*Institute for Evolutionary Biology and Ecology, University of Bonn, Bonn, Germany;
School of Biological Sciences, Victoria University of Wellington, Wellington, New Zealand;
and
Laboratoire d'Entomologie, Museum National d'Histoire Naturelle, Paris, France
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Abstract |
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Introduction |
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Depending on the author's preferences, dragonflies, Anisoptera, are subdivided into 615 families. Three probably monophyletic groups (based on morphological data) are species-rich, the families Gomphidae and Aeshnidae and the "Corduliidae" + Libellulidae complex, accounting for almost 90% of all species within dragonflies. The remaining families, Petaluridae, Austropetaliidae, Cordulegastridae, Neopetaliidae, Chlorogomphidae, Gomphomacromiidae, Macromiidae, and Synthemistidae, to name the most prominent ones, are species-poor assemblages, often restricted to small geographical areas or refugia. Imagoes of all dragonflies display a restricted ecological diversity; all dragonflies are essentially fast flying insect predators, whereas larval ecology shows a pronounced degree of diversification, from (semi)terrestrial life (e.g., Petaluridae) to life in high-current water bodies (e.g., Austropetaliidae, most Gomphidae).
Recent morphological work on a phylogeny of dragonflies (Pfau 1991
; Carle 1995
; Bechly 1996
; Lohmann 1996
; Trueman 1996
; Bechly et al. 1998
) left many interfamily relationships unresolved. The analyses of Pfau (1991)
, Carle (1995
), Fleck (1996
), Lohmann (1996
), Trueman (1996
), and Bechly et al. (1998
) contradict each other in phylogenetic positions of Petaluridae, Gomphidae, Aeshnidae, Austropetaliidae, and Cordulegastridae. All of those analyses are based on morphological characters, some of them exclusively on wing characters (Trueman 1996
); others are based on more exhaustive analyses of secondary sexual characters (Pfau 1991
) and head characters (Fleck 1996
), and others include information on fossil and extant species (Carle 1995
; Lohmann 1996
; Bechly et al. 1998
). Even within families, most phylogenetic relationships remain obscure (e.g., within Gomphids [Carle 1986
] or Libellulidae [Kambhampati and Charleton 1999
]).
The phylogenetic analysis of mitochondrial SSU and LSU gene fragments therefore has the potential to assess resolution at the intrafamily and interfamily levels. Several questions have to be addressed: (1) Does the molecular analysis recover generally accepted monophyletic clades based on morphological arguments, thus supporting the interpretation of specific morphological characters? (2) What is the position of the aeshnids, petalurids, and gomphids? All three major evolutionary lineages are characterized by a complex pattern of primitive and autapomorphic features. For example, wing structures appear to be plesiomorphic in all three groups, whereas larval ecology is specialized in petalurids to semiterrestrial life, to active freshwater predators in aeshnids and buried sit-and-wait predators in gomphids. (3) Do "Cavilabiata" (monophyly of Cordulegastridae, Neopetaliidae, Chlorogomphidae, Synthemistidae, Macromiidae, Corduliidae, and Libellulidae based, e.g., on the shape of the labial mask and gizzard structures) really exist, or is this a polyphyletic group? "Cavilabiata" are characterized by a specific larval type, a mostly semiburied sit-and-wait predator with a spoonlike labial mask which enables larvae to perform an upward stroke into the water body to rescue prey. Several groups within the assumed monophyletic "Cavilabiata" developed active predatory larvae, still showing the spoon-shaped labium. These larval characters are considered derived diagnostic features, which led to the inclusion of the Neopetaliidae, formerly within Aeshnoids, within "Cavilabiata" (Carle and Louton 1994
).
Phylogenetic information in gene fragments is built up by nucleotide substitutions over time. Due to the limited number of character states, phylogenetic signal also erodes by similar processes, and its progression is dependent on rate of change. In this study, we analyzed the quantity of phylogenetic signal and its decay depending on cladistic level, genetic distance, and rate of change.
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Materials and Methods |
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DNA Extraction, PCR Amplification, and DNA Sequencing
DNA samples were prepared from individual insects by extraction of total DNA from frozen or ethanol-preserved animals. After excision of muscle tissue, voucher specimens used for DNA extractions where deposited in absolute ethanol at -20°C at the University of Bonn, Institute for Evolutionary Biology and Ecology, and at the Museum National d'Histoire Naturelle, Paris.
