TY - JOUR
AU - Wills, Matthew, A
AB - Abstract Notwithstanding the rapidly increasing sampling density of molecular sequence data, morphological characters still make an important contribution to our understanding of the evolutionary relationships of arthropod groups. In many clades, characters relating to the number and morphological specialization of appendages are ascribed particular phylogenetic significance and may be preferentially sampled. However, previous studies have shown that partitions of morphological character matrices often imply significantly different phylogenies. Here, we ask whether a similar incongruence is observed in the appendage and non-appendage characters of arthropods. We apply tree length (incongruence length difference, ILD) and tree distance (incongruence relationship difference, IRD) tests to these partitions in an empirical sample of 53 published neontological datasets for arthropods. We find significant incongruence about one time in five: more often than expected, but markedly less often than in previous partition studies. We also find similar levels of homoplasy in limb and non-limb characters, both in terms of internal consistency and consistency relative to molecular trees. Taken together, these findings imply that sampled limb and non-limb characters are of similar phylogenetic utility and quality, and that a total evidence approach to their analysis is preferable. thoracopod morphology, fossil record, character conflict, character sampling, consistency index, homoplasy, morphological phylogenetics, phylogenetics INTRODUCTION Despite the increasing ease and economy of obtaining ever larger volumes of molecular phylogenetic data – coupled with progressively more sophisticated models for their analysis – morphological characters can still contribute significantly to resolving the phylogeny of many clades (Wiens, 2004; O’Leary & Gatesy, 2008; Gainett et al., 2014; also see discussion in: Lopardo & Hormiga, 2015). Morphological and molecular data are often reciprocally illuminating (e.g. Houde, 1994; Nicolalde-Morejón et al., 2009) and can reveal hidden support when combined in a single total evidence analysis (Kluge, 1989; Gatesy et al., 1999; Gatesy & Arctander, 2000; Wahlberg et al., 2005; Damgaard, 2008; O’Leary & Gatesy, 2008; Padial et al., 2010; Mounce et al., 2016). For fossil species, morphology is typically the only source of phylogenetic data, despite impressive strides in obtaining subfossil DNA (e.g. Dabney et al., 2013; reviewed in: Shapiro & Hofreiter, 2014; Orlando et al., 2015) and the value of stratigraphic time series in a few special cases (Wills et al., 2008, 2009; O’Connor & Wills, 2016). Unlike molecular sequence data, there are no widely implemented standard frameworks for coding and archiving morphological data (but see MorphoBank: O’Leary & Kaufman, 2011; Davies et al., 2017). Partly as a result of this, there is little systematic knowledge concerning rates of evolution and levels of homoplasy in morphological characters from different anatomical regions in different clades. Similarly, there is no consensus on the types of morphological characters that are likely to be informative for cladogeneses of different geological ages. Despite this, trees are often inferred from relatively restricted morphological character sets (Sánchez-Villagra & Williams, 1998; Arratia, 2009; Song & Bucheli, 2010; Mounce et al., 2016), a practice that may be analogous to early molecular phylogenies that used small numbers of loci that may not always have evolved at appropriate rates (Bateman, 1999). For fossil taxa, this may reflect various preservation biases (Sansom et al., 2010, 2017; Sansom & Wills, 2013; Pattinson et al., 2015). For example, molluscs typically lack all soft-part data (Castelin et al., 2017), while ostracods are almost exclusively known from their sculpted, bivalved carapaces (Briggs et al., 1993; Whatley et al., 1993). Character sampling in arthropods Biased character sampling may be a particular problem in arthropods, where there is growing evidence that overall levels of homoplasy are greater than in many other higher taxa (Engel, 2015). Examples include the genital morphology of acarine mites (Klimov et al., 2017) and insects (Bennik et al., 2016; Yoshizawa et al., 2016), the wing morphology of lepidopterans (Finkbeiner et al., 2017), the limbs of amphipod crustaceans (Verheye et al., 2016) and the overall morphology of cave-dwelling Diplopoda (Liu et al., 2017) and Collembola (Christiansen, 1960). Moreover, historically, even the deep phylogeny of arthropods has been addressed with restricted character sets, and with a striking diversity of results (e.g. Wheeler et al., 1993; Boore et al., 1995; Giribet et al., 2001; Regier et al., 2005). Characters pertaining to the number and morphological adaptations of limbs are particularly important for arthropod systematics and phylogenetics (Størmer, 1939; Shultz, 2007; Gainett et al., 2014). Unfortunately, such characters are often poorly recorded in fossil arthropods, and several major groups – notably trilobites (Størmer, 1939; Hughes, 2003) and ostracods (Smith, 2000) – preserve limbs only under the most exceptional circumstances. Here, we address two questions in a sample of 38 arthropod data matrices comprising predominantly extant taxa and coding a broad sample of characters from both the limbs/mouthparts/antennae (appendages, or sometimes “limbs” hereafter for short) and the rest of the body. First, we ask whether levels of homoplasy differ between appendage and non-limb/body characters, such that the quality of data in either partition might be deemed superior (see: Pettigrew, 1991; Sánchez-Villagra & Williams, 1998; Williams, 2007; Song & Bucheli, 2010; Parker, 2016). Second, we ask whether the hierarchical signals conveyed by appendage and body characters imply different phylogenies (see: Mounce et al., 2016; Sansom & Wills, 2017; Sansom et al., 2017). Why examine morphological character partitions in arthropods? The rationale for this partitioning is twofold. First, suites of characters can evolve in functionally or developmentally integrated modules (Clarke & Middleton, 2008; Klingenberg, 2008; Lü et al., 2010). These can be subject to different selection pressures and evolve at different speeds (Maynard Smith, 1993; Lü et al., 2010; Parker, 2016), thereby exhausting their character spaces at different rates (Wagner, 1995, 1997; Oyston et al., 2015, 2016) and containing different levels of homoplasy as a result. For example, Sánchez-Villagra & Williams (1998) tested whether functional selection for feeding and locomotion increases the evolutionary lability of dental and postcranial characters relative to cranial characters in the skeletons of mammals, while Sansom et al. (2017) showed that mammalian dental data exhibit relatively poor congruence with independent molecular phylogenies. Similarly, the mouthparts of insects (Angelini & Kaufman, 2005) and other arthropods (Řezáč et al., 2008; Baiocco et al., 2017) are highly labile and are extensively modified in lineage-specific ways, reflecting the trophic resources that they exploit. The same is true of other appendages, which are highly conserved in their underlying structure, but which possess a great diversity of form and function across taxa of all ranks (Angelini & Kaufman, 2005). Relatively high levels of homoplasy can also be found in arthropod body characters. For example, the classification of ostracod crustaceans is heavily contingent on characters of the carapace (Tinn & Oakley, 2008), despite marked and misleading convergence in form. Characters of the copulatory limbs, by contrast, appear to be more conserved and less homoplastic (Park et al., 2002; Cohen & Morin, 2003). Second, much of the arthropod (particularly insect) fossil record is concentrated within a relatively small number of Konservat-Lägerstatten (Sepkoski, 1981; Martinez-Delclòs et al., 2004; Baalbergen & Donovan, 2013). Outside of these exceptional localities, there are usually conspicuous biases in the suites of characters or anatomical regions preserved. For example, Baalbergen & Donovan (2013) found only the chelae of decapod crustaceans preserved (despite unusually good preservation of other arthropod groups at the same site), while Stempien (2005) reported that the chelipeds and carapaces of Brachyura were more likely to fossilize than their walking legs. Similarly, tough, sclerotized structures, such as the elytrae (Martinez-Delclòs et al., 2004; Baalbergen & Donovan, 2013) of insects, are more frequently preserved than many other body parts. The calcite carapaces of ostracods frequently preserve highly homoplastic and functionally constrained details of sculpture and ornamentation, whereas limbs are only rarely fossilized (Smith, 2000). Among fossil Arachnomorpha, the taxonomically diagnostic chelicerae are rarely preserved, obfuscating the systematic placement of many specimens (Dunlop, 1997). Hence, body characters, such as differentiation of the opisthosoma and segmentation of the post-abdomen, are more useful in fossil chelicerate systematics (Dunlop, 1997). Such anatomical biases on character sampling could mislead attempts to infer the relationships of fossil arthropods, particularly if homoplasy is concentrated within the more readily preserved characters. MATERIAL AND METHODS Datasets The character matrices utilized in this study were obtained from peer-reviewed papers published between 2000 and 2017. We sought to sample all major living arthropod groups [Chelicerata, Pancrustacea (Crustacea and Hexapoda), Myriapoda], including matrices of varying dimensions and clades of both lower and higher ranks (genera through classes). Wherever possible, more recent and more inclusive matrices were used. We utilized Graeme Lloyd’s online compilation of matrices (Lloyd, 2018) and searches of Web of Science using higher taxon names plus the root keywords ‘phylog* + morphol*’. The resulting sample of 52 matrices contained representatives of 21 orders in seven classes (see Tables 1, 2). Thirty-eight matrices were collected for the incongruence tests and internal consistency tests and 15 crustacean matrices were collected for the molecular consistency tests (see below and Supplementary Information, S1, S2)). Table 1. Summary of the 38 published morphological datasets across all arthropod groups utilized in this study, and the results of all tests. ILD test results based upon 999 randomizations. IRD test results based upon 499 randomizations (where quoted to three decimal places) or 99 randomizations (where quoted to two decimal places, and where P > 0.20), and are calculated based upon mean nearest neighbour distances between sets of trees Author, Year Clade Taxa Limb chrs Body chrs Percentage missing limb Percentage missing body IRDRF IRDD1 ILD CI limb CI body RI limb RI body Chelicerata Bochkov et al., 2011 Acari: Psoroptidae: Makialginae 11 27 23 1.01 5.93 0.084 0.142 0.751 0.70 0.79 0.77 0.81 Botero-Trujillo et al., 2017 Solifugae: Mummuciidae 15 14 6 2.38 9.33 0.072 0.152 1.000 1.00 0.90 1.00 0.75 Klompen et al., 2013 Acari: Heterozerconidae 10 23 6 3.04 1.67 0.56 0.81 0.202 0.60 0.75 0.58 0.80 Kuntner, 2005 Araneae: Nephilidae: Nephilinae 28 69 88 13.35 13.46 0.002 0.022 0.002 0.52 0.42 0.72 0.73 Mendes, 2011 Opiliones: Laniatores: Gonyleptidae 21 46 56 11.49 13.10 0.030 0.500 0.061 0.56 0.44 0.71 0.63 Prendini & Esposito, 2010 Scorpiones: Buthidae 29 28 38 0.37 1.72 0.182 0.020 0.097 0.55 0.48 0.81 0.77 Shultz, 2007 Arachnida 44 77 86 7.76 10.31 0.72 0.86 0.014 0.61 0.56 0.88 0.84 Wood et al., 2012 Araneae: Archaeidae 37 75 51 28.43 21.67 0.75 0.56 0.010 0.48 0.48 0.78 0.79 Crustacea George, 2017 Copepoda: Laophontodinae 9 32 18 4.17 0.62 0.022 0.020 0.033 0.49 0.44 0.64 0.66 Jenner et al., 2009 Eumalacostraca 24 99 63 9.60 19.44 0.016 0.159 0.008 0.49 0.44 0.64 0.66 McLaughlin et al., 2007 Anomura: Paguroidea 20 34 45 1.18 0.22 0.230 0.388 0.507 0.56 0.47 0.66 0.59 Olesen, 2009 Branchiopoda 15 44 28 30.45 29.76 0.630 0.460 0.122 0.84 0.85 0.75 0.84 Richter & Scholtz, 2001 Malacostraca 19 34 41 10.22 22.21 0.098 0.364 0.161 0.59 0.58 0.68 0.65 Riehl et al., 2014 Isopoda: Asellota: Urstylidae 28 283 124 15.57 26.64 0.294 0.572 0.002 0.52 0.56 0.76 0.70 Vereshchaka et al., 2016 Decapoda: Luciferidae 29 119 48 33.20 24.31 0.390 0.330 0.213 0.72 0.74 0.85 0.91 Vereshchaka & Lunina, 2015 Decapoda: Sergestidae 23 100 48 24.40 19.02 0.51 0.22 0.223 0.72 0.78 0.72 0.83 Hexapoda Banks & Paterson, 2004 Phthiraptera: Philopteridae 16 14 41 1.79 7.47 0.59 0.29 0.624 0.85 0.58 0.94 0.74 Blagoderov et al., 2009 Diptera: Sciaroidea: Lygistorrhinidae 18 25 35 10.47 7.14 0.81 0.920 0.121 0.55 0.45 0.67 0.60 Calor & Holzenthal, 2008 Trichoptera: Leptoceridae 11 10 21 9.09 12.99 0.098 0.270 0.411 