalpidae 58 Grylloblatta Grylloblattodea 99 Lobophasma 34 Tyrannophasma 21 Sclerophasma Mantophasmatodea Timema 32 100 Phyllium 29 Carausius "Phasmatodea" Antipaluria 100 Australembia 90 91 Brasilembia 21 53 55 Biguembia Embioptera 96 Oligotoma 100 Teratembia Diradius 100 Paraoxypilus Kongobatha 90 85 Tenodera 91 Archimantis Mantodea 80 Creobroter 100 Gongylus 38 Polyphaga 44 Supella 62 54 Gromphadorhina Blattella Periplaneta "Blattaria" 34 Cryptocercus 58 Mastotermes 100 91 Termitidae 38 Serritermes Isoptera 23 Dictyoptera 64 Coptotermes 65 Hodotermopsis 42 Neotermes 33 Microhodotermes Hodotermopsis 95 Philaenus 100 Okanagana 93 Ranatra 100 Corythucha Hesperocorixa Hemiptera 51 100 Graphosoma Paraneoptera 82 60 Rhaphigaster 77 Lygus Phymata 91 Ectopsocidae Valenzuela "Psocoptera" 81 100 Frankliniella Thysanoptera 75 Idolothrips 48 67 Liposcelis 81 Heterodoxus "Psocoptera" 81 Neophilopterus 94 Pediculus "Pthiraptera" Haematomyzus 100 Distocupes 82 Dynastes Coleoptera 94 Phaeostigma 94 Sialis Neuropteroidea 92 Hemerobius 94 Polistes Endopterygota Athalia Hymenoptera 84 Archaeopsylla 41 95 Boreus Siphonaptera 100 Merope 59 Panorpa "Mecoptera" 100 Tipula Chrysops Diptera 54 100 Micropterix 100 Galleria Lepidoptera 100 Wormaldia Pycnopsyche Trichoptera Fig. 2. Results from a weighted parsimony analysis of the combined “conservative” data. Numerals above the nodes are bootstrap values. 42 KJER et al.: Molecular phylogeny of Hexapoda

Limulus Myriapoda "Crustacea" Collembola Protura Diplura Archaeognatha Zygentoma Odonata

Ephemeroptera Pterygota Mantodea

"Blattaria" Cryptocercus Isoptera

Grylloblatta Mantophasmatodea Timema Euphasmida Neoptera Embioptera

Orthoptera

Plecoptera

Neuropteroids Coleoptera Hymenoptera "Mecoptera+Siphonaptera" Lepidoptera Trichoptera Thysanoptera "Dermaptera" Diptera "Psocoptera" Liposcelis "Phthiraptera"

Hemiptera 0.1

Fig. 3. Phylogram of the most likely tree from a Bayesian analysis of nuclear rRNA data. The lengths of branches are proportional to the number of nucleotide changes per site; scale: 0.1. Arthropod Systematics & Phylogeny 64 (1) 43

Tab. 1. Clades from the combined analysis that were supported by multiple independent datasets in partitioned analyses. MtB = Bayesian analysis of mitochondrial data. MtWP = weighted parsimony analysis of mitochondrial data. NrRNA = nuclear rRNA. Morph = morphology.

Pancrustacea EF-2 MtWP MtB NrRNA Hexapoda MtWP MtB NrRNA Morph Insecta EF-1 MtB NrRNA Morph Dicondylia NrRNA Morph Pterygota EF-1 MtB NrRNA Morph Neoptera EF-1 NrRNA Morph Dictyoptera MtWP MtB NrRNA Morph Cryptocercus + Isoptera MtB NrRNA Paraneoptera MtWP MtB NrRNA Morph Endopterygota EF-1 NrRNA Morph Amphiesmenoptera NrRNA Morph

5. References

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