Total DNA was extracted with two different methods, of which the classical phenol/chloroform extraction method was preferably used on most frozen specimens (Hadrys, Balick, and Schierwater 1992
). The CHELEX extraction method (Gerken et al. 1998
) was used for most ethanol-preserved specimens for which the phenol/chloroform extraction did not yield template DNA suitable for PCR amplification.
PCR amplifications were performed on a Perkin Elmer 9600 (Perkin Elmer) and a Primus 96 (MWG) according to Misof, Anderson, and Hadrys (2000). Amplification of the 12S fragments was only successful using a touch-down PCR set up. The chemical composition of the PCR reaction was similar to the regular reaction conditions (Misof, Anderson, and Hadrys 2000), except that the 12.5 U of Taq polymerase and 50% of the sterile water were added after 2 min denaturation at 92°C. Within the first 15 cycling steps, the annealing temperature was decreased by 1°C each cycle, starting at 50°C and ending at 35°C. These initial 15 cycles were followed by 25 cycles of constant annealing temperature at 50°C and 1 min extension time at 72°C to enrich the amplification product. Primers used and developed in this investigation are listed in table 1 .
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Analyses of Phylogenetic Information Content and Substitutional Saturation
Sequences were initially aligned with the CLUSTAL X program package (Higgins and Sharp 1988
) and subsequently corrected by eye. We explored the quality of our alignment by varying alignment parameter specifications (gap opening cost, gap extension cost) and comparing alignments. Positions which showed variable alignments where excluded from phylogenetic analyses (Gatesy, DeSalle, and Wheeler 1993
; Milinkovitch, Berube, and Palsboll 1998
). Additionally, we used secondary-structure information from the 16S Van de Peer model (de Rijk, Van De Peer, and de Wachter 1997
; Gutell et al., personal communication), secondary-structure information for odonates and other insects (Buckley et al. 2000), and secondary-structure information on 12S domain III (Hickson et al. 1996
) to confirm identification of ambiguous alignment positions.
Prior to performing phylogenetic reconstructions, we analyzed the quality of our data set concerning phylogenetic information content and saturation of variation caused by multiple substitutions. We decided to use the approach introduced by (Lyons-Weiler, Hoelzer, and Tausch 1996
), since it offers theoretical advantages over the permutation tests commonly used to check for hierarchical structure in a data matrix (e.g., Hillis 1991
). This method makes use of the fact that in a hierarchically structured data matrix, the proportion between potential synapomorphies and potential informative similarity for any two taxa increases above that expected by chance alone. This offers avenues for statistical evaluation of hierarchical structure in any data set. The technique is implemented in the program RASA (for details of the method, see Lyons-Weiler, Hoelzer, and Tausch 1996
). The results of RASA gave us information about the overall phylogenetic information content of the 16S and 12S data sets.
Additionally, RASA gave us information about potential long branches in the data set associated with single taxa. Taxa associated with long-branch effects were readily recognized by their significantly larger cladistic variance compared with other taxa (Lyons-Weiler and Hoelzer 1997
). A significant deviation of the cladistic variance of single taxa was determined with F-statistics as implemented in RASA, version 2.3.
We used saturation plots to estimate areas in which phylogenetic information was expected to decay. We plotted frequencies of each substitution type against maximum-likelihood distances estimated by using the maximum-likelihood model fitted to the data (see below).
Outgroup Analysis
The selection of an optimal outgroup is crucial to derive a robust phylogenetic conclusion (from the extensive literature, see, e.g., Farris [1982]
and, more recently, Milinkowitch and Lyons-Weiler [1998]). We used saturation plots to check for potential decay of phylogenetic signal caused by saturated nucleotide variation between outgroup and ingroup taxa.
In parallel, we used the RASA technique to evaluate our choice of outgroup taxa. An optimal outgroup is characterized by a relatively small number of autapomorphies and predominant plesiomorphies compared with ingroup taxa. Using this criterion, an optimal outgroup can also be selected prior to phylogenetic inference with RASA by constraining the relative synapomorphy scores (RAS) of taxon pairs with outgroup character states. A relative best outgroup maximizes the hierarchical structure in a rooted RASA analysis (Milinkowitch and Lyons-Weiler 1998
). This can be verified by comparing the tRASA (the test statistic in a test for homogeneity of slopes) value of the unrooted ingroup with that of the rooted ingroup analysis. With suitable outgroup assemblages, tRASA increases.