0.86 0.74 0.93 0.82 Chamorro & Konstantinov, 2011 Coleoptera: Chrysomelidae: Lamprosomatinae 13 5 21 12.30 3.66 0.042 0.110 0.103 1.00 0.81 1.00 0.85 Clarke, 2011 Coleoptera: Staphylinidae 24 26 104 2.16 1.64 0.32 0.190 0.616 0.68 0.55 0.86 0.77 Del Rio et al., 2012 Coleoptera: Curculionidae: Entiminae 11 9 40 0.00 5.23 0.45 0.35 0.691 0.68 0.58 0.58 0.55 Author, Year Clade Taxa Limb chrs Body chrs Percentage missing limb Percentage missing body IRDRF IRDD1 ILD CI limb CI body RI limb RI body Di Giulio et al., 2003 Coleoptera: Carabidae 9 26 30 0.85 16.30 0.46 0.57 0.703 0.76 0.75 0.76 0.77 Gerstmeier & Eberle, 2011 Coleoptera: Cleridae: Clerinae 12 10 13 2.50 8.33 0.180 0.820 0.062 0.61 0.50 0.72 0.68 Grebennikov & Newton, 2009 Coleoptera: Scydmaenidae 38 106 105 3.80 5.66 0.57 0.51 0.042 0.34 0.30 0.70 0.66 Grebennikov, 2010 Coleoptera: Curculionoidea 16 10 13 6.25 7.21 0.220 0.210 0.014 0.84 0.96 0.82 0.94 Liu et al., 2012 Megaloptera: Chauliodinae 24 17 24 18.38 6.08 0.450 0.044 0.921 0.85 0.54 0.95 0.81 Michel-Salzat et al., 2004 Hymenoptera: Apinae: Euglossini 23 19 18 4.58 0.00 0.088 0.056 0.191 0.79 0.76 0.95 0.95 Packer et al., 2017 Hymenoptera: Megachilidae 27 87 127 1.53 6.27 0.23 0.79 0.362 0.41 0.36 0.68 0.65 Wipfler et al., 2011 Grylloblattodea 18 49 55 6.24 8.83 0.33 0.61 0.924 0.64 0.67 0.67 0.72 Yoshizawa, 2004 Psocoptera: Psocidae 14 11 22 7.79 2.92 0.29 0.23 0.390 0.75 0.81 0.84 0.90 Yoshizawa & Leinhard, 2010 Psocoptera: Liposcelididae 14 9 16 0.00 8.93 0.32 0.41 0.845 0.71 0.93 0.81 0.83 Myriapoda Blanke & Wesener, 2014 Diplopoda 16 23 33 2.99 5.11 0.014 0.026 0.094 0.87 0.74 0.94 0.86 Edgecombe & Barrow, 2007 Chilopoda: Scutigeromorpha 21 41 14 10.57 17.35 0.53 0.99 0.407 0.91 0.79 0.97 0.92 Koch et al., 2009 Chilopoda: Scolopen- dromorpha 30 46 34 2.54 19.31 0.030 0.520 0.089 0.60 0.60 0.85 0.86 Pena-Barbosa et al., 2013 Diplopoda: Polydesmida: Chelodesmidae 15 31 16 17.20 8.33 0.458 0.904 0.689 0.61 0.62 0.76 0.80 Pitz & Sierwald, 2010 Diplopoda: Helminthomorpha 33 34 20 7.75 0.00 0.98 0.24 0.800 0.46 0.63 0.74 0.78 Wesener & Vanden- Spiegel, 2009 Diplopoda: Sphaerotheriida 38 48 41 1.15 1.16 0.110 0.240 0.053 0.55 0.60 0.83 0.83 Author, Year Clade Taxa Limb chrs Body chrs Percentage missing limb Percentage missing body IRDRF IRDD1 ILD CI limb CI body RI limb RI body Chelicerata Bochkov et al., 2011 Acari: Psoroptidae: Makialginae 11 27 23 1.01 5.93 0.084 0.142 0.751 0.70 0.79 0.77 0.81 Botero-Trujillo et al., 2017 Solifugae: Mummuciidae 15 14 6 2.38 9.33 0.072 0.152 1.000 1.00 0.90 1.00 0.75 Klompen et al., 2013 Acari: Heterozerconidae 10 23 6 3.04 1.67 0.56 0.81 0.202 0.60 0.75 0.58 0.80 Kuntner, 2005 Araneae: Nephilidae: Nephilinae 28 69 88 13.35 13.46 0.002 0.022 0.002 0.52 0.42 0.72 0.73 Mendes, 2011 Opiliones: Laniatores: Gonyleptidae 21 46 56 11.49 13.10 0.030 0.500 0.061 0.56 0.44 0.71 0.63 Prendini & Esposito, 2010 Scorpiones: Buthidae 29 28 38 0.37 1.72 0.182 0.020 0.097 0.55 0.48 0.81 0.77 Shultz, 2007 Arachnida 44 77 86 7.76 10.31 0.72 0.86 0.014 0.61 0.56 0.88 0.84 Wood et al., 2012 Araneae: Archaeidae 37 75 51 28.43 21.67 0.75 0.56 0.010 0.48 0.48 0.78 0.79 Crustacea George, 2017 Copepoda: Laophontodinae 9 32 18 4.17 0.62 0.022 0.020 0.033 0.49 0.44 0.64 0.66 Jenner et al., 2009 Eumalacostraca 24 99 63 9.60 19.44 0.016 0.159 0.008 0.49 0.44 0.64 0.66 McLaughlin et al., 2007 Anomura: Paguroidea 20 34 45 1.18 0.22 0.230 0.388 0.507 0.56 0.47 0.66 0.59 Olesen, 2009 Branchiopoda 15 44 28 30.45 29.76 0.630 0.460 0.122 0.84 0.85 0.75 0.84 Richter & Scholtz, 2001 Malacostraca 19 34 41 10.22 22.21 0.098 0.364 0.161 0.59 0.58 0.68 0.65 Riehl et al., 2014 Isopoda: Asellota: Urstylidae 28 283 124 15.57 26.64 0.294 0.572 0.002 0.52 0.56 0.76 0.70 Vereshchaka et al., 2016 Decapoda: Luciferidae 29 119 48 33.20 24.31 0.390 0.330 0.213 0.72 0.74 0.85 0.91 Vereshchaka & Lunina, 2015 Decapoda: Sergestidae 23 100 48 24.40 19.02 0.51 0.22 0.223 0.72 0.78 0.72 0.83 Hexapoda Banks & Paterson, 2004 Phthiraptera: Philopteridae 16 14 41 1.79 7.47 0.59 0.29 0.624 0.85 0.58 0.94 0.74 Blagoderov et al., 2009 Diptera: Sciaroidea: Lygistorrhinidae 18 25 35 10.47 7.14 0.81 0.920 0.121 0.55 0.45 0.67 0.60 Calor & Holzenthal, 2008 Trichoptera: Leptoceridae 11 10 21 9.09 12.99 0.098 0.270 0.411 0.86 0.74 0.93 0.82 Chamorro & Konstantinov, 2011 Coleoptera: Chrysomelidae: Lamprosomatinae 13 5 21 12.30 3.66 0.042 0.110 0.103 1.00 0.81 1.00 0.85 Clarke, 2011 Coleoptera: Staphylinidae 24 26 104 2.16 1.64 0.32 0.190 0.616 0.68 0.55 0.86 0.77 Del Rio et al., 2012 Coleoptera: Curculionidae: Entiminae 11 9 40 0.00 5.23 0.45 0.35 0.691 0.68 0.58 0.58 0.55 Author, Year Clade Taxa Limb chrs Body chrs Percentage missing limb Percentage missing body IRDRF IRDD1 ILD CI limb CI body RI limb RI body Di Giulio et al., 2003 Coleoptera: Carabidae 9 26 30 0.85 16.30 0.46 0.57 0.703 0.76 0.75 0.76 0.77 Gerstmeier & Eberle, 2011 Coleoptera: Cleridae: Clerinae 12 10 13 2.50 8.33 0.180 0.820 0.062 0.61 0.50 0.72 0.68 Grebennikov & Newton, 2009 Coleoptera: Scydmaenidae 38 106 105 3.80 5.66 0.57 0.51 0.042 0.34 0.30 0.70 0.66 Grebennikov, 2010 Coleoptera: Curculionoidea 16 10 13 6.25 7.21 0.220 0.210 0.014 0.84 0.96 0.82 0.94 Liu et al., 2012 Megaloptera: Chauliodinae 24 17 24 18.38 6.08 0.450 0.044 0.921 0.85 0.54 0.95 0.81 Michel-Salzat et al., 2004 Hymenoptera: Apinae: Euglossini 23 19 18 4.58 0.00 0.088 0.056 0.191 0.79 0.76 0.95 0.95 Packer et al., 2017 Hymenoptera: Megachilidae 27 87 127 1.53 6.27 0.23 0.79 0.362 0.41 0.36 0.68 0.65 Wipfler et al., 2011 Grylloblattodea 18 49 55 6.24 8.83 0.33 0.61 0.924 0.64 0.67 0.67 0.72 Yoshizawa, 2004 Psocoptera: Psocidae 14 11 22 7.79 2.92 0.29 0.23 0.390 0.75 0.81 0.84 0.90 Yoshizawa & Leinhard, 2010 Psocoptera: Liposcelididae 14 9 16 0.00 8.93 0.32 0.41 0.845 0.71 0.93 0.81 0.83 Myriapoda Blanke & Wesener, 2014 Diplopoda 16 23 33 2.99 5.11 0.014 0.026 0.094 0.87 0.74 0.94 0.86 Edgecombe & Barrow, 2007 Chilopoda: Scutigeromorpha 21 41 14 10.57 17.35 0.53 0.99 0.407 0.91 0.79 0.97 0.92 Koch et al., 2009 Chilopoda: Scolopen- dromorpha 30 46 34 2.54 19.31 0.030 0.520 0.089 0.60 0.60 0.85 0.86 Pena-Barbosa et al., 2013 Diplopoda: Polydesmida: Chelodesmidae 15 31 16 17.20 8.33 0.458 0.904 0.689 0.61 0.62 0.76 0.80 Pitz & Sierwald, 2010 Diplopoda: Helminthomorpha 33 34 20 7.75 0.00 0.98 0.24 0.800 0.46 0.63 0.74 0.78 Wesener & Vanden- Spiegel, 2009 Diplopoda: Sphaerotheriida 38 48 41 1.15 1.16 0.110 0.240 0.053 0.55 0.60 0.83 0.83 Open in new tab Table 1. Summary of the 38 published morphological datasets across all arthropod groups utilized in this study, and the results of all tests. ILD test results based upon 999 randomizations. IRD test results based upon 499 randomizations (where quoted to three decimal places) or 99 randomizations (where quoted to two decimal places, and where P > 0.20), and are calculated based upon mean nearest neighbour distances between sets of trees Author, Year Clade Taxa Limb chrs Body chrs Percentage missing limb Percentage missing body IRDRF IRDD1 ILD CI limb CI body RI limb RI body Chelicerata Bochkov et al., 2011 Acari: Psoroptidae: Makialginae 11 27 23 1.01 5.93 0.084 0.142 0.751 0.70 0.79 0.77 0.81 Botero-Trujillo et al., 2017 Solifugae: Mummuciidae 15 14 6 2.38 9.33 0.072 0.152 1.000 1.00 0.90 1.00 0.75 Klompen et al., 2013 Acari: Heterozerconidae 10 23 6 3.04 1.67 0.56 0.81 0.202 0.60 0.75 0.58 0.80 Kuntner, 2005 Araneae: Nephilidae: Nephilinae 28 69 88 13.35 13.46 0.002 0.022 0.002 0.52 0.42 0.72 0.73 Mendes, 2011 Opiliones: Laniatores: Gonyleptidae 21 46 56 11.49 13.10 0.030 0.500 0.061 0.56 0.44 0.71 0.63 Prendini & Esposito, 2010 Scorpiones: Buthidae 29 28 38 0.37 1.72 0.182 0.020 0.097 0.55 0.48 0.81 0.77 Shultz, 2007 Arachnida 44 77 86 7.76 10.31 0.72 0.86 0.014 0.61 0.56 0.88 0.84 Wood et al., 2012 Araneae: Archaeidae 37 75 51 28.43 21.67 0.75 0.56 0.010 0.48 0.48 0.78 0.79 Crustacea George, 2017 Copepoda: Laophontodinae 9 32 18 4.17 0.62 0.022 0.020 0.033 0.49 0.44 0.64 0.66 Jenner et al., 2009 Eumalacostraca 24 99 63 9.60 19.44 0.016 0.159 0.008 0.49 0.44 0.64 0.66 McLaughlin et al., 2007 Anomura: Paguroidea 20 34 45 1.18 0.22 0.230 0.388 0.507 0.56 0.47 0.66 0.59 Olesen, 2009 Branchiopoda 15 44 28 30.45 29.76 0.630 0.460 0.122 0.84 0.85 0.75 0.84 Richter & Scholtz, 2001 Malacostraca 19 34 41 10.22 22.21 0.098 0.364 0.161 0.59 0.58 0.68 0.65 Riehl et al., 2014 Isopoda: Asellota: Urstylidae 28 283 124 15.57 26.64 0.294 0.572 0.002 0.52 0.56 0.76 0.70 Vereshchaka et al., 2016 Decapoda: Luciferidae 29 119 48 33.20 24.31 0.390 0.330 0.213 0.72 0.74 0.85 0.91 Vereshchaka & Lunina, 2015 Decapoda: Sergestidae 23 100 48 24.40 19.02 0.51 0.22 0.223 0.72 0.78 0.72 0.83 Hexapoda Banks & Paterson, 2004 Phthiraptera: Philopteridae 16 14 41 1.79 7.47 0.59 0.29 0.624 0.85 0.58 0.94 0.74 Blagoderov et al., 2009 Diptera: Sciaroidea: Lygistorrhinidae 18 25 35 10.47 7.14 0.81 0.920 0.121 0.55 0.45 0.67 0.60 Calor & Holzenthal, 2008 Trichoptera: Leptoceridae 11 10 21 9.09 12.99 0.098 0.270 0.411 0.86 0.74 0.93 0.82 Chamorro & Konstantinov, 2011 Coleoptera: Chrysomelidae: Lamprosomatinae 13 5 21 12.30 3.66 0.042 0.110 0.103 1.00 0.81 1.00 0.85 Clarke, 2011 Coleoptera: Staphylinidae 24 26 104 2.16 1.64 0.32 0.190 0.616 0.68 0.55 0.86 0.77 Del Rio et al., 2012 Coleoptera: Curculionidae: Entiminae 11 9 40 0.00 5.23 0.45 0.35 0.691 0.68 0.58 0.58 0.55 Author, Year Clade Taxa Limb chrs Body chrs Percentage missing limb Percentage missing body IRDRF IRDD1 ILD CI limb CI body RI limb RI body Di Giulio et al., 2003 Coleoptera: Carabidae 9 26 30 0.85 16.30 0.46 0.57 0.703 0.76 0.75 0.76 0.77 Gerstmeier & Eberle, 2011 Coleoptera: Cleridae: Clerinae 12 10 13 2.50 8.33 0.180 0.820 0.062 0.61 0.50 0.72 0.68 Grebennikov & Newton, 2009 Coleoptera: Scydmaenidae 38 106 105 3.80 5.66 0.57 0.51 0.042 0.34 0.30 0.70 0.66 Grebennikov, 2010 Coleoptera: Curculionoidea 16 10 13 6.25 7.21 0.220 0.210 0.014 0.84 0.96 0.82 0.94 Liu et al., 2012 Megaloptera: Chauliodinae 24 17 24 18.38 6.08 0.450 0.044 0.921 0.85 0.54 0.95 0.81 Michel-Salzat et al., 2004 Hymenoptera: Apinae: Euglossini 23 19 18 4.58 0.00 0.088 0.056 0.191 0.79 0.76 0.95 0.95 Packer et al., 2017 Hymenoptera: Megachilidae 27 87 127 1.53 6.27 0.23 0.79 0.362 0.41 0.36 0.68 0.65 Wipfler et al., 2011 Grylloblattodea 18 49 55 6.24 8.83 0.33 0.61 0.924 0.64 0.67 0.67 0.72 Yoshizawa, 2004 Psocoptera: Psocidae 14 11 22 7.79 2.92 0.29 0.23 0.390 0.75 0.81 0.84 0.90 Yoshizawa & Leinhard, 2010 Psocoptera: Liposcelididae 14 9 16 0.00 8.93 0.32 0.41 0.845 0.71 0.93 0.81 0.83 Myriapoda Blanke & Wesener, 2014 Diplopoda 16 23 33 2.99 5.11 0.014 0.026 0.094 0.87 0.74 0.94 0.86 Edgecombe & Barrow, 2007 Chilopoda: Scutigeromorpha 21 41 14 10.57 17.35 0.53 0.99 0.407 0.91 0.79 0.97 0.92 Koch et al., 2009 Chilopoda: Scolopen- dromorpha 30 46 34 2.54 19.31 0.030 0.520 0.089 0.60 0.60 0.85 0.86 Pena-Barbosa et al., 2013 Diplopoda: Polydesmida: Chelodesmidae 15 31 16 17.20 8.33 0.458 0.904 0.689 0.61 0.62 0.76 0.80 Pitz & Sierwald, 2010 Diplopoda: Helminthomorpha 33 34 20 7.75 0.00 0.98 0.24 0.800 0.46 0.63 0.74 0.78 Wesener & Vanden- Spiegel, 2009 Diplopoda: Sphaerotheriida 38 48 41 1.15 1.16 0.110 0.240 0.053 0.55 0.60 0.83 0.83 Author, Year Clade Taxa Limb chrs Body chrs Percentage missing limb Percentage missing body IRDRF IRDD1 ILD CI limb CI body RI limb RI body Chelicerata Bochkov et al., 2011 Acari: Psoroptidae: Makialginae 11 27 23 1.01 5.93 0.084 0.142 0.751 0.70 0.79 0.77 0.81 Botero-Trujillo et al., 2017 Solifugae: Mummuciidae 15 14 6 2.38 9.33 0.072 0.152 1.000 1.00 0.90 1.00 0.75 Klompen et al., 2013 Acari: Heterozerconidae 10 23 6 3.04 1.67 0.56 0.81 0.202 0.60 0.75 0.58 0.80 Kuntner, 2005 Araneae: Nephilidae: Nephilinae 28 69 88 13.35 13.46 0.002 0.022 0.002 0.52 0.42 0.72 0.73 Mendes, 2011 Opiliones: Laniatores: Gonyleptidae 21 46 56 11.49 13.10 0.030 0.500 0.061 0.56 0.44 0.71 0.63 Prendini & Esposito, 2010 Scorpiones: Buthidae 29 28 38 0.37 1.72 0.182 0.020 0.097 0.55 0.48 0.81 0.77 Shultz, 2007 Arachnida 44 77 86 7.76 10.31 0.72 0.86 0.014 0.61 0.56 0.88 0.84 Wood et al., 2012 Araneae: Archaeidae 37 75 51 28.43 21.67 0.75 0.56 0.010 0.48 0.48 0.78 0.79 Crustacea George, 2017 Copepoda: Laophontodinae 9 32 18 4.17 0.62 0.022 0.020 0.033 0.49 0.44 0.64 0.66 Jenner et al., 2009 Eumalacostraca 24 99 63 9.60 19.44 0.016 0.159 0.008 0.49 0.44 0.64 0.66 McLaughlin et al., 2007 Anomura: Paguroidea 20 34 45 1.18 0.22 0.230 0.388 0.507 0.56 0.47 0.66 0.59 Olesen, 2009 Branchiopoda 15 44 28 30.45 29.76 0.630 0.460 0.122 0.84 0.85 0.75 0.84 Richter & Scholtz, 2001 Malacostraca 19 34 41 10.22 22.21 0.098 0.364 0.161 0.59 0.58 0.68 0.65 Riehl et al., 2014 Isopoda: Asellota: Urstylidae 28 283 124 15.57 26.64 0.294 0.572 0.002 0.52 0.56 0.76 0.70 Vereshchaka et al., 2016 Decapoda: Luciferidae 29 119 48 33.20 24.31 0.390 0.330 0.213 0.72 0.74 0.85 0.91 Vereshchaka & Lunina, 2015 Decapoda: Sergestidae 23 100 48 24.40 19.02 0.51 0.22 0.223 0.72 0.78 0.72 0.83 Hexapoda Banks & Paterson, 2004 Phthiraptera: Philopteridae 16 14 41 1.79 7.47 0.59 0.29 0.624 0.85 0.58 0.94 0.74 Blagoderov et al., 2009 Diptera: Sciaroidea: Lygistorrhinidae 18 25 35 10.47 7.14 0.81 0.920 0.121 0.55 0.45 0.67 0.60 Calor & Holzenthal, 2008 Trichoptera: Leptoceridae 11 10 21 9.09 12.99 0.098 0.270 0.411 0.86 0.74 0.93 0.82 Chamorro & Konstantinov, 2011 Coleoptera: Chrysomelidae: Lamprosomatinae 13 5 21 12.30 3.66 0.042 0.110 0.103 1.00 0.81 1.00 0.85 Clarke, 2011 Coleoptera: Staphylinidae 24 26 104 2.16 1.64 0.32 0.190 0.616 0.68 0.55 0.86 0.77 Del Rio et al., 2012 Coleoptera: Curculionidae: Entiminae 11 9 40 0.00 5.23 0.45 0.35 0.691 0.68 0.58 0.58 0.55 Author, Year Clade Taxa Limb chrs Body chrs Percentage missing limb Percentage missing body IRDRF IRDD1 ILD CI limb CI body RI limb RI body Di Giulio et al., 2003 Coleoptera: Carabidae 9 26 30 0.85 16.30 0.46 0.57 0.703 0.76 0.75 0.76 0.77 Gerstmeier & Eberle, 2011 Coleoptera: Cleridae: Clerinae 12 10 13 2.50 8.33 0.180 0.820 0.062 0.61 0.50 0.72 0.68 Grebennikov & Newton, 2009 Coleoptera: Scydmaenidae 38 106 105 3.80 5.66 0.57 0.51 0.042 0.34 0.30 0.70 0.66 Grebennikov, 2010 Coleoptera: Curculionoidea 16 10 13 6.25 7.21 0.220 0.210 0.014 0.84 0.96 0.82 0.94 Liu et al., 2012 Megaloptera: Chauliodinae 24 17 24 18.38 6.08 0.450 0.044 0.921 0.85 0.54 0.95 0.81 Michel-Salzat et al., 2004 Hymenoptera: Apinae: Euglossini 23 19 18 4.58 0.00 0.088 0.056 0.191 0.79 0.76 0.95 0.95 Packer et al., 2017 Hymenoptera: Megachilidae 27 87 127 1.53 6.27 0.23 0.79 0.362 0.41 0.36 0.68 0.65 Wipfler et al., 2011 Grylloblattodea 18 49 55 6.24 8.83 0.33 0.61 0.924 0.64 0.67 0.67 0.72 Yoshizawa, 2004 Psocoptera: Psocidae 14 11 22 7.79 2.92 0.29 0.23 0.390 0.75 0.81 0.84 0.90 Yoshizawa & Leinhard, 2010 Psocoptera: Liposcelididae 14 9 16 0.00 8.93 0.32 0.41 0.845 0.71 0.93 0.81 0.83 Myriapoda Blanke & Wesener, 2014 Diplopoda 16 23 33 2.99 5.11 0.014 0.026 0.094 0.87 0.74 0.94 0.86 Edgecombe & Barrow, 2007 Chilopoda: Scutigeromorpha 21 41 14 10.57 17.35 0.53 0.99 0.407 0.91 0.79 0.97 0.92 Koch et al., 2009 Chilopoda: Scolopen- dromorpha 30 46 34 2.54 19.31 0.030 0.520 0.089 0.60 0.60 0.85 0.86 Pena-Barbosa et al., 2013 Diplopoda: Polydesmida: Chelodesmidae 15 31 16 17.20 8.33 0.458 0.904 0.689 0.61 0.62 0.76 0.80 Pitz & Sierwald, 2010 Diplopoda: Helminthomorpha 33 34 20 7.75 0.00 0.98 0.24 0.800 0.46 0.63 0.74 0.78 Wesener & Vanden- Spiegel, 2009 Diplopoda: Sphaerotheriida 38 48 41 1.15 1.16 0.110 0.240 0.053 0.55 0.60 0.83 0.83 Open in new tab Table 2. Summary of the 15 published crustacean morphological and molecular datasets used for molecular consistency tests Morphological Author(s), Year Molecular Author(s), Year Clade Taxa Limb chrs Body chrs CI limb CI body RI limb RI body Crustacea Admowicz & Purvis, 2006 Meland & Willasen, 2004 Pseudomma 18 26 5 0.31 0.28 0.30 0.23 Bradford-Grieve et al., 2010. Blanco-Bercial et al., 2011 Calanoida 29 93 7 0.29 0.53 0.58 0.75 Bradford-Grieve et al., 2017 Bradford-Grieve et al., 2017 Megacalanidae 12 37 5 0.29 0.53 0.58 0.75 Chang et al. 2016 Chang et al. 2016 Nephropidae 13 23 28 0.62 0.65 0.75 0.75 Dreyer & Wägele, 2001 Dreyer & Wägele, 2001 Bopyridae 21 37 13 0.50 0.65 0.66 0.73 Hermoso-Salazar et al., 2008 Hultgren et al., 2014 Synalpheus 13 22 12 0.45 0.44 0.40 0.17 Karasawa et al., 2013 Bracken-Grissom et al., 2014 Pleocyemata 19 22 43 0.87 0.51 0.95 0.72 Lörz & Brandt, 2004 Lörz & Held, 2004 Epimeriidae 16 41 49 0.45 0.46 0.67 0.54 Oakley et al., 2012 Tinn & Oakley, 2008 Ostracoda 34 22 12 0.77 0.75 0.92 0.93 Robalino et al., 2016 Ma et al., 2009 Penaeidae 37 103 94 0.34 0.27 0.63 0.54 Schnabel et al., 2011 Schnabel et al., 2011 Anomura 64 58 61 0.32 0.35 0.76 0.76 Tshudy et al., 2007 Chan et al., 2009 Metanephrops 10 8 14 0.47 0.54 0.44 0.64 Wills et al., 2009 Jenner et al., 2009 Eumalacostraca 14 59 54 0.35 0.39 0.23 0.32 Wilson, 2009 Wilson, 2009 Peracarida 75 124 55 0.29 0.27 0.69 0.68 Wyngaard et al., 2010 Wyngaard et al., 2010 Mesocyclops 15 41 9 0.62 0.40 0.67 0.40 Morphological Author(s), Year Molecular Author(s), Year Clade Taxa Limb chrs Body chrs CI limb CI body RI limb RI body Crustacea Admowicz & Purvis, 2006 Meland & Willasen, 2004 Pseudomma 18 26 5 0.31 0.28 0.30 0.23 Bradford-Grieve et al., 2010. Blanco-Bercial et al., 2011 Calanoida 29 93 7 0.29 0.53 0.58 0.75 Bradford-Grieve et al., 2017 Bradford-Grieve et al., 2017 Megacalanidae 12 37 5 0.29 0.53 0.58 0.75 Chang et al. 2016 Chang et al. 2016 Nephropidae 13 23 28 0.62 0.65 0.75 0.75 Dreyer & Wägele, 2001 Dreyer & Wägele, 2001 Bopyridae 21 37 13 0.50 0.65 0.66 0.73 Hermoso-Salazar et al., 2008 Hultgren et al., 2014 Synalpheus 13 22 12 0.45 0.44 0.40 0.17 Karasawa et al., 2013 Bracken-Grissom et al., 2014 Pleocyemata 19 22 43 0.87 0.51 0.95 0.72 Lörz & Brandt, 2004 Lörz & Held, 2004 Epimeriidae 16 41 49 0.45 0.46 0.67 0.54 Oakley et al., 2012 Tinn & Oakley, 2008 Ostracoda 34 22 12 0.77 0.75 0.92 0.93 Robalino et al., 2016 Ma et al., 2009 Penaeidae 37 103 94 0.34 0.27 0.63 0.54 Schnabel et al., 2011 Schnabel et al., 2011 Anomura 64 58 61 0.32 0.35 0.76 0.76 Tshudy et al., 2007 Chan et al., 2009 Metanephrops 10 8 14 0.47 0.54 0.44 0.64 Wills et al., 2009 Jenner et al., 2009 Eumalacostraca 14 59 54 0.35 0.39 0.23 0.32 Wilson, 2009 Wilson, 2009 Peracarida 75 124 55 0.29 0.27 0.69 0.68 Wyngaard et al., 2010 Wyngaard et al., 2010 Mesocyclops 15 41 9 0.62 0.40 0.67 0.40 Open in new tab Table 2. Summary of the 15 published crustacean morphological and molecular datasets used for molecular consistency tests Morphological Author(s), Year Molecular Author(s), Year Clade Taxa Limb chrs Body chrs CI limb CI body RI limb RI body Crustacea Admowicz & Purvis, 2006 Meland & Willasen, 2004 Pseudomma 18 26 5 0.31 0.28 0.30 0.23 Bradford-Grieve et al., 2010. Blanco-Bercial et al., 2011 Calanoida 29 93 7 0.29 0.53 0.58 0.75 Bradford-Grieve et al., 2017 Bradford-Grieve et al., 2017 Megacalanidae 12 37 5 0.29 0.53 0.58 0.75 Chang et al. 2016 Chang et al. 2016 Nephropidae 13 23 28 0.62 0.65 0.75 0.75 Dreyer & Wägele, 2001 Dreyer & Wägele, 2001 Bopyridae 21 37 13 0.50 0.65 0.66 0.73 Hermoso-Salazar et al., 2008 Hultgren et al., 2014 Synalpheus 13 22 12 0.45 0.44 0.40 0.17 Karasawa et al., 2013 Bracken-Grissom et al., 2014 Pleocyemata 19 22 43 0.87 0.51 0.95 0.72 Lörz & Brandt, 2004 Lörz & Held, 2004 Epimeriidae 16 41 49 0.45 0.46 0.67 0.54 Oakley et al., 2012 Tinn & Oakley, 2008 Ostracoda 34 22 12 0.77 0.75 0.92 0.93 Robalino et al., 2016 Ma et al., 2009 Penaeidae 37 103 94 0.34 0.27 0.63 0.54 Schnabel et al., 2011 Schnabel et al., 2011 Anomura 64 58 61 0.32 0.35 0.76 0.76 Tshudy et al., 2007 Chan et al., 2009 Metanephrops 10 8 14 0.47 0.54 0.44 0.64 Wills et al., 2009 Jenner et al., 2009 Eumalacostraca 14 59 54 0.35 0.39 0.23 0.32 Wilson, 2009 Wilson, 2009 Peracarida 75 124 55 0.29 0.27 0.69 0.68 Wyngaard et al., 2010 Wyngaard et al., 2010 Mesocyclops 15 41 9 0.62 0.40 0.67 0.40 Morphological Author(s), Year Molecular Author(s), Year Clade Taxa Limb chrs Body chrs CI limb CI body RI limb RI body Crustacea Admowicz & Purvis, 2006 Meland & Willasen, 2004 Pseudomma 18 26 5 0.31 0.28 0.30 0.23 Bradford-Grieve et al., 2010. Blanco-Bercial et al., 2011 Calanoida 29 93 7 0.29 0.53 0.58 0.75 Bradford-Grieve et al., 2017 Bradford-Grieve et al., 2017 Megacalanidae 12 37 5 0.29 0.53 0.58 0.75 Chang et al. 2016 Chang et al. 2016 Nephropidae 13 23 28 0.62 0.65 0.75 0.75 Dreyer & Wägele, 2001 Dreyer & Wägele, 2001 Bopyridae 21 37 13 0.50 0.65 0.66 0.73 Hermoso-Salazar et al., 2008 Hultgren et al., 2014 Synalpheus 13 22 12 0.45 0.44 0.40 0.17 Karasawa et al., 2013 Bracken-Grissom et al., 2014 Pleocyemata 19 22 43 0.87 0.51 0.95 0.72 Lörz & Brandt, 2004 Lörz & Held, 2004 Epimeriidae 16 41 49 0.45 0.46 0.67 0.54 Oakley et al., 2012 Tinn & Oakley, 2008 Ostracoda 34 22 12 0.77 0.75 0.92 0.93 Robalino et al., 2016 Ma et al., 2009 Penaeidae 37 103 94 0.34 0.27 0.63 0.54 Schnabel et al., 2011 Schnabel et al., 2011 Anomura 64 58 61 0.32 0.35 0.76 0.76 Tshudy et al., 2007 Chan et al., 2009 Metanephrops 10 8 14 0.47 0.54 0.44 0.64 Wills et al., 2009 Jenner et al., 2009 Eumalacostraca 14 59 54 0.35 0.39 0.23 0.32 Wilson, 2009 Wilson, 2009 Peracarida 75 124 55 0.29 0.27 0.69 0.68 Wyngaard et al., 2010 Wyngaard et al., 2010 Mesocyclops 15 41 9 0.62 0.40 0.67 0.40 Open in new tab Definition of character partitions The ‘appendage’ character partition included those pertaining to the legs and leg-derived appendages. This encompassed all podomeres of the walking legs and modified legs such as brooding limbs (e.g. Jenner et al., 2009) and the spinnerets of spiders (Selden et al., 2008). Also included were characters pertaining to the mouthparts, including mandibles, maxillae and the labium (Angelini & Kaufman, 2005), as well as the palps, chelicerae and glossae. The labrum, hypopharynx and epipharynx were also included in the ‘appendage’ partition as they are closely functionally associated with the other mouthparts and in some groups form a feeding apparatus for sucking or piercing in conjunction with these other elements (Angelini & Kaufman, 2005); as such, we suspect that they are subject to similar selective pressures (Klingenberg, 2008). Antennae were also included (Angelini & Kaufman, 2005), as were genital structures derived from legs or fused coxae, such as the hypandria. Characters pertaining to setation or other elaborations of leg, mouthpart or appendage podomeres were also included, as were characters referring to limb musculature. The ‘body’ character partition was defined, by default, as all those characters not encompassed above. This included the wings and elytrae of insects, since we consider these to be derived from the carapace of the thorax rather than from pre-existing limb structures (Clark-Hatchel & Tomoyasu, 2016). The ‘body’ partition also included all characters encoding genital structures that were not derived from appendages, such as those pertaining to the vulva, genital pore, spermatheca and ovipositor. Characters pertaining to elaborations and ornamentations of body segments were included with the ‘body’ partition, as were characters of the eyes and internal organs. Behavioural, molecular, developmental and sperm characters were removed from each matrix (these accounted for just 3% of those analysed). Missing and inapplicable codes Poorly known taxa (or those that were otherwise scored for only a small number of characters) can be highly mobile in sets of optimal trees; particularly those inferred using maximum parsimony. This can, in turn, result in large numbers of most parsimonious trees (MPTs), prohibitively long search times and poor resolution of consensus trees (Wilkinson, 1995; Mounce et al., 2016). Where data matrices were found to be subject to these issues empirically, we edited them (using MESQUITE v.3.40: Maddison & Maddison, 2018) by removing taxa with more than 75% of characters scored as missing (‘?’) or inapplicable (‘-’) in either partition [50% for the dataset of Shultz (2007)]. We also removed taxa found to be taxonomically equivalent to others (sensuWilkinson, 1995). Any characters rendered uninformative or invariant by this process were also deleted. Means of just 0.47 taxa (~2.2%) and 3.34 characters (~3.6%) were removed from each dataset in this manner (for a list of the precise taxa and characters deleted, see Supporting Information, S3). We did not set out to analyse matrices of fossils, because our intention was to compare signals in limb and non-limb characters. Fossil taxa often tend to contain larger proportions of missing codings (Wilkinson, 1995; Wiens, 1998; Mounce et al., 2016) and these missing codes tend to be concentrated in characters pertaining to regions of anatomy with lower preservation potential. In particular, fossils tend to lack data for limbs and other appendages. However, fossils are often informative in phylogenetic analyses of arthropods (Legg et al., 2013) and other taxa (Cobbett et al., 2007), so fossil taxa in matrices of predominantly extant taxa (e.g. Shultz, 2007; Olesen, 2009; Liu et al., 2012) were not discounted a priori, but only as a consequence of obfuscating analyses as described above. Measuring homoplasy We took two approaches to measuring homoplasy: internal consistency of morphological characters relative to the most parsimonious trees derived from those same morphological characters, and consistency of morphological characters when optimized onto independent molecular trees (e.g. Sansom & Wills, 2017; Sansom et al., 2017). With both approaches, we used the ensemble consistency index (CI; Kluge & Farris, 1969) and ensemble retention index (RI; Farris, 1989). CI is a commonly used and well-characterized index of homoplasy. However, it is