Searching factors causing implausible non-monophyly: ssu rDNA phylogeny of Isopoda Asellota (Crustacea: Peracarida) and faster evolution in marine than in freshwater habitats

MOLECULAR PHYLOGENETICS AND EVOLUTION Molecular Phylogenetics and Evolution 28 (2003) 536–551 www.elsevier.com/locate/ympev

Searching factors causing implausible non-monophyly: ssu rDNA phylogeny of Isopoda Asellota (Crustacea: Peracarida) and faster evolution in marine than in freshwater habitats Johann-Wolfgang W€ agele,a,* Barbara Holland,a Hermann Dreyer,b and Beate Hackethala a

Lehrstuhl Spezielle Zoologie, Fakult€at Biologie, Ruhr-Universit€at Bochum, 44780 Bochum, Germany b Institute of Zoology, University of Vienna, Austria Received 17 July 2002; revised 4 January 2003

Abstract This contribution addresses two questions: which alignment patterns are causing non-monophyly of the Asellota and what is the phylogenetic history of this group? The Asellota are small benthic crustaceans occurring in most aquatic habitats. In view of the complex morphological apomorphies known for this group, monophyly of the Asellota has never been questioned. Using ssu rDNA sequences of outgroups and of 16 asellote species from fresh water, littoral marine habitats and from deep-sea localities, the early divergence between the lineages in fresh water and in the ocean, and the monophyly of the deep-sea taxon Munnopsidae are confirmed. Relative substitution rates of freshwater species are much lower than in other isopod species, rates being highest in some littoral marine genera (Carpias and Jaera). Furthermore, more sequence sites are variable in marine than in freshwater species, the latter conserve outgroup character states. Monophyly is recovered with parsimony methods, but not with distance and maximum likelihood analyses, which tear apart the marine from the freshwater species. The information content of alignments was studied with spectra of supporting positions. The scarcity of signal ( ¼ apomorphic nucleotides) supporting monophyly of the Asellota is attributed to a short stem-line of this group or to erosion of signal in fast evolving marine species. Parametric boostrapping in combination with spectra indicates that a tree model cannot explain the data and that monophyly of the Asellota should not be rejected even though many topologies do not recover this taxon.
2003 Elsevier Science (USA). All rights reserved. Keywords: Crustacea; Isopoda; Asellota; Deep-sea; Molecular systematics; Long-branch-effects; Covariotids; Spectrum of supporting positions; Phylogenetic signal

1. Introduction The Asellota are a species-rich group of isopods (about 1940 known species) that occur in marine and freshwater habitats. Eurasian inland species are often found in ponds and streams, but some also live in caves, others are very small stygobionts ( ¼ inhabitants of underground waters, see e.g., Coineau, 1968; Henry et al., 1986). In the sea they colonize all depths from shallow water down to hadal trenches, they occur at all latitudes (e.g., Barnard et al., 1962; Brandt, 2000; Kussakin, 1999; *

Corresponding author. Fax: +39-(0)-234-32-4114. E-mail address: [email protected] (J.-W. Wa¨gele).

Menzies, 1963; Sars, 1909; Siebenaller and Hessler, 1981; Svavarsson et al., 1993; Wolff, 1962). The Asellota are adapted to a benthic mode of life and most of them cannot swim. Only a single monophylum of deep-sea isopods (the Munnopsidae) acquired secondarily the ability to swim: in these animals the posterior pereopods (primarily walking legs) are flattened and used to paddle backwards (Hessler, 1993; Hessler and Str€ omberg, 1989). In the deep-sea, the Asellota are one of the most conspicuous taxa of benthic invertebrates. Locally, single sledge-samples of Atlantic deep-sea fauna contained up to 109 different isopod species, most of which were asellotes (Wilson, 1998). Preliminary phylogenetic analyses based on known morphological characters

1055-7903/$ - see front matter
2003 Elsevier Science (USA). All rights reserved. doi:10.1016/S1055-7903(03)00053-8

J.-W. Wa¨gele et al. / Molecular Phylogenetics and Evolution 28 (2003) 536–551

(W€ agele, 1989) have suggested that large groups of deep-sea taxa are monophyletic. Obviously, the speciesrichness of deep-sea asellotes is the result of in situ speciation and not of immigration from shallower areas. During a previous analysis of the phylogenetic information content of 18S rDNA sequences we noted that monophyly of the Asellota was not recovered (Dreyer and W€ agele, 2001, 2002), with the sequences of marine species (Janiroidea) being closer to the base of the tree and the freshwater species (Aselloidea) branching off nearer to the higher isopods (the Scutocoxifera). This was a surprising result because none of the taxonomists familiar with isopods ever suggested paraphyly of this group. Monophyly is much more probable because it is supported by inferred groundpattern apomorphies such as: the shortening of the pleon and the fusion of pleonites 3–5 with the pleotelson; the migration of the anus to the caudal end of the telson; pleopods with short setae unsuitable for swimming; pleopods 1 and 2 sexually dimorphic; pleopod 1 absent in females, medially fused and uniramous in males; pleopod 2 without endopod in females, transformed into a gonopodium in males (without the copulatory stylet present in other isopods; W€agele, 1989). The posterior swimming appendages (pleopods), which in other Malacostraca are typically leaf-like and armed with long swimming setae, are modified in asellote isopods. The tail-fan (used for steering in swimming malacostracans and in some isopods, e.g., in Cirolanidae) is reduced, the uropods are styliform. The Asellota also share a plesiomorphic (mostly ringlike) coxa, while the ‘‘higher isopods’’ (Scutocoxifera) are characterized by rigid coxal plates. Persons not familiar with morphological analyses should accept that a rejection of the monophyly of asellotes has (from the point of view of taxonomists) a similar high probability of being incorrect as the assumption that birds are polyphyletic. The available information allows us to postulate a ‘‘morphological hypothesis’’ for the larger clades present in our alignment (Fig. 1). This tree is congruent with the result of previous phylogenetic analyses (Brusca and Wilson, 1991; W€ agele, 1989) and contains above all monophyletic Asellota. The fact that groups of Asellota cluster separately in some of the molecular analyses is the motivation to search for causes of errors that without a priori knowledge would remain unnoticed. In an 18S rDNA alignment with fewer asellote sequences, Dreyer and W€ agele (2002) have neither found strong signals contradicting monophyly of the Asellota nor supporting signals. In the present paper, we add some further asellote sequences, describe the divergence between freshwater, marine littoral and deep-sea asellotes and concentrate on phenomena presumably causing the implausible non-monophyly of the Asellota.

537

Fig. 1. Topology for the studied crustaceans as expected from morphological data (see cladistic analyses of the Malacostraca and Peracarida: Kobusch, 1999; Richter, 1999; Richter and Scholtz, 2001; cladistic analyses of the Isopoda: Brusca and Wilson, 1991; W€agele, 1989). The Asellota should be monophyletic according to important morphological evidence.

2. Materials and methods 2.1. Specimens and sequences Specimens were collected by the authors or were donated by colleagues (data published as electronic supplementary material). For the purpose of this study we did not include all available 18S rDNA sequences present in our isopod-DNA database, but rather restricted the analysis to the Asellota and some outgroups to reduce computation time. 2.2. DNA sequencing and alignment Primer sequences, methods used for fixation, amplification, and sequencing are described in Dreyer and W€agele (2001, 2002). Initially, sequences were aligned using CLUSTAL X (Thompson et al., 1997) and the result was edited by eye with GeneDoc (Nicholas et al., 1997) and corrected using secondary structure information to reduce the positional variability. Alignment of these sequences is difficult and has a strong influence on the results. Due to the enormous length variation of the isopod ssu rRNA

