Department of Evolution, Ecology, and Organismal Biology
Department of Entomology, Ohio State University, Columbus
![]() |
Abstract |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
![]() |
Introduction |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Lyons-Weiler, Hoelzer, and Tausch (1996
, p. 754) distinguished RASA from two other measures of phylogenetic signal, g1 (Hillis 1991
) and permutation tail probability (PTP; Archie 1989
; Faith and Cranston 1991
), by noting that "(1) [RASA] is an a priori measure of phylogenetic signal, (2) it is a tree-independent statistical measure of phylogenetic signal, and (3) it is not at all based on the assumptions of maximum parsimony." Källersjö et al. (1992
, p. 275) demonstrated that g1 can be misleading because it "is too sensitive to character state frequencies, is not sensitive enough to number of characters (degree of corroboration) and relies on counts of arbitrarily-resolved bifurcating trees." Källersjö et al. (1992)
and Farris et al. (1994)
demonstrated that PTP can indicate strong signal for ambiguous data matrices.
Lyons-Weiler and his colleagues have proposed RASA to be used to measure phylogenetic signal (Lyons-Weiler, Hoelzer, and Tausch 1996
), select outgroups (Lyons-Weiler, Hoelzer, and Tausch 1998
), identify terminals subject to long-branch attraction (Lyons-Weiler and Hoelzer 1997
), and to detect lineage sorting (Lyons-Weiler and Milinkovitch 1997
). Others have used RASA as a basis to select their tree-building method (maximum likelihood was used instead of parsimony or distance methods because some terminals were indicated to be on long branches; Spaulding and von Dohlen 1998
), to determine on which parts of the tree most of the phylogenetic signal is located (Adams, Burnell, and Powers 1998
), and to select among alignments (Mardulyn and Whitfield 1999
). RASA has been interpreted as "an ingenious method of distinguishing at least some apparent synapomorphy from evolutionary synapomorphy..." (Zander 1998
, p. 690) and as a means of solving the question of superiority of molecular data relative to morphological data (Milinkovitch and Thewissen 1997
). In this paper we evaluate the ability of RASA to measure phylogenetic signal, select outgroups, and identify terminals subject to long-branch attraction, using both hypothetical and empirical examples.
![]() |
Measurement of Phylogenetic Signal |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
The ability of RASA to measure phylogenetic signal is based on the presumption that if data are uninformative to RAS, the slope of the regression of observed RAS values plotted on observed E values will be indistinguishable from a permutation-based estimate of the null slope (Lyons-Weiler and Hoelzer 1999
). We will demonstrate that this presumption is false, first with a matrix that is phylogenetically uninformative yet yields an observed RAS/E slope that is significantly different from the null slope, and second with a matrix in which all characters are completely congruent that yields an observed slope indistinguishable from the null slope.
The data matrix in table 1
is taken from Källersjö et al.'s (1992)
data matrix Two. This original matrix has 20% state 1 and 80% state 0 for each character. There are two most parsimonious trees for this matrix, the strict consensus of which is completely unresolved as each character is in conflict with the two other characters. Although the data matrix is uninformative regarding relationships among the terminals, its extraordinarily high unrooted tRASA value of 15,031.04 is highly significant (P < 0.001; table 2
).
|
|
RASA may fail to detect phylogenetic signal for matrices with strong character congruence, regardless of character-state frequencies. A matrix of 10 terminals and 10 characters was constructed, similar to the matrix in table 1
. The matrix consists of two, three, four, or five terminals, respectively, with the character state 1 for all 10 characters, whereas all other terminals have character state 0 for all 10 characters. Although the phylogenetic signal is very strong as the characters are completely congruent, unrooted tRASA is nearly zero in all cases (table 3
). These values did not appreciably change when the data matrix was expanded to include 100 identical characters (though suggesting a slight decrease in signal in four of five cases; table 3
), indicating that tRASA is not necessarily sensitive to the amount of phylogenetic signal, contra Lyons-Weiler, Hoelzer, and Tausch (1996)
. In contrast, Källersjö et al.'s (1992)
total support is 20 and highly significant (alpha = 0.001) for all matrices with 10 characters, and 200 and highly significant (alpha = 0.001) for all matrices with 100 characters.