subject to known biases, notably a correlation with the number of characters and taxa in the dataset (Archie, 1989; Mounce et al., 2016). For the internal CI, we removed these biases empirically by comparing the residuals from regression analyses of CI on both matrix dimensions. For comparisons of the CI of morphological character partitions optimized into molecular trees, there were no such biases. This is because the molecular trees were inferred from an independent source of data (molecules), rather than from the morphological data. For molecular consistency tests, we sought independent molecular trees (Sansom & Wills, 2017; Sansom et al., 2017). Taxa were pruned (typically from the morphological dataset) such that both morphological and molecular trees had the same residual leaf set. This had the potential to render some morphological characters uninformative, and these were subsequently removed from the matrix. Internal consistency measures were derived using PAUP* 4.0a (build 154) (Swofford, 2002) whilst molecular consistency measures were derived using TNT (Goloboff et al., 2008) and MESQUITE (Maddison & Maddison, 2018). Statistical tests for incongruence The incongruence length difference (ILD) test (Mickevich & Farris, 1981; Farris et al., 1995a, b) is a widely implemented partition homogeneity test based upon the difference in MPT length for a matrix when analysed as a whole and the sum of MPT lengths for the partitions of the matrix analysed in isolation (MPTs). More formally, the ILD for a bipartitioned matrix is given by LAB – (LA+ LB)/LAB, where LAB is the optimal tree length (in steps) from the analysis of the entire matrix (the total evidence analysis) and LA and LB are the optimal tree lengths for partitions A and B analysed independently. This ILD is compared with a distribution of ILD values (here, 999) for random bipartitions of the matrix in the same proportions as the original and a P value is derived from the fraction of these that are as large or larger than the original. The ILD test has been criticized on philosophical grounds and because it has a high Type I error rate (Dolphin et al., 2000; Barker & Lutzoni, 2002; Ramirez, 2006; Sansom et al., 2017). However, it remains very widely applied (Mounce et al., 2016) and is used here as a measure of matrix partition incongruence rather than as a criterion for combining those partitions (Fig. 1). Figure 1. Open in new tabDownload slide Calculation of P values associated with the incongruence length difference (ILD) test (Mickevich & Farris, 1981; Farris et al., 1995a; Farris et al., 1995b) and the incongruence relationship difference (IRD) test (Ruta & Wills 2016; Mounce et al., 2016) using the Robinson Foulds (RF) distance (IRDRF). A, a hypothetical dataset is partitioned into ‘limb’ characters (left hand) and ‘non-limb’ or body characters (right hand). For illustrative purposes, appendage (limb) and non-appendage (non-limb) character numbers are both contiguous and both partitions are the same size. This need not be the case. Each matrix partition is then analysed independently using PAUP* and a single most parsimonious tree (MPT) is inferred from each. The lengths of these are summed (marked *). The incongruence length difference (ILD) is not shown here, but would be equivalent to the difference between this summed length and the length of the MPT(s) resulting from the analysis of both partitions simultaneously). The number of nodes unique to one or both trees is also tallied as the Robinson Foulds (RF) distance (†). B, characters are partitioned at random to yield null distributions of sums of lengths and RF distances. Random partitions contain the same number of characters as the original partitions and the procedure is repeated a large number of times (999 in this example, and 499 times for our empirical data sets). C, the randomized partitions in ‘B’ yield empirical distributions of sums of tree lengths (left hand histogram, ILD) and RF distances (right hand histogram, IRDRF). The ILD P-value is calculated as the fraction of the random partitions (plus the original partition) for which the sum of MPT tree lengths is less than or equal to that for the original partition (P = 126/1000 = 0.126). Random partitions with sums of lengths less than the original are those in which the internal consistency of each partition (‘appendage’ or ‘body’) is greater than that in the original. The IRDRFP-value is calculated as the fraction of the random partitions (plus the original partition) for which the sum of MPT tree lengths is greater than or equal to that for the original partition (P = 384/1000 = 0.384). Figure 1. Open in new tabDownload slide Calculation of P values associated with the incongruence length difference (ILD) test (Mickevich & Farris, 1981; Farris et al., 1995a; Farris et al., 1995b) and the incongruence relationship difference (IRD) test (Ruta & Wills 2016; Mounce et al., 2016) using the Robinson Foulds (RF) distance (IRDRF). A, a hypothetical dataset is partitioned into ‘limb’ characters (left hand) and ‘non-limb’ or body characters (right hand). For illustrative purposes, appendage (limb) and non-appendage (non-limb) character numbers are both contiguous and both partitions are the same size. This need not be the case. Each matrix partition is then analysed independently using PAUP* and a single most parsimonious tree (MPT) is inferred from each. The lengths of these are summed (marked *). The incongruence length difference (ILD) is not shown here, but would be equivalent to the difference between this summed length and the length of the MPT(s) resulting from the analysis of both partitions simultaneously). The number of nodes unique to one or both trees is also tallied as the Robinson Foulds (RF) distance (†). B, characters are partitioned at random to yield null distributions of sums of lengths and RF distances. Random partitions contain the same number of characters as the original partitions and the procedure is repeated a large number of times (999 in this example, and 499 times for our empirical data sets). C, the randomized partitions in ‘B’ yield empirical distributions of sums of tree lengths (left hand histogram, ILD) and RF distances (right hand histogram, IRDRF). The ILD P-value is calculated as the fraction of the random partitions (plus the original partition) for which the sum of MPT tree lengths is less than or equal to that for the original partition (P = 126/1000 = 0.126). Random partitions with sums of lengths less than the original are those in which the internal consistency of each partition (‘appendage’ or ‘body’) is greater than that in the original. The IRDRFP-value is calculated as the fraction of the random partitions (plus the original partition) for which the sum of MPT tree lengths is greater than or equal to that for the original partition (P = 384/1000 = 0.384). In addition to the ILD test, we also implemented the incongruence relationship difference (IRD) test of Ruta & Wills (2016) and Mounce et al. (2016). This is analogous to the ILD test in that a measure of incongruence for the original data partition is compared with a distribution of incongruence values for a large number of random partitions. However, whereas for the ILD incongruence is measured in terms of additional tree length, a tree-to-tree distance metric is used for the IRD. Many such metrics are available, but here we use two tests based upon the symmetrical-difference (RF) distance (IRDRF; Robinson & Foulds, 1981) and maximum agreement subtree (MAST) distance (IRDD1; Goddard et al., 1994; de Vienne et al., 2007). We acknowledge that other metrics may have more desirable properties, but the RF distance, in particular, is well characterized and widely applied. It is unusual for a single most parsimonious tree (MPT) to result from a parsimony search, and we therefore followed Mounce et al. (2016) in calculating the mean nearest neighbour distance (NND) between each tree resulting from one partition and the most similar tree in the other partition. In addition, we calculated the distances between strict, semi-strict and 50% majority rule (plus compatible groupings) trees for the two partitions, although we caution that these offer poor or positively misleading summaries of the differences between sets of trees (Mounce et al., 2016). We illustrate this latter approach for the eumalacostracan data of Jenner et al. (2009) and Wills et al. (2009) (Fig. 2) and for the diplopod data of Blanke & Wesener (2014) (Fig. 3). IRD tests were initially based upon 99 random partitions of the data (cf. 999 for the computationally much faster ILD). However, in those cases where P ≤ 0.10, we re-ran the test for that dataset using 499 random partitions (Fig. 1). Figure 2. Open in new tabDownload slide Tanglegram of the 50% majority rule consensus (plus compatible groupings) trees inferred from the ‘limbs’ (appendage) (left) and ‘body’ (non appendage) (right) partitions of the eumalacostracan data of Jenner et al. (2009) and Wills et al. (2009). The IRDRF test revealed the partitions to be significantly incongruent (P = 0.016). Nodes unique to each tree are marked with black dots: only five nodes are shared by the trees inferred from the ‘limb’ and ‘body’ partitions. Majority rule trees are figured for illustrative purposes. We advocate measures based upon the mean distance between nearest neighbours in the two partitions. Figure 2. Open in new tabDownload slide Tanglegram of the 50% majority rule consensus (plus compatible groupings) trees inferred from the ‘limbs’ (appendage) (left) and ‘body’ (non appendage) (right) partitions of the eumalacostracan data of Jenner et al. (2009) and Wills et al. (2009). The IRDRF test revealed the partitions to be significantly incongruent (P = 0.016). Nodes unique to each tree are marked with black dots: only five nodes are shared by the trees inferred from the ‘limb’ and ‘body’ partitions. Majority rule trees are figured for illustrative purposes. We advocate measures based upon the mean distance between nearest neighbours in the two partitions. Figure 3. Open in new tabDownload slide Tanglegram of majority consensus trees implied by a ‘limbs’ (left) and ‘body’ (right) partition of the diplopod data of Blanke & Wesener (2014), shown to be significantly incongruent by IRDRF (P = 0.015) and IRDD1 (P = 0.025). Unique nodes in each phylogeny are indicated by black dots. In this case, the tree inferred from the ‘limbs’ partition contains all of the same nodes as the strict consensus tree derived from the entire dataset by Blanke & Wesener (2014). Figure 3. Open in new tabDownload slide Tanglegram of majority consensus trees implied by a ‘limbs’ (left) and ‘body’ (right) partition of the diplopod data of Blanke & Wesener (2014), shown to be significantly incongruent by IRDRF (P = 0.015) and IRDD1 (P = 0.025). Unique nodes in each phylogeny are indicated by black dots. In this case, the tree inferred from the ‘limbs’ partition contains all of the same nodes as the strict consensus tree derived from the entire dataset by Blanke & Wesener (2014). All parsimony searches were implemented using 25 random additions of taxa, followed by tree bisection and reconnection branch swapping and retaining ten trees at each step. To expedite the searches, we limited the number of trees stored in memory to 100 000, and for the IRD tests we calculated nearest neighbour tree-to-tree distances based upon no more than 1000 trees from each partition (2000 trees in total and 1 999 000 tree-to-tree distances calculated for each metric in order to find the minima). Consensus trees were calculated from all MPTs up to the 100 000 buffer. We also condensed the resulting most parsimonious trees by collapsing branches with a minimum length of zero (Goloboff’s ‘amb-’) and removing all but one of any consequently identical trees. All analyses were implemented in PAUP* 4.0a for Macintosh (Swofford, 2002), using scripts (by MAW) that produced batch files for PAUP* and summarized the log files that it produced (see Supporting Information, S4). RESULTS No difference in levels of homoplasy (CI) or retained synapomoprhy (RI) for limb and body characters There are no significant differences in mean levels of internal homoplasy (as measured by the ensemble consistency index, CI) between limb and body partitions, either or the 38 datasets in combination (paired t-test, P = 0.060) or for subphyla considered in isolation (P > 0.05 in all cases) (Fig. 4). To account for the known biases in CI, residuals from regression analyses of internal CI on both the log of the number of characters and the log of the number of taxa were also compared across partitions. The results differ little from those for raw CI (Fig. 4) and no significant differences are detected. A similar set of analyses