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Classification or Phylogenetic Estimates?
Cladistics 16, 411–419 (2000) doi:10.1006/clad.2000.0139, available online at http://www.idealibrary.com on
Classification or Phylogenetic Estimates? Robert W. Scotland and Mark A. Carine* Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, United Kingdom; and *Centre for Plant Diversity and Systematics, The University of Reading, Whiteknights, Reading RG6 6AS, United Kingdom Accepted July 15, 2000; published online January 2, 2001
[m]3ta is a method that seeks to implement a taxic view of homology. The method is consistent with Patterson’s tests for discriminating homology from nonhomology. Contrary to the claims of Kluge and Farris, (1999, Cladistics 15, 205–212), m3ta is not a phenetic method—nor does it necessarily place the basal split in a tree between the phenetically most divergent taxa. [m]3ta does not seek to accurately recover phylogeny but rather it seeks to maximize the information content of taxic homology propositions. [m]3ta is a method of classification in which the unit of analysis is the relation of homology. [m]3ta differs from all phylogenetic methods because the units of analyses in phylogenetic methods, including sca, are transformation series. 䉷 2000 The Willi Hennig Society
tree contains the maximum amount of homology. 3ta seeks only to discriminate homology from nonhomology within the conceptual framework of tests of homology derived from Patterson (1982). Patterson (1982) discussed his tests in the context of constructing classifications. Patterson (1982, p. 67) stated that the role of homology in phylogenetic reconstruction is limited to the production of cladograms, or classifications.
Patterson’s (1982) tests sought to discriminate homology from nonhomology among those data that were considered to be evidence for grouping. De Pinna (1991, p. 371) stated: Therefore, by making all characters operationally equivalent to putative synapomorphies, the procedure of discovering a common pattern among taxa can be carried out.
3TA AND PATTERSON’S (1982) TESTS OF HOMOLOGY
Table 1 shows the relationship between Patterson’s (1982) tests of homology, 3ta and sca (standard cladistic analysis). Table 1a shows Patterson’s similarity and congruence tests of homology. Homology is the sole relation that passes the tests of both similarity and congruence. Parallelism and convergence are nonhomology; parallelism fails the congruence test and convergence fails both the similarity and the congruence tests. The sole purpose of Patterson’s (1982) tests is to distinguish homology from nonhomology.
Classifications have been described as a hierarchy of homology (Rieppel, 1988) and 3ta (three-taxon statement analysis) is a method of data analysis that seeks to maximize the information content of taxic homology propositions for the purpose of constructing classifications. For the purposes of analysis, 3ta maximizes propositions of homology, in the form of accommodated three-taxon statements such that the minimal (optimal)
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TABLE 1 (a) Patterson’s (1982) tests of homology Convergence
Parallelism
Homology
Fail Fail
Pass Fail
Pass Pass
Similarity Congruence
(b) Three-taxon statements, analogy, homology, and nonhomology
Similarity Fit
Analogy
Homology
Nonhomology
Fail N/A
Pass Exact
Pass Fail
(c) Patterson’s tests reinterpreted in the context of standard cladistic analysis
The explanation includes plesiomorphy, synapomorphy, and homoplasy and is claimed to be a simple model of character evolution (Nelson, 1996; Siebert and Williams, 1999; Scotland, 2000b). Thus, Table 1 illustrates an important distinction between sca and 3ta: the latter is consistent with Patterson’s tests, whereas the former is not. Compatibility analysis (Meacham and Estabrook, 1985) is similarly consistent with Patterson’s tests, although the unit of comparison (the rooted character) differs from that of 3ta (the three-item statement). The similarity between compatibility methods and 3ta was discussed by Wilkinson (1994) and Williams and Siebert (2000).
Analogy Homoplasy Apomorphy Plesiomorphy Similarity Fit
Fail N/A
Homology
No
Pass Not exact ci less than 1 Yes/no
Pass Exact ci ⫽ 1
Pass Exact ci ⫽ 1
Yes
Yes
Note. Sca and m3ta in the context of Patterson’s (1982) tests of homology. (a) Patterson’s similarity and congruence tests. (b) m3ta and Patterson’s tests. Analogy fails the similarity test. Three-taxon statements either fit the tree (homology) or do not fit the tree (nonhomology). (c) Sca and Patterson’s tests. Homoplasy, plesiomorphy, and apomorphy can result in homology at particular hierarchical levels. The table shows why hypotheses of primary homology are not tested in an absolute sense but are fitted to the most-parsimonious unrooted tree.
Table 1b shows these tests reinterpreted in the context of 3ta (after Scotland, 2000a). Table 1b demonstrates that in 3ta a given three-taxon statement derived from a homology proposition that passes the similarity test either fits a tree as evidence of relationship or fails to fit the tree. The test of homology implemented in 3ta is therefore consistent with Patterson’s (1982) tests of homology. 3ta thus provides a test for discriminating homology from nonhomology. Table 1c shows Patterson’s tests reinterpreted in the context of sca (Scotland, 2000a). Table 1c demonstrates that hypotheses of transformational homology, i.e., those that pass the similarity test, are not tested in an absolute sense but that all data are rather fitted to the most parsimonious tree. Consequently sca does not provide a method for implementing Patterson’s (1982) tests of homology despite suggestions to the contrary (De Pinna, 1991; Kitching et al., 1998, p. 26). Rather, it seeks to explain all data relative to an unrooted tree.
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PAIRED HOMOLOGUES AND POLARITY 3ta is a parsimony-based method of systematics that evaluates data with reference to rooted trees. 3ta sensu Nelson and Platnick (1991) code data relative to functional outgroups or a priori estimates of putative synapomorphy (Williams, 1996). 3ta as implemented in Carine and Scotland (1999, hereafter m3ta) and proposed by Scotland (2000b), differs from 3ta of Nelson and Platnick (1991) only in that all observations from all terminal taxa are available as evidence of sister group relationships, although prior inferences with regard to character generality may result in the exclusion of certain homologues from the analysis (Scotland, 2000b; Fig. 1). Thus, Scotland (2000b) stated that in situations where homologue generality is straightforward, any given paired homologue (x and x⬘) reduces to one informative homologue for analysis (Fig. 1b). For example, at the level of Acanthaceae, some taxa have retinacula (modified funiculi), whereas other taxa within Acanthaceae have unmodified funiculi, the condition present in all other flowering plant families. Homologue generality is clear in this example and the paired homologue consequently reduces to one informative homologue for analysis within Acanthaceae, i.e., retinacula (Scotland, 2000b; Scotland and Vollesen, 2000). The only difference between m3ta and 3ta is that in m3ta some paired homologues in which character generality is unknown, both homologues are treated as a hypothesis of groups (Fig. 1c). All systematic methods seeking
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homologues that can demonstrate that fins do not form a group.