|
![]() |
Optimal Outgroup Analysis |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
The data matrix in table 4
for ingroup terminals AL is composed entirely of 14 nonadditive (unordered), congruent characters, 13 of which are parsimony-informative (the first character is a parsimony-uninformative synapomorphy for the ingroup). tRASA for the unrooted ingroup is -0.32. The outgroup M is composed entirely of plesiomorphic character states for the ingroup. The single most parsimonious tree, rooted with outgroup M, is in figure 1a
. The consistency index (CI) for this tree is 1.0, affirming that the outgroup has no convergent character states with ingroup terminals. tRASA for the ingroup rooted with the entirely plesiomorphic outgroup, which is not on a long branch (one step between the ingroup and the outgroup), is -2.68. This decrease of 2.36 in tRASA suggests that the entirely plesiomorphic outgroup "is likely to be inappropriate for rooted phylogenetic analyses" (Lyons-Weiler 2000
, section 2.2.3).
|
|
|
![]() |
Detecting Long Branches |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
RASA has been used to check for terminals subject to long-branch attraction (Belshaw et al. 2000
; Liu and Kipreos 2000
; Morin 2000
; O'Donnell et al. 2000
; Wägele et al. 1999
), remove terminals subject to long-branch attraction (Stiller and Hall 1999
; Bowe, Coat, and de Pamphilis 2000
; Culligan et al. 2000
; Teeling et al. 2000
), exclude characters (Barkman et al. 2000a, 2000b
), and postulate long-branch attraction on a maximum-likelihood tree (Mardulyn and Cameron 1999
).
The data matrix in table 6 for eight terminals AH is composed of 30 nonadditive (unordered) characters. Although only 15 characters are parsimony-informative, all 30 characters are used by RASA in the calculation of tRASA. The most parsimonious tree for the entire data matrix is presented in figure 2a . All characters are congruent except for character state 3 in characters 1420, which is derived independently in terminals A and H. The branches leading to terminals A and H are the longest on the tree and their convergence in characters 1420 can potentially cause long-branch attraction. However, long-branch attraction does not occur because the phylogenetic signal between these disparate terminals is greater than the misleading, convergent signal.
|
|
![]() |
An Empirical Example of How RASA Can Be Misleading |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
One most parsimonious gene tree of 1,045/3,135 steps was found for the nucleotide characters (CI = 0.43; retention index (RI) = 0.35; fig. 3a
). tRASA, based on 100 permutations, for the unrooted data matrix is strongly negative (-7.85; table 7
), indicating that RASA cannot distinguish the phylogenetic signal in the matrix from randomly distributed signal. No long branches for the data matrix (or any of the modified data matrices, rooted or unrooted) were indicated in RASA's taxon-variance ratio diagram, so no terminals were removed from the analysis. Following Lyons-Weiler and Hoelzer's (1997)
suggestions for ways to recode the data to increase phylogenetic signal (cited in the context of alleviating problematic long branches), we recoded the matrix, using only first and second codon positions, purines, and pyrimidines (i.e., only considering transversions), or amino-acid characters, respectively.
|
|
Using a permutation-based estimate of the null slope, RASA failed to recognize phylogenetic signal in the nucleotide-based matrix of all three codon positions, even in the replicated matrix. This matrix produces a fully resolved most parsimonious tree for which many of the clades are well supported and congruent with relationships based on more widely sampled analyses (Chase et al. 2000
; Stevenson et al. 2000
). Furthermore, based on both analytical and permutation-based estimates of the null slope, RASA indicated increasing phylogenetic signal for matrices for which the strict consensus of the most parsimonious trees is increasingly poorly resolved, clades are increasingly poorly supported, and for which many relationships are in conflict with more widely sampled analyses. For instance, the two outgroup terminals are resolved in disparate parts of the most parsimonious tree for nucleotide characters from first and second codon positions (fig. 3b
). Also, Acorus, the well-supported sister group (or member of the sister group) of the rest of the monocots (Davis et al. 1998
; Chase et al. 2000
; Stevenson et al. 2000
), is resolved as a derived member of the monocots in the amino acid strict-consensus tree (fig. 3d
). Finally, RASA failed to recognize the threefold increase in phylogenetic signal in the replicated matrices.
In contrast to RASA, Källersjö et al.'s (1992)
total support is highest for the nucleotide characters from all three codon positions, and, appropriately, lower for all three codon positions coded as purines and pyrimidines and the first and second codon positions only (table 7
). For all three matrices, the total support values increased nearly linearly when the characters were replicated. The total support test recognized highly significant signal in all matrices except the original matrix of first and second codon positions only (alpha = 0.253), which is the least well supported of the matrices examined for total support. The program RNA does not recognize amino acid characters, and therefore, total support was not calculated for the matrix of amino acid characters.