for retained synapomorphy (as measured by the retention index, RI) also reveal no differences between limb and body partitions, either overall (paired t-test, P = 0.227) or within subphyla (P > 0.05 in all cases). Our findings are similar for the 15 crustacean datasets for which we have independent molecular trees: there are no differences between the CI or the RI of limb versus body character partitions when optimized onto those molecular trees (Wilcoxon tests P = 0.589 and 0.625 for CI and RI respectively) (Fig. 5). Figure 4. Open in new tabDownload slide A, B, box and whisker plots of the distribution of ensemble CI (A) and RI (B) values obtained for limb and non-limb partitions of 38 datasets across all arthropod groups (summarized in Table 1). There were no significant differences in CI or RI between partitions overall or in any individual taxonomic grouping. C, D, boxplots comparing residual CI (C) and RI (D) values for the same sample of datasets, modelling out the effects of data matrix dimesnsions (number of characters and number of taxa). There were no significant differences between the partitions, either overall or in any individual taxonomic grouping. Figure 4. Open in new tabDownload slide A, B, box and whisker plots of the distribution of ensemble CI (A) and RI (B) values obtained for limb and non-limb partitions of 38 datasets across all arthropod groups (summarized in Table 1). There were no significant differences in CI or RI between partitions overall or in any individual taxonomic grouping. C, D, boxplots comparing residual CI (C) and RI (D) values for the same sample of datasets, modelling out the effects of data matrix dimesnsions (number of characters and number of taxa). There were no significant differences between the partitions, either overall or in any individual taxonomic grouping. Figure 5. Open in new tabDownload slide Box and whisker plots of the distribution of ensemble CI and RI values obtained for limb and non-limb partitions of 15 morphological datasets of crustaceans. Characters have been optimized onto corresponding but independently derived molecular trees for the same leaf set (summarized in Table 2). There were no significant differences in CI or RI between partitions. Figure 5. Open in new tabDownload slide Box and whisker plots of the distribution of ensemble CI and RI values obtained for limb and non-limb partitions of 15 morphological datasets of crustaceans. Characters have been optimized onto corresponding but independently derived molecular trees for the same leaf set (summarized in Table 2). There were no significant differences in CI or RI between partitions. Limb and body partitions imply significantly different trees one time in five Both the ILD test and the IRDRF test for nearest neighbours report significant (P < 0.05) incongruence between the trees inferred from limb and body character partitions in about one in five cases (8/38 and 7/38, respectively). The IRDD1 test for nearest neighbours reports significant (P < 0.05) incongruence slightly less often (5/38). We note that the different tests assess different aspects of incongruence and the P values for ILD, IRDRF and IRDD1 do not precisely coincide. Hence, a significant P-value (P < 0.05) is obtained for both IRDRF and IRDD1 in three datasets and for all three tests (including the ILD) in only two cases. Rates of significant incongruence are summarized in Table 1. For the ILD test, our finding that eight from 38 datasets are incongruent with P ≤ 0.05 means that incongruence is significantly more common than expected by chance (two would be anticipated: binomial test P = 0.0005). The IRDRF test also detected significant incongruence significantly more often than expected (P = 0.0025). Whilst reporting significant incongruence at the lowest rate, the IRDD1 test also detects a significantly higher rate of incongruence than would be expected (P = 0.03973, binomial test). The outcome of the ILD and IRD tests is not significantly influenced by dataset parameters or by taxonomic group We sought to determine whether various dataset dimensions and imbalances might determine the outcome of our incongruence tests (P ≤ 0.05 or P > 0.05). In addition to data matrix dimensions, previous studies (e.g. Sansom & Wills, 2013, 2017; Mounce et al., 2016) have accounted for (or variously controlled) amounts of missing data within partitions or regions. In general, we find that there is no significant difference in the median percentage of cells scored as missing/inapplicable for limb and body partitions across the entire dataset (Mann–Whitney U = 36.9636, P = 0.4242). Neither are there significant differences in the mean or variances of percentages of missing/inapplicable codings for limb and body partitions in individual subphyla: myriapods (paired t = –0.3868, P = 0.7148), crustaceans (paired t = –0.5852, P = 0.5768), chelicerates (paired t = –0.7982, P = 0.4510), hexapods (paired t = –0.4896, P = 0.6315). For each dataset we also take account of the difference in percentage of missing data between partitions [this was a marginally significant factor in the study of Mounce et al. (2016)]. However, a logistic regression model (see Supporting Information, S5) shows that the outcome of the ILD is not significantly influenced by the log of the percentage of missing data across both partitions (P = 0.6127), the difference in the percentage of missing data between partitions (P = 0.1551), the difference between partition sizes (P = 0.1564), the log of the number of taxa (P = 0.0606), the log of the number of characters (P = 0.0667) or the interaction between these last two variables (P = 0.0619). The model also shows that higher taxonomic groups (i.e. Chelicerata, Crustacea, Hexapoda, Myriapoda) have no effect on ILD outcome. Similarly, a log-likelihood ratio test (G-test) reveals no difference in the frequencies of significant or non-significant outcomes across these higher taxa (G = 4.0863, P = 0.2523). We find similar results from logistic modelling of the outcome of the IRDRF and IRDD1 tests, with no significant effect for overall percentage of missing data (P = 0.511 and P = 0.396), the difference in percentage of missing data between partitions (P = 0.330 and P = 0.987), the log of the number of taxa (P = 0.838 and P = 0.379), the log of the number of characters (P = 0.692 and P = 0.417) or the interaction between characters and taxa (P = 0.727 and P = 0.381). Higher taxonomic group also has no effect for either test and G-tests also reveal no difference in the frequency of significant outcomes for the four groups (IRDRF, G = 2.7948, P = 0.4244: IRDD1, G = 1.4049, P = 0.7044). Limb and body character sampling Overall there is no significant difference in the log of number of characters sampled from each partition of the datasets in Table 1 (t = –0.3461, P = 0.7312, paired t-test of logs). Furthermore, no significant difference is observed in chelicerates (t = –0.5679, P = 0.5907, paired t-test) or myriapods (t = 2.1830, P = 0.0808, paired t-test). However, differences are observed in crustaceans (t = 2.7658, P = 0.0279, paired t-test of logs) and hexapods (t = –4.4382, P = 0.0005, paired t-test of logs). Crustacean datasets contain significantly more limb characters than those from the body, while the opposite tendency pertains in hexapod datasets. We do not assume that these differences reflect a bias of sampling from the hypothetical universe of possible leg and body characters, since there is no reason to suppose that the two partitions should yield identical character numbers (a naïve null hypothesis). Rather, we merely report that the numbers do, in fact, differ in the case of crustaceans and hexapods. DISCUSSION Levels of incongruence Rates of significant (P < 0.05) incongruence between limb and body partitions across our sample of arthropod matrices are significantly higher than expected for all of our tests. We find eight from 38 significant with P ≤ 0.05 for the ILD (one in five) and seven from 38 for the IRDRF, whereas two (one in 20) would be expected by chance (binomial test P = 0.0005). The only previous systematic studies of partition homogeneity using similar approaches to those deployed here concerned the craniodental and postcranial characters of vertebrates (Mounce et al., 2016), the dental and osteological characters of mammals (Sansom et al., 2017) and hard and soft part characters across a diversity of animal clades (Sansom & Wills, 2017). Higher rates of significant (P < 0.05) incongruence were reported in those earlier studies: about one in three (ILD and IRD) for craniodental/body characters and hard/soft characters and up to one in two (ILD) for dental/osteological characters (compared with one in five for the ILD and IRD across our arthropods). There is no reason to expect limb versus body partitions for arthropods to yield similar rates of null rejection to functionally and anatomically different partitions in other groups. However, levels of limb to body incongruence for our sample of arthropods are not especially high, which is good news for those attempting to infer the relationships of fossil arthropods that lack details of appendage morphology, provided there is enough character data overall. Lack of partition homogeneity can result from a variety of factors other than conflict between the phylogenetic signals inherent in partitions (Dolphin et al., 2000; Planet, 2006; Mounce et al., 2016). However, we demonstrate that there are no significant (P < 0.05) differences in overall levels of either internal or molecular consistency between the partitions of our datasets (CI and RI; Figs 4, 5) and neither are there differences in amounts of missing data. Although the levels of homoplasy contained in each partition may be comparable, the quality of this noise often misinforms the inference of phylogenies in different ways, thereby resulting in incongruence. Implications of incongruence Whatever the cause of the incongruence between partitions, it is still observed more often than we would expect, with several implications. Focusing on restricted suites of characters to the exclusion of others is questionable practice, unless it has been demonstrated a priori (e.g. in a large empirical sample: Sansom & Wills, 2017; Sansom et al., 2017) that some classes of characters are intrinsically more informative and less prone to homoplasy than others. This is not the case for the appendage and body characters of arthropods. Nevertheless, uneven character sampling is commonplace in arthropod systematics (Clarke, 2011) and we find these biases in some higher taxa here. Such biases probably reflect previous expectations that certain characters are of more value or contain a stronger phylogenetic signal than others (see: Sánchez-Villagra & Williams, 1998; Williams, 2007; Song & Bucheli, 2010; Mounce et al., 2016; Parker, 2016; Sansom et al., 2017). For example, Gainett et al. (2014) focused upon appendicular characters in their phylogeny of harvestmen, while Dunlop (1997) found that characters of body segmentation and segment differentiation were particularly helpful in determining the higher-level relationships of chelicerates. Our sample of datasets does not support this idea for limb and body characters across arthropods. Such biases are most acute (and often unavoidable) in many fossil groups, where the more heavily mineralized or sclerotized cuticle of the carapace and tergites typically preserve more readily than that of the limbs. Hence, many fossil arthropod taxa lack details of the appendages and out of necessity focus on ‘body’ characters of segmentation and ornamentation. In ostracods, for example, body characters are the most readily available (Tinn & Oakley, 2008), despite suggestions that appendicular characters are of much greater utility (Park et al., 2002; Cohen & Morin, 2003). Notwithstanding, many arthropod studies uncover hidden support and hidden branch support (Gatesy et al., 1999) from combined suites of morphological characters (Clarke, 2011) and from the combination of morphological and molecular data (e.g. Wahlberg et al., 2005; Damgaard, 2008). We, therefore, advocate holistic character sampling (Song & Bucheli, 2010) and principles of total evidence (Kluge, 1989; Gatesy & Springer, 2014; Mounce et al., 2016; see also: Gatesy & Arctander, 2000) in arthropod phylogenetics. There are other systematic problems that may occur when trees are inferred from non-random character samples, although these are usually framed in terms of the effects of missing data. In this regard, it is not the number of missing entries in a matrix so much as the amount of data that are present that influences the resolution of trees and the stability of taxa within them (Wiens, 2003a, b; Cobbett et al., 2007). Non-random blocks of missing data – such as those that typically result from the concatenation of molecular datasets with different taxon samples (Chernomor et al., 2016; Dillman et al., 2016; Dobrin et al., 2018) or morphological datasets containing a mix of fossil and extant taxa (Pattinson et al., 2015; Sansom, 2015) – bring their own particular set of problems. The processes of decay prior to fossilization obliterate soft-part character data, but a recent and surprising finding is that such characters tend to optimize along branches further from the root of the tree than their more fossilizable counterparts. The simulated removal of soft-part data from species in real neontological datasets, therefore, tends to result in the disproportionate ‘stemward slippage’ of lineages towards the root of the tree (Sansom & Wills, 2013; Sansom, 2015). It is, therefore, likely that many fossils appear more plesiomorphic and erroneously resolve closer to the roots of phylogenies as a function of taphonomic filters (Sansom et al., 2017). This needs to be explored in greater detail across the phylogeny of arthropods. SUPPORTING INFORMATION Additional Supporting Information may be found in the online version of this article at the publisher's web-site. S1. Morphological data and partitions for arthropods.tar. S2. Morphological data and molecular trees for crustaceans.tar. S3. List of characters and taxa deleted from each matrix. S4. Basic code for IRD test.tar. S5. Regression models. ACKNOWLEDGEMENTS We thank two anonymous referees for suggestions that helped us to significantly improve this manuscript. We are grateful to the BBSRC for funding (BB/K015702/1 and BB/K006754/1 to MAW), as well as to the John Templeton Foundation (Grant 43915 to Mark Wilkinson and M.A.W.). Analyses were run in the Tarr Bioinformatics Suite. REFERENCES Adamowicz SJ , Purvis A . 2006 . From more to fewer? Testing an allegedly pervasive trend in the evolution of morphological structure . Evolution 60 : 1402 – 1416 . Google Scholar Crossref Search ADS PubMed WorldCat Angelini DR , Kaufman TC . 2005 . Insect appendages and comparative ontogenetics . Developmental Biology 286 : 57 – 77 . Google Scholar Crossref Search ADS PubMed WorldCat Archie JW . 1989 . Homoplasy excess ratios: new indices for measuring levels of homoplasy in phylogenetic systematics and a critique of the consistency index . Systematic Biology 38 : 253 – 269 . WorldCat Arratia G . 2009 . Identifying patterns of diversity of the actinopterygian fulcra . Acta Zoologica 90 : 220 – 235 . Google Scholar Crossref Search ADS WorldCat Baalbergen E , Donovan SK . 2013 . Terrestrial arthropods from the Late Pleistocene of Jamaica: systematics, palaeoecology and taphonomy . Geological Journal 48 : 628 – 645 . Google Scholar Crossref Search ADS WorldCat Baiocco M , Bonato L , Cardini A , Fusco G . 2017 . Shape variation of prey-catching structures in geophilomorph centipedes: a preliminary investigation using geometric morphometrics . Zoologischer Anzeiger 268 : 11 – 18 . Google Scholar Crossref Search ADS WorldCat Banks JC , Paterson AM . 2004 . A penguin-chewing louse (Insecta: Phthiraptera) phylogeny derived from morphology . Invertebrate Systematics 18 : 89 – 100 . Google Scholar Crossref Search ADS WorldCat Barker FK , Lutzoni FM . 2002 . The utility of the Incongruence Length Difference test . Systematic Biology 51 : 625 – 637 . Google Scholar Crossref Search ADS PubMed WorldCat Bateman RM . 1999 . Integrating molecular and morphological evidence of evolutionary radiations . In: Hollingsworth PM , Bateman RM , Gornall RJ , eds. Molecular systematics and plant evolution . London: CRC Press, 432 – 471 . Google Preview WorldCat COPAC Bennik RM , Buckley TR , Hoare RJB , Holwell GI . 2016 . Molecular phylogeny reveals the repeated evolution of male genital traits in the New Zealand moth genus Izatha (Lepidoptera: Xyloryctidae) . Systematic Entomology 41 : 309 – 322 . Google Scholar Crossref Search ADS WorldCat Blagoderov V , Hippa H , Sevcik J . 2009 . Asiorrhina, a new Oriental genus of Lygistorrhinidae (Diptera: Sciaroidea) and its phylogenetic position . Zootaxa 2295 : 31 – 45 . Google Scholar Crossref Search ADS WorldCat Blanco-Bercial L , Bradford-Grieve JM , Bucklin A . 2011 . Molecular phylogeny of the Calanoida (Crustacea: Copepoda) . Molecular Phylogenetics and Evolution 59 : 103 – 113 . Google Scholar Crossref Search ADS PubMed WorldCat Blanke A , Wesener T . 2014 . Revival of forgotten characters and modern imaging techniques help to produce a robust phylogeny of the Diplopoda (Arthropoda, Myriapoda) . Arthropod Structure & Development 43 : 6375 . Google Scholar Crossref Search ADS WorldCat Bochkov AV , Klimov PB , Wauthy G . 2011 . Phylogeny and coevolutionary associations of makialgine mites (Acari, Psoroptidae, Makialginae) provide insight into evolutionary history of their hosts, strepsirrhine primates . Zoological Journal of the Linnean Society 162 : 1 – 14 . Google Scholar Crossref Search ADS WorldCat Boore JL , Collins TM , Stanton D , Daehler LL , Brown WM . 1995 . Deducing the pattern of arthropod phylogeny from mitochondrial DNA rearrangements . Nature 376 : 163 – 165 . Google Scholar Crossref Search ADS PubMed WorldCat Botero-Trujillo R , Ott R , Carvalho LS . 2017 . Systematic revision and phylogeny of the South American sun-spider genus Gaucha Mello-Leitão (Solifugae: Mummuciidae), with description of four new species and two new generic synonymies . Arthropod Systematics & Phylogeny 75 : 3 – 44 . WorldCat Bracken-Grissom HD , Ahyong ST , Wilkinson RD , Feldmann RM , Schweitzer CE , Breinholt JW , Bendall M , Palero F , Chan TY , Felder DL , Robles R , Chu KH , Tsang LM , Kim D , Martin JW , Crandall KA . 2014 . The emergence of lobsters: phylogenetic relationships, morphological evolution and divergence time comparisons of an ancient group (Decapoda: Achelata, Astacidea, Glypheidea, Polychelida) . Systematic Biology 63 : 457 – 479 . Google Scholar Crossref Search ADS PubMed WorldCat Bradford-Grieve JM , Boxshall GA , Ahyong ST , Ohtsuka S . 2010 . Cladistic analysis of the calanoid Copepoda . Invertebrate Systematics 24 : 291 – 321 . Google Scholar Crossref Search ADS WorldCat Bradford-Grieve JM , Blanco-Bercial L , Boxshall GA . 2017 . Revision of the family Megacalanidae (Copepoda: Calanoida) . Zootaxa 4229 : 1 – 183 . Google Scholar Crossref Search ADS WorldCat Briggs DEG , Weedon MJ , Whyte MA . 1993 . Arthropoda (Crustacea, excluding Ostracoda) . In: Benton MJ , ed. The fossil record 2 . London : Chapman & Hall , 321 – 342 . Google Preview WorldCat COPAC Calor AR , Holzenthal RW . 2008 . Phylogeny of Grumichellini Morse, 1981 (Trichoptera: Leptoceridae) with the description of a new genus from southeastern Peru . Aquatic Insects 30 : 245 – 259 . Google Scholar Crossref Search ADS WorldCat Castelin M , Williams ST , Buge B , Maestrati P , Lambourdiere J , Ozawa T , Utge J , Couloux A , Alf A , Samadi S . 2017 . Untangling species identity in gastropods with polymorphic shells in the genus Bolma Risso, 1826 (Mollusca, Vetigastropoda) . European Journal of Taxonomy 288 : 1 – 21 . WorldCat Chamorro ML , Konstantinov AS . 2011 . Cachiporrini, a remarkable new tribe of Lamprosomatinae (Coleoptera, Chrysomelidae) from South America . ZooKeys 78 : 43 – 59 . Google Scholar Crossref Search ADS WorldCat Chan TY , Ho KC , Li CP , Chu KH . 2009 . Origin and diversification of the clawed lobster genus Metanephrops (Crustacea: Decapoda: Nephropidae) . Molecular Phylogenetics and Evolution 50 : 411 – 422 . Google Scholar Crossref Search ADS PubMed WorldCat Chang S , Tshudy D , Sornhannus U , Ahyong ST , Chan T . 2016 . Evolution of the thaumastocheliform lobsters (Crustacea, Decapoda, Nephropidae) . Zoologica Scripta 46 : 373 – 387 . WorldCat Chernomor O , von Haeseler A , Bui Quang M . 2016 . Terrace aware data structure for phylogenomic inference from supermatrices . Systematic Biology 65 : 997 – 1008 . Google Scholar Crossref Search ADS PubMed WorldCat Christiansen K . 1960 . Convergence and parallelism in cave Entomobryinae . Evolution 15 : 288v301 . WorldCat Clarke DJ . 2011 . Testing the phylogenetic utility of morphological character systems, with a revision of Creophilus Leach (Coleoptera: Staphylinidae) . Zoological Journal of the Linnean Society 163 : 723 – 812 . Google Scholar Crossref Search ADS WorldCat Clarke JA , Middleton KM . 2008 . Mosaicism, modules, and the evolution of birds: results from a Bayesian approach to the study of morphological evolution using discrete character data . Systematic Biology 57 : 185 – 201 . Google Scholar Crossref Search ADS PubMed WorldCat Clark-Hatchel CM , Tomoyasu Y . 2016 . Exploring the origin of insect wings from an evo-devo perspective . Current Opinion in Insect Science 13 : 77 – 85 . Google Scholar Crossref Search ADS PubMed WorldCat Cobbett A , Wilkinson M , Wills MA . 2007 Fossils impact as hard as living taxa in parsimony analyses of morphology . Systematic Biology 56 : 753 – 766 . Google Scholar Crossref Search ADS PubMed WorldCat Cohen AC , Morin JG . 2003 . Sexual morphology, reproduction and the evolution of bioluminescence in Ostracoda . The Paleontological Society Papers 9 : 37 – 70 . Google Scholar Crossref Search ADS WorldCat Dabney J , Knapp M , Glocke I , Gansauge M-T , Weihmann A , Nickel B , Valdiosera C , García N , Pääbo S , Arsuaga J-L , Meyer M . 2013 . Complete mitochondrial genome sequence of a Middle Pleistocene cave bear reconstructed from ultrashort DNA fragments . Proceedings of the National Academy of Sciences of the United States 110 : 15758 – 15763 . Google Scholar Crossref Search ADS WorldCat Damgaard J . 2008 . Phylogeny of the semiaquatic bugs (Hemiptera-Heteroptera, Gerromorpha) . Insect Systematics & Evolution 39 : 431 – 460 . Google Scholar Crossref Search ADS WorldCat Davies TG , Rahman IA , Lautenschlager S , Cunningham JA , Asher RJ , Barrett PM , Bates KT , Bengston S , Benson RBJ , Boyer DM , Braga J , Bright JA , Claessens LPAM , Cox PG , Don XP , Evans AR , Falkingham PL , Friedman M , Garwood RJ , Goswami A , Hutchinson JR , Jeffery S , Johanson Z , Lebrun R , Martínez-Pérez C , Marúgan-Lobón J , O’Higgins PM , Metscher B , Orliac M , Rowe TB , Rücklin M , Sánchez-Villagra MR , Shubin NH , Smith SY , Starck JM , Stringer C , Summers AP , Sutton MD , Walsh ST , Weisbecker V , Witmer LM , Wroe S , Yin Z , Rayfield EJ , Donoghue PCJ . 2017 . Open data and digital morphology . Proceedings of the Royal Society B 284 : 20170194 . Google Scholar Crossref Search ADS PubMed WorldCat Del Rio MG , Malvardi AE , Lanteri A . 2012 . Systematics and cladistics of a new Naupactini genus (Coleoptera: Curculionidae: Entiminae) from the Andes of Colombia and Ecuador . Zoological Journal of the Linnean Society 166 : 54 – 71 . Google Scholar Crossref Search ADS WorldCat De Vienne DM , Giraud T , Martin OC . 2007 . A congruence index for testing topological similarity between trees . Bioinformatics 23 : 3119 – 3124 . Google Scholar Crossref Search ADS PubMed WorldCat Di Giulio A , Fattorini S , Kaupp A , Taglianti AV , Nagel P . 2003 . Review of competing hypotheses of phylogenetic relationships of Paussinae (Coleoptera: Carabidae) based on larval characters . Systematic Entomology 28 : 509 – 537 . Google Scholar Crossref Search ADS WorldCat Dillman CB , Sidlauskas BL , Vari RP . 2016 . A morphological supermatrix-based phylogeny for the Neotropical fish superfamily Anostomoidea (Ostariophysi: Characiformes): phylogeny, missing data and homoplasy . Cladistics 32 : 276 – 296 . Google Scholar Crossref Search ADS WorldCat Dobrin BH , Zwickl DJ , Sanderson MJ . 2018 . The prevalence of terraced treescapes in analyses of phylogenetic datasets . BMC Evolutionary Biology 18 : 46 . Google Scholar Crossref Search ADS PubMed WorldCat Dolphin K , Belshaw R , Orme CDL , Quicke DLJ . 2000 . Noise and incongruence: interpreting results of the Incongruence Length Difference test . Molecular Phylogenetics and Evolution 17 : 401 – 406 . Google Scholar Crossref Search ADS PubMed WorldCat Dreyer H , Wägele JW . 2001 . Parasites of crustaceans (Isopoda: Bopyridae) evolved from fish parasites: molecular and morphological evidence . Zoology 103 : 157 – 178 . WorldCat Dunlop JA . 1997 . Palaeozoic arachnids and their significance for arachnid phylogeny. In: Zabka M, ed. Proceedings of the 16th European colloquium of arachnology. Siedlce: Wyzsza Szkola Rolnicko-Pedagogiczna, 65 – 82 . WorldCat Edgecombe GD , Barrow L . 2007 . A new genus of scutigerid centipedes (Chilopoda) from Western Australia, with new characters for morphological phylogenetics of Scutigeromorpha . Zootaxa 1409 : 23 – 50 . WorldCat Engel MS . 2015 . Insect evolution . Current Biology Magazine 25 : 868 – 872 . Google Scholar Crossref Search ADS WorldCat Farris JS . 