538

J.-W. Wa¨gele et al. / Molecular Phylogenetics and Evolution 28 (2003) 536–551

gene many gaps had to be inserted, and the initial alignment obtained with CULSTAL X proved to be reliable only in the most conserved areas. Long insertions occur especially in the V4 and V7 regions (see nomenclature in Nelles et al., 1984) wherefore isopod sequences are distinctly longer than those of outgroup species. Even though initial phylogenetic analyses of the complete alignment produced some plausible results, we decided to cut out those areas that did not show alignable patterns. The initial alignment had 28 sequences and was 4114 bp long. We reduced it to 2363 bp deleting the following positions (in parentheses corresponding positions in the sequence of Asellus aquaticus): 248–301 (194–207), 1030–2213 (674–860), 3057–3489 (1571–1672), 3932–4012 (2011–2034). The alignment is available from the authors and is published in the EMBL data bank (ID 1017755945). 2.3. Statistics and phylogenetic analyses Base composition was examined with the tools implemented in PAUP v. 4.x (Swofford, 1998). The relative rate test was carried out with the program K2WuLi (Jermiin, 1996), which uses Kimura-2-parameter distances as proposed by Wu and Li (1985). Differences in the number of variable positions were detected by comparison of individual isopod sequences with basal outgroups and the phreatocid (specifically: sequences of Tethysbaena scabra, Leptochelia sp., and Colubotelson hodgsoni) and counting the visible variable positions using MEGA (Kumar et al., 1993). The phylogenetic information content of the alignment was visualized by constructing a spectrum of splitsupporting positions with PHYSID (for details see W€agele and R€ odding, 1998a,b). The relevance of the method has been explained elsewhere (e.g., in W€agele et al., 1999; W€ agele and Misof, 2001). Working with PHYSID, the number of supporting positions is used as a measure for the amount of phylogenetic signal conserved in the alignments. Monophyly of a group has a higher probability if the signal is high and the group is compatible with other groups of high support. Longbranch phenomena can also be detected in these spectra because highly modified sequences appear in many nonsense groupings of species due to the higher number of chance similarities (e.g., combinations of a single amphipod species with different isopods, or repeated combinations of Jaera spp. with a different isopod). PHYSID also allows the output of alignment patterns with conserved positions that support a redefined split (see Table 2 in the electronic supplement). (Note that ‘‘nonsense groupings’’ can be identified either with a priori knowledge (e.g., a group with a bird appearing among mammals) or by the fact that a group of taxa is incompatible with any of the topologies estimated from the given data.)

We used PAUP v. 4.x (Swofford, 1998) for tree construction with distance methods (neighbor-joining: Saitou and Nei, 1987), maximum likelihood (Felsenstein, 1981), and parsimony analyses. Parameters for substitution models were also estimated with PAUP v. 4.x. Distance trees, corrected pairwise distances, and relative rate tests were used to identify long-branch taxa. The model for maximum likelihood analyses was chosen with a likelihood ratio test (Huelsenbeck and Bull, 1996; Huelsenbeck and Crandall, 1997, see also Crandall et al., 2001) using the program Modeltest Version 2.0 (Posada and Crandall, 1998). The optimal model was the GTR + G + I model with six substitution types, nucleotide frequencies estimated via maximum likelihood, c-distribution of rate variation among sites as well as proportion of invariable sites estimated from the data, the molecular clock not enforced (estimated proportion of invariable sites ¼ 0.22, a-shape parameter 0.60). Other models were also used. Analyses with complex models required a reduction of the number of sequences to shorten computation time. For the same reason bootstrap runs with the maximum likelihood criterion were restricted to 100 replicates (each with 10 replicates of random sequence additions). In most phylogenetic analyses which are based on explicit models of nucleotide substitution (e.g., maximum likelihood) some effort is expended to find the most appropriate model to use, for example, nested likelihood ratio tests such as performed by Modeltest (Posada and Crandall, 1998). However, once the best model has been found it is also interesting to ask how well the observed data is explained by this model. Here we use parametric bootstrapping in combination with spectral analysis to test how well our proposed model of nucleotide substitution, in combination with a given weighted tree, explains the data. First a reduced alignment of 1414 bp was created by removing all gaps or ambiguous sites. This was necessary as simulated bootstrap sequences cannot contain any special characters and we wished to compare the observed alignment with simulated alignments. The program Modeltest was used to find the best model of nucleotide substitution (TrNef + I + G) on this reduced alignment. Fixing this model of nucleotide substitution with all the estimated parameters from Modeltest, PAUP v. 4.x (Swofford, 1998) was used to find the maximum likelihood tree. This was done first without any constraint on topology, and then with the constraint that the Asellota should be monophyletic. Finally, to generate the parametric bootstrap samples the program Seq-gen (Rambuat and Grassly, 1997) was used to simulate 100 sequence alignments on the ML tree and the constrained Asellota tree, respectively, again using the model found by Modeltest. As with the methodology of PHYSID we count those sites in the alignment that support a hypothesis of

J.-W. Wa¨gele et al. / Molecular Phylogenetics and Evolution 28 (2003) 536–551

monophyly, that is they have a single shared character that defines a subset of the taxa. For example the site [AACCCGGG] supports the monophyly of taxa 1 and 2 based on the novel character A, it also supports the monophyly of taxa 3–5 based on the novel character C, and similarly the monophyly of taxa 6–8. For each of the parametric bootstrap datasets the support for each hypothesis of monophyly was tabulated and compared to the support seen in the observed alignment. These results are displayed in Fig. 6, the bars below the x-axis indicate the support for the reverse hypothesis of the bar above the x-axis (e.g., from the small example above, support for the reverse of the monophyletic group 1–2 would be those sites where taxa 3–8 shared a common character to the exclusion of taxa 1 and 2). The proportion (out of 100) of the bootstrap samples that had higher support for each hypothesis of monophyly than was observed in the real alignment is indicated above or below the corresponding bar in the spectrum (Fig. 6). These numbers can be interpreted as p values indicating how likely is it to see the observed amount of support for that hypothesis of monophyly under both the ML tree and the constrained Asellota tree. The SOWH-test (Goldman et al., 2000) was used to test the significance of the difference in ln likelihood between the maximum likelihood tree and the best tree with the constraint that the Asellota taxa be monophyletic. The test was performed on a reduced alignment in which all sites with gaps or ambiguous characters were removed. As full optimization of the maximum likelihood tree along with model parameters for each parametric bootstrap replicate was computationally time consuming, the partial optimization version of the test was used.

3. Results 3.1. Base composition, relative rate test, and variability of sites Sequence length varies within isopods used for this study between 2098 and 3402 bp (details are given in the

539

electronic supplement). The alignment has 2365 characters of which 717 are parsimony informative. Most sequences deviate only little from the mean base frequencies (A:C:G:T ¼ 0.26:0.23:0.27:0.24). An exception are the species of Jaera, with an AT-content of 0.59. For the complete alignment the null hypothesis of homogeneity of base composition was confirmed with a v2 test (p < 0:001). The relative rate test supports the observation that some species evolve distinctly faster than others (see below). We took the tanaidacean Leptochelia sp. as the closest available outgroup of the Isopoda and compared all other sequences with the one of the freshwater asellid A. aquaticus. Greatest rate differences occur in the Asellota and among these the highest rates are found in Carpias and Jaera (range of Z-scores: 5.51– 6.86), followed by Janira (2.35), Iathrippa (1.31), and the Munnopsidae (range: 0.62–1.27). Clearly, in all marine species relative rates are higher than in the freshwater species (Z-score range: 0.23–0.36). Other isopod taxa also show higher rates (Oniscidea: 2.84– 4.16; Valvifera: 1.58–2.26). Repeating this analysis with both alignments, i.e., including or excluding the hypervariable regions, similar values are obtained. Differences between the two alignments are not great because positions with gaps, which are frequent in hypervariable regions, cannot be considered for distance estimations. As seen in Table 1, the number of variable positions is lowest in the freshwater isopods (Aselloidea) when compared to basal outgroup sequences: adding single aselloid sequences to an alignment of outgroup taxa, the number of variable positions increases only slightly. The fast evolving shallow-water marine Janiroidea have on average five times more additional variable positions. This means that sites that normally are not free to vary show substitutions in these fast evolving sequences, a fact also obvious when the total alignment is inspected by eye (see also Table 2 of the electronic supplement). Munnopsidae (deep-sea species) and the higher isopods (Scutocoxifera) have more than two respectively nearly three times the number of additional variable positions than the freshwater species.