KLUGE AND FARRIS (1999)
FIG. 1. (a–c) Observations, homologues, grouping, and character generality. (a) A homologue present in A and B but absent from C and D (complement relation) results in only one hypothesis. A and B are in a group to which C and D do not belong. The character generality is contained in the data. (b) Paired homologues distributed (AB) and (CD). In this case the generality of the homologues is given prior to analysis and it is only the AB homologue that is a hypothesis of a group. (c) In some cases both homologues from a pair are a hypothesis of groups because character generality is unknown.
to analyze morphological data demand selecting, filtering, and determining homologues for analysis (Stevens, 1991; Thiele, 1993; Gift and Stevens, 1997; Hawkins, 2000). 3ta and m3ta seek to include in a matrix discrete homologues shared among organisms that hypothesize groups. Scotland (2000b) distinguished complement relation homologues from paired homologues. An important aspect of this distinction is that, at certain hierarchical levels, there are homologues that are pertinent only to a subset of taxa and no alternative homologue is detectable for the remaining taxa (Fig. 1a). For example, at the level of seed plants, angiosperms possess carpels but the homologue of carpels in other seed plants is not known. Therefore, in this example, lacking carpels provides no information for constructing a classification (Fig. 1a). Traditional or phenetic classifications that have utilized the absence homologues for grouping have neither failed to distinguish plesiomorphy from apomorphy nor grouped on plesiomorphy but simply grouped on the lack of a particular homologue. For genuine paired homologues the situation is quite different. [m]3ta as implemented by Carine and Scotland (1999), in advocating [m]3ta, adopt the view that a group diagnosed by the homologue fins relative to forelimbs should be tested for congruence relative to other data (Fig. 1c). In such situations it is only other
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Kluge and Farris (1999) provide a number of criticisms of 3ta and particularly of m3ta. Within the framework of taxic homology and the relationship between m3ta and Patterson’s tests of homology, we address these criticisms.
Is [m]3ta a Phenetic Method? Kluge and Farris (1999, p. 207) claim that Carine and Scotland (1999) have reinvented phenetics. However, there are fundamental differences between phenetics and m3ta. First, in contrast to most phenetic clustering algorithms, m3ta does not group on the absence of a homologue. Second, phenetic clustering algorithms (equivalent to phenetics sensu Farris, 1979), group on overall similarity and make no attempt to test or discriminate homology from nonhomology. Phenetics is not designed to explicitly test hypotheses of taxic homology, but neither then is sca. In contrast, m3ta represents a method for implementing a taxic view of homology (Carine and Scotland, 1999) within a framework consistent with Patterson’s tests of homology (Table 1).
Symmetry Kluge and Farris (1999, p. 207) claim that “the new method [3ta] simply places the basal split of the tree between the phenetically most divergent groups.” Figure 2 shows for five taxa, one paired homologue (column 1) and six complement relation homologues (columns 2–7). The sca matrix for these data is shown in
FIG. 2. One paired homologue and six complement relation homologues shared between five taxa.
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Table 2a. Analysis of these data with UPGMA and sca results in Figs. 3a and 3b, respectively. If “phenetically most divergent groups” is defined as the longest branch on a tree, then for these data groups (CDE) and (AB) are the phenetically most divergent taxa (Table 2b). The m3ta view of these data, treating all homologues as potentially informative yield 33 three-taxon statements (Table 2c) and one minimal rooted tree (Fig. 3c). The basal dichotomy is not placed between the phenetically most divergent groups (AB)(CDE) as Kluge and Farris (1999) claim. Kluge and Farris’s (1999) assertion that m3ta simply places the basal split between phenetically most divergent groups is simply false. A related criticism of m3ta by Kluge and Farris’ (1999) and the purpose of their Fig. 2 (our Table 3 and Fig. 4) is to show that m3ta, as implemented in Carine and Scotland (1999), is sensitive to plesiomorphy and autapomorphy (Fig. 4a). Although the example is taken
TABLE 2 (a) The data from Fig. 2 coded in binary format Homologues Taxa A B C D E
1
2
3
4
5
6
7
1 1 0 0 0
1 1 1 0 0
1 1 1 0 0
1 1 0 0 0
1 1 0 0 0
1 1 0 0 0
1 1 0 0 0
(b) Pairwise distances between taxa
1A 2B 3C 4D 5E
1
2
3
4
5
— 0 5 7 7
5 7 7
2 2
0
—
(c) Three-taxon statements derived from matrix
FIG. 3. (a) UPGMA tree for the data from Table 2a.(b) Most parsimonious sca unrooted tree for the data from Table 2a. (c) m3ta minimal tree for the data from Table 2a.
as given here, in relation to Table 3, if taxon O is a priori taken to be a functional outgroup for assessing character generality as in Kluge and Farris (1999), m3ta results in Fig. 4b, which is the same result as sca. The example of Hawkins et al. (1997) may be used to illustrate the issue of character generality in the context of classification. The example comprised a group of organisms (A–F) given by other data in which A and B lack tails, C and D have red tails, and E and F have blue tails (Table 4a). Hawkins et al. (1997) showed that in the context of phylogeny reconstruction the coding protocol has a profound effect on the resulting topology for these data. Sca of these data coded using conventional binary characters (Table 4a) results
TABLE 3 Data Matrix from Kluge and Farris (1999)
Homologues 1a 1b 2 3 4 5 6 7
Three-taxon statements (AB)C, (AB)D, (AB)E (CD)A, (CD)B, (CE)A, (CE)B, (DE)A, (DE)B (AB)D, (AB)E, (AC)D, (AC)E, (BC)D, (BC)E (AB)D, (AB)E, (AC)D, (AC)E, (BC)D, (BC)E (AB)C, (AB)D, (AB)E (AB)C, (AB)D, (AB)E (AB)C, (AB)D, (AB)E (AB)C, (AB)D, (AB)E
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O A B C D E F G H
cc gg gg gg gg gg gg gg gg
c g g g g c c c c
c g g g c c c c c
c g g c c c c c c
c c c c c g g g g
c c c c c c g g g
c c c c c c c g g
ccccccccc ggggggggg ccccccccc ccccccccc ccccccccc ccccccccc ccccccccc ccccccccc ccccccccc
c c g c c c c c c
c c c g c c c c c
c c c c g c c c c
c c c c c g c c c
c c c c c c g c c
c c c c c c c g c
cccccccccc cccccccccc cccccccccc cccccccccc cccccccccc cccccccccc cccccccccc cccccccccc gggggggggg
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Classification or Phylogeny
FIG. 4. (a) m3ta minimal tree for the data from Table 3. (b) Strict consensus tree of sca analysis of the data from Table 3.
in the strict consensus tree shown in Fig. 5, which suggests that tail color provides no unambiguous information for grouping. In the context of classification, however, it is clear that these data contain more unambiguous evidence on grouping of these taxa than the results from sca would suggest. The classification that results from m3ta (Fig. 6a) contains a group (CDEF) diagnosed by possession of tails and two subgroups (CD) and (EF) diagnosed by possession of red and blue tails, respectively. These data contain no evidence for the classification of A and B. Consider the same example but with the number of
taxa possessing each homologue as shown in Table 4b. In this case tail color is a priori uninformative of relationships for sca because the character-state red is autapomorphic, and only one partition (Fig. 5) is supported. In contrast the topology from m3ta is as shown in Fig. 6b. Sca cannot treat tail color as containing unambiguous evidence because it is primarily concerned with phylogeny and character evolution. In contrast, m3ta recognizes a taxonomic group with blue tails for Tables 4a and 4b because the data contain this information, irrespective of the truth of phylogeny, or indeed plesiomorphy and autapomorphy.
