Application of a Discrete-Character Parsimony Phylogeny-Inference Algorithm to Classical Text...

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Adam Breindel

Departm ent of Classics, Brow n UniversityMay 1998

The Application of a D iscrete-Character Parsimony

Phylogeny-Inference Algorithm to Classical Text Stemmata

The purp ose of this paper is to present two interdisciplinary observations; a new

technique for stemmatic analysis; and preliminary results from an app lication of this

technique.

The first interd isciplinary observation is that the method s and pu rpose of

stemmatics overlaps substantially with the method s and p urp ose of the biological sub-

discipline of cladistic analysis. While this fact is rarely em ph asized or exploited, it is not

a new discovery, and its history w ill be discussed. The second interd isciplinary

observation is that compu ter software which has been developed for biologists in ord er

to solve problems in clad istic analysis now offers us the possibility of advances in the

construction of textual stemm ata, through a non-traditional use of trad itional

manuscript collations.

The analytic technique contained herein wh ich d oes not appear ever to have

been attempted heretoforeis the application of an existing cladistic analysis software

package to the stemmatic analysis of a manu script collation. The use of this technique to

analyze part of the Sallustian corpus is thorough ly documented in this stud y.

Preliminary results indicate that th e technique produ ces a stemm a nearly iden tical to


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that pu blished by L.D. Reynold s, the editor of the Oxford text. Hence, this method

app ears to offer an effective new ap proach to evaluating t he relationships amon g extant

versions of a text.

The interp lay between the disciplines of biological systematics, genetics, and

textual criticism, w hich m akes this pap er p ossible, has a somewh at Byzantine history

spann ing the last thirty years. I ask the read er to consider with charity my exposition of

this history. For it seems that the relative uniqueness of this pap er demand s an

unusu ally large amou nt of background information.1

Background

In 1968, John G. Griffith p ublished a p aper entitled A Taxonomic Stud y of the

Manuscript Trad ition of Juvenal.2In this study , Griffith app lied m ethods of numerical

taxonomy to the classification of Juvenal manuscripts. The taxonomic methods, as he

explains in a similar article the following year ,3

he had in turn learned from biologist

Rober t Sokals 1966 Scientific American article on that top ic.

Griffith describes the biological advances wh ich he exploits in analyzing the texts

of Juv enal:

1I have found it necessary in the course of this paper to refer to some technical aspects of systematics and

genetics. I have attemp ted to r estrict to an elemen tary level the familiarity requ ired w ith these disciplines,

in order to make this work accessible to a broad aud ience. Non etheless, readers seeking an introd uctory

exposition may find ap propriate sections of the following textbooks useful:

Gamblin, Linda and Gail Vines, eds. (1991) The Evolution of Life, Oxford, chapter 3.

Maxson, Lind a R. and Ch arles H. Dau gherty (1992) Genetics: A Human Perspective,

Dubu que, Iowa, chapter s 8, 10.

Minkoff, Eli C. (1983)Evolut ionary Biology , Reading, Mass., chapter 22.2Griffith, John G. (1968) A Taxonomic Stud y of the Manu script Trad ition of Juv enal Museum Helveticum

25:101-38.3Griffith, John G. (1969) Nu merical Taxonomy an d Some Prim ary Man uscript s of the Gospels, Journal of

Theological Studies 20:389-406.


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Scientist have long been aware of the limitations of the traditional

meth ods of classifying specimen s; biologists in particular have laboured

under this hand icap. Within th e last 10-15 years considerable ad vances

have been made, largely because techniques developed for comp uter u se

have enabled specialists in this activity, who style themselves numerical

taxonomists, to sift w ith speed and precision large masses ofunprom isingly heterogeneous material, and thereby to isolate groups or

taxa of related specimens, on the basis of which further inquiry may be

conducted. ...4

Thus, Griffith identifies a requirem ent which textual criticism has in comm on with

taxonomy: in both d isciplines, objects mu st be group ed based on small numbers of

distinctions among v ast amounts of similarity. The numeric taxonomy m ethods ap pear,

he says, to offer new quan titative approaches app licable to both p roblems. He then

expresses the hop e that w e might find associations between specimens by evaluating

large amounts of data w ith machine assistance. In light of the existing resou rces,

though, he remarks that for a textual critic operating with only a few thousand lines of

text it is simply not w orth the trouble of program ming the data for machine-

processing...5

The limitations to Griffiths pioneering app roach were unfortun ately several. His

procedu re w as, first, extraordinarily laborious: for the fourteen Gospel manuscripts

analyzed in h is article of 1969, up to fifty-six manua l record ing acts were required for

every variant among one or more of the manu scripts. Thu s he was constrained to look

at only small samples of the d ata. Moreover, if he had had access to more data, he may

likely have lacked access to the technology to evaluate it.

4Griffith, op. cit. 1968, pp. 113-14.

5ibid.


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Griffiths procedure (and , in a ll fairness, the biological methods with h e worked )

had a more troublesome limitation in that they resulted only in associations of objects.

Griffith could assert the d istribution of manu scripts into various sub -groups w ith

statistically-argued accuracy, but th e mere grou ping of the manu scripts d oes not seem

to have accomp lished m uch. His method s said nothing abou t the genealogical

relationships of the manuscripts. For example, if manuscripts A, B, and C are found to

be in a single taxon, we have only formalized their external similarity. As useful as such

formalization m ight be, little is ind icated abou t the genealogical relationships likely to

inhere between the manu scripts.

Thus, Griffith su cceeded in bringing n um erical taxