Integrative taxonomy of ciliates: Assessment of molecular phylogenetic content and morphological homology testing

Accepted Manuscript Title: Integrative taxonomy of ciliates: assessment of molecular phylogenetic content and morphological homology testing Author: Peter Vd’aˇcn´y PII: DOI: Reference:

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To appear in: Please cite this article as: Vd’aˇcn´y, Peter, Integrative taxonomy of ciliates: assessment of molecular phylogenetic content and morphological homology testing.European Journal of Protistology http://dx.doi.org/10.1016/j.ejop.2017.02.001 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Integrative taxonomy of ciliates: assessment of molecular phylogenetic content and morphological homology testing

Peter Vďačný

Department of Zoology, Faculty of Natural Sciences, Comenius University in Bratislava, Ilkovičova 6, Mlynská dolina B-1, 842 15 Bratislava, Slovak Republic

Corresponding author. Tel.: +421 2 602 96 170; fax: +421 2 602 96 333. E-mail address: [email protected]

Abstract The very diverse and comparatively complex morphology of ciliates has given rise to numerous taxonomic concepts. However, the information content of the utilized molecular markers has seldom been explored prior to phylogenetic analyses and taxonomic decisions. Likewise, robust testing of morphological homology statements and the apomorphic nature of diagnostic characters of ciliate taxa is rarely carried out. Four phylogenetic techniques that may help address these issues are reviewed. (1) Split spectrum analysis serves to determine the exact number and quality of nucleotide positions supporting individual nodes in phylogenetic trees and to discern long-branch artefacts that cause spurious phylogenies. (2) Network analysis can depict all possible evolutionary trajectories inferable from the dataset and locate and measure the conflict between them. (3) A priori likelihood mapping tests the suitability of data for reconstruction of a well resolved tree, visualizes the tree-likeness of quartets, and assesses the support of an internal branch of a given tree topology. (4) Reconstruction of ancestral morphologies can be applied for analyzing homology and apomorphy statements without circular reasoning. Since these phylogenetic tools are rarely used, their principles and interpretation are introduced and exemplified using various groups of ciliates. Finally, environmental sequencing data are discussed in this light.

Key words: Apomorphies; Phylogenetic networks; Quartet mapping; Reconstruction of ancestral morphologies; Split spectrum

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Introduction The attempt to classify organisms according to their phylogenetic relationships is called evolutionary taxonomy (Mayr 1981). Such an approach is based on reconstruction of past events, which in essence requires integration of various disciplines ranging from comparative morphology through population genetics and evolution to statistics and probability mathematics. Indeed, only a combination of diverse methods can recover the journey of organisms in the course of evolution and unite them in natural groups according to their kinships (Wägele 2005). There is a general agreement that the principal taxonomic unit is a species. However, it is important to differentiate between the meaning of species as a distinct lineage of organisms and the meaning of species as a category (Bachmann 1998; Boenigk et al. 2012; Claridge et al. 1997; Hey 2001; Schlegel and Meisterfeld 2003). In principle, the former meaning requires practical delineation of boundaries among groups of organisms (Coyne and Orr 2004), while the latter is a philosophical and ontological matter (Wägele 2005). Ciliates are a suitable taxon to explore species concepts because they are considered to be morphologically and evolutionary cohesive. The morphological species concept is already the standard in delimitation of ciliate taxa, while the evolutionary species concept is still in its infancy (e.g. Berger 1999, 2006, 2008, 2011; Foissner 2015; Foissner and Xu 2007; Foissner et al. 2002; Kahl 1930, 1931, 1932, 1935; Vďačný and Foissner 2012). Nevertheless, the rapidly growing molecular data on ciliates bring inconsistent results: some morphologically delineated species form monophyletic entities, while others are depicted as para- or even polyphyletic assemblages (e.g. Catania et al. 2009; Katz et al. 2005; Shazib et al. 2016; Simon et al. 2008; Sun et al. 2013; Thamm et al. 2010). Likewise, the application of the morpho-concept to ciliate genera often does not bring straightforward results in the light of molecular data (e.g., Rajter and Vďačný 2016). To overcome these problems, delineation of units of life‘s diversity needs to be reciprocally illuminated by an integrative approach that takes into account and critically weights both morphological and molecular data. The very diverse and comparatively complex morphology of ciliates has given rise to many taxonomic concepts and hypotheses on the topology of the ciliate tree of life (Corliss 1979; Gao et al. 2016; Jankowski 2007; Lynn and Small 2002; Puytorac 1994; Small and Lynn 1981, 1985; Vďačný et al. 2010, 2011a, b). Most of the morphology-based hypotheses about suprageneric taxa are generally congruent with gene trees (Gentekaki et al. 2014; Lynn