Models TABLE 4 Taxa
Data
Binary
(a) A B C D E F
Tail absent Tail absent Red tail Red tail Blue tail Blue tail
0? 0? 10 10 11 11
(b) A B C D E F
Tail absent Tail absent Red tail Blue tail Blue tail Blue tail
Note. In part a, data are from Hawkins et al. (1997) coded in binary form. Part B shows amended data with taxa D–F sharing blue tails.
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Kluge and Farris criticize m3ta, as implemented by Carine and Scotland (1999), because it fails to incorporate evolutionary changes into the process of classification. Kluge and Farris (1999, p. 208) state that [n]o one but a creationist could think it realistic to exclude transformational considerations from the process of grouping. Character patterns are the product of changes. It would be astonishing if trying to analyze those patterns, while ignoring this fact, did not lead to paradox.
FIG. 5. Single most-parsimonious tree for the data from Table 4a.
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However, these criticisms are at odds with Kluge’s recent pronouncement that if we discover tomorrow that all life is the product only of special creation, we can still do cladistics, operationally, in terms of summarizing the observed character generalities. (Siddall and Kluge, 1997, p. 320)
It seems therefore that in the context of disagreement with proponents of maximum likelihood, sca is about character generalities (Siddall and Kluge, 1997) but in the context of m3ta, it is “astonishing” to ignore the process of change (Kluge and Farris, 1999). If systematics is about change, then surely the rate of change is an important variable and the estimation of that rate, a model of character evolution, is necessary. The practice of assessing tree building methods relative to a known tree and known unequal rate variation was discussed by Felsenstein (1978), who demonstrated that sca is inconsistent for certain tree models. Indeed, recovering the correct tree has been shown to be problematic for all methods relative to certain tree models (Felsenstein, 1978; Hillis et al., 1994; Siddall, 1998). The extent to which accuracy is a goal of sca, however, remains unclear. Siddall and Kluge (1997, p. 319) stated that “[i]n phylogenetics, however, we are not interested in some abstract generality regarding the group of taxa we are working with. We are concerned with uncovering the actual spatio-temporally real history of divergence, the species genealogy” [our italics]. While this is in contrast to Kluge (1995, p. 77), who argued “. . . accuracy as it pertains to knowing the truth is not an obsession of cladists,” it is evident from Kluge and Farris (1999) that in the context of the debate surrounding 3ta, issues relating to phylogenetic accuracy are paramount. Kluge and Farris take a tree (Fig. 4b), invent a matrix (Table 3), and demonstrate that m3ta fails to recover the tree given this matrix. This example is used to
FIG. 6. (a) m3ta minimal tree for the data from Table 4a. (b) m3ta minmal tree for the data from Table 4b.
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Scotland and Carine TABLE 5 Data Matrix with Two Long Branches pertaining to Taxa A and H O A B C D E F G H
cc gg gg gg gg gg gg gg gg
c g g g g c c c c
c g g g c c c c c
c g g c c c c c c
c c c c c g g g g
c c c c c c g g g
c c c c c c c g g
ccccccccc ggggggggg ccccccccc ccccccccc ccccccccc ccccccccc ccccccccc ccccccccc ccccccccc
c c g c c c c c c
c c c g c c c c c
c c c c g c c c c
c c c c c g c c c
c c c c c c g c c
c c c c c c c g c
cccccccccc cccccccccc cccccccccc cccccccccc cccccccccc cccccccccc cccccccccc cccccccccc cccccccccc
demonstrate that m3ta gets the wrong tree because it groups on symplesiomorphies. However, the expediency of their argument is made clear relative to Table 5, which is similar to Table 3 in that the data indicate two long branches pertaining to taxa A and H. The strict consensus tree from sca of Table 5 is shown in Fig. 7. If we assume that the “true tree” is that shown in Fig. 4b, then comparison of Fig. 4b and Fig. 7 shows that sca obtains the wrong tree. Table 5 demonstrates that sca is sensitive to long branches and unequal rates of change, a phenomenon that is widely known (Felsenstein, 1978; Hillis et al., 1994; Siddall, 1998). However, both the examples of Kluge and Farris (1999) and that presented in Table 5 are trivial and provide no insight into the performance of methods in the context of phylogenetic accuracy. In contrast, simulated data matrices or either known or well-corroborated phylogenies may be used to assess the performance of methods (e.g., Hillis, 1996) and as an example of this approach, we compare sca and 3ta relative to the emerging consensus that human is more closely related to chimpanzee than to gorilla (Mann and Weiss, 1996; Pilbeam, 1996; Ruvolo, 1996; Shoshani et al., 1996; Page and Holmes, 1998). Brown et al. (1982) published five mitochondrial DNA sequences from human, chimpanzee, gorilla, orang-utan, and gibbon. These data are used here to
FIG. 7. Single most-parsimonious tree for the data from Table 5.
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compare the results for 3ta and sca relative to the threetaxon problem in relation to human, chimpanzee, and gorilla. For the purposes of outgroup comparison for sca and assessing the generality of homologues for 3ta, gibbon and orang-utan are included in the analysis. The sequences are 896 bp in length and contain the genes for three transfer RNAs and part of two proteins. The sequences have a total of 281 variable positions. Sca of these sequences yields a single unrooted tree (Fig. 8a) of length 145 (excluding autapomorphies), as reported in Brown et al. (1982). It is shown here rooted on the branch between the ingroup and outgroup and gorilla is placed as sister group to chimpanzee. With n taxa, there are [2n ⫺ (n ⫹ 2)] possible types of cladistically informative characters; with five taxa there are 25 such types—10 grouping two taxa (xxxAA), 10 grouping three taxa (xxAAA), and 5 grouping four taxa (xAAAA) (Patterson, 1988). The shared possession of a base is treated as a homologue. In the anthropoid mtDNA sequences all 25 possible types of cladistically informative characters occur (Patterson, 1988), and the number of occurrences of each type is shown in Table 6. Also shown are the number of three-taxon statements that result from these data, the total number of statements, the individual weight of each statement, and the total weight of all statements. The m3ta matrix (not shown) contains a total of 1926 three-taxon statements. Analysis of the m3ta matrix for these data yields a single minimal tree (Fig. 8b) (Scotland, 2000a). The minimal tree (Fig. 8b) from the m3ta analysis, which pairs human with chimpanzee, agrees with the results of Hasegawa and Yano (1984) and Bishop and Friday (1986) for these data using maximum likelihood. The same tree was found by Patterson (1988) using an eclectic form of compatibility analysis. Nei et al. (1985) found
FIG. 8. (a) Sca most parsimonious solution for the data from Brown et al. (1982). (b) Minimal m3ta tree with fractional weighting. (c) Minimal m3ta tree without fractional weighting. C, Pan; G, Gorilla; H, Hylobates; M, Homo; O, Pongo.