Finding an Appropriate Substitution Model
We used the approach described by Sullivan, Holsinger, and Simon (1995
), Frati et al. (1997)
, and Buckley, Simon, and Chambers (2000)
to select an appropriate substitution model for further phylogenetic analysis and distance estimation. We made use of the MODELTEST program (Posada and Crandall 1998
), which implements a model test routine based on the likelihood ratio statistics and the Akaike information criterion (Akaike 1974
), in combination with PAUP, version 4.0b3a*, maximum-likelihood calculations. The likelihood ratio test (Goldman 1993a, 1993b
; Yang et al. 1994
) indicated that every increase in model complexity significantly improved fit to data. In total, we tested 42 different models, including rate heterogeneity and number of invariable sites. Parameters of the model (substitution parameters, shape of gamma distribution, proportion of invariable sites) were estimated from the data set using a neighbor-joining tree generated with JC-distances. The general time reversible (GTR) + %I +
model was found to be of adequate complexity. The Akaike information criterion implied the same conclusion.
Estimation of Rate Heterogeneity
Estimation of rate heterogeneity is relatively insensitive to topology as long as long branches in the tree are correctly identified (Sullivan, Holsinger, and Simon 1996
). We compared shape estimates for six alternative phylogenies of Anisoptera and found no deviation in shape parameters. Subsequently, shape estimates were used to calculate posterior probabilities that a site belonged to a given rate category (Uzzell and Corbin 1971
; Yang 1994
) assuming a gamma distribution with eight categories by using PAUP, version 4.0b3a*. Due to different rates of change, character partitions associated with single-site rates are expected to display differing levels of resolution within the data set but are expected to support compatible topologies, assuming that homoplasy is distributed randomly (this does not necessarily have to be the case; see Naylor and Wesley Brown 1998
). Fast-changing characters might introduce predominant noise to phylogenetic reconstructions due to an increased likelihood of multiple substitutions. We tested this hypothesis with two different approaches. First, we successively subtracted character sets associated with single-site-specific rates from the data set and calculated the remaining phylogenetic signal of the reduced data sets using RASA. The increase/decrease of the test parameter tRASA was plotted against the number of included character sets. If a character subset harbors predominantly random structure, the test parameter tRASA is expected to reflect this through an increase after removal of that character set. Second, we compared robustness of tree topologies reconstructed with single character partitions using bootstrapping and Bremer support (Bremer 1988
; Baker and DeSalle 1997
) implemented in TreeRot, version 2 (Sorensen 1999
). Bremer support allowed us to study how fast support for nodes of interest eroded.
Phylogenetic Reconstructions
Prior to phylogenetic reconstructions, we tested for homogeneity of base frequencies among taxa using the 2 test as implemented in PAUP, version 4.0b3a* (which ignores correlations due to phylogenetic structure); over all sites, over parsimony-informative sites only, without constant sites (parsimony-uninformative and constant sites will mislead the
2 test); and over each character subset associated with single-site rates separately. All phylogenetic reconstructions were performed with PAUP, version 4.0b3a* (Swofford 1999
). We performed maximum-parsimony, minimum-evolution, and maximum-likelihood reconstructions from the pruned data set (without fast-changing characters) and compared results with those of a logDet (Lockhart et al. 1994
) reconstruction on the complete data set. LogDet reconstructions were performed on the complete data set, with invariable sites removed in proportion to the base frequencies estimated from constant sites (Waddell and Steel 1997
).
Prior to the maximum-parsimony analyses, we evaluated the congruence of the two data sets with an ILD test (Farris et al. 1995
; Hillis, Moritz, and Mable 1996
; Swofford et al. 1996
), as implemented in PAUP, version 4.0b3a*. Parsimony reconstructions were performed with heuristic searches on parsimony-informative characters, and gaps treated as missing characters, with random addition of taxa for 100 replications and tree bisection-reconnection (TBR) branch swapping. Bootstrapping was conducted on parsimony-informative characters for a total of 10,000 replicates of heuristic searches, with random addition of taxa.
We used the combined data set in our maximum-likelihood analysis. Separate analyses for the 12S and 16S fragments all found that a GTR + %I + model fitted the data best. We assumed that deviation from the model parameters between the two sequenced gene fragments would not dramatically influence the results of the combined analyses. Heuristic tree searches under the maximum-likelihood criterion were performed with random addition of taxa for one replication and TBR branch swapping. Since tree reconstruction with maximum likelihood is extremely time-consuming, we restricted our search to the puzzling technique to evaluate statistical support (number of quartets = 101,270; number of puzzling steps = 100,000), being aware that quartet puzzling support tends to overestimate the robustness of nodes (Strimmer and von Haeseler 1996
; Cao, Adachi, and Hasegawa 1998
).
Minimum-evolution trees were reconstructed using maximum-likelihood distances of the pruned data set employing the GTR + I% + model. Bootstrapping was performed with 10,000 replicates.
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Results |
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Alignment
The net number of unambiguously aligned positions totaled 1,540, and the net number of parsimony-informative characters was 645 for the combined data set. The data matrix is available on request.
Analyses of Phylogenetic Information Content and Substitutional Saturation
A hierarchical structure is clearly present in the combined data matrix (tRASA = 10.13076, df = 857). Removing Ephemera sp., Platycnemis pennipes, Calopteryx splendens, Chalcolestes viridis, and Heteragrion sp. as the most distant outgroup taxa enhances the footprint of a hierarchical structure in the data matrix (tRASA = 20.34925, df = 699) (fig. 1
). For the complete data set, no single taxon exhibited an excess of cladistic variation typical for long-branch effects.
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Test of Homogeneity of Base Frequencies
The shape parameter indicated strong rate heterogeneity within both rRNA fragments. Of 1,540 characters, 129 characters of the 16S fragment and 50 characters of the 12S fragment were estimated to belong within the fastest rate category (G8) of the discrete gamma distribution. The remaining 410 sites were estimated to fall within rate categories 17 (G1G7) (for rate estimates, see table 2
).
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Parsimony analyses of both character partitions (G8 and G1G7) resulted in two incongruent topologies. Support for the most parsimonious trees of character set G8 quickly decayed for the major nodes, and Bremer support (BS) values were generally low, except for the monophyly of the Aeshnidae (BS = 9) and Cordulegastridae (BS = 20) and two subgroups within the gomphids. Gomphids and libellulids appear to be polyphyletic, petalurids appear to be paraphyletic, and Neopetaliidae and Chlorogomphidae appear to be the most basal taxa within Anisoptera. Bootstrap support did not climb beyond 40% in any node except for Aeshnidae and Cordulegastridae, and two subgroups within gomphids (Ophiogomphus + Onychogomphus, and Gomphus s.l.). Apparently, character set G8 is devoid of strong phylogenetic structure beyond some intrafamily relationships and might even contain misleading signal due to the nonstationarity of base frequencies across taxa.
For character set G7-1, the monophyly of Australopetaliidae + Aeshnidae, Gomphidae, Corduliidae s.l., and Libellulidae was robust against immediate decay; however, the monophyly of "Cavilabiata" quickly decayed (within three steps). Bootstrap support was generally high within families and low at the interfamily level.
In figure 3 , the decrease in Bremer support is plotted against the logDet distances. Additionally, the total number of substitutions is also plotted against logDet distances. As can be expected, the median of decay indices starts out higher in fast-changing sites (fig. 3B ) and quickly decreases above logDet distances of 12% in both fast-changing (G8) (fig. 3B ) and slow-changing (G1G7) (fig. 3A ) characters. However, variance in Bremer support is much higher for slow-changing characters. Bremer support decreases before saturation of nucleotide substitution becomes apparent. Thus, both character partitions seemed to have experienced erosion of phylogenetic signal above logDet distances of 15%, although this was less pronounced in character set G7-1.
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Discussion |
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Second, we achieved robust reconstructions of interfamily relationships only for parts of the tree. A crucial problem turned out to be the recognition of the "Cavilabiata," as is the case in morphological studies (Pfau 1991
; Carle 1995
; Lohmann 1996
; Trueman 1996
; Bechly et al. 1998
). Despite an ecological diversity of larval and imaginal life history features, an apomorphic spoon-shaped development of the larval labial mask suggests a monophyly of the "Cavilabiata." Morphological analyses emphasized this character as an apomorphic structure (e.g., Carle 1995
; Bechly et al. 1998
; in contrast to Pfau 1991
), which led to the inclusion of Neopetalia punctata (Neopetaliidae) in the "Cavilabiata" (Carle and Louton 1994
). The recognition of this monophylum is contradicted by an analysis of secondary sexual characters (Pfau 1991
) and, additionally, leads to a paleontological paradox. The assumed most-ancestral representatives, the Cordulegastridae, the Neopetaliidae, and also the Chlorogomphidae, are unknown from fossil records (Bechly et al. 1998
) in contrast to Lower Cretaceous representatives of the MCL complex (Nel and Paicheler 1994
; Fleck, Nel, and Martinez-Delclòs 1999
). Based on two wing characters, Bechly et al. (1998
) proposed recognition of the extinct Hemeroscopidae as close relatives of the Chlorogomphidae, relaxing this paradoxical situation. Imaginal characters of Chlorogomphidae display many plesiomorphic features compared with species of the MCL complex, closely resembling representatives of the Cordulegastridae and N. punctata. The molecular data did not conclusively confirm the monophyly of the "Cavilabiata," but it is compatible with this hypothesis. Chlorogomphus brunneus is at the stem of the monophyletic MCL complex, supporting a view based on predominantly larval characters (Bechly et al. 1998
). Genetic distances of Chlorogomphidae to species of the MCL complex illustrate a pronounced molecular distinction of Chlorogomphidae from Neopetaliidae and Cordulegastridae not immediately apparent from morphological data. Thus, our molecular data have added structure to the stem of the group. Future inclusion of presumably basal cordulegastrid species, e.g., Zoraena sp. and Chlorogomphus ssp. and species of the families Gomphomacromiidae and Synthemistidae, will possibly further improve resolution.