Optimal outgroup analysis was then performed for each of the matrices (except the matrix of nucleotide characters coded as purines and pyrimidines, for which all characters are binary and therefore optimal outgroup analysis cannot be performed) to determine if RASA recognized either of the two outgroup terminals (Piper and Saruma) as the best outgroups. Broader level analyses have supported Piper and Saruma as appropriate outgroups for the monocots (Chase et al. 1993
; Soltis et al. 2000
). In no case did RASA indicate that either of the outgroup terminals was the best outgroup. RASA indicated a different ingroup terminal (Veratrum, Smilax, and Vellozia, respectively) as the best outgroup for each of the matrices (table 8
). In all cases, RASA indicated that the two actual outgroup terminals are the 11th or worse of the 20 terminals to root the trees. In the original matrix of nucleotides from all three codon positions, Saruma was indicated to be the 13th best candidate to root the tree and Piper was indicated to be the 15th best candidate. As a comparison to the tRASA values for which the putative best outgroup was used, Saruma was designated as the outgroup for rooted RASA analyses. In all three cases, no phylogenetic signal was recognized by RASA when Saruma was the designated outgroup (table 8
).
|
![]() |
Conclusions |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
In all cases except for equal character-state frequencies, RASA indicated extraordinarily high levels of phylogenetic signal for hypothetical data matrices that are uninformative regarding relationships among the terminals. Yet, regardless of the number of characters or character-state frequencies, RASA failed to detect phylogenetic signal for hypothetical matrices with strong phylogenetic signal. In our empirical example, RASA indicated increasing phylogenetic signal for matrices for which the strict consensus of the most parsimonious trees is increasingly poorly resolved, clades are increasingly poorly supported, and for which many relationships are in conflict with more widely sampled analyses. RASA's presumption that if data are uninformative to RAS, the slope of the regression of observed RAS values plotted on observed E values will be indistinguishable from a permutation-based estimate of the null slope is unfounded.
RASA is an ineffective approach to identify outgroup terminal(s) with the most plesiomorphic character states for the ingroup. Our hypothetical example demonstrated that RASA preferred outgroup terminals with increasing numbers of convergent character states with ingroup terminals, and rejected the outgroup terminal with all plesiomorphic character states. Our empirical example demonstrated that RASA, in all three cases examined, selected an ingroup terminal, rather than an outgroup terminal, as the best outgroup. In no case was one of the two outgroup terminals even close to being considered the optimal outgroup by RASA.
RASA is an ineffective means of identifying problematic long-branch terminals. In our hypothetical example, RASA indicated a terminal as being a problematic long-branch terminal in spite of the terminal being on a zero-length branch and having no possibility of undergoing long-branch attraction with another terminal. RASA also failed to identify actual problematic long-branch terminals that did undergo long-branch attraction, but only after following Lyons-Weiler and Hoelzer's (1997)
three-step process to identify and remove terminals subject to long-branch attraction. Siddall and Whiting (1999
, p. 15) described an appropriate method to test for long-branch attraction: "branches can only attract each other when they are simultaneously part of an analysis. If two branches are attracting each other, the absence of one of the branches should allow the remaining branch to place elsewhere in the pruned tree. That is, if each of the two branches individually group in precisely the same place as the other when they are allowed to stand alone in an analysis, one can hardly argue that they are attracted to this placement by the absent branch."
RASA, relying on three-taxon statements, cannot avoid their inherent problem of dependence, as described by Farris et al. (1995)
. RAS for two terminals is the number of times a terminal other than the two terminals has a different character state when the two terminals have the same character state, summed for all characters. Therefore, RASA is strongly affected by the frequencies of character states. Lyons-Weiler and Hoelzer (1999)
recognized the sensitivity of RASA to unequal character-state frequencies. However, rather than recognizing the problem of dependence to be an inherent flaw of RASA, Lyons-Weiler and Hoelzer (1999
, p. 1401) ascribed the problem to "excessive compositionally induced character state biases limit the phylogenetic information that can be encoded in a data matrix...." But that is not the root of the problem, as demonstrated by our first hypothetical example in which RASA interpreted character-state biased data matrices that are uninformative regarding relationships among the terminals as having extraordinarily high levels of phylogenetic information. Lyons-Weiler and Hoelzer's (1999)
permutation-based estimate of the null parameter is an ineffective correction.