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TABLE 6 The Data from Brown et al. (1982) in m3ta Format Showing the Number of Three-Taxon Statements Derived from Each Shared Base, the Total Number of Statements, the Individual Weight of Each Statement, and the Total Weight of All Statements
Shared pairs
Number of statements
Running total of statements
Individual weight
Total weight
12 6 14 4 8 9 7 5 32 3
36 18 42 12 24 27 21 15 96 9
36 54 96 108 132 159 180 195 291 300
1 1 1 1 1 1 1 1 1 1
36 18 42 12 24 27 21 15 96 9
CGH CGM CGO CHM CHO CMO GHM GHO GMO HMO
Shared threes 3 31 7 7 5 11 3 10 10 10
18 186 42 42 30 66 18 60 60 60
318 504 546 588 618 684 702 762 822 882
0.66 0.66 0.66 0.66 0.66 0.66 0.66 0.66 0.66 0.66
12 124 28 28 20 44 12 40 40 40
CGHM CGHO CGMO CHMO GHMO
Shared fours 52 15 64 24 19
312 90 384 144 114
1194 1284 1668 1812 1926
0.5 0.5 0.5 0.5 0.5
156 45 192 72 57
Taxa CG CH CM CO GH GM GO HM HO MO
the same tree from a UPGMA analysis of these data as did Page and Holmes (1998) using spectral analysis. Although the signal in these data is weak (Brown et al., 1982; Patterson, 1988; Page and Holmes, 1998), there are 19 more three-taxon statements accommodated on the (human⫹chimpanzee) tree than the (gorilla⫹chimpanzee) tree, which is the most parsimonious result using sca. The m3ta tree (Fig. 8b) is in agreement with those of several other studies but at odds with the results of sca. Analysis of these data without fractional weighting result in the tree shown in Fig. 8c. What does this example demonstrate? It demonstrates that some methods yield congruent results for some data sets while other methods yield conflicting
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results. The argument adopted by Kluge and Farris (1999, p. 208) rests on their claim that m3ta “directly contradicts evolution.” In the case of hominoid evolution it would appear that it is the result of sca that conflict with the phylogeny of this group.
CONCLUSIONS We agree with Kluge (1995) in that accuracy as it pertains to knowing the truth is not a concern of cladists. Phylogenetic accuracy is not the goal or aim of m3ta. Rather, m3ta seeks to explore a taxic view of homology, in that homology is that which remains constant in the course of change (Rieppel, 1988). It is a method of classification based on the relation of homology. We agree with Platnick (1979), who argued that our knowledge of phylogeny largely stems from what we know about classification. This does not mean that homologue trees are not phylogenies, it simply means that the concept of homology is the stuff of classification, whereas models of character evolution are the stuff of phylogenetic change (phylogenetic trees). As Patterson (1982, p. 57) stated “. . . belief in or knowledge of phylogeny is superfluous to homology analysis.”
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character states in quantitative variation—An experimental study. Syst. Biol. 46, 112–125. Hasegawa, M., and Yano, T. (1984). Phylogeny and classification of Hominoidea as inferred from DNA sequence data. Proc. Jpn. Acad. 60B, 389–392. Hawkins, J. A. (2000). A survey of primary homology assessment: Different workers perceive and define characters in different ways. In “Homology and Systematics: Coding Characters for Phylogenetic Analysis” (R. W. Scotland and R. T. Pennington, Eds.). Taylor and Francis, London. Hawkins, J. A., Hughes, C. E., and Scotland, R. W. (1997). Primary homology assessment, characters and character states. Cladistics 13, 275–283. Hillis, D. M. (1996). Inferring complex phylogenies. Nature 383, 130–131. Hillis, D. M., Huelsenbeck, J. P., and Cunningham, C. W. (1994). Application and accuracy of molecular phylogenies. Science 264, 671–677. Kitching, I. J., Forey, P. L., Humphries, C. J., and Williams, D. M. (1998). “Cladistics: The Theory and Practice of Parsimony Analysis.” Oxford Univ. Press, Oxford. Kluge, A. (1995). Parsimony. Herpetol. Rev. 26, 76–77. Kluge, A., and Farris, J. S. (1999). Taxic homology ⫽ Overall similarity. Cladistics 15, 205–212. Mann, A., and Weiss, M. (1996). Hominoid phylogeny and taxonomy: A consideration of the molecular and fossil evidence in an historical perspective. Mol. Phylogenet. Evol. 5, 169–181. Meacham, C. A., and Estabrook, G. F. (1985). Compatibility methods in systematics. Annu. Rev. Ecol. Syst. 116, 431–446. Nei, M., Stephens, J. C., and Saitou, N. (1985). Methods for computing the standard errors in branching points in an evolutionary tree and their application to molecular data from humans and apes. Mol. Biol. Evol. 2, 66–85.
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Ruvolo, M. (1996). A new approach to studying modern human origins:
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Classification or Phylogeny Hypothesis testing with coalescence time distributions. Mol. Phylogenet. Evol. 5, 202–219. Scotland, R. W. (2000a). Homology, coding and three-taxon statement analysis. In “Homology and Systematics, Coding Characters for Phylogenetic Analysis” (R. W. Scotland and R. T. Pennington, Eds.). Taylor and Francis, London. Scotland, R. W. (2000b). Taxic homology and three-taxon statement analysis. Syst. Biol. 49(3), 480–500. Scotland, R. W., and Vollesen, K. (2000). Classification of Aeanthaceae. Kew. Bull. 55, 513–589. Shoshani, J., Groves, C. P., Simons, E. L., and Gunnell, G. F. (1996). Primate phylogeny: Morphological vs molecular results. Mol. Phylogenet. Evol. 5, 102–154. Siddall, M. E. (1998). Success of parsimony in the four-taxon case: Long branch repulsion by likelihood in the Farris zone. Cladistics 14, 209–220.