Table 1 Number of variable sites when individual isopod sequences are compared to outgroups Taxa considered

Total variable sites

Range of additional variable sites

Mean number of additional variable sites

Outgroup taxa alone Outgroup plus single Aselloidea Outgroup plus Jaera spp. and Carpias Outgroup plus single Munnopsidae Outgroup plus single Janiroidea Outgroup plus single Scutocoxifera

452 474–489 578–591 508–521 508–591 497–556

—

—

22–37 126–139 56–69 56–139 45–104

26.75  6.9 134  7 62.16  5.2 81  32.9 73.5  31.85

540

J.-W. Wa¨gele et al. / Molecular Phylogenetics and Evolution 28 (2003) 536–551

Table 2 Genetic distances of asellote isopods to more basal outgroup sequences, estimated via maximum likelihood (GTR model) and with LogDet correction Species

Distance to ourgroup 1 (Leptochelia)

Lirceus fontinalis Proasellus slavus Stenasellus racovitzai

GTR: 0.154 LogDet: 0.155 GTR: 0.146 LogDet: 0.147 GTR: 0.154 LogDet: 0.155 GTR: 0.156 LogDet: 0.157

Iathrippa trilobatus Jaera albifrons Jaera nordmanni Janira maculosa Joeropsis coralicola Acanthocope galathea Eurycopen sp. Eurycope inermis Munnopsis typica Echinozone spinosa

Range GTR: 0.181–0.192 LogDet: 0.181–0.191

GTR: 0.192 LogDet: 0.191 GTR: 0.181 LogDet: 0.181 GTR: 0.182 LogDet: 0.182 GTR: 0.183 LogDet: 0.183 GTR: 0.156–0.231 LogDet: 0.159–0.233

Janiroidea Carpias nereus

Distance to outgroup 2 (Tethysbaena)

GTR: 0.146–0.156 LogDet: 0.147–0.157

Aselloidea Asellus aquaticus

Range

GTR: 0.214 LogDet: 0.215 GTR: 0.163 LogDet: 0.164 GTR: 0.227 LogDet: 0.230 GTR: 0.231 LogDet: 0.233 GTR: 0.174 LogDet: 0.176 GTR: 0.160 LogDet: 0.161 GTR: 0.163 LogDet: 0.164 GTR: 0.163 LogDet: 0.164 GTR: 0.161 LogDet: 0.162 GTR: 0.158 LogDet: 0.159 GTR: 0.157 LogDet: 0.159

3.2. Maximum parsimony The most parsimonious tree (tree length ¼ 2562, Fig. 1 of the electronic supplement) is consistent with some recent phylogenetic analyses based on morphological data (Brusca and Wilson, 1991; W€ agele, 1989, further hypotheses are discussed later). The oldest extant isopod group in our data set would be the Phreatoicidea, followed by the Asellota with the sistertaxon Scutocoxifera (not considering the Calabozoidea Van Lieshout, 1983 and the engimatic genus Tainisopus Wilson and Ponder, 1992, not represented in our dataset). The Asellota are monophyletic and partitioned into two clades corresponding to the Aselloidea (freshwater isopods) and the Janiroidea (the common marine asellote species). The sequenced deep-sea species of the Family Munnopsidae Sars, 1869 are also monophyletic. The sequences of Leptochelia, Gammarus, and Tethysbaena are grouped in one clade, a peculiarity that is not plausible because this is a mixture of peracarid and thermosbaenacean crustaceans (for recent phylogenetic

GTR: 0.196–0.266 LogDet: 0.196–0.257 GTR: 0.241 LogDet: 0.242 GTR: 0.196 LogDet: 0.196 GTR: 0.228 LogDet: 0.256 GTR: 0.266 LogDet: 0.257 GTR: 0.211 LogDet: 0.209 GTR: 0.200 LogDet: 0.199 GTR: 0.204 LogDet: 0.203 GTR: 0.208 LogDet: 0.207 GTR: 0.207 LogDet: 0.206 GTR: 0.206 LogDet: 0.205 GTR: 0.202 LogDet: 0.202

analysis of peracarid crustaceans see Kobusch, 1999; Richter, 1999; Richter and Scholtz, 2001). In the 50% majority-rule bootstrap consesus-tree (Fig. 2), the monophyly of the Asellota is lost but not rejected, i.e., it is compatible with a polytomy and alternative sistergroup-relationships for asellote subgroups are not obtained. The consensus tree contains a polytomy with the following taxa: Aselloidea, Janiroidea, Colubotelson, Trachelipus, and Ligia (i.e., a signal for the monophyly of Oniscidea is not detected in this case), Valvifera ( ¼ Idotea plus Glyptonotus). The support for the Isopoda is not high (65%). Including or excluding other species does not alter the splitting of asellote sequences into the two groups seen in Fig. 2 (Aselloidea and Janiroidea) and the monophyly of the Asellota is not recovered in the bootstrap consensus tree. However, the sequence of Leptochelia sp. sometimes groups closer to the Janiroidea, when other basal outgroups are missing in the dataset. Also, excluding valviferan sequences the Aselloidea can group closer to the basal outgroups while the Janiroidea appear as

J.-W. Wa¨gele et al. / Molecular Phylogenetics and Evolution 28 (2003) 536–551

541

Fig. 2. Fifty percent majority-rule bootstrap consensus tree with bootstrap support values (first value on branches: 1000 replicates for maximum parsimony; second value: 100 replicates with 10 random sequence additions for maximum likelihood). Lower value: bootstrap support values from neighbor-joining tree with distances estimated via maximum likelihood (GTR model, 1000 replicates). Note that basal nodes of the isopod trees collapsed. The two monophyletic lineages of the Asellota (Aselloidea, Janiroidea) are recovered. The names marked with a dark bar represent deepsea species.

sistergroup to the Oniscidea. These variations also indicate that the phylogenetic signal for several basal dichotomies of the ‘‘real’’ phylogenetic tree must be weak. 3.3. Genetic distances Of the species selected for this study the closest outgroup of the Isopoda is Leptochelia sp., a mediterranean representative of the Tanaidacea. The closest outgroup of the Peracarida is the sequence of the thermosbaenacean Tethysbaena scabra. We estimated distances of asellote

species to basal outgroups using different substitution rate models. Table 2 shows two examples (rate matrices estimated via maximum likelihood for the GTR model and with LogDet correction of p-distances). The GTR model (Lanave et al., 1984; Tavare, 1986) needs the assumption that rate distribution is equal across variable sites. It uses six substitution types and relies on constancy of base frequencies, while the LogDet correction (Lake, 1994; Lockhart et al., 1994; Steel, 1994) allows variations of base frequencies and of substitution rates in different branches of a topology. Even though in practice the

542

J.-W. Wa¨gele et al. / Molecular Phylogenetics and Evolution 28 (2003) 536–551

LogDet transformation often produces the greatest differences when topologies constructed under different models are compared, it is obvious from Table 2 that a general tendency emerges independently of the model used: the distances of the marine species (Janiroidea) to the basal outgroup are distinctly higher than those of the freshwater species (Aselloidea). The ranges of distances found for these two asellote groups do not even overlap. This indicates that on average the number of substitutions is higher in the marine species, a result that is consistent with the relative-rate test. Because the stem-line leading from the outgroup to the last common ancestor of all Asellota is the same, differences are caused by events that occurred after the divergence of both lines, one evolving in freshwater habitats, the other one in the sea. Distance topologies constructed by neighbor-joining and with distances estimated via maximum likelihood (GTR model, empricial nucleotide frequencies: A ¼ 0.26, C ¼ 0.23, G ¼ 0.27, T ¼ 0.24) tear the two asellote groups apart. The resulting polyphyly indicates that sequences of freshwater isopods are more similar to those of the Scutocoxifera than to those of the marine asellotes. Exclusion of fast evolving sequences (Gammarus spp., Jaera spp.) did not alter the result. Interestingly, even though the longest isopod branches are those leading to Carpias and Jaera, these taxa are not placed outside the Janiroidea. A misplacement due to long-branch effects is not observed because the corresponding sequences preserve many autapomorphies of the Janiroidea (partly seen in Table 2 of the electronic supplement). 3.4. Maximum likelihood Using the model selected by a previous likelihood ratio test a topology is obtained (Fig. 3) that is close to the tree derived from morphology (Fig. 1): the Peracarida, the Isopoda, the Scutocoxifera, the Janiroidea, and the Aselloidea are monophyletic. Groupings that are not plausible and probably erroneous are the clades {Amphipoda and Tanaidacea} and {Phreatoicidea and Scutocoxifera}. Furthermore, the Asellota are paraphyletic with the Aselloidea being closer to the Scutocoxifera. There are only few differences to the distance topologies. In the lines starting with the putative last common ancestor of the Asellota to the terminal species the average number of estimated substitutions per site is 0.044 (range: 0.037–0.052) within the Aselloidea and 0.14 (range 0.081–0.307) within the Janiroidea. The highest rates occur in the littoral genera Carpias, Jaera, and Janira. Excluding these taxa, the average rate is still roughly twice as high in the Janiroidea (0.086 substitutions per site) in comparison with the freshwater isopods. Unfortunately, a calibration of rates is not possible because asellote fossils are not known and indirect biogeographic evidence is not available.