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2003, 2008). When incongruencies occur, they usually center upon paraphyletic groups. These contain only some and not all descendants of a single evolutionary lineage. Paraphyly problems are generally connected to a high amount of phenotypic change in the derivative taxa that underwent adaptive evolution in a new ecological situation (Mayr and Bock 2002). Ancestral (plesiomorphic) conditions remain in some taxa, causing them to be grouped together, while some derivative taxa lost or strongly modified the plesiomorphic state and hence are being excluded from the group. Paraphyletic taxa are thus typically created when plesiomorphic characters are erroneously used as apomorphies, i.e., derived characters. Although paraphyly is not accepted by many taxonomists, there are also some authors arguing that paraphyletic taxa are natural units of biological classification, when reflecting evolutionary history (Hörandl 2006, 2007; Hörandl and Stuessy 2010). The most serious problem in evolutionary taxonomy is polyphyly. Polyphyletic groups contain species derived from different evolutionary lineages and hence are artificial taxa (Wiley and Lieberman 2011). These are founded on analogies (homoplasies) erroneously used as synanpomorphies, i.e., as shared derived homologous characters (Wägele 2005). Homology assessment of morphological characters is, however, often very difficult in microbial groups and robust testing of morphological homology statements is still only rarely performed in ciliates (Dunthorn and Katz 2008). Both morphological and molecular data can lead to misleading phylogenetic conclusions. As concerns morphological data, the reason is an incorrect assumption about homology of characters or about the apomorphic nature of homologous characters. In the case of molecular data, it is the presence of a non-historical signal, the lack of historical signal, and stochastic errors that lead to misleading frameworks (Wägele and Mayer 2007). Molecular synapomorphies may be rare if species radiated quickly and chance similarities that evolved later can dominate in the form of a signal-like pattern. Further, multiple substitutions may destroy historical signals (molecular synapomorphies) or produce non-historical signal (molecular homoplasies) (e.g., Bergsten 2005; Wenzel and Siddall 1999; Wägele and Mayer 2007). Many published studies about evolutionary taxonomy and phylogenetic systematics of ciliates do not take these problems into account and are based on data whose quality has not been explored prior to taxonomic implications. Neglecting information content of a dataset may lead to unsubstantiated taxonomic conclusions, because conflicting signal tends to be suppressed by conventional tree-building methods and high statistical support values may be present even if there is no distinct phylogenetic signal in the dataset (Wägele and Mayer 2007). In this review, I will suggest what tools can be applied in a priori examination of data 4

quality and what steps could be taken as standard practice in evolutionary taxonomic studies. Specifically, four methods will be introduced: split spectrum analyses, phylogenetic networks, likelihood mapping, and reconstruction of ancestral morphologies. Their application and interpretation of results will be exemplified using litostomatean ciliates as the model taxon.