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419 Siddall, M. E., and Kluge, A. G. (1997). Probabilism and phylogenetic inference. Cladistics 13, 313–336. Siebert, D. J., and Williams, D. M. (1998). Recycled. Cladistics 14, 339–348. Stevens, P. F. (1991). Character states, morphological variation, and phylogenetic analysis: A review. Syst. Bot. 16, 553–583. Thiele, K. (1993). The holy grail of the perfect character: The cladistic treatment of morphometric data. Cladistics 9, 275–304. Wilkinson, M. (1994). Three-taxon statements: When is a parsimony analysis also a clique analysis. Cladistics 10, 221–224. Williams, D. M. (1996). Fossil species of the diatom genus Tetracyclus (Bacillariophyta, ‘ellipticus’ species group): Monophyly, interrelationships and the relevance of ontogeny. Philos, Trans. R. Soc. London B 351, 1759–1782. Williams, D. M., and Siebert, D. J. (2000). Characters, homology and three-item analysis. In “Homology and Systematics, Coding Characters for Phylogenetic Analysis” (R. W. Scotland and R. T. Pennington, Eds.). Taylor and Francis, London.
Classification or Phylogenetic Estimates? Robert W. Scotland and Mark A. Carine* Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, United Kingdom; and *Centre for Plant Diversity and Systematics, The University of Reading, Whiteknights, Reading RG6 6AS, United Kingdom Accepted July 15, 2000; published online January 2, 2001
[m]3ta is a method that seeks to implement a taxic view of homology. The method is consistent with Patterson’s tests for discriminating homology from nonhomology. Contrary to the claims of Kluge and Farris, (1999, Cladistics 15, 205–212), m3ta is not a phenetic method—nor does it necessarily place the basal split in a tree between the phenetically most divergent taxa. [m]3ta does not seek to accurately recover phylogeny but rather it seeks to maximize the information content of taxic homology propositions. [m]3ta is a method of classification in which the unit of analysis is the relation of homology. [m]3ta differs from all phylogenetic methods because the units of analyses in phylogenetic methods, including sca, are transformation series. 䉷 2000 The Willi Hennig Society
tree contains the maximum amount of homology. 3ta seeks only to discriminate homology from nonhomology within the conceptual framework of tests of homology derived from Patterson (1982). Patterson (1982) discussed his tests in the context of constructing classifications. Patterson (1982, p. 67) stated that the role of homology in phylogenetic reconstruction is limited to the production of cladograms, or classifications.
Patterson’s (1982) tests sought to discriminate homology from nonhomology among those data that were considered to be evidence for grouping. De Pinna (1991, p. 371) stated: Therefore, by making all characters operationally equivalent to putative synapomorphies, the procedure of discovering a common pattern among taxa can be carried out.
3TA AND PATTERSON’S (1982) TESTS OF HOMOLOGY
Table 1 shows the relationship between Patterson’s (1982) tests of homology, 3ta and sca (standard cladistic analysis). Table 1a shows Patterson’s similarity and congruence tests of homology. Homology is the sole relation that passes the tests of both similarity and congruence. Parallelism and convergence are nonhomology; parallelism fails the congruence test and convergence fails both the similarity and the congruence tests. The sole purpose of Patterson’s (1982) tests is to distinguish homology from nonhomology.
Classifications have been described as a hierarchy of homology (Rieppel, 1988) and 3ta (three-taxon statement analysis) is a method of data analysis that seeks to maximize the information content of taxic homology propositions for the purpose of constructing classifications. For the purposes of analysis, 3ta maximizes propositions of homology, in the form of accommodated three-taxon statements such that the minimal (optimal)
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TABLE 1 (a) Patterson’s (1982) tests of homology Convergence
Parallelism
Homology
Fail Fail
Pass Fail
Pass Pass
Similarity Congruence
(b) Three-taxon statements, analogy, homology, and nonhomology
Similarity Fit
Analogy
Homology
Nonhomology
Fail N/A
Pass Exact
Pass Fail
(c) Patterson’s tests reinterpreted in the context of standard cladistic analysis
The explanation includes plesiomorphy, synapomorphy, and homoplasy and is claimed to be a simple model of character evolution (Nelson, 1996; Siebert and Williams, 1999; Scotland, 2000b). Thus, Table 1 illustrates an important distinction between sca and 3ta: the latter is consistent with Patterson’s tests, whereas the former is not. Compatibility analysis (Meacham and Estabrook, 1985) is similarly consistent with Patterson’s tests, although the unit of comparison (the rooted character) differs from that of 3ta (the three-item statement). The similarity between compatibility methods and 3ta was discussed by Wilkinson (1994) and Williams and Siebert (2000).
Analogy Homoplasy Apomorphy Plesiomorphy Similarity Fit
Fail N/A
Homology
No
Pass Not exact ci less than 1 Yes/no
Pass Exact ci ⫽ 1
Pass Exact ci ⫽ 1
Yes
Yes
Note. Sca and m3ta in the context of Patterson’s (1982) tests of homology. (a) Patterson’s similarity and congruence tests. (b) m3ta and Patterson’s tests. Analogy fails the similarity test. Three-taxon statements either fit the tree (homology) or do not fit the tree (nonhomology). (c) Sca and Patterson’s tests. Homoplasy, plesiomorphy, and apomorphy can result in homology at particular hierarchical levels. The table shows why hypotheses of primary homology are not tested in an absolute sense but are fitted to the most-parsimonious unrooted tree.
Table 1b shows these tests reinterpreted in the context of 3ta (after Scotland, 2000a). Table 1b demonstrates that in 3ta a given three-taxon statement derived from a homology proposition that passes the similarity test either fits a tree as evidence of relationship or fails to fit the tree. The test of homology implemented in 3ta is therefore consistent with Patterson’s (1982) tests of homology. 3ta thus provides a test for discriminating homology from nonhomology. Table 1c shows Patterson’s tests reinterpreted in the context of sca (Scotland, 2000a). Table 1c demonstrates that hypotheses of transformational homology, i.e., those that pass the similarity test, are not tested in an absolute sense but that all data are rather fitted to the most parsimonious tree. Consequently sca does not provide a method for implementing Patterson’s (1982) tests of homology despite suggestions to the contrary (De Pinna, 1991; Kitching et al., 1998, p. 26). Rather, it seeks to explain all data relative to an unrooted tree.