It is highly questionable to measure phylogenetic signal using characters that are not used, or are used in an entirely different manner, by the tree-construction method. RASA considers all variable characters to be informative, whether they are parsimony-informative or not. Uninformative characters do not reveal cladistic hierarchy and yet may be counted as apparent synapomorphies by RASA. For example, in the hypothetical example from the detecting long branches section, RASA measured phylogenetic signal in twice the number of characters that were used to construct the trees using parsimony. When these parsimony-uninformative characters were removed, four more terminals were indicated to be problematic long branches, though terminals A and H were still not recognized as such.
In the same sense that neighbor joining and parsimony treat characters differently and therefore often create different optimal tree topologies, RASA, measuring phylogenetic signal outside the parsimony and maximum likelihood frameworks, will often give results that have little to no meaning to these methods of tree construction. Lyons-Weiler, Hoelzer, and Tausch (1996
, p. 754) distinguished RASA from g1 and PTP in part by noting that RASA is not at all based on the assumptions of maximum parsimony, presumably considering this to be an advantage. However, we see no consistently meaningful alternative to using the same methodology to construct trees as to measure signal. Källersjö et al.'s (1992)
total support is consistent with the parsimony approach.
![]() |
Acknowledgements |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
![]() |
Footnotes |
---|
Keywords: relative apparent synapomorphy analysis
RASA
phylogenetic signal
long-branch attraction
outgroup selection
Address for correspondence and reprints: Mark P. Simmons, Department of Biology, Colorado State University, E106 Anatomy/Zoology Building, Fort Collins, Colorado 80523-1878. psimmons{at}lamar.colostate.edu
.
![]() |
References |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Adams B. J., A. M. Burnell, T. O. Powers, 1998 A phylogenetic analysis of Heterorhabditisrr (Nemata: Rhabditidae) based on internal transcribed spacer 1 DNA sequence data J. Nematol 30:22-39[ISI]
Archie J. W., 1989 A randomization test for phylogenetic information in systematic data Syst. Zool 38:219-252
Atibalentija N., G. R. Noel, L. L. Domier, 2000 Phylogenetic position of the North American isolate of Pasteuria that parasitizes the soybean cyst nematode, Heterodera glycines, as inferred from 16S rDNA sequence analysis Int. J. Syst. Evol. Microbiol 50:605-613[Abstract]
Barkman T. J., J. R. McNeal, G. Chenery, C. W. de Pamphilis, 2000a. Evolutionary genomic analyses converge on basal angiosperm phylogeny Am. J. Bot. Suppl 6:112
Barkman T. J., G. Chenery, J. R. McNeal, J. Lyons-Weiler, W. J. Elisens, G. Moore, A. D. Wolfe, C. W. dePamphilis, 2000b. Independent and combined analyses of sequences from all three genomic compartments converge on the root of flowering plant phylogeny Proc. Natl. Acad. Sci. USA 97:13166-13171
Belshaw R., M. Dowton, D. L. J. Quicke, A. D. Austin, 2000 Estimating ancestral geographic distributions: a Gondwanan origin for aphid parasitoids Proc. R. Soc. Lond. Ser. B: Biol. Sci 267:491-496[ISI][Medline]
Bowe L. M., G. Coat, C. W. de Pamphilis, 2000 Phylogeny of seed plants based on all three genomic compartments: extant gymnosperms are monophyletic and Gnetales' closest relatives are conifers Proc. Natl. Acad. Sci. USA 97:4092-4097
Chase M. W., D. E. Soltis, R. G. Olmstead, et al. (39 co-authors) 1993 Phylogenetics of seed plants: an analysis of nucleotide sequences from the plastid gene rbcL Ann. Mo. Bot. Gard 80:528-580
Chase M. W., D. E. Soltis, P. S. Soltis, et al. (13 co-authors) 2000 Higher-level systematics of the monocotyledons: an assessment of current knowledge and a new classification Pp. 316 in K. L. Wilson and D. A. Morrison, eds. Monocots: systematics and evolution. CSIRO Publishing, Melbourne, Australia
Culligan K. M., G. Meyer-Gauen, J. Lyons-Weiler, J. B. Hays, 2000 Evolutionary origin, diversification and specialization of eukaryotic MutS homolog mismatch repair proteins Nucleic Acids Res 28:463-471
Davis J. I., M. P. Simmons, D. W. Stevenson, J. F. Wendel, 1998 Data decisiveness, data quality, and incongruence in phylogenetic analysis: an example from the monocotyledons using mitochondrial atpA sequences Syst. Biol 47:282-310[ISI][Medline]
Donoghue M., M. Sanderson, W. Piel, 1996 TreeBASE: a database of phylogenetic knowledge Deposited 26 July 2001 on the World Wide Web: http://www.herbaria.harvard.edu/treebase/
Faith D., P. Cranston, 1991 Could a cladogram this short have arisen by chance alone? Cladistics 7:1-28[ISI]
Farris J. S., 1989 The retention index and the rescaled consistency index Cladistics 5:417-419[ISI]
. 1994 RNA (computer software and manual) Distributed by the author, Molekylärsystematiska laboratoriet, Stockholm, Sweden
Farris J. S., M. K, A. G. Kluge, C. Bult, 1994 Permutations Cladistics 10:65-76[ISI]
Farris J. S., M. K, V. A. Albert, et al. (30 co-authors) 1995 Explanation Cladistics 11:211-218[ISI]
Felsenstein J., 1978 Cases in which parsimony or compatibility methods will be positively misleading Syst. Zool 27:401-410[ISI]
Hall J. S., B. Adams, T. J. Parsons, R. French, L. C. Lane, S. G. Jensen, 1998 Molecular cloning, sequencing, and phylogenetic relationships of a new potyvirus: sugarcane streak mosaic virus, and a reevaluation of the classification of the Potyviridae Mol. Phylogenet. Evol 10:323-332[ISI][Medline]
Hillis D. M., 1991 Discriminating between phylogenetic signal and random noise in DNA sequences Pp. 278294 in M. M. Miyamoto and J. Cracraft, eds. Phylogenetic analysis of DNA sequences. Oxford University Press, Oxford
Holmdahl O. J. M., D. A. Morrison, J. T. Ellis, L. T. T. Huong, 1999 Evolution of ruminant Sarcocystis (Sporozoa) parasites based on small subunit rDNA sequences Mol. Phylogenet. Evol 11:27-37[ISI][Medline]
Johns G. C., J. C. Avise, 1998 Tests for ancient species flocks based on molecular phylogenetic appraisals of Sebastes rockfishes and other marine fishes Evolution 52:1135-1146[ISI]
K M., J. S. Farris, A. G. Kluge, C. Bult, 1992 Skewness and permutation Cladistics 8:275-287[ISI]
Kluge A. G., J. S. Farris, 1969 Quantitative phyletics and the evolution of Anurans Syst. Zool 18:1-32[ISI]
Liu J., R. Berry, G. Poinar, A. Moldenke, 1997 Phylogeny of Photorhabdus and Xenorhabdus species and strains as delimited by comparison of partial 16S rRNA gene sequences Int. J. Syst. Bacteriol 47:948-951
Liu J., E. T. Kipreos, 2000 Evolution of cyclin-dependent kinases (CDKs) and CDK- activating kinases (CAKs): differential conservation of CAKs in yeast and metazoa Mol. Biol. Evol 17:1061-1074
Lyons-Weiler J., 2000 RASA 2.5 software and documentation for the Mac Distributed by the author, University of Massachusetts, Lowell, Mass
Lyons-Weiler J., G. A. Hoelzer, 1997 Escaping from the Felsenstein zone by detecting long branches in phylogenetic data Mol. Phylogenet. Evol 14:375-384
. 1999 Null model selection, composition bias, character state bias, and the limits of phylogenetic information Mol. Biol. Evol 16:1400-1405
Lyons-Weiler J., M. C. Milinkovitch, 1997 A phylogenetic approach to the problem of differential lineage sorting Mol. Biol. Evol 14:968-975
Lyons-Weiler J., G. A. Hoelzer, R. J. Tausch, 1996 Relative apparent synapomorphy analysis (RASA) I: the statistical measurement of phylogenetic signal Mol. Biol. Evol 13:749-757[Abstract]
. 1998 Optimal outgroup analysis Biol. J. Linn. Soc 64:493-511[ISI]