Gene Informativeness Prior to any phylogenetic analysis, informativeness of the utilized gene sequences should be examined. The most promising tool for assessing the sequence information content is the spectral analysis. This approach finds splits present in a DNA dataset, evaluates them and compares them with respect to nucleotide patterns (Wägele and Mayer 2007). By definition, a split is a bipartition in a species set, which separates all species of the dataset into two groups: a functional in-group and a functional out-group (Huson et al. 2010). According to Wägele and Rödding (1998), there are three groups of split-supporting nucleotide positions: (i) symmetric or binary positions have a different character state in the functional in-group and out-group, i.e., each group has one distinct character state that is different from the other group and hence support both groups of a split equally; (ii) asymmetric positions support only one group which possesses the same nucleotide at particular position, while the other group contains different and more than one character state at this position; and (iii) noisy positions include the same character states present in all sequences of the functional in-group, but also at least in one sequence of the functional out-group and thus represent convergences or chance similarities between the in-group and the out-group (Fig. 1A). The rationale behind spectral analysis is that if the dataset contains conserved nucleotide apomorphies supporting real monophyletic groups, then these patterns should be mutually compatible. In other words, different species groups supported by nucleotide patterns should fit to a single tree. The alignment is informative, if compatible signal-like patterns are based on more conserved nucleotide positions than contradicting (mutually incompatible) patterns. Then the signal is discernible from the background noise of the data (Wägele and Mayer 2007). Lento et al. (1995) filtered out a large part of incompatible signal using Hadamard conjugation. This method uses a special case of the Fourier transform to make a direct measurement of how much support is there in the data for all possible partitions of taxa (for introduction to Hadamard transform methods, see Felsenstein 2004). If there are n taxa, there will be 2n–1 bipartitions in the split spectrum and the Hadamard conjugation will require n  2n operations (for details, see Felsenstein 2004). Because the Hadamard machinery processes the

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complete split space of an alignment, the computing time grows exponentially with the number of taxa (Hendy and Penny 1993; Felsenstein 2004). The computer programs Spectrum (Charleston 1998) or Spectronet (Huber et al. 2002) can handle only about 20–30 taxa due to the huge amount of mathematical operations required. Thus for 30 taxa, there are as many as 30  230 ≈ 3.2  1010 operations, which is close to the computational limits at present. Therefore, Wägele and Mayer (2007) developed the program SAMS that searches only for splits present in the data. The calculated split spectrum can be subsequently graphically visualized as the so-called Lento plots (Lento et al. 1995; Wägele and Mayer 2007; Wägele et al. 2009) in which supporting position numbers of in-group partition are shown above and those of out-group partition below the horizontal axis for each split (Fig. 1B). The phylogenetically informative content of the 18S rRNA gene has been assessed in only two ciliate classes: Heterotrichea (Shazib et al. 2014) and Litostomatea (Rajter and Vďačný 2016; Vďačný and Rajter 2015; Vďačný et al. 2014, 2015). I will document the meaning and interpretation of spectral analysis with the example of the class Litostomeatea. According to the tree-building phylogenetic analyses, there are six main monophyletic litostomatean lineages that are very strongly or fully statistically supported: Rhynchostomatia, Trichostomatia, Haptorida, Lacrymariida, Pleurostomatida, and Didiniida. On the other hand, the monophyletic origin of the order Spathidiida is not consistently supported statistically. The subclass Trichostomatia is nested within the order Spathiidida, causing the subclass Haptoria to be paraphyletic. Distinct nucleotide patterns supporting all main litostomatean lineages, except for the Spathidiida, were found already within the first 20 splits (Vďačný et al. 2014). For instance, the subclass Rhynchostomatia was supported with 2 binary and 25 noisy nucleotide positions, the order Haptorida with 3 binary, 25 asymmetric and 3 noisy positions, the order Lacrymariida with 3 binary and 21 asymmetric positions, the order Pleurostomatida with 7 binary, 16 asymmetric and 7 noisy positions, and the order Didinida with 1 binary and 36 asymmetric positions. This documents that the high posterior probability and bootstrap values for monophylies of these higher taxa are based on conserved nucleotide primary homologies, i.e., binary and asymmetric nucleotide positions whose numbers range from one to seven and from 16 to 36, respectively. On the other hand, the high posterior probabilities for classification of the Trichostomatia within the Spathidiida in phylogenetic trees may be a result of chance similarities. Specifically, the grouping of the Trichostomatia and Epispathidium papilliferum isolates has a total support of only 4, with 1 asymmetric and 3 noisy nucleotide positions. There are no 6

binary positions supporting this node and statistical topology tests do not reject trees in which the Trichostomatia cluster outside Spathidiida as a sister group of the Haptoria. Thus, there is no distinct signal in the 18S rRNA gene to support the spathidiid home for the Trichostomatia, thought it receives full support in Bayesian inferences. Consequently, the traditional morphology-based classifications of the trichostomatians and the haptorians as a distinct subclass each cannot be rejected (Foissner and Foissner 1988; Grain 1994; Jankowski 2007; Lynn 2008). On the basis of spectral analysis, we also recognized that Chaenea and Trachelotractus form a large number of mutually incompatible but strongly supported splits with various armophorean taxa (Vďačný et al. 2014). Such a split spectrum is clear evidence for class III long-branch effects that occur when homoplasies outnumber apomorphies (Wägele and Mayer 2007). Homoplasies lead to nonsense clades that are supported only by chance and attracted due to non-homologous similarities. Thus, the fully statistically supported basal position of Chaenea and Trachelotractus in the phylogenetic trees (e.g. Zhang et al. 2012; Gao et al. 2016) is very likely due to the attraction by long branches of the out-group taxa. This is also indicated by the statistical tree topology tests that do not reject the sister relationship of Chaenea and Trachelotractus as well as their classification within the monophyletic Haptoria (Kwon et al. 2014; Vďačný et al. 2014). To sum up, spectral analysis is a useful tool for: (1) determining the exact number and quality of nucleotide positions supporting particular nodes in phylogenetic trees; (2) recognizing which high statistical support values may be a result of chance similarities; and (3) discerning long-branch artefacts present in molecular trees.

Visualization and Quantification of Phylogenetic Content Conventional tree-building methods are unable to visualize and quantify phylogenetic conflict present in a DNA dataset. However, phylogenetic networks are able to depict the consistent and conflicting information in a single graph (Huson et al. 2010; Morrison 2011). Likelihood mapping is another technique permitting assessment of the tree-, net- and star-like structure of the dataset (Nieselt-Struwe and von Haeseler 2001; Strimmer and von Haeseler 1997).

Phylogenetic networks Phylogenetic networks provide an alternative to phylogenetic trees and may be more

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suitable for datasets whose evolution involves reticulate events caused by hybridization, horizontal gene transfer, recombination, gene conversion or gene duplication and loss (e.g. Doolittle 1999; Sneath 1975). However, even for a set of taxa that have evolved according to a tree-based model, phylogenetic networks can be usefully employed to explicitly represent conflicts in a dataset that may, for example, be due to mechanisms such as incomplete lineage sorting or to inadequacies of an assumed evolutionary model (Huson et al. 2010; Morrison 2011). Consistent phylogenetic information is documented by treeness of the network, and noisy and conflicting information by star-like patterns and parallelograms (Fig. 2A). The length of edges (branches) is a measure for the support of a split and hence the proportion of the support for all splits (i.e., alternative evolutionary trajectories) becomes visible quantitatively at once. The currently most widely used software for computing phylogenetic networks is: SplitsTree (Huson 1998; Huson and Bryant 2006), TCS (Clement et al. 2000) and Dendroscope (Huson et al. 2007). Application of phylogenetic networks in studies about ciliates can be found in Bourland et al. (2014), Jang et al. (2014, 2015), Kim et al. (2014), Rajter and Vďačný (2016), Shazib et al. (2014, 2016), Vďačný and Rajter (2015) and Vďačný et al. (2014, 2015). I shall exemplify the utilization of network analyses using the class Litostomatea. Monophylies

of

Rhynchostomatia,

Trichostomatia,

Haptorida,

Lacrymariida,

Pleurostomatida, and Didiniida were supported with distinct sets of long parallel edges in the networks. On the other hand, the signal for monophyly of the order Spathiidida was not distinct, as this part of the network was not separated from the star-like central part, which indicates an explosive radiation or ancestral polymorphism of the rRNA locus (Vďačný et al. 2014). Alternatively, the short edges in the central part of the network may be a result of noise, i.e., chance similarities (Rajter and Vďačný 2016). Further, there is only a very weak support for classification of Trichostomatia within the spathidiid cluster, as shown by several very short parallelograms connecting the trichostomatian clade with two Epispathidium papilliferum isolates. On the other hand, there are also many short edges connecting the subclass Trichostomatia with the subclass Rhynchostomatia, a relationship not indicated in phylogenetic trees at all. Phylogenetic networks constructed only for the subclass Rhynchostomatia basically displayed a tree-like structure, indicating presence of real phylogenetic signal in the datasets (Fig. 2B). Moreover, complexity of networks decreased when the 18S rRNA gene matrix was concatenated with ITS1-5.8S rRNA-ITS2 region sequences and morphological data, documenting that they have consistent phylogenetic signal (Vďačný and Rajter 2015). Further, synergistic effects of combining multiple markers also 8

diminished the number of short parallelograms, indicating that the phylogenetic signal present in the particular datasets prevails over noise. To sum up, network analysis is a useful tool for: (1) depicting all possible evolutionary trajectories present in the dataset, (2) locating and measuring the conflict among them, and (3) detecting rapid radiation and ancestral polymorphism.

Likelihood mapping This technique is implemented in the computer program Tree-Puzzle (Schmidt et al. 2002) and provides an a priori assessment of the phylogenetic content of a sequence alignment (Strimmer and von Haeseler 1997). The procedure of likelihood mapping is based on three maximum likelihoods calculated for three possible quartet trees. The three likelihoods are then transformed into barycentric coordinates that have the statistical interpretation of posterior probabilities and define one point inside an equilateral triangle that is partitioned in seven regions (Fig. 3A). The three tips of the triangle represent the wellresolved phylogeny, i.e., tree-likeness of the data. The three rectangles on the sides of the triangle are quartets with network pattern and represent conflicting signal, i.e., a situation where it is difficult to distinguish between two of the three trees. The central region of the triangle represents star-like evolution, i.e., noisy signal (Nieselt-Struwe and von Haeseler 2001; Strimmer and von Haeseler 1997; Fig. 3B). To get an overall impression of the phylogenetic signal present in the dataset, probability vectors for all quartets (sets of four sequences) are computed and corresponding points are plotted in the triangle. If there is no phylogenetic information, i.e., quartets form a star topology, then the probability vectors are concentrated in the centre of the triangle with rays spreading to the corners of the triangle. On the other hand, tree-likeness is documented by placing quartets in corners of the triangle. We investigated the information content of the spathidiid 18S rRNA gene and ITS1-5.8SITS2 region using quartet puzzling (Rajter and Vďačný 2016). In the 18S rRNA gene analyses, 94% of probability vectors of all possible quartets fall in the tree-like areas, 4.8% were in the network-like areas, and only 1.1% of quartets remained unresolved (Fig. 3C). However, the situation was very different for the ITS region: 20.7% of all possible quartets were star-like, 7.9% of quartets had a network pattern, and only 71.4% of data points fell in the triangle corners (Fig. 3D). Thus, it can be deduced that the 18S rRNA gene bears distinctly more tree-like information than the ITS region does. On the other hand, due to the synergistic effect of concatenation of the ITS region with the 18S rRNA gene, tree-like structure of the data (95%) distinctly prevailed over conflict (4.4%) and noise (0.6%) (Rajter 9

and Vďačný 2016). A further application of likelihood mapping allows testing of an internal branch of a given tree topology when sequences are grouped in four clusters (Strimmer and von Haeseler 1997). This approach also effectively leads to a reduction of noise (Wägele et al. 2009) and was used in ciliates to test the sister group status within the main lineages of the Heterotrichea (Shazib et al. 2014) and Litostomatea (Vďačný and Rajter 2015; Vďačný et al. 2014, 2015). I shall document this technique with the example of the litostomatean subclass Rhynchostomatia, which includes four distinct lineages: Tracheliidae, Dileptidae, Rimaleptinae and Dimacrocaryoninae. Although their interrelationships were not clearly resolved in phylogenetic trees, quartet likelihood mapping of the concatenated 18S-ITS region dataset brought a very clear picture: 91.1% of probability vectors of all quartets supported the sister