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PAIRED HOMOLOGUES AND POLARITY 3ta is a parsimony-based method of systematics that evaluates data with reference to rooted trees. 3ta sensu Nelson and Platnick (1991) code data relative to functional outgroups or a priori estimates of putative synapomorphy (Williams, 1996). 3ta as implemented in Carine and Scotland (1999, hereafter m3ta) and proposed by Scotland (2000b), differs from 3ta of Nelson and Platnick (1991) only in that all observations from all terminal taxa are available as evidence of sister group relationships, although prior inferences with regard to character generality may result in the exclusion of certain homologues from the analysis (Scotland, 2000b; Fig. 1). Thus, Scotland (2000b) stated that in situations where homologue generality is straightforward, any given paired homologue (x and x⬘) reduces to one informative homologue for analysis (Fig. 1b). For example, at the level of Acanthaceae, some taxa have retinacula (modified funiculi), whereas other taxa within Acanthaceae have unmodified funiculi, the condition present in all other flowering plant families. Homologue generality is clear in this example and the paired homologue consequently reduces to one informative homologue for analysis within Acanthaceae, i.e., retinacula (Scotland, 2000b; Scotland and Vollesen, 2000). The only difference between m3ta and 3ta is that in m3ta some paired homologues in which character generality is unknown, both homologues are treated as a hypothesis of groups (Fig. 1c). All systematic methods seeking
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homologues that can demonstrate that fins do not form a group.
KLUGE AND FARRIS (1999)
FIG. 1. (a–c) Observations, homologues, grouping, and character generality. (a) A homologue present in A and B but absent from C and D (complement relation) results in only one hypothesis. A and B are in a group to which C and D do not belong. The character generality is contained in the data. (b) Paired homologues distributed (AB) and (CD). In this case the generality of the homologues is given prior to analysis and it is only the AB homologue that is a hypothesis of a group. (c) In some cases both homologues from a pair are a hypothesis of groups because character generality is unknown.
to analyze morphological data demand selecting, filtering, and determining homologues for analysis (Stevens, 1991; Thiele, 1993; Gift and Stevens, 1997; Hawkins, 2000). 3ta and m3ta seek to include in a matrix discrete homologues shared among organisms that hypothesize groups. Scotland (2000b) distinguished complement relation homologues from paired homologues. An important aspect of this distinction is that, at certain hierarchical levels, there are homologues that are pertinent only to a subset of taxa and no alternative homologue is detectable for the remaining taxa (Fig. 1a). For example, at the level of seed plants, angiosperms possess carpels but the homologue of carpels in other seed plants is not known. Therefore, in this example, lacking carpels provides no information for constructing a classification (Fig. 1a). Traditional or phenetic classifications that have utilized the absence homologues for grouping have neither failed to distinguish plesiomorphy from apomorphy nor grouped on plesiomorphy but simply grouped on the lack of a particular homologue. For genuine paired homologues the situation is quite different. [m]3ta as implemented by Carine and Scotland (1999), in advocating [m]3ta, adopt the view that a group diagnosed by the homologue fins relative to forelimbs should be tested for congruence relative to other data (Fig. 1c). In such situations it is only other
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Kluge and Farris (1999) provide a number of criticisms of 3ta and particularly of m3ta. Within the framework of taxic homology and the relationship between m3ta and Patterson’s tests of homology, we address these criticisms.
Is [m]3ta a Phenetic Method? Kluge and Farris (1999, p. 207) claim that Carine and Scotland (1999) have reinvented phenetics. However, there are fundamental differences between phenetics and m3ta. First, in contrast to most phenetic clustering algorithms, m3ta does not group on the absence of a homologue. Second, phenetic clustering algorithms (equivalent to phenetics sensu Farris, 1979), group on overall similarity and make no attempt to test or discriminate homology from nonhomology. Phenetics is not designed to explicitly test hypotheses of taxic homology, but neither then is sca. In contrast, m3ta represents a method for implementing a taxic view of homology (Carine and Scotland, 1999) within a framework consistent with Patterson’s tests of homology (Table 1).
Symmetry Kluge and Farris (1999, p. 207) claim that “the new method [3ta] simply places the basal split of the tree between the phenetically most divergent groups.” Figure 2 shows for five taxa, one paired homologue (column 1) and six complement relation homologues (columns 2–7). The sca matrix for these data is shown in
FIG. 2. One paired homologue and six complement relation homologues shared between five taxa.
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Table 2a. Analysis of these data with UPGMA and sca results in Figs. 3a and 3b, respectively. If “phenetically most divergent groups” is defined as the longest branch on a tree, then for these data groups (CDE) and (AB) are the phenetically most divergent taxa (Table 2b). The m3ta view of these data, treating all homologues as potentially informative yield 33 three-taxon statements (Table 2c) and one minimal rooted tree (Fig. 3c). The basal dichotomy is not placed between the phenetically most divergent groups (AB)(CDE) as Kluge and Farris (1999) claim. Kluge and Farris’s (1999) assertion that m3ta simply places the basal split between phenetically most divergent groups is simply false. A related criticism of m3ta by Kluge and Farris’ (1999) and the purpose of their Fig. 2 (our Table 3 and Fig. 4) is to show that m3ta, as implemented in Carine and Scotland (1999), is sensitive to plesiomorphy and autapomorphy (Fig. 4a). Although the example is taken
TABLE 2 (a) The data from Fig. 2 coded in binary format Homologues Taxa A B C D E
1
2
3
4
5
6
7
1 1 0 0 0
1 1 1 0 0
1 1 1 0 0
1 1 0 0 0
1 1 0 0 0
1 1 0 0 0
1 1 0 0 0
(b) Pairwise distances between taxa
1A 2B 3C 4D 5E
1
2
3
4
5
— 0 5 7 7
5 7 7
2 2
0
—
(c) Three-taxon statements derived from matrix
FIG. 3. (a) UPGMA tree for the data from Table 2a.(b) Most parsimonious sca unrooted tree for the data from Table 2a. (c) m3ta minimal tree for the data from Table 2a.
as given here, in relation to Table 3, if taxon O is a priori taken to be a functional outgroup for assessing character generality as in Kluge and Farris (1999), m3ta results in Fig. 4b, which is the same result as sca. The example of Hawkins et al. (1997) may be used to illustrate the issue of character generality in the context of classification. The example comprised a group of organisms (A–F) given by other data in which A and B lack tails, C and D have red tails, and E and F have blue tails (Table 4a). Hawkins et al. (1997) showed that in the context of phylogeny reconstruction the coding protocol has a profound effect on the resulting topology for these data. Sca of these data coded using conventional binary characters (Table 4a) results
TABLE 3 Data Matrix from Kluge and Farris (1999)
Homologues 1a 1b 2 3 4 5 6 7
Three-taxon statements (AB)C, (AB)D, (AB)E (CD)A, (CD)B, (CE)A, (CE)B, (DE)A, (DE)B (AB)D, (AB)E, (AC)D, (AC)E, (BC)D, (BC)E (AB)D, (AB)E, (AC)D, (AC)E, (BC)D, (BC)E (AB)C, (AB)D, (AB)E (AB)C, (AB)D, (AB)E (AB)C, (AB)D, (AB)E (AB)C, (AB)D, (AB)E
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O A B C D E F G H
cc gg gg gg gg gg gg gg gg
c g g g g c c c c
c g g g c c c c c
c g g c c c c c c
c c c c c g g g g
c c c c c c g g g
c c c c c c c g g
ccccccccc ggggggggg ccccccccc ccccccccc ccccccccc ccccccccc ccccccccc ccccccccc ccccccccc
c c g c c c c c c
c c c g c c c c c
c c c c g c c c c
c c c c c g c c c
c c c c c c g c c
c c c c c c c g c
cccccccccc cccccccccc cccccccccc cccccccccc cccccccccc cccccccccc cccccccccc cccccccccc gggggggggg
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Classification or Phylogeny
FIG. 4. (a) m3ta minimal tree for the data from Table 3. (b) Strict consensus tree of sca analysis of the data from Table 3.