1989 . The retention index and the rescaled consistency index . Cladistics 5 : 417 – 419 . Google Scholar Crossref Search ADS WorldCat Farris JS , Kallersjo M , Kluge AG , Bult C . 1995a . Constructing a significance test for incongruence . Systematic Biology 44 : 570 – 572 . Google Scholar Crossref Search ADS WorldCat Farris JS , Kallersjo M , Kluge AG , Bult C . 1995b . Testing significance of incongruence . Cladistics 10 : 315 – 331 . Google Scholar Crossref Search ADS WorldCat Finkbeiner SD , Briscoe AD , Mullen SP . 2017 . Complex dynamics underlie the evolution of imperfect wing pattern convergence in butterflies . Evolution 71 : 949 – 959 . Google Scholar Crossref Search ADS PubMed WorldCat Gainett G , Sharma PP , Pinto-da-rocha R , Giribet G , Willemart RH . 2014 . Walk it off: predictive power of appendicular characters toward inference of higher-level relationships in Laniatores (Arachnida: Opiliones) . Cladistics 30 : 120 – 138 . Google Scholar Crossref Search ADS WorldCat Gatesy J , Arctander P . 2000 . Hidden morphological support for the phylogenetic placement of Pseudoryx nghetinhensis with bovine bovids: a combined analysis of gross anatomical evidence and DNA sequences from five genes . Systematic Biology 49 : 515 – 538 . Google Scholar Crossref Search ADS PubMed WorldCat Gatesy J , Springer MS . 2014 . Phylogenetic analysis at deep timescales: unreliable gene trees, bypassed hidden support, and the coalescence/concatalescence conundrum . Molecular Phylogenetics and Evolution 80 : 231 – 266 . Google Scholar Crossref Search ADS PubMed WorldCat Gatesy J , O’Grady P , Baker RH . 1999 . Corroboration among datasets in simultaneous analysis: hidden support for phylogenetic relationships among higher level artiodactyl taxa . Cladistics 15 : 271 – 313 . Google Scholar Crossref Search ADS WorldCat George KH . 2017 . Phylogeny of the taxon Paralaophontodes Lang (Copepoda, Harpacticoida, Laophontodinae), including species descriptions, chorological remarks, and a key to species . Zoosystematics and Evolution 93 : 211 – 241 . Google Scholar Crossref Search ADS WorldCat Gerstmeier R , Eberle J . 2011 . Definition and revision of the Orthrius-group of genera (Coleoptera, Cleridae, Clerinae) . ZooKeys 92 : 35 – 60 . Google Scholar Crossref Search ADS WorldCat Giribet G , Edgecombe GD , Wheeler WC . 2001 . Arthropod phylogeny based on eight molecular loci and phylogeny . Nature 413 : 157 – 161 . Google Scholar Crossref Search ADS PubMed WorldCat Goddard W , Kubicka E , Kubicki G , McMorris FR . 1994 . The agreement metric for labelled binary trees . Mathematical Biosciences 123 : 215 – 226 . Google Scholar Crossref Search ADS PubMed WorldCat Goloboff PA , Farris JS , Nixon KC . 2008 . TNT, a free program for phylogenetic analysis . Cladistics 24 : 774 – 786 . Google Scholar Crossref Search ADS WorldCat Grebennikov VV . 2010 . First Alaocybites weevil (Insecta: Coleoptera: Curculionoidea) from the eastern Palaearctic: a new microphthalmic species and generic relationships . Arthropod Systematics & Phylogeny 68 : 331 – 365 . WorldCat Grebennikov VV , Newton AF . 2009 . Good-bye Scydmaenidae, or why the ant-like stone beetles should become megadiverse Staphylinidae sensu latissimo (Coleoptera) . European Journal of Entomology 106 : 275 – 301 . Google Scholar Crossref Search ADS WorldCat Hermoso-Salazar M , Wicksten M , Morrone JJ . 2008 . Phylogenetic analysis of the Paulsoni species group (Decapoda: Alpheidae) from the American Pacific, with implications for the phylogenetic classification of the genus Synalpheus . Zootaxa 1744 : 19 – 30 . Google Scholar Crossref Search ADS WorldCat Houde P . 1994 . Evolution of Heliornithidae: reciprocal illumination by morphology, biogeography and DNA hybridization (Aves: Gruiformes) . Cladistics 10 : 1 – 19 . Google Scholar Crossref Search ADS WorldCat Hultgren KM , Hurt C , Anker A . 2014 . Phylogenetic relationships within the snapping shrimp genus Synalpheus (Decapoda: Alpheidae) . Molecular Phylogenetics and Evolution 77 : 116 – 125 . Google Scholar Crossref Search ADS PubMed WorldCat Hughes NC . 2003 . Trilobite tagmosis and body patterning from morphological and developmental perspectives . Integrative and Comparative Biology 43 : 185 – 206 Google Scholar Crossref Search ADS PubMed WorldCat Jenner RA , Ní Dhubhghaill CN , Ferla MP , Wills MA . 2009 . Eumalacostran phylogeny and total evidence: limitations of the usual suspects . BMC Evolutionary Biology 9 : 21. WorldCat Karasawa H , Schweitzer CE , Feldmann RM . 2013 . Phylogeny and systematics of extant and extinct lobsters . Journal of Crustacean Biology 33 : 78 – 123 . Google Scholar Crossref Search ADS WorldCat Klimov PB , Mironov SV , OConnor BM . 2017 . Convergent and unidirectional evolution of extremely long aedeagi in the largest feather mite genus, Proctophyllodes (Acari: Proctophyllodidae): evidence from comparative molecular and morphological phylogenetics . Molecular Phylogenetics and Evolution 114 : 212 – 224 . Google Scholar Crossref Search ADS PubMed WorldCat Klingenberg CP . 2008 . Morphological integration and developmental modularity . Annual Review of Ecology, Evolution and Systematics 39 : 115 – 132 . Google Scholar Crossref Search ADS WorldCat Klompen H , Amin M , Gerdeman BS . 2013 . A revision of the genus Afroheterozercon (Acari: Heterozerconidae) . Zootaxa 3626 : 301 – 325 . Google Scholar Crossref Search ADS PubMed WorldCat Kluge AG . 1989 . A concern for evidence and a phylogenetic hypothesis of relationships among Epicrates (Boidae, Serpentes) . Systematic Zoology 38 : 7 – 25 . Google Scholar Crossref Search ADS WorldCat Kluge AG , Farris JS . 1969 . Quantitative phyletics and the evolution of anurans . Systematic Zoology 18 : 1 – 32 . Google Scholar Crossref Search ADS WorldCat Koch M , Parschke S , Edgecombe GD . 2009 . Phylogenetic implications of gizzard morphology in scolopendromorph centipedes (Chilopoda) . Zoologica Scripta 38 : 269 – 288 . Google Scholar Crossref Search ADS WorldCat Kuntner M . 2005 . A revision of Herennia (Araneae: Nephilidae: Nephilinae), the Australasian ‘coin spiders’ . Invertebrate Systematics 19 : 391 – 436 . Google Scholar Crossref Search ADS WorldCat Legg DA , Sutton MD , Edgecombe GD . 2013 . Arthropod fossil data increase congruence of morphological and molecular phylogenies . Nature Communications 4 : 2485 . Google Scholar Crossref Search ADS PubMed WorldCat Liu X , Wang Y , Shih C , Ren D , Yang D . 2012 . Early evolution and historical biogeography of fishflies (Megaloptera: Chauliodinae): Implications from a phylogeny combining fossil and extant taxa . PLoS One 9 : e40345 . Google Scholar Crossref Search ADS WorldCat Liu W , Golovatch S , Wesener T , Tian M . 2017 . Convergent evolution of unique morphological adaptations to a subterranean environment in cave millipedes (Diplopoda) . PLoS One 12 : e0170717 . Google Scholar Crossref Search ADS PubMed WorldCat Lloyd GT , 2018 . Matrices: arthropods. Available at: www.graemetlloyd.com (accessed 10/01/18) . Lopardo L , Hormiga G . 2015 . Out of the twilight zone: phylogeny and evolutionary morphology of the orb-weaving spider family Mysmenidae, with a focus on spinneret spigot morphology in symphytognathoids (Araneae, Araneoidea) . Zoological Journal of the Linnean Society 173 : 527 – 786 . Google Scholar Crossref Search ADS WorldCat Lörz AN , Brandt A . 2004 . Phylogeny of Antarctic Epimeria (Epimeriidae: Amphipoda) . Journal of the Marine Biological Association of the United Kingdom 84 : 179 – 190 . Google Scholar Crossref Search ADS WorldCat Lörz AN , Held C . 2004 . A preliminary molecular and morphological phylogeny of the Antarctic Epimeriidae and Iphimediidae (Crustacea, Amphiopoda) . Molecular Phyogenetics and Evolution 31 : 4 – 15 . Google Scholar Crossref Search ADS WorldCat Lü J , Unwin DM , Jin X , Liu Y , Ji Q . 2010 . Evidence for modular evolution in a long-tailed pterosaur with a pterodactyloid skull . Proceedings of the Royal Society B 277 : 383 – 389 . Google Scholar Crossref Search ADS PubMed WorldCat Ma KY , Chan TY , Chu KH . 2009 . Phylogeny of penaeoid shrimps (Decapoda: Penaeoidea) inferred from nuclear protein-coding genes . Molecular Phylogenetics and Evolution 53 : 45 – 55 . Google Scholar Crossref Search ADS PubMed WorldCat Maddison WP , Maddison DR . 2018 . Mesquite: a modular system for evolutionary analysis, v.3.51. Available at: http://www.mesquiteproject.org. Accessed 29 June 2018. Martinez-Delclòs X , Briggs DEG , Peñalver E . 2004 . Taphonomy of insects in carbonates and amber . Palaeogeography, Palaeoclimatology, Paleoecology 203 : 19 – 64 . Google Scholar Crossref Search ADS WorldCat Maynard Smith J . 1993 . The theory of evolution . Cambridge : Cambridge University Press. Google Preview WorldCat COPAC McLaughlin PA , Lemaitre R , Sorhannus U . 2007 . Hermit crab phylogeny: a reappraisal and its ‘fall-out’ . Journal of Crustacean Biology 27 : 97 – 115 . Google Scholar Crossref Search ADS WorldCat Meland K , Willasen E . 2004 . Molecular phylogeny and biogeography of the genus Pseudomma (Peracarida: Mysida) . Journal of Crustacean Biology 24 : 541 – 557 . Google Scholar Crossref Search ADS WorldCat Mendes AC . 2011 . Phylogeny and taxonomic revision of Heteropachylinae (Opiliones: Laniatores: Gonyleptidae) . Zoological Journal of the Linnean Society 163 : 437 – 483 . Google Scholar Crossref Search ADS WorldCat Michel-Salzat A , Cameron SA , Oliveira ML . 2004 . Phylogeny of the orchid bees (Hymenoptera: Apinae: Euglossini): DNA and morphology yield equivalent patterns . Molecular Phylogenetics and Evolution 32 : 309 – 323 . Google Scholar Crossref Search ADS PubMed WorldCat Mickevich MF , Farris JS . 1981 . The implications of congruence in Menidia . Systematic Zoology 30 : 351 – 370 . Google Scholar Crossref Search ADS WorldCat Mounce RCP , Sansom R , Wills MA . 2016 . Sampling diverse characters improves phylogenies: craniodental and postcranial characters of vertebrates often imply different trees . Evolution 70 : 666 – 686 . Google Scholar Crossref Search ADS PubMed WorldCat Nicolalde-Morejón F , Vergara-Silva F , Vovides AP , de los Monteros AE . 2009 . Reciprocal illumination of morphological characters upon a molecular hypothesis supports proposal of a new species of cycad from Mexico . Systematics and Biodiversity 7 : 73 – 79 . Google Scholar Crossref Search ADS WorldCat Oakley TH , Wolfe JM , Lindgren AR , Zaharoff AK . 2012 . Phylotranscriptomics to bring the understudied into the fold: monophyletic Ostracoda, fossil placement, and pancrustacean phylogeny . Molecular Biology and Evolution 30 : 215 – 233 . Google Scholar Crossref Search ADS PubMed WorldCat O’Connor A , Wills MA . 2016 . Measuring stratigraphic congruence across trees, higher taxa and time . Systematic Biology 65 : 792 – 811 . Google Scholar Crossref Search ADS PubMed WorldCat O’Leary MA , Gatesy J . 2008 . Impact of increased character sampling on the phylogeny of Cetartiodactyla (Mammalia): combined analysis including fossils . Cladistics 24 : 397 – 442 . Google Scholar Crossref Search ADS WorldCat O’Leary MA , Kaufman S . 2011 . MorphoBank: phylophenomics in the ‘cloud’ . Cladistics 27 : 1 – 9 . Google Scholar Crossref Search ADS WorldCat Olesen J . 2009 . Phylogeny of Branchiopoda (Crustacea) – character evolution and contribution of uniquely preserved fossils . Arthropod Systematics & Phylogeny 67 : 3 – 39 . WorldCat Orlando L , Gilbert MTP , Willerslev E . 2015 . Reconstructing ancient genomes and epigenomes . Nature Reviews Genetics 16 : 395 – 408 . Google Scholar Crossref Search ADS PubMed WorldCat Oyston JW , Hughes M , Wagner PJ , Gerber S , Wills MA . 2015 . What limits the morphological disparity of clades? Interface Focus 5 : 20150042 . Google Scholar Crossref Search ADS PubMed WorldCat Oyston JW , Hughes M , Gerber S , Wills MA . 2016 . Why should we investigate the morphological disparity of plant clades? Annals of Botany 117 : 859 – 879 . Google Scholar Crossref Search ADS PubMed WorldCat Packer L , Litman J , Praz CJ . 2017 . Phylogenetic position of a remarkable new fideliine bee from northern Chile (Hymenoptera: Megachilidae) . Systematic Entomology 42 : 473 – 488 . Google Scholar Crossref Search ADS WorldCat Padial JM , Miralles A , De la Riva I , Vences M . 2010 . The integrative future of taxonomy . Frontiers in Zoology 7 : 16 . Google Scholar Crossref Search ADS PubMed WorldCat Park LE , Martens K , Cohen AS . 2002 . Phylogenetic relationships of Gomphcythere (Ostracoda) in Lake Tanganyika, East Africa . Journal of Crustacean Biology 22 : 15 – 27 . Google Scholar Crossref Search ADS WorldCat Parker WG , 2016 . Revised phylogenetic analysis of the Aetosauria (Archosauria: Pseudosuchia); assessing the effects of incongruent morphological characters sets . PeerJ 4 : e1583 . Google Scholar Crossref Search ADS PubMed WorldCat Pattinson DJ , Thompson RS , Piotrowski AK , Asher RJ . 