The bootstrap consensus topology is very similar to that obtained with the parsimony criterion, results are included in Fig. 2. Comparing the topology of Fig. 3 with a constrained topology containing monophyletic Asellota, the Kishino–Hasegawa test (Kishino and Hasegawa, 1989) as implemented in PAUP does not reject monophyly (p ¼ 0:073). 3.5. Spectra of supporting positions Spectra of putative apomorphies are useful to visualize the signal content of alignments. Fig. 4 shows the 49 best splits of the alignment. Each column indicates how many supporting positions are present in the alignment. Based on relatively simple statistics that compensate for multiple hits to some degree (allowing noise to occur in supporting patterns, see W€ agele and R€ odding, 1998a,b) and without the need to specify a substitution model, those positions are identified that have a majority of one character state occurring in the ingroup but not in the corresponding outgroup (which is the rest of the bipartition of the sequences). Supporting positions contain putative apomorphies. Only those splits that appear also in the previously mentioned tree topologies have been named (arrows in Fig. 4). All the remaining splits seen in the spectrum are different partitions of species that are mutually incompatible (in the sense that they do not fit on a single tree-like topology). These splits show the amount of background noise present in this alignment: many splits are nonsense groupings supported by a large number of positions. The fact that the signals supporting meaningful splits are in most cases not better than the background noise shows that little useful phylogenetic information is retained in conserved positions. We know that the present version of PHYSID used to construct these spectra is too conservative, part of the information is lost. But the spectra are nevertheless a good visualization of the information extracted from the more conserved positions. Among the meaningful splits, the clade Janiroidea and some of its subgroups have more autapomorphies than the Aselloidea with only six supporting positions (wherefore the latter do not appear among the best 49 splits). Interestingly, the split with the group {Aselloidea, Scutocoxifera, Colubotelson} that is seen in distance- and ML-trees (Fig. 3) is not present in the spectrum, which means that no conserved binary or even moderately noisy sites support this split. Therefore it is not surprising that the bootstrap consensus topologies (Fig. 2) only show a polytomy for these taxa. Since the long-branch sequences of Jaera spp. and Gammarus spp. are causing many random combinations with other taxa, exclusion of these sequences reduces the noise contained in this alignment (Fig. 5). Several of the nonsense splits (not named in Fig. 4) are combinations containing Carpias or Tethysbaena, an indication for

J.-W. Wa¨gele et al. / Molecular Phylogenetics and Evolution 28 (2003) 536–551

543

Fig. 3. Result of maximum likelihood analysis using a substitution matrix and base frequencies estimated via a likelihood ratio test (c-shape parameter: 0.60, four rate categories, molecular clock not enforced, score of best tree: 14777). The Asellota are torn apart in the same way as in the distance analyses. Bootstrap support values are shown in Fig. 2.

further long-branch effects (chance similarities are shared with unrelated species, such as Carpias + oniscids, Carpias + Leptochelia, etc.). In Fig. 5, the Janiroidea and Munnopsidae are supported by many autapomorphies, but neither the Asellota nor the Aselloidea do appear among the best splits. This result is congruent with the observation of higher rates in the marine species (see foregoing results). Interestingly, using the complete alignment, i.e., including the hypervariable regions, a distinct signal in

favour of the Aselloidea is found (rank no. 4 in the spectrum, not shown). This indicates that autapomorphies of the Aselloidea are found in those variable positions that have been excluded for this study. 3.6. Parametric bootstrap combined with spectra Fig. 6 shows the proportion of parametric boostrap samples (out of 100) that support a bipartition in the ML topology or in the constrained tree with monophyletic

544

J.-W. Wa¨gele et al. / Molecular Phylogenetics and Evolution 28 (2003) 536–551

Asellota. Each bar represents a bipartition found in tree topologies. These results give us further evidence to discuss whether this dataset has sufficient information to accept or reject the Asellota hypothesis. The vast majority of bipartitions (splits) shown support various groupings of the seven longest branch taxa (Jaera spp, Gammarus spp, Carpias, Tethysbaena, and Leptochelia). Fig. 6 shows that either the ML or the Asellota tree are equally capable of explaining these patterns which clearly result from convergent evolution along the long branches, or, in the case of support for the reverse hypotheses, erosion of the ground pattern. Those splits that show support for edges in both trees, e.g., the Jaera spp. group and the Gammarus spp. group, are also equally well explained by either tree. In addition there are a number of splits that are equally poorly explained by either tree (black columns). By chance we would expect that 1 in 10 splits would have a p value with lower than 5 or higher than 95, in practice 11 out of 43 splits (of which only the first 29 are shown in Fig. 6) are either over or underrepresented in the observed data according to what is expected on the ML tree, similarly 8 out of 43 splits are either over or underrepresented in the observed data according to what is expected on the Asellota tree. In other words, the data cannot be wholy explained by a tree model at all, the main signals left in the data are the result of convergent evolution. Certainly it would seem unwise to reject the Asellota hypothesis on the basis of these data alone. The SOWH-test (Goldman et al., 2000) (results are shown in Fig. 2 of the electronic supplement), based on 100 parametric bootstrap replicates, rejects the null hypothesis that the difference in likelihoods is not significant at a p value of 0.5 which means that nonmonophyly of the Asellota is rejected. In contrast, the Kishino–Hasegawa test does not reject the null hypothesis (with a p value of 0.073). We note, however, that both these tests rely on using an accurate model of nucleotide substitution and that the results of the spectral analysis indicate that the model in combination with the ML tree is inadequate to explain the observed data.

4. Discussion 4.1. Causes of errors and of non-monophyly Depending on the method of data analysis the Asellota are monophyletic or not. Only the most parsimonious tree resembles the tree derived from morphology, while with other methods the tree is either not resolved or the topologies are not plausible (para- or polyphyletic Asellota). Wrong topologies are obtained in molecular phylogenetic analyses when the data are not informative (Adoutte and Philippe, 1993; Chang and Campbell,

2000; Colgan et al., 1998; Emerson et al., 1999; Littlewood et al., 2001; Mitchell et al., 1997; Shultz and Regier, 1997; Steiner and M€ uller, 1996), i.e., when sequences evolve too slowly to contain synapomorphies, or when they evolve too fast so that synapomorphies are substituted (i.e. variable positions are saturated, e.g., Philippe and Forterre, 1999). In the latter case symplesiomophies may support false clades that in reality are paraphyletic (erosion effects: Fuellen et al., 2001a,b). It is not surprising that often ssu rDNA alignments do not resolve deeper phylogenies (Abouheif et al., 1998; Philippe and Laurent, 1998), because signals erode in the course of time due to multiple substitutions. Mistakes also occur when rates of sister-taxa differ in such a way that chance similarities occurring in fast evolving sequences support non-monophyletic groupings (long-branch attraction: Felsenstein, 1978; Hendy and Penny, 1989; Huelsenbeck, 1997; Lyons-Weiler and Hoelzer, 1997; Philippe and Laurent, 1998; etc.), but also when real sister taxa with long branches are separated in maximum likelihood analyses (‘‘long-branch repulsion,’’ not seen in parsimony analyses: Pol and Siddall, 2001; Siddall, 1988). In general, artefacts occur when substitution processes vary across the tree and across sites (e.g., Fitch and Markowitz, 1970; Lockhart et al., 1994; Shoemaker and Fitch, 1989; Steel et al., 1993; Steel et al., 2000; Waddell et al., 1997; Yang, 1996). Long branches may be undetectable when sequences are saturated, and this effect will occur more often in deep phylogenies than in younger clades (Philippe and Laurent, 1998). Compositional bias, which sometimes has distinct effects (e.g., the grouping of nematodes and bees discussed by Foster and Hickey, 1999), is not correlated with a major misplacement of taxa in our case. The Jaera species found their correct place among the Janiroidea despite their higher AT-content. In our example, the data structure that causes para- or polyphyly of the Asellota is not clear-cut: the stem-line of the Asellota and of the subclade Aselloidea evolved slowly so that little phylogenetic signal is present in the ssu rDNA for these groups, while other species considered herein, especially also some of the Janiroidea, evolved fast und show considerably more variable positions in relation to outgroup sequences. The faster asellotes are not grouped with some of the more basal long-branch outgroup taxa (Gammarus species, see Fig. 3), as expected in an obvious case of long-branch attraction, however, they remain closer to these taxa, while the slower asellotes are placed nearer to those outgroup species that occur higher up in the tree. Studying splitsupporting positions (Table 2, electronic supplement) one can identify those variable sequence positions that have a majority character state for all species except in the Janiroidea, the latter having different states in most species. It can be seen that most of the substitutions must have occurred in the stem-line of the Janiroidea (new