in the strict consensus tree shown in Fig. 5, which suggests that tail color provides no unambiguous information for grouping. In the context of classification, however, it is clear that these data contain more unambiguous evidence on grouping of these taxa than the results from sca would suggest. The classification that results from m3ta (Fig. 6a) contains a group (CDEF) diagnosed by possession of tails and two subgroups (CD) and (EF) diagnosed by possession of red and blue tails, respectively. These data contain no evidence for the classification of A and B. Consider the same example but with the number of
taxa possessing each homologue as shown in Table 4b. In this case tail color is a priori uninformative of relationships for sca because the character-state red is autapomorphic, and only one partition (Fig. 5) is supported. In contrast the topology from m3ta is as shown in Fig. 6b. Sca cannot treat tail color as containing unambiguous evidence because it is primarily concerned with phylogeny and character evolution. In contrast, m3ta recognizes a taxonomic group with blue tails for Tables 4a and 4b because the data contain this information, irrespective of the truth of phylogeny, or indeed plesiomorphy and autapomorphy.
Models TABLE 4 Taxa
Data
Binary
(a) A B C D E F
Tail absent Tail absent Red tail Red tail Blue tail Blue tail
0? 0? 10 10 11 11
(b) A B C D E F
Tail absent Tail absent Red tail Blue tail Blue tail Blue tail
Note. In part a, data are from Hawkins et al. (1997) coded in binary form. Part B shows amended data with taxa D–F sharing blue tails.
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Kluge and Farris criticize m3ta, as implemented by Carine and Scotland (1999), because it fails to incorporate evolutionary changes into the process of classification. Kluge and Farris (1999, p. 208) state that [n]o one but a creationist could think it realistic to exclude transformational considerations from the process of grouping. Character patterns are the product of changes. It would be astonishing if trying to analyze those patterns, while ignoring this fact, did not lead to paradox.
FIG. 5. Single most-parsimonious tree for the data from Table 4a.
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However, these criticisms are at odds with Kluge’s recent pronouncement that if we discover tomorrow that all life is the product only of special creation, we can still do cladistics, operationally, in terms of summarizing the observed character generalities. (Siddall and Kluge, 1997, p. 320)
It seems therefore that in the context of disagreement with proponents of maximum likelihood, sca is about character generalities (Siddall and Kluge, 1997) but in the context of m3ta, it is “astonishing” to ignore the process of change (Kluge and Farris, 1999). If systematics is about change, then surely the rate of change is an important variable and the estimation of that rate, a model of character evolution, is necessary. The practice of assessing tree building methods relative to a known tree and known unequal rate variation was discussed by Felsenstein (1978), who demonstrated that sca is inconsistent for certain tree models. Indeed, recovering the correct tree has been shown to be problematic for all methods relative to certain tree models (Felsenstein, 1978; Hillis et al., 1994; Siddall, 1998). The extent to which accuracy is a goal of sca, however, remains unclear. Siddall and Kluge (1997, p. 319) stated that “[i]n phylogenetics, however, we are not interested in some abstract generality regarding the group of taxa we are working with. We are concerned with uncovering the actual spatio-temporally real history of divergence, the species genealogy” [our italics]. While this is in contrast to Kluge (1995, p. 77), who argued “. . . accuracy as it pertains to knowing the truth is not an obsession of cladists,” it is evident from Kluge and Farris (1999) that in the context of the debate surrounding 3ta, issues relating to phylogenetic accuracy are paramount. Kluge and Farris take a tree (Fig. 4b), invent a matrix (Table 3), and demonstrate that m3ta fails to recover the tree given this matrix. This example is used to
FIG. 6. (a) m3ta minimal tree for the data from Table 4a. (b) m3ta minmal tree for the data from Table 4b.
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Scotland and Carine TABLE 5 Data Matrix with Two Long Branches pertaining to Taxa A and H O A B C D E F G H
cc gg gg gg gg gg gg gg gg
c g g g g c c c c
c g g g c c c c c
c g g c c c c c c
c c c c c g g g g
c c c c c c g g g
c c c c c c c g g
ccccccccc ggggggggg ccccccccc ccccccccc ccccccccc ccccccccc ccccccccc ccccccccc ccccccccc
c c g c c c c c c
c c c g c c c c c
c c c c g c c c c
c c c c c g c c c
c c c c c c g c c
c c c c c c c g c
cccccccccc cccccccccc cccccccccc cccccccccc cccccccccc cccccccccc cccccccccc cccccccccc cccccccccc
demonstrate that m3ta gets the wrong tree because it groups on symplesiomorphies. However, the expediency of their argument is made clear relative to Table 5, which is similar to Table 3 in that the data indicate two long branches pertaining to taxa A and H. The strict consensus tree from sca of Table 5 is shown in Fig. 7. If we assume that the “true tree” is that shown in Fig. 4b, then comparison of Fig. 4b and Fig. 7 shows that sca obtains the wrong tree. Table 5 demonstrates that sca is sensitive to long branches and unequal rates of change, a phenomenon that is widely known (Felsenstein, 1978; Hillis et al., 1994; Siddall, 1998). However, both the examples of Kluge and Farris (1999) and that presented in Table 5 are trivial and provide no insight into the performance of methods in the context of phylogenetic accuracy. In contrast, simulated data matrices or either known or well-corroborated phylogenies may be used to assess the performance of methods (e.g., Hillis, 1996) and as an example of this approach, we compare sca and 3ta relative to the emerging consensus that human is more closely related to chimpanzee than to gorilla (Mann and Weiss, 1996; Pilbeam, 1996; Ruvolo, 1996; Shoshani et al., 1996; Page and Holmes, 1998). Brown et al. (1982) published five mitochondrial DNA sequences from human, chimpanzee, gorilla, orang-utan, and gibbon. These data are used here to
FIG. 7. Single most-parsimonious tree for the data from Table 5.