2015 . Phylogeny, paleontology, and primates: do incomplete fossils bias the Tree of Life? Systematic Biology 64 : 169 – 186 . Google Scholar Crossref Search ADS PubMed WorldCat Pena-Barbosa JPP , Sierwald P , Brescovit AD . 2013 . On the largest chelodesmid millipedes: taxonomic review and cladistic analysis of the genus Odontopeltis Pocock, 1894 (Diplopoda; Polydesmida; Chelodesmidae) . Zoological Journal of the Linnean Society 169 : 737 – 764 . Google Scholar Crossref Search ADS WorldCat Pettigrew JD . 1991 . Wings or brain? Convergent evolution in the origins of bats . Systematic Zoology , 40 : 199 – 216 . Google Scholar Crossref Search ADS WorldCat Pitz KM , Sierwald P . 2010 . Phylogeny of the millipede order Spirobolida (Arthropoda: Diplopoda: Helminthomorpha) . Cladistics 26 : 497 – 525 . Google Scholar Crossref Search ADS WorldCat Planet , P . 2006 . Tree disagreement: measuring and testing incongruence in phylogenies . Journal of Biomedical Informatics 39 : 86 – 102 . Google Scholar Crossref Search ADS PubMed WorldCat Prendini L , Esposito LA . 2010 . A reanalysis of Parabuthus (Scorpiones: Buthidae) phylogeny with descriptions of two new Parabuthus species endemic to the Central Namib gravel plains, Namibia . Zoological Journal of the Linnean Society 159 : 673 – 710 . Google Scholar Crossref Search ADS WorldCat Ramirez MJ . 2006 . Further problems with the incongruence length difference test: ‘hypercongruence’ effect and multiple comparisons . Cladistics 22 : 289 – 295 . Google Scholar Crossref Search ADS WorldCat Regier JC , Schultz JW , Kambic RE . 2005 . Pancrustacean phylogeny: hexapods are terrestrial crustaceans and maxillopods are not monophyletic . Proceedings of the Royal Society B 272 : 395 – 401 . Google Scholar Crossref Search ADS PubMed WorldCat Řezáč M , Pekár S , Lubin Y . 2008 . How oniscophagous spiders overcame woodlouse armour . Journal of Zoology 275 : 64 – 71 . Google Scholar Crossref Search ADS WorldCat Richter S , Scholtz G . 2001 . Phylogenetic analysis of the Malacostraca (Crustacea) . Journal of Zoological Systematics and Evolutionary Research 39 : 113 – 136 . Google Scholar Crossref Search ADS WorldCat Riehl T , Wilson GDF , Malyutina MV . 2014 . Urstylidae – a new family of abyssal isopods (Crustacea: Asellota) and its phylogenetic implications . Zoological Journal of the Linnean Society 170 : 245 – 296 . Google Scholar Crossref Search ADS WorldCat Robalino J , Wilkins B , Bracken-Grissom HD , Chan TY , O’Leary M . 2016 . The origin of large-bodied shrimp that dominate modern global aquaculture . PLoS One 11 : e0158840 . Google Scholar Crossref Search ADS PubMed WorldCat Robinson DR , Foulds LR . 1981 . Comparison of phylogenetic trees . Mathematical Biosciences 53 : 131 – 147 . Google Scholar Crossref Search ADS WorldCat Ruta M , Wills MA . 2016 . Comparable disparity in the appendicular skeleton across the fish-tetrapod transition, and the morphological gap between fish and tetrapod postcrania . Palaeontology 59 : 249 – 267 . Google Scholar Crossref Search ADS WorldCat Sánchez-Villagra MR , Williams BA . 1998 . Levels of homoplasy in the evolution of the mammalian skeleton . Journal of Mammalian Evolution 5 : 113 – 126 . Google Scholar Crossref Search ADS WorldCat Sansom RS . 2015 . Bias and sensitivity in the placement of fossil taxa resulting from interpretations of missing data . Systematic Biology 64 : 256 – 266 . Google Scholar Crossref Search ADS PubMed WorldCat Sansom RS , Wills MA . 2013 . Fossilization causes organisms to appear erroneously primitive by distorting evolutionary trees . Scientific Reports 3 : 2545 . Google Scholar Crossref Search ADS PubMed WorldCat Sansom RS , Wills MA . 2017 . Differences between hard and soft phylogenetic data . Proceedings of the Royal Society B 284 : 20172150 . Google Scholar Crossref Search ADS PubMed WorldCat Sansom RS , Gabbott SE , Purnell MA . 2010 . Non-random decay of chordate characters causes bias in fossil interpretation . Nature 463 : 797 – 800 . Google Scholar Crossref Search ADS PubMed WorldCat Sansom RS , Wills MA , Williams T . 2017 . Dental data perform relatively poorly in reconstructing mammal phylogenies: Morphological patterns evaluated with molecular benchmarks . Systematic Biology 66 : 813 – 822 . Google Scholar PubMed WorldCat Schnabel KE , Ahyong ST , Maas EW . 2011 . Galatheoidea are not monophyletic – Molecular and morphological phylogeny of the squat lobsters (Decapoda: Anomura) with recognition of a new superfamily . Molecular Phylogenetics and Evolution 58 : 157 – 168 . Google Scholar Crossref Search ADS PubMed WorldCat Selden PA , Shear WA , Sutton MD . 2008 . Fossil evidence for the origin of spider spinnerets, and a proposed arachnid order . Proceedings of the National Academy of Sciences of the USA 105 : 20781 – 20785 . Google Scholar Crossref Search ADS PubMed WorldCat Sepkoski JJ . 1981 . A factor analytic description of the Phanerozoic marine fossil record . Paleobiology 7 : 36 – 53 . Google Scholar Crossref Search ADS WorldCat Shapiro B , Hofreiter M . 2014 . A paleogenomic perspective on evolution and gene function: new insights from ancient DNA . Science 343 : 1236573 . Google Scholar Crossref Search ADS PubMed WorldCat Shultz JW . 2007 . A phylogenetic analysis of the arachnid orders based on morphology . Zoological Journal of the Linnean Society 150 : 221 – 265 . Google Scholar Crossref Search ADS WorldCat Smith RJ . 2000 . Morphology and ontogeny of Cretaceous ostracods with preserved appendages from Brazil . Palaeontology 43 : 63 – 98 . Google Scholar Crossref Search ADS WorldCat Song H , Bucheli SR . 2010 . Comparison of phylogenetic signal between male genitalia and non-genital characters in insect systematics . Cladistics 26 : 23 – 35 . Google Scholar Crossref Search ADS WorldCat Stempien JA . 2005 . Brachyuran taphonomy in a modern tidal-flat environment: preservation potential and anatomical bias . Palaios 20 : 400 – 410 . Google Scholar Crossref Search ADS WorldCat Størmer L . 1939 . Studies on trilobite morphology: part I. The thoracic appendages and their phylogenetic significance . Norsk Geologisk Tidsskrift 19 : 143 – 273 . WorldCat Swofford DL . 2002 . PAUP*: Phylogenetic Analysis Using Parsimony (*and other methods), v.4.0. Sunderland, Massachusetts : Sinauer Associates. Google Preview WorldCat COPAC Tinn O , Oakley TH . 2008 . Erratic rates of molecular evolution and incongruence of fossil and molecular divergence time estimates in Ostracoda (Crustacea) . Molecular Phylogenetics and Evolution 48 : 157 – 167 . Google Scholar Crossref Search ADS PubMed WorldCat Tshudy D , Chan TY , Sornhannus U . 2007 . Morphology based cladistics analysis of Metanephrops: the most diverse extant genus of clawed lobster (Nephropidae) . Journal of Crustacean Biology 27 : 463 – 476 . Google Scholar Crossref Search ADS WorldCat Vereshchaka AL , Lunina AA . 2015 . Phylogeny and taxonomy of the enigmatic genus Petalidium (Decapoda, Sergestidae), with biological remarks . Zoological Journal of the Linnean Society 174 : 459 – 472 . Google Scholar Crossref Search ADS WorldCat Vereshchaka AL , Olesen J , Lunina AA , 2016 . A phylogeny-based revision of the family Luciferidae (Crustacea: Decapoda) . Zoological Journal of the Linnean Society 178 : 15 – 32 . Google Scholar Crossref Search ADS WorldCat Verheye ML , Martin P , Backeljau T , d’Udekem d’Acoz C . 2016 . DNA analyses reveal abundant homoplasy in taxonomically important morphological characters of Eusiroidea (Crustacea, Amphipoda) . Zoologica Scripta 45 : 300 – 321 . Google Scholar Crossref Search ADS WorldCat Wahlberg N , Braby MF , Brower AVZ , de Jong R , Lee MM , Nylin S , Pierce NE , Sperling FAH , Vila R , Warren AD , Zakharov E . 2005 . Synergistic effects of combining morphological and molecular data in resolving the phylogeny of butterflies and skippers . Proceedings of the Royal Society B 272 : 1577 – 1586 . Google Scholar Crossref Search ADS PubMed WorldCat Wagner PJ . 1995 . Testing evolutionary constraint hypotheses with early Paleozoic gastropods . Paleobiology 21 : 248 – 272 . Google Scholar Crossref Search ADS WorldCat Wagner PJ . 1997 . Patterns of morphologic diversification among the Rostroconchia . Paleobiology 23 : 115 – 150 . Google Scholar Crossref Search ADS WorldCat Wesener T , VandenSpiegel D . 2009 . A first phylogenetic analysis of giant pill-millipedes (Diplopoda: Sphaerotheriida), a new model Gondwanan taxon, with special emphasis on island gigantism . Cladistics 25 : 545 – 573 . Google Scholar Crossref Search ADS WorldCat Whatley RC , Siveter DJ , Boomer ID . 1993 . Arthropoda (Crustacea: Ostracoda) . In: Benton MJ , ed. The fossil record 2 . London : Chapman & Hall , 343 – 356 . Google Preview WorldCat COPAC Wheeler WC , Cartwright P , Hayashi CY . 1993 . Arthropod phylogeny: a combined approach . Cladistics 9 : 1 – 39 . Google Scholar Crossref Search ADS WorldCat Wiens JJ . 1998 . Does adding characters with missing data increase or decrease phylogenetic accuracy? Systematic Biology 47 : 625 – 640 . Google Scholar Crossref Search ADS PubMed WorldCat Wiens JJ . 2003a . Missing data, incomplete taxa, and phylogenetic accuracy . Systematic Biology 52 : 528 – 538 . Google Scholar Crossref Search ADS WorldCat Wiens JJ . 2003b . Incomplete taxa, incomplete characters, and phylogenetic accuracy: is there a missing data problem? Journal of Vertebrate Paleontology 23 : 297 – 310 . Google Scholar Crossref Search ADS WorldCat Wiens JJ . 2004 . The role of morphological data in phylogeny reconstruction . Systematic Biology 53 : 653 – 661 . Google Scholar Crossref Search ADS PubMed WorldCat Wilkinson M . 1995 . Coping with abundant missing data in phylogenetic inference using parsimony . Systematic Biology 44 : 501 – 514 . Google Scholar Crossref Search ADS WorldCat Williams BA . 2007 . Comparing levels of homoplasy in the primate skeleton . Journal of Human Evolution 52 : 480 – 489 . Google Scholar Crossref Search ADS PubMed WorldCat Wills MA , Barrett PM , Heathcote JF . 2008 . The modified Gap Excess Ratio (GER*) and the stratigraphic congruence of dinosaur phylogenies . Systematic Biology 57 : 891 – 904 . Google Scholar Crossref Search ADS PubMed WorldCat Wills MA , Jenner RA , Ní Dhubhghaill CN . 2009 . Eumalacostracan evolution: conflict between three sources of data . Arthropod Systematics and Phylogeny 67 : 71 – 90 . WorldCat Wilson GDF . 2009 . The phylogenetic position of the Isopoda in the Peracarida (Crustacea: Malacostraca) . Arthropod Systematics and Phylogeny 67 : 159 – 198 . WorldCat Wipfler B , Machida R , Müller B , Beutel RG . 2011 . On the head morphology of Grylloblattodea (Insecta) and the systematic position of the order, with a new nomenclature for the head muscles of Dicondylia . Systematic Entomology 36 : 241 – 266 . Google Scholar Crossref Search ADS WorldCat Wood HM , Griswold CE , Gillespie RG . 2012 . Phylogenetic placement of pelican spiders (Archaeidae, Araneae), with insight into evolution of the ‘neck’ and predatory behaviours of the superfamily Palpimanoidea . Cladistics 28 : 598 – 626 . Google Scholar Crossref Search ADS WorldCat Wyngaard GA , Hołyńska M , Schulte JA . 2010 . Phylogeny of the freshwater copepod Mesocyclops (Crustacea: Cyclopidae) based on combined molecular and morphological data, with notes on biogeography . Molecular Phylogenetics and Evolution 55 : 753 – 764 . Google Scholar Crossref Search ADS PubMed WorldCat Yoshizawa K . 2004 . Molecular phylogeny of major lineages of Trichadenotecnum and a review of diagnostic morphological characters (Psocoptera: Psocidae) . Systematic Entomology 29 : 383 – 394 . Google Scholar Crossref Search ADS WorldCat Yoshizawa K , Lienhard C . 2010 . In search of the sister group of the true lice: a systematic review of booklice and their relatives, with an updated checklist of Liposcelididae (Insecta: Psocodea) . Arthropod Systematics & Phylogeny 68 : 181 – 195 . WorldCat Yoshizawa K , Yao I , Lienhard C . 2016 . Molecular phylogeny reveals genital convergences and reversals in the barklouse genus Trichadenotecnum (Insecta: Psocodea: ‘Psocoptera’: Psocidae) . Molecular Phylogenetics and Evolution 94 : 358 – 364 . Google Scholar Crossref Search ADS PubMed WorldCat © 2019 The Linnean Society of London, Zoological Journal of the Linnean Society This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)
TI - Phylogenetic incongruence and homoplasy in the appendages and bodies of arthropods: why broad character sampling is best
JF - Zoological Journal of the Linnean Society
DO - 10.1093/zoolinnean/zlz024
DA - 2019-08-23
UR - https://www.deepdyve.com/lp/oxford-university-press/phylogenetic-incongruence-and-homoplasy-in-the-appendages-and-bodies-xG96z1fTV6
SP - 100
VL - 187
IS - 1
DP - DeepDyve
ER -