J.-W. Wa¨gele et al. / Molecular Phylogenetics and Evolution 28 (2003) 536–551

states present in all Janiroidea), some additional substitutions evolved for example in the stem-line of the Munnopsidae. It seems that the long-branch effect caused by this type of positions consists in a placement of the Janiroidea at the base of the isopod tree (in the distance and maximum likelihood topologies, Fig. 3). A similar misplacement is observed in the case of Colubotelson. This representative of the Phreatoicidea (a group with the oldest fossil record among all isopods: Schram, 1970) should appear at the base of the Isopoda (Brusca and Wilson, 1991; W€ agele, 1989), but in distance and maximum likelihood topologies (Fig. 3) the sequence is inserted among the higher isopods, the Scutocoxifera. Rate differences that might cause these problems could also be visualized with spectra of supporting positions (Figs. 4 and 5). Signals in favour of the Aselloidea are weak, and positions favouring the clade Asellota are too noisy (with a third or fourth character state in more than 1 species) to have a supporting effect. Checking the alignment by eye, some positions are found with a majority character state shared by the Asellota as a whole, but these usually also show similar states in few outgroup sequences (e.g., position 1194 with a T in the Asellota that also occurs in Colubotelson, 1231 with a T shared with Glyptonotus, 2267 with a unique A). However, we cannot conclude that the alignment in general has no phylogenetic signal. Signal evolved and is preserved in an unpredictable way in some clades, in others not. As shown in Table 1 (and Table 2 of the electronic supplement), not only the rates vary, but also the number of variable positions. Obviously, a correct covarion-model (Fitch, 1971; Galtier, 2001; Lockhart et al., 1996; Miyamoto and Fitch, 1996) that allows for site variation in part of the tree and invariability in other parts is needed to simulate the evolution of the ssu rDNA in these crustaceans. We tried several of the available corrections to see if topologies with monophyletic Asellota can be obtained. Use of distance methods in combination with different substitution models always produced topologies with para- or polyphyletic Asellota. To correct for positional rate heterogeneity we used the LogDet/ paralinear transformation and neighbor joining as implemented in PAUP v. 4.x, which is an application of an invariable sites model (Lake, 1994; Lockhart et al., 1994; Steel, 1994). The resulting topology, however, does not differ from a neighbor-joining tree estimated with maximum likelihood distances based on a model without invariable sites and with equal rates at all variable sites. Selection of a model via likelihood ratio test and using maximum likelihood as optimality criterion for tree construction did not help, the Asellota remain paraphyletic (Fig. 3). In other published cases where rate differences caused problems, maximum likelihood analyses allowed estimation of the expected tree while distance methods failed. A beautiful example of this kind is that of the planktonic

545

Foraminifera which evolve 50–100 times faster than benthic ones (Pawlowski et al., 1997). However, the rate differences are not so dramatic in our example, signals are probably weaker than in the Foraminifera and the maximum likelihood (Fig. 3) analyses did not produce the tree expected from morphology. Maximum likelihood may fail because distantly related species can be clustered due to similar distributions of invariable sites (Germot and Philippe, 1999; Lockhart et al., 1996, 1998). We assume that for our alignment a model is needed that allows large variations of the number of variable sites across the tree (a covarion model) and in addition variation of site rates in different branches and variations across sites to get the optimal tree. However, the bootstrap consensus topology we obtained does not contradict the morphological hypothesis. Its polytomies only indicate absence of phylogenetic signal. The fact that the Kishino–Hasegawa rejects test monophyly of the Asellota should not persuade isopod specialists to abandon the use of this taxon name. The test is valid for a priori hypotheses, in our case, for the comparison of a topology containing monophyletic Asellota with a different one obtained via maximum likelihood analysis. It requires the assumption that the model used for tree inferrence is correct. Furthermore, this test tells us nothing about the quality of the data and of taxon sampling. In our example, non-monophyly of the Asellota is probably caused by several factors: • Lack of strong signal in favour of the Asellota. Causes may be: a short stem-line (either due to slow evolution or rapid radiation) or signal erosion in the marine species. In the latter case, some of the autapomorphies of the Aselloidea (i.e., novelties occurring only in this group) would be in reality apomorphies of the Asellota that eroded ( ¼ were substituted) in the marine lineage. The lack of signal is seen in bootstrap consensus trees and in spectra of supporting positions. • Long branches in the marine species. The result is a placement at the base of the isopod tree in distance and likelihood analyses, because few similarities shared with other isopods remain (Table 2, electronic supplement). The rate difference between the marine and freshwater lineage of the Asellota was detected with several methods (see results). • Inability of the maximum likelihood method implemented in PAUP v. 4.x to find the ‘‘correct’’ substitution model. 4.2. Molecular evolution and phylogeny of the Asellota The number of taxa represented in our alignment is still too small to allow for far-reaching conclusions about the phylogeny of the Asellota. However, the

546

J.-W. Wa¨gele et al. / Molecular Phylogenetics and Evolution 28 (2003) 536–551

dichotomy between the freshwater species (Aselloidea) and the marine ones considered herein (Janiroidea) is very pronounced. And it is obvious that the deep-sea species (Munnopsidae) are derived from a group of marine shallow-water ones which traditionally are classified as members of the ‘‘Janiridae’’ (Hessler et al., 1979; Hessler and Wilson, 1983; Kussakin, 1973; W€agele, 1989). This latter taxon probably is not monophyletic, an observation that was already derived from analyses of morphological data (W€ agele, 1989; Wilson, 1994). For a more comprehensive study of the phylogeny of the Asellota, representatives of taxa intermediate between the Aselloidea and the Janiroidea have to be sequenced (Gnathostenetroidoidea, Protojaniroidea, Stenetrioidea, and Pseudojaniridae). Unfortunately, most of these are very rare species. And, it is obvious that for more trustworthy phylogenetic analyses of the deeper nodes the alignment must be improved by addition of further genes. The observation that freshwater isopods evolve slower than the marine ones is surprising. This has to be confirmed with additional genes. Unfortunately, there exists no fossil evidence that would allow an estimation of the age of these taxa. Published phylogenetic analyses (W€ agele, 1989; Wilson, 1987) suggest that the Janiroidea and the Aselloidea belong to two lineages that diverged very early from a common asellote ancestor. Since the oldest known fossil phreatoicids have an age of about 300 million years, the Asellota, which have to be placed near the Phreatoicidea (Brusca and Wilson, 1991; W€agele, 1989) can well be older than 200 milion years. The minimum age has been estimated to be between the Carboniferous and the Triassic (Wilson, 1998). Since aselloids occur in Eurasia, North America and Africa (e.g., Banarescu, 1990; Bowman and Holmquist, 1975; Chappuis, 1965; Chappuis and Delamare-Deboutteville, 1957; Collinge, 1944; Henry and Magniez, 1970; Henry et al., 1986; Magniez, 1983, 1997; Van Name, 1936), this freshwater group should at least be older than the Atlantic Ocean. This implies that the low number of substitutions seen in sequences of freshwater species is not caused by young age of the group but by slow rates. It may be that selective pressure constrains the variability in freshwater habitats while the marine realm offers more ecological niches that trigger adaptive radiations. Other factors causing rate difference, such as generation time effects (e.g., differences beween annual and perennial plants: Andreasen and Baldwin, 2001), body size, metabolic rates (e.g., Bleiweiss, 1998; Bromham et al., 1996; Hafner et al., 1994; Martin and Palumbi, 1993) should to our knowledge not differ drastically in freshwater and marine species. Within the marine asellotes, the shallow-water species have longer branches than the deep-sea ones. This might reflect real differences in rates. It is also very probable that the deep-sea taxa are relatively younger, however,

in comparison with other deep-sea crustaceans the asellotes belong to the more ancient elements of the abyssal fauna. Their large number of species reflects the intense radiation in the deep-sea, many genera have a world-wide distribution (e.g., Hessler and Wilson, 1983; Wilson, 1999).