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Classification or Phylogeny
compare the results for 3ta and sca relative to the threetaxon problem in relation to human, chimpanzee, and gorilla. For the purposes of outgroup comparison for sca and assessing the generality of homologues for 3ta, gibbon and orang-utan are included in the analysis. The sequences are 896 bp in length and contain the genes for three transfer RNAs and part of two proteins. The sequences have a total of 281 variable positions. Sca of these sequences yields a single unrooted tree (Fig. 8a) of length 145 (excluding autapomorphies), as reported in Brown et al. (1982). It is shown here rooted on the branch between the ingroup and outgroup and gorilla is placed as sister group to chimpanzee. With n taxa, there are [2n ⫺ (n ⫹ 2)] possible types of cladistically informative characters; with five taxa there are 25 such types—10 grouping two taxa (xxxAA), 10 grouping three taxa (xxAAA), and 5 grouping four taxa (xAAAA) (Patterson, 1988). The shared possession of a base is treated as a homologue. In the anthropoid mtDNA sequences all 25 possible types of cladistically informative characters occur (Patterson, 1988), and the number of occurrences of each type is shown in Table 6. Also shown are the number of three-taxon statements that result from these data, the total number of statements, the individual weight of each statement, and the total weight of all statements. The m3ta matrix (not shown) contains a total of 1926 three-taxon statements. Analysis of the m3ta matrix for these data yields a single minimal tree (Fig. 8b) (Scotland, 2000a). The minimal tree (Fig. 8b) from the m3ta analysis, which pairs human with chimpanzee, agrees with the results of Hasegawa and Yano (1984) and Bishop and Friday (1986) for these data using maximum likelihood. The same tree was found by Patterson (1988) using an eclectic form of compatibility analysis. Nei et al. (1985) found
FIG. 8. (a) Sca most parsimonious solution for the data from Brown et al. (1982). (b) Minimal m3ta tree with fractional weighting. (c) Minimal m3ta tree without fractional weighting. C, Pan; G, Gorilla; H, Hylobates; M, Homo; O, Pongo.
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TABLE 6 The Data from Brown et al. (1982) in m3ta Format Showing the Number of Three-Taxon Statements Derived from Each Shared Base, the Total Number of Statements, the Individual Weight of Each Statement, and the Total Weight of All Statements
Shared pairs
Number of statements
Running total of statements
Individual weight
Total weight
12 6 14 4 8 9 7 5 32 3
36 18 42 12 24 27 21 15 96 9
36 54 96 108 132 159 180 195 291 300
1 1 1 1 1 1 1 1 1 1
36 18 42 12 24 27 21 15 96 9
CGH CGM CGO CHM CHO CMO GHM GHO GMO HMO
Shared threes 3 31 7 7 5 11 3 10 10 10
18 186 42 42 30 66 18 60 60 60
318 504 546 588 618 684 702 762 822 882
0.66 0.66 0.66 0.66 0.66 0.66 0.66 0.66 0.66 0.66
12 124 28 28 20 44 12 40 40 40
CGHM CGHO CGMO CHMO GHMO
Shared fours 52 15 64 24 19
312 90 384 144 114
1194 1284 1668 1812 1926
0.5 0.5 0.5 0.5 0.5
156 45 192 72 57
Taxa CG CH CM CO GH GM GO HM HO MO
the same tree from a UPGMA analysis of these data as did Page and Holmes (1998) using spectral analysis. Although the signal in these data is weak (Brown et al., 1982; Patterson, 1988; Page and Holmes, 1998), there are 19 more three-taxon statements accommodated on the (human⫹chimpanzee) tree than the (gorilla⫹chimpanzee) tree, which is the most parsimonious result using sca. The m3ta tree (Fig. 8b) is in agreement with those of several other studies but at odds with the results of sca. Analysis of these data without fractional weighting result in the tree shown in Fig. 8c. What does this example demonstrate? It demonstrates that some methods yield congruent results for some data sets while other methods yield conflicting
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results. The argument adopted by Kluge and Farris (1999, p. 208) rests on their claim that m3ta “directly contradicts evolution.” In the case of hominoid evolution it would appear that it is the result of sca that conflict with the phylogeny of this group.
CONCLUSIONS We agree with Kluge (1995) in that accuracy as it pertains to knowing the truth is not a concern of cladists. Phylogenetic accuracy is not the goal or aim of m3ta. Rather, m3ta seeks to explore a taxic view of homology, in that homology is that which remains constant in the course of change (Rieppel, 1988). It is a method of classification based on the relation of homology. We agree with Platnick (1979), who argued that our knowledge of phylogeny largely stems from what we know about classification. This does not mean that homologue trees are not phylogenies, it simply means that the concept of homology is the stuff of classification, whereas models of character evolution are the stuff of phylogenetic change (phylogenetic trees). As Patterson (1982, p. 57) stated “. . . belief in or knowledge of phylogeny is superfluous to homology analysis.”
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character states in quantitative variation—An experimental study. Syst. Biol. 46, 112–125. Hasegawa, M., and Yano, T. (1984). Phylogeny and classification of Hominoidea as inferred from DNA sequence data. Proc. Jpn. Acad. 60B, 389–392. Hawkins, J. A. (2000). A survey of primary homology assessment: Different workers perceive and define characters in different ways. In “Homology and Systematics: Coding Characters for Phylogenetic Analysis” (R. W. Scotland and R. T. Pennington, Eds.). Taylor and Francis, London. Hawkins, J. A., Hughes, C. E., and Scotland, R. W. (1997). Primary homology assessment, characters and character states. Cladistics 13, 275–283. Hillis, D. M. (1996). Inferring complex phylogenies. Nature 383, 130–131. Hillis, D. M., Huelsenbeck, J. P., and Cunningham, C. W. (1994). Application and accuracy of molecular phylogenies. Science 264, 671–677. Kitching, I. J., Forey, P. L., Humphries, C. J., and Williams, D. M. (1998). “Cladistics: The Theory and Practice of Parsimony Analysis.” Oxford Univ. Press, Oxford. Kluge, A. (1995). Parsimony. Herpetol. Rev. 26, 76–77. Kluge, A., and Farris, J. S. (1999). Taxic homology ⫽ Overall similarity. Cladistics 15, 205–212. Mann, A., and Weiss, M. (1996). Hominoid phylogeny and taxonomy: A consideration of the molecular and fossil evidence in an historical perspective. Mol. Phylogenet. Evol. 5, 169–181. Meacham, C. A., and Estabrook, G. F. (1985). Compatibility methods in systematics. Annu. Rev. Ecol. Syst. 116, 431–446. Nei, M., Stephens, J. C., and Saitou, N. (1985). Methods for computing the standard errors in branching points in an evolutionary tree and their application to molecular data from humans and apes. Mol. Biol. Evol. 2, 66–85.
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