relationship

of

Tracheliidae

and

Dileptidae

and

of

Rimaleptinae

and

Dimacrocaryoninae. When trees are rooted with Tracheliidae, then the family Dileptidae becomes a sister group of the family Dimacrocaryonidae, which includes the subfamilies Dimacrocaryoninae and Rimaleptinae (Vďačný and Rajter 2015). To sum up, likelihood mapping is a useful tool for: (1) testing the suitability of data to reconstruct a well-resolved tree; (2) visualizing the tree-likeness of quartets in a single graph; and (3) assessing the support of an internal branch of a given tree topology.

Testing for Morphological Homology Natural groups of organisms can be founded only on homologous characters. Among them only apomorphies have the power to support hypotheses about phylogenetic kinships among taxa and, in fact, only the correctly identified apomorphies represent the signal in phylogenetic analyses (Wägele 2005). However, the reliable identification of apomorphies and their distinction from chance similarities, i.e., analogies or homoplasies, is the most difficult task in evolutionary taxonomy. The assertion that a particular character is an apomorphy of a particular monophyletic group is de facto a probability conjecture (Haszprunar 1998; Sober 2000) whose veracity is open to further testing (Rieppel 1980; Wiley and Lieberman 2011). Long before the advent of computer-assisted analysis, Hennig (1966) clearly saw the process of sorting out homologies and homoplasies as intimately connected to phylogenetic hypotheses: ―…in deciding whether corresponding characters of several species are to be regarded as synapomorphies, convergences, homologies, or parallelisms we must determine

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whether the same character was already present in a stem species that is common only to the bearers of the identical character.‖ At present, several complex statistical approaches have been developed to perform reconstruction of the character evolution of species for which a phylogeny or a sample of phylogenies is available. For instance, the computer program Mesquite implements parsimony and likelihood reconstruction methods (Maddison and Maddison 2007). The former methods find the ancestral states that minimize the number of steps of character changes given the tree and observed character distribution, while the latter methods find the ancestral states that maximize the probability the observed states would evolve under a stochastic model of evolution (Pagel 1999; Schulter et al. 1997). A strength of parsimony is its simplicity and computational efficiency, but its limitations include lack of explicit assumptions about evolution and non-detection of multiple changes in the course of evolution (Yang and Rannala 2012). On the other hand, the computer package BayesTraits uses Markov Chain Monte Carlo (MCMC) methods to derive posterior distributions of estimates of character states at ancestral nodes of trees, accounting for uncertainties in estimates of phylogenies (Pagel and Meade 2014; Pagel et al. 2004). With the aid of these approaches, morphologies of stem lineages could be reconstructed and compared to recent species. In this way, the a priori assumed apomorphic nature of morphological characters could be discerned without circular reasoning since molecular trees can be used as an independent scaffold for reconstruction analyses. The advantage of morphological data in reconstruction of evolutionary history is that they are less susceptible to long-branch artefacts than nucleotide datasets (Bergsten 2005). Firstly, because usually a much larger possible number of character states exist in morphology, as opposed to the limited four possible different states in DNA sequence data (Jenner 2004). From this follows that convergent evolution will, to a higher degree, already be detected as not homologous at the character scoring state, and homoplasy thereby avoided to a larger degree than is possible with nucleotides. There also exists empirical evidence that morphological datasets in general experience less homoplasy than molecular datasets (Baker et al. 1998). Testing of morphological homology and apomorphy statements was performed in ciliates either by classic Hennigian argumentation (e.g., Agatha 2004a, b, 2011; Agatha and StrüderKypke 2012, 2014; Berger and Foissner 1997; Foissner et al. 2007; Vďačný et al. 2011a, 2015) and/or by reconstruction of ancestral morphologies on provided molecular or combined morphological-molecular phylogenies (Bourland et al. 2012; Rajter and Vďačný 2016; Shazib et al. 2014; Vďačný and Foissner 2013; Vďačný and Rajter 2015; Vďačný et al. 2015). I will 11

document here the meaning of homology testing with the example of the order Spathidiida whose taxonomy is based, inter alia, on oral ciliary patterns (Foissner and Xu 2007). Our reconstruction analyses suggested the Spathidium oral ciliary pattern to be an apomorphy of the last common ancestor of the order Spathidiida and, hence, a plesiomorphy for all spathidiids (Vďačný and Foissner 2013). The Arcuospathidium, Epispathidium, Enchelys and Enchelyodon patterns were recognized to be homoplastic and formed several times independently from the Spathidium pattern. Only the Acaryophrya pattern was found to be a real synapomorphy uniting the genera Acaryophrya and Pseudoholophrya (Rajter and Vďačný 2016). Thus, at least five spathidiid genera were based on plesiomorphies and homoplasies, i.e., characters inappropriate to create a natural classification framework (Wägele 2005; Wiley and Lieberman 2011). This problem is even more pronounced in hypotrich ciliates, where many supraspecies taxa are founded on plesiomorphies and homoplasies (for reviews, see Berger 1999, 2006, 2008, 2011). To sum up, reconstruction of character evolution is indispensable for robust testing of apomorphy statements. Only apomorphies can support hypotheses about monophyly and about phylogenetic relationships among taxa. Plesiomorphies typically lead to establishment of paraphyletic taxa, while homoplasies to polyphyletic groups of organisms.

Environmental Sequences in Phylogenetic Framework Taxon sampling is known to affect the accuracy of phylogenetic inference (Bergsten 2005; Rannala et al. 1998). This problem is also well known for the phylum Ciliophora, where taxon sampling is comparatively low for identified cultured isolates and high for unidentified environmental sequences. High-throughput next-generation sequencing (NGS) now adds far more data, indicating substantially more molecular operational taxonomic units than recognized by culture-dependent and Sanger sequencing methodologies (e.g., Dunthorn et al. 2014; Stoeck et al. 2009). One way to analyze environmental diversity and NGS data is to place them in a phylogenetic context. Using the example of the order Prorodontida, Yi et al. (2010) showed that increasing taxon sampling with unidentified environmental sequences changes both topology and nodal support in clades which have low sampling for identified cultured isolates. These differences were suggested to be beneficial for phylogenetic inferences of prorodontinds and this strategy was recommended also for other undersampled ciliate groups. On the other hand, a potential problem in using NGS data in phylogenetic analyses is the

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relatively short sequence length, which is potentially associated with a paucity of signal for accurate systematic placement of amplicons (Huber et al. 2009). Dunthorn et al. (2014) analyzed this problem and suggested that inclusion of V4 amplicons into an alignment of fulllength 18S rRNA gene sequences and maximum likelihood with RAxML is the best approach for phylogenetic inferences from NGS data. However, positional homology in hypervariable V4 and V9 amplicons is questionable and wrong assumptions about positional homology can have detrimental effects on the accuracy of phylogenies, as already mentioned by Dunthorn et al. (2014). This problem could be, at least partially, overcome by the strategy proposed by Gimmler and Stoeck (2015), which allows to retrieve the full-length 18S rRNA gene sequences using a targeted polymerase chain reaction primer design and to construct probes for in situ hybridization. In this way, the taxon associated with the desired amplicons can be visualized and its phylogenetic position can be more accurately analyzed. I envisage that this approach to the incorporation of NGS data may help to significantly improve our understanding of evolutionary history of ciliates and may be beneficial also for evolutionary inferences.

Conclusions Neglecting information content of the utilized molecular markers and nucleotide positional/morphological homology may lead to misleading and inconsistent phylogenetic and taxonomic frameworks. We should be also aware that none or only very few conserved nucleotide positions may stand behind well-supported nodes in phylogenetic trees and that correctly identified morphological apomorphies also represent strong signal in phylogenetic analyses. Therefore, thorough examination of phylogenetic signal and robust testing of homology statements needs to be carried out prior to any phylogenetic analyses and taxonomic implications. To this end, I suggest the following phylogenetic tools to be taken into account: 1. Spectral analysis of a DNA alignment to determine the exact number and quality of nucleotide positions supporting particular nodes in phylogenetic trees, because phylogenetic support values are not necessarily informative. 2. Phylogenetic network analysis to visualize all possible evolutionary trajectories inferable from a DNA alignment, because conflicting signals tend to be suppressed by conventional tree-building methods. 3. Quartet mapping analysis to assess the tree-likeness of a DNA alignment and to test

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for an internal branch of a given tree topology, because deeper phylogenetic relationships might be left unresolved in phylogenetic trees due to noise. 4. Reconstruction of ancestral morphologies to recognize the phylogenetic nature of the diagnostic characters, because apomorphies are often difficult to distinguish from plesiomorphies and homoplasies in microbial groups in general and ciliates in particular. Acknowledgements. I would like to thank Prof. Denis H. Lynn and two anonymous reviewers for their thoughtful comments that led to improvements in the manuscript. I am grateful to the International Research Coordination Network on Biodiversity of Ciliates funded by the National Science Foundation of the United States of America (NSF) and the National Natural Science Foundation of China (NSFC) for travel support. This work was supported by the Slovak Research and Development Agency under the contract No. APVV0147-15.

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Figure Legends Fig. 1. Split spectrum. A: Example for split-supporting positions. Binary nucleotide positions of a split are marked by arrows, asymmetric positions by arrowheads, and noisy positions by asterisks. Modified from Fig. 1 of Wägele and Rödding (1998). B: Example of a split spectrum showing 20 first best bipartitions. Column height represents the number of cladesupporting nucleotide positions. Column parts above the y-axis represent the in-group partition, while those below the y-axis correspond to the out-group partition. Modified from Fig. 7B of Vďačný et al. (2014).

Fig. 2. Phylogenetic networks. A: Example of a phylogenetic five-taxon network consisting of eight numbered splits. Each split represents a particular bipartition of the taxa, as shown at the right. All splits are pairwise compatible, except for split 8 which is not compatible with split 3, i.e, the groupings contradict each other. Incompatibility is displayed by pairs of parallel edges, i.e., net-like structure. On the other hand, compatibility of splits is shown as a tree-like structure, consisting of dichotomous edges (branches). Modified from Fig. 3.4. of Morrison (2011). B: A phylogenetic network of the subclass Rhynchostomatia, showing interrelationships among its four fundamental lineages and the proportion of support for all splits. Long parallel edges (arrows) represent consistent phylogenetic information, while reticulate and star-like structure (arrowheads) indicates noisy and/or conflicting phylogenetic signal. Modified from Fig. 4 of Vďačný and Rajter (2015).

Fig. 3. Likelihood mapping. A: Plot of the probability vector P = (p1, p2, p3) onto an equilateral triangle. The lengths of the perpendiculars from point P to the triangle sides are equal to the posterior probabilities pi and represent barycentric coordinates of the vector. The corners T1, T2, and T3 represent three quartet topologies with corresponding coordinates (probabilities): (1, 0, 0), (0, 1, 0), and (0, 0, 1). Modified from Fig. 2 of Strimmer and von Haeseler (1997). B: The equilateral triangle is partitioned in seven regions: the three vertices represent the well-resolved phylogeny, the three rectangles on the sides represent the network pattern with conflicting signal, and the central triangle represents star-like evolution, where all trees are equally likely (1/3, 1/3, 1/3). Modified from Fig. 1 of Nieselt-Struwe and von Haeseler (2001). C, D: Likelihood mapping of the spathidiid 18S rRNA gene and ITS region sequences. The occupancies (in percent) of probability vectors are shown for the seven 23

regions. The spathidiid 18S rRNA gene bears distinctly more tree-like information (94% of probability vectors fall in the tree-like areas) than the ITS region does (71.4%). Modified from Fig. 5 of Rajter and Vďačný (2016).

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