Maddison W. P., D. R. Maddison, 1992 MacClade: analysis of phylogeny and character evolution Version 3.0. Sinauer. Sunderland, Mass
Mardulyn P., S. A. Cameron, 1999 The major opsin in bees (Insecta: Hymenoptera): a promising nuclear gene for higher level phylogenetics Mol. Phylogenet. Evol 12:168-176[ISI][Medline]
Mardulyn P., J. B. Whitfield, 1999 Phylogenetic signal in the COI, 16S, and 28S genes for inferring relationships among genera of Microgastrinae (Hymenoptera: Braconidae): evidence of a high diversification rate in this group of parasitoids Mol. Phylogenet. Evol 12:282-294[ISI][Medline]
Milinkovitch M. C., J. Lyons-Weiler, 1998 Finding optimal ingroup topologies and convexities when the choice of outgroups is not obvious Mol. Phylogenet. Evol 9:348-357[ISI][Medline]
Milinkovitch M. C., J. G. M. Thewissen, 1997 Even-toed fingerprints to whale ancestry Nature 388:622-624[ISI][Medline]
Morin L., 2000 Long branch attraction effects and the status of "basal eukaryotes": phylogeny and structural analysis of the ribosomal RNA gene cluster of the free-living diplomonad Trepomonas agilis J. Eukaryot. Microbiol 47:167-177[ISI][Medline]
Nelson G., N. I. Platnick, 1981 Systematics and biogeography: cladistics and vicariance Columbia University Press, New York
. 1991 Three-taxon statements: a more precise use of parsimony? Cladistics 7:351-366[ISI]
O'Donnell K., H. I. Nirenberg, T. Aoki, E. Cigelnik, 2000 A multigene phylogeny of the Gibberella fujikuroi species complex: detection of additional phylogenetically distinct species Mycoscience 41:61-78
Parnell J., 1999 Numerical analysis of Thai members of the Eugena-Syzygium group (Myrtaceae) Blumea 44:351-379[ISI]
Pruess K. P., B. J. Adams, T. J. Parsons, X. Zhu, T. O. Powers, 2000 Utility of the mitochondrial cytochrome oxidase II gene for resolving relationships among black flies (Diptera: Simuliidae) Mol. Phylogenet. Evol 16:286-295[ISI][Medline]
Siddall M. E., M. F. Whiting, 1999 Long-branch abstractions Cladistics 15:9-24[ISI]
Soltis D. E., P. S. Soltis, M. W. Chase, et al. (11 co-authors) 2000 Angiosperm phylogeny inferred from a combined data set of 18S rDNA, rbcL, and atpB sequences Bot. J. Linn. Soc 133:381-461[ISI]
Spaulding A. W., C. D. von Dohlen, 1998 Phylogenetic characterization and molecular evolution of bacterial endosymbionts in Psyllids (Hemiptera: Sternorrhyncha) Mol. Biol. Evol 15:1506-1513
Stevenson D. W., J. I. Davis, J. V. Freudenstein, C. R. Hardy, M. P. Simmons, C. D. Specht, 2000 A phylogenetic analysis of the monocotyledons based on morphological and molecular characters, with comments on the placement of Acorus and Hydatellaceae Pp. 1724 in K. L. Wilson and D. A. Morrison, eds. Monocots: systematics and evolution. CSIRO Publishing, Melbourne, Australia
Stiller J. W., B. D. Hall, 1999 Long-branch attraction and the rDNA model of early eukaryotic evolution Mol. Biol. Evol 16:1270-1279
Swofford D. L., 1998 PAUP*: phylogenetic analysis using parsimony (*and other methods) Version 4.0. Sinauer, Sunderland, Mass
Teeling E. C., M. Scally, D. J. Kao, M. L. Romagnoli, M. S. Springer, M. J. Stanhope, 2000 Molecular evidence regarding the origin of echolocation and flight in bats Nature 403:188-192[ISI][Medline]
van Tuinen M., C. G. Sibley, S. B. Hedges, 2000 The early history of modern birds inferred from DNA sequences of nuclear and mitochondrial ribosomal genes Mol. Biol. Evol 17:451-457
Wägele J. W., T. Erikson, P. Lockhart, B. Misof, 1999 The ecdyosoma: artifact or monophylum J. Zool. Syst. Evol. Res 37:211-223[ISI]
Wolf P. G., 1997 Evaluation of atpB nucleotide sequences for phylogenetic studies of ferns and other pteridophytes Am. J. Bot 84:1429-1440[Abstract]
Zander R. H., 1998 Phylogenetic reconstruction, a critique Taxon 47:681-693[ISI]