5. General conclusions In our example, the single optimal trees obtained with different optimality criteria and with carefully selected substitution models do not reflect the signal contained in the data. Even though the most parsimonious tree agrees with the universally accepted morphological hypothesis, it is safer to bootstrap the data to visualize the effect of contradictions contained in the data. The bootstrap consensus topologies obtained with maximum likelihood and parsimony methods (not the distance trees) are more reliable, the lack of resolution indicates that there is not sufficient phylogenetic information conserved in these data for the placement of those taxa that emerge from the polytomy. However, even when ‘‘good’’ bootstrap support is found for a clade, the question remains whether it is produced by chance similarities or by signal, a question that can be explored with spectral analyses (see e.g., W€agele et al., 1999; W€agele and Misof, 2001). Since likelihood methods are very time consuming, bootstrapping is often avoided or simply not possible. In this case independent spectral analyses can show if clades present in the maximum likelihood toplogy are also supported by alignment sites that are not too variable and more frequent than background noise. Otherwise ‘‘surprising new results’’ based on weak data will be propagated. Spectra of supporting positions are independent of tree constructing methods and of single models applied to all branches in tree space and therefore ideal tools for explorative data analysis.

Acknowledgments Dr. D. Jaume (Mallorca, Spain) accompanied us in a caving expedition and helped to collect Tethysbaena scabra from a cave on Mallorca. Dr. G. Messana donated specimens of Stenasellus racovitzai, Dr. D.L. Danielopol collected for us Proasellus slavus, Dr. T.C. Sparkes has sent us Lirceus fontinalis. The first author is grateful to Prof. A. Brandt for her cooperation during the deep-sea expedition ‘‘DIVA 1.’’ Dr. Christoph Held (Bochum) helped with his advice concerning laboratory methods and data analyses. Dr. Ulrike Englisch (Bochum) gave us the amphipod sequences she gained during her sequencing project.

J.-W. Wa¨gele et al. / Molecular Phylogenetics and Evolution 28 (2003) 536–551

Appendix A

547

548

J.-W. Wa¨gele et al. / Molecular Phylogenetics and Evolution 28 (2003) 536–551

b Fig. 4 (Top). Spectrum of supporting positions showing the 49 best splits. Note that only few splits are based on a large number of autapomorphies (Gammarus spp., Jaera spp.), while most mutually compatible groupings that also occur in the most parsimonious tree (marked with arrows) have no signal better than other meaningless splits. The latter (without names) indicate the amount of background noise. (Estimated with PHYSID W€agele and R€ odding, 1998a,b; up to 25% tolerated noise per line and column of the alignment). Fig. 5 (Bottom). Spectrum as in Fig. 4, however, without the long-branch sequences of Gammarus spp. and Jaera spp. Note that the number of nonsense splits is reduced and more meaningful splits are found on the left part of the spectrum. This difference visualizes the amount of noise introduced by long-branch sequences. Janiroidea and Munnopsidae are supported by many autapomorphies, the Aselloidea do not appear among the best splits.

Fig. 6. Each bar shows the number of pure supporting positions for a split in decreasing order of magnitude, showing all splits with more than 2 supporting positions. Splits that occur in both trees are shown in white and labelled A–H (split C only occurs in the tree with monophyletic Asellota). Splits that appear in neither tree are shown in black and are not labelled, in all cases these splits are combinations of the seven longest branch taxa. The numbers in bold above or below each bar are the p values for data simulated on the ML tree. The numbers in italics are the p values for data simulated on the constrained asellota tree. The p values are the number of the simulated datasets in which that split had more supporting positions than in the observed data. Low p values indicate the split is under-represented in the observed data compared to expectations arising from the model and tree, conversely high p values indicate that the split is over-represented in the observed data.

References Abouheif, E., Zardoya, R., Meyer, A., 1998. Limitations of metazoan 18S rRNA sequence data: implications for reconstructing a phylogeny of the animal kingdom and inferring the reality of the Cambrian explosion. J. Mol. Evol. 47, 394–405. Adoutte, A., Philippe, H., 1993. The major lines of metazoan evolution: summary of traditional evidence and lessons from ribosomal RNA sequence analysis. In: Pichon, Y. (Ed.), Comparative Molecular Neurobiology. Birkh€auser, Basel, pp. 1–30. Andreasen, K., Baldwin, B.G., 2001. Unequal evolutionary rates between annual and perennial lineages of checker mallows (Sidalcea, Malvaceae): evidence from 18S–26S rDNA internal and external transcribed spacers. Mol. Biol. Evol. 18, 936–944.

Banarescu, P., 1990. Zoogeography of Fresh Waters. Aula-Verlag, Wiesbaden. Barnard, J.L., Menzies, R.J., Bacescu, M.C., 1962. Abyssal Crustacea. Columbia University Press, New York. Bleiweiss, R., 1998. Slow rate of molecular evolution in high-elevation hummingbirds. Proc. Natl. Acad. Sci. USA 95, 612–616. Bowman, T.E., Holmquist, C., 1975. Asellus (Asellus) alaskensis n.sp., the first Alaskan Asellus, with remarks on its Asian affinities (Crustacea: Isopoda: Asellidae). Proc. Biol. Soc. Wash. 88, 59–72. Brandt, A., 2000. Hypotheses on Southern Ocean peracarid evolution and radiation (Crustacea, Malacostraca). Antarctic Sci. 12, 269–275. Bromham, L., Rambaut, A., Harvey, P.H., 1996. Determinants of rate variation in mammalian DNA sequence evolution. J. Mol. Evol. 43, 610–621.

J.-W. Wa¨gele et al. / Molecular Phylogenetics and Evolution 28 (2003) 536–551 Brusca, R.C., Wilson, G.D.F., 1991. A phylogenetic analysis of the Isopoda with some classificatory recommendations. Mem. Queens. Mus. 31, 143–204. Chang, B.S.W., Campbell, D.L., 2000. Bias in phylogenetic reconstruction of vertebrate rhodopsin sequences. Mol. Biol. Evol. 17, 1220–1231. Chappuis, P.A., 1965. Remarques generales sur le genre Asellus et description de quatre especes nouvelles. Notes Biospeologiques 10, 163–182. Chappuis, P.A., Delamare-Deboutteville, C., 1957. Un nouvel Asellide de LÕAfrique du sud. Notes Biospeologiques 12, 29–36. Coineau, N., 1968. Contribution a lÕetude de la faune interstitielle Isopodes et Amphipodes. Mem. Mus. Natn. Hist. Nat. 55 (3), 147–216. Colgan, D.J., McLauchlan, A., Wilson, G.D.F., Livingston, S.P., Edgecombe, G.D., Macaranas, J., Cassis, G., Gray, M.R., 1998. Histone H3 and U2 snRNA DNA sequences and arthropod molecular evolution. Austral. J. Zool. 46, 419–437. Collinge, W.E., 1944. On the freshwater isopod genus Caecidotea Packard. Ann. Mag. Nat. Hist. 11 (84), 815–817. Crandall, K.A., Harris, D.J., Fetzner, J.W., 2001. The monophyletic origin of freshwater crayfish estimated from nuclear and mitochondrial DNA sequences. Proc. R. Soc. Lond. B Biol. 267, 1679–1686. Dreyer, H., W€ agele, J.-W., 2001. Parasites of crustaceans (Isopoda: Bopyridae) evolved from fish parasites: molecular and morphological evidence. Zoology 103, 157–178. Dreyer, H., W€ agele, J.W., 2002. The Scutocoxifera tax. nov. (Crustacea, Isopoda) and the information content of nuclear ssu rDNA sequences for reconstruction of isopod phylogeny (Peracarida: Isopoda). J. Crust. Biol. 22, 217–234. Emerson, G.L., Kilpatrick, C.W., McNiff, B.E., Ottenwalder, J., Allard, M.W., 1999. Phylogenetic relationships of the order Insectivora based on complete 12SrRNA sequences from mitochondria. Cladistics 15, 221–230. Felsenstein, J., 1978. Cases in which parsimony or compatibility methods will be positively misleading. Syst. Zool 27, 410–410. Felsenstein, J., 1981. Evolutionary trees from DNA sequences: a maximum likelihood approach. J. Mol. Evol. 17, 368–376. Fitch, W.M., 1971. The nonidentity of invariable positions in the cytochrome c of different species. Biochem. Genet. 5, 231–241. Fitch, W.M., Markowitz, E., 1970. An improved method for determining codon variability in a gene and its application to the rate of fixation of mutations in evolution. Biochem. Genet. 4, 579–593. Foster, P.G., Hickey, D.A., 1999. Compositional bias may affect both DNA-based and protein-based phylogenetic reconstructions. J. Mol. Evol. 48, 284–290. Fuellen, G., W€ agele, J.W., Giegerich, R., 2001a. Best systematist practice transferred to molecular data. Org. Divers. Evol. 1, 257–272. Fuellen, G., W€ agele, J.W., Giegerich, R., 2001b. Minimum conflict: a divide-and-conquer approach to phylogeny estimation. Bioinformatics 17, 1168–1178. Galtier, N., 2001. Maximum-likelihood phylogenetic analysis under a covarion-like model. Mol. Biol. Evol. 18, 866–873. Germot, A., Philippe, H., 1999. Critical analysis of eukaryotic phylogeny: a case study based on the HSP70 family. J. Euk. Microbiol. 46, 116–124. Goldman, N., Anderson, J.P., Rodrigo, A.G., 2000. Likelihood-based tests of topologies in phylogenetics. Syst. Biol. 49, 652–670. Hafner, M.S., Sudman, P.D., Villablanca, F.X., Spradling, T.A., Demastes, J.W., Nadler, S.A.., 1994. Disparate rates of molecular evolution in cospeciating hosts and parasites. Science 265, 1087–1090. Hendy, M.D., Penny, D., 1989. A framework for the quantitative study of evolutionary trees. Syst. Zool. 38, 310–321.

549

Henry, J.P., Magniez, G., 1970. Contribution a la systematique des Asellides (Crustacea, Isopoda). Ann. Speleol. 25 (2), 335–367. Henry, J.P., Lewis, J.J., Magniez, G., 1986. Isopoda: Asellota: Aselloidea, Gnathostenetroidoidea, Stenetrioidea. In: Botosaneanu, L. (Ed.), Stygofauna Mundi. E.J.Brill and Dr.W.Backhuys, Leiden. Hessler, R.R., 1993. Swimming morphology in Eurycope cornuta (Isopoda: Asellota). J. Crust. Biol. 13, 667–674. Hessler, R.R., Str€ omberg, J.O., 1989. Behavior of janiroidean isopods (Asellota), with special reference to deep-sea genera. Sarsia 74, 145–159. Hessler, R.R., Wilson, G.D.F., Thistle, D., 1979. The deep-sea isopods: a biogeographic and phylogenetic overview. Sarsia 64, 67–75. Hessler, R.R., Wilson, G.D.F., 1983. The origin and biogeography of malacostracan crustaceans in the deep sea. In: Sims, R.W., Price, J.H., Whalley, P.E.S. (Eds.), Evolution in Time and Space: The Emergence of the Biosphere. Academic Press, London and New York, pp. 227–254. Huelsenbeck, J.P., 1997. Is the Felsenstein zone a fly trap. Syst. Biol. 46, 69–74. Huelsenbeck, J.P., Bull, J.J., 1996. A likelihood ratio test to detect conflicting phylogenetic signal. Syst. Bio.l 45, 92–98. Huelsenbeck, J.P., Crandall, K.A., 1997. Phylogeny estimation and hypothesis testing using maximum likelihood. Annu. Rev. Ecol. Syst. 28, 437–466. Jermiin, L.S., 1996. K2WuLi—Version 1.0: computer program for the relative rate test. The Australian National University. Kishino, H., Hasegawa, M., 1989. Evaluation of the maximum likelihood estimate of the evolutionary tree topologies from DNA sequence data, and the branching order in Hominoidea. J. Mol. Evol. 29, 170–179. Kobusch, W., 1999. The phylogeny of the Peracarida (Crustacea, Malacostraca). Cuvillier Verlag, G€ ottingen. Kumar, S., Tamura, K., Nei, M., 1993. MEGA: Molecular Evolutionary Genetics Analysis, version 1.01. The Pennsylvania State University, University Park, PA 16802. Kussakin, O.G., 1973. Peculiarities of the geographical and vertical distribution of marine isopods and the problem of deep sea fauna origin. Mar. Biol. 23, 19–34. Kussakin, O.G., 1999. Marine and brackish-water Isopoda of the cold and temperate waters of the Northern Hemisphere. III. Suborder Asellota, Part 2, Akademia Nauk (Ed.), Petersburg. Lake, J., 1994. Reconstructing evolutionary trees from DNA and protein sequences: Paralinear distances. Proc. Natl. Acad. Sci. USA 91, 1455–1459. Lanave, C.G., Preparata, G., Saccone, C., Serio, G., 1984. A new method for calculating evolutionary substitution rates. J. Mol. Evol. 20, 86–93. Littlewood, D.T.J., Olson, P.D., Telford, M.J., Herniou, E.A., Riutort, M., 2001. Elongation factor 1-alpha sequences alone do not assist in resolving the position of the Acoela within the Metazoa. Mol. Biol. Evol. 18, 437–442. Lockhart, P.J., Larkum, A.W., Steel, M., Waddell, P.J., Penny, D., 1996. Evolution of chlorophyll and bacteriochlorophyll: the problem of invariant sites in sequence analysis. Proc. Natl. Acad. Sci. USA 93, 1930–1934. Lockhart, P.J., Steel, M.A., Hendy, M.D., Penny, D., 1994. Recovering evolutionary trees under a more realistic model of sequence evolution. Mol. Biol. Evol. 11, 605–612. Lockhart, P.J., Steel, M., Barbrook, A., Huson, D., Charleston, M.A., Howe, C.J., 1998. A covariotide model explains apparent phylogenetic structure of oxygenic photosynthetic lineages. Mol. Biol. Evol. 15, 1183–1188. Lyons-Weiler, J., Hoelzer, G.A., 1997. Escaping from the Felsenstein zone by detecting long branches in phylogenetic data. Mol. Phylog. Evol. 8, 375–384.

550

J.-W. Wa¨gele et al. / Molecular Phylogenetics and Evolution 28 (2003) 536–551

Magniez, G., 1983. Biogeographie et Paleobiogeographie des Stenasellides (Crustaces Isopodes Asellotes des eaux souterraines continentales). Mem. Biospeol. 10, 187–191.  tat actuel des connaissances sur les Stenasellidae Magniez, G.J., 1997. E (Crustacea, Isopoda; Asellota des eaux souterraines continentales). Bull. Sci. Bourg. 49, 21–28. Martin, A.P., Palumbi, S.R., 1993. Body size, metabolic rate, generation time, and the molecular clock. Proc. Natl. Acad. Sci. USA 90, 4087–4091. Menzies, R.J., 1963. The abyssal fauna of the sea floor of the Arctic Ocean. Proc. Arctic Basin Symp. 1963, 46–66. Mitchell, A., Cho, S., Regier, J.C., Mitter, C., Poole, R.W., Matthews, M., 1997. Phylogenetic utility of elongation factor-1 alpha in Noctuoidea (Insecta: Lepidoptera:) The limits of synonymous substitution. Mol. Biol. Evol. 14, 381–390. Miyamoto, M.M., Fitch, W.M., 1996. Constraints on protein evolution and the age of eubacteria/eukaryote split. Syst. Biol. 45, 568–575. Nelles, L., Fang, B.L., Volckaert, G., Vandenberghe, A., Wachter, R., 1984. Nucleotide sequence of a crustacean 18S ribosomal RNA gene and secondary structure of eukaryotic small subunit ribosomal RNAs. Nucleic Acids Res. 12, 8749–8768. Nicholas, K.B., Nicholas, H.B., Deerfield, D.W., 1997. GeneDoc: Analysis and Visualization of Genetic Variation. Embnews 4, 14 and http://www.cris.com/~Ketchup/genedoc.shtml. Pawlowski, J., Bolivar, I., Fahrni, J.F., Vargas, C., Gouy, M., Zaninetti, L., 1997. Extreme differences in rates of molecular evolution of Foraminifera revealed by comparison of ribosomal DNA sequences and the fossil record. Mol. Biol. Evol. 14, 498–505. Philippe, H., Forterre, P., 1999. The rooting of the universal tree of life is not reliable. J. Mol. Evol. 49, 509–523. Philippe, H., Laurent, J., 1998. How good are deep phylogenetic trees? Curr. Opin. Genet. Dev. 8, 616–623. Pol, D., Siddall, M.E., 2001. Biases in maximum likelihood and parsimony: a simulation approach to a 10-taxon case. Cladistics 17, 266–281. Posada, D., Crandall, K.A., 1998. MODELTEST: testing the model of DNA substitution. Bioinformatics Appl. Note 14, 817–818. Rambuat, A.E., Grassly, N.C., 1997. Seq-gen: an application for the Monte Carlo simulation of DNA sequence evolution along phylogenetic trees. Comput. Appl. Biosci. 1, 235–238. Richter, S., 1999. The structure of the ommatidia of the Malacostraca (Crustacea)—a phylogenetic approach. Verh. naturwiss. Ver. Hamburg 38, 161–204. Richter, S., Scholtz, G., 2001. Phylogenetic analysis of the Malacostraca (Crustacea). J. Zool. Syst. Evol. Res. 39, 113–136. Saitou, N., Nei, M., 1987. The neighbour-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4, 406–425. Sars, G.O., 1909. Crustacea. Rep. Second Norwegian Arctic Exp. ‘‘Fram’’ 1989–1902 18, 1–47. Schram, F.R., 1970. Isopod from the Pennsylvanian of Illinois. Science 169, 854–855. Shoemaker, J.S., Fitch, W.B., 1989. Evidence from nuclear sequences that variable sites should be considered when sequence divergence is calculated. Mol. Biol. Evol. 6, 270–289. Shultz, J.W., Regier, J.C., 1997. Progress toward a molecular phylogeny of the centipede orders (Chilopoda). Ent. Scand. Suppl. 51, 25–32. Siddall, M.E., 1988. Success of parsimony in the four-taxon case: longbranch repulsion by likelihood in the Farris zone. Cladistics 14, 209–220. Siebenaller, J.F., Hessler, R.R., 1981. The genera of the Nannoniscidae (Isopoda, Asellota). Trans. San Diego Soc. Nat. Hist. 19, 227–250. Steiner, G., M€ uller, M., 1996. What can 18S rDNA do for bivalve phylogeny? J. Mol. Evol. 43, 58–70.

Steel, M.A., 1994. Recovery of a tree from the leaf coloration it generates under a Markov model. Appl. Math. Lett. 7, 19–23. Steel, M.A., Lockhart, P.J., Penny, D., 1993. Confidence in evolutionary trees from biological sequence data. Nature 364, 440–442. Steel, M.A., Huson, D., Lockhart, P.J., 2000. Invariable sites models and their use in phylogeny reconstruction. Syst. Biol. 49, 225–232. Svavarsson, J., Str€ omberg, J.O., Brattegard, T., 1993. The deep-sea asellote (Isopoda, Crustacea) fauna of the Northern Seas: species composition, distributional patterns and origin. J. Biogeogr. 20, 537–555. Swofford, D.L., 1998. PAUP*. Phylogenetic Analysis Using Parsimony (* and other methods). Version 4*. Sinauer Associates, Sunderland, MA. Tavare, S., 1986. Some probabilistic and statistical problems on the analysis of DNA sequences. Lec. Math. Life Sci. 17, 57–86. Thompson, J.D., Gibson, T.J., Plewniak, F., Jeanmougin, F.H., 1997. The ClustalX windows interface: flexible strategies for multiple sequece alignment aided by quality analysis tools. Nucleic Acids Res. 24, 4876–4882. Lieshout, S.E.N., 1983. Calabozoidea, a new suborder of stygobiont Isopoda, discovered in Venezuela. Bijdr. Dierk. 53, 165–177. Name, W.G., 1936. The American land and fresh-water isopod crustacea. Bull. Am. Mus. Nat. Hist. 71, 1–535. Waddell, P.J., Penny, D., Moore, T., 1997. Hadamard conjugations and modeling sequence evolution with unequal rates across sites. Mol. Phylog. Evol. 8, 33–50. W€agele, J.W., 1989. Evolution und phylogenetisches System der Isopoda. Stand der Forschung und neue Erkenntnisse. Zoologica 140, 1–262. W€agele, J.W., Erikson, T., Lockhart, P., Misof, B., 1999. The Ecdysozoa: artifact or monophylum? J. Zool. Syst. Evol. Res. 37, 211–223. W€agele, J.W., Misof, B., 2001. On quality of evidence in phylogeny recosntruction: a reply to ZrzavyÕs defence of the ÔEcdysozoaÕ hypothesis. Z. Zool. Syst. Evol. Res. 39, 165–176. W€agele, J.W., R€ odding, F., 1998a. A priori estimation of phylogenetic information conserved in aligned sequences. Mol. Phylog. Evol. 9, 358–365. W€agele, J.W., R€ odding, F., 1998b. Origin and phylogeny of metazoans as reconstructed with rDNA sequences. Prog. Mol. Subcell. Biol. 21, 45–70. Wilson, G.D.F., 1987. The road to the Janiroidea: comparative morphology and evolution of the asellote isopod crustaceans. Z. f. Zool. Syst. Evolutionsforsch. 25, 257–280. Wilson, G.D.F., 1994. A phylogenetic analysis of the isopod family Janiridae (Crustacea). Invertebr. Taxon 8, 749–766. Wilson, G.D.F., 1998. Historical influences on deep-sea isopod diversity in the Atlantic Ocean. Deep-Sea Res. II 45, 279–301. Wilson, G.D.F., 1999. Some of the deep-sea fauna is ancient. Crustaceana 72, 1019–1030. Wilson, G.D.F., Ponder, W.F., 1992. Extraordinary new subterranean isopods (Peracarida: Crustacea) from the Kimberley Region, Western Australia. Rec. Austr. Mus. 44, 279–298. Wolff, T., 1962. The systematics and biology of bathyal and abyssal Isopoda Aselllota. Galathea Rep. 6, 1–320. Wu, C.-I., Li, W.-H., 1985. Evidence for higher rates of nucleotide substitution in rodents than in man. Proc. Natl. Acad. Sci. USA 82, 1741–1745. Yang, Z., 1996. Among-site rate variation and its impact on phylogenetic analyses. Trends Ecol. Evol. 11, 367–372.

Further reading Bremer, K., 1988. The limits of amino acid sequence data in angiosperm phylogenetic reconstruction. Evolution 42, 795–803.

J.-W. Wa¨gele et al. / Molecular Phylogenetics and Evolution 28 (2003) 536–551 Lopez, P., Forterre, P., Philippe, H., 1999. The root of the tree of life in the light of the covarion model. J. Mol. Evol. 49, 496–508. Miyamoto, M.M., Fitch, W.M., 1995. Testing the covarion hypothesis of molecular evolution. Mol. Biol. Evol. 12, 503–513.

551

Sanders, H.L., 1968a. Marine benthic diversity; a comparative study. Amer. Nat. 102, 243–282. Sanders, H.L., 1968b. Marine benthic diversity and the stability-time hypothesis. In: Woodwell, G.M., Smith, H.H. (Eds.), Diversity and Stability in Ecological Systems, Brookhaven Symposium on Biology, vol. 22. Brookhaven National Laboratory, pp. 71–81.