Genetic distances of E. superstes from the anisopterous taxa imply a comparatively recent split between E. superstes and sampled anisopterous taxa, contrary to the view expressed in Nel et al. (1993)
based on wing characters. Morphological analyses of "Anisozygoptera" larvae including larvae of E. superstes (unpublished data) support the results of the molecular analysis. Among other characters, larvae of Epiophlebia ssp. and Anisoptera share an absence of external larval gills. Comparison of Anisoptera and Epiophlebia larvae should provide clues as to which ecological factors induced loss of external larval gills in this monophyletic group.
Genetic distances of zygopterous taxa and anisopterous taxa are obviously inflated by saturated nucleotide variation, implying an old split between Zygoptera and Anisoptera, making them unfit outgroup taxa in our analysis; similarly, Ephemera sp. is unsuitable to serve as an outgroup taxon for Anisoptera. Assuming a relatively constant rate of change at the molecular level, our data indicate an ancient split between extant Zygoptera and Anisoptera + Epiophlebia. A direct relationship of extinct Permian zygopterous forms and extant Zygoptera might be worth considering.
Phylogenetic Signal Associated with Single-Site Rates
The characters assumed to change the most rapidly do not harbor phylogenetic signal sufficient for resolving most interfamily and intrafamily relationships, in contrast to characters assumed to change with rate 17. Lack of phylogenetic resolution in character set G8 must be attributed to the erosion of phylogenetic information over time due to multiple hits. In figure 3B,
a decrease in Bremer support coincides with saturation of nucleotide substitution. At roughly 12% logDet distances, decay values of character set G8 level off at 34. We assume this pattern to be due to the high evolutionary rates of these characters. Simulations will show whether this interpretation is correct. Character set G7-1 exhibits a pattern similar to that of character set G8, but signal decay is delayed (fig. 3A
). Variance of decay indices remains high up to 24% logDet distances, indicating that there is still some phylogenetic signal. It is clear that comparing genetic distances, decay indices of common nodes, and the inferred number of substitutions provides only a rough record of signal dilution. Nevertheless, these plots demonstrate that the robustness of tree reconstruction declines with or before substitutional saturation becomes detectable in both data sets. This is in contrast to recently published reports (e.g., Yang 1998
) in which good performance of reconstruction methods was recorded despite extensive substitutional saturation. The disparity between Yang's results (1998) and ours might indicate that even the best evolutionary substitution model is still an insufficient description of the actual biological processes shaping the pattern of nucleotide variation within these gene fragments.
Base composition among fast-changing sites (subset G8) deviated significantly from homogeneity, further emphasizing their problematic nature (table 3
) by possibly introducing misleading information. Saturation, base composition, and decay analyses imply that decay of phylogenetic signal associated with among-site rate variation is the important player in our analysis. Our findings agree with empirical results of Sullivan, Holsinger, and Simon 1995
) and simulation results of Yang (1995, 1996)
, which demonstrated that effects of transition/transversion rate bias can be minor in comparison with those of among-site rate variation and unequal branch lengths.
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Conclusions |
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Acknowledgements |
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Footnotes |
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1 Present address: Zoology Department, North Carolina Central University.
2 Keywords: Anisoptera
phylogeny
mitochondrial SSU and LSU rRNA
signal decay
base frequency
3 Address for correspondence and reprints: B. Misof, Institute for Evolutionary Biology and Ecology, University of Bonn, An der Immenburg 1, 53121 Bonn, Germany. E-mail: bmisof{at}evolution.uni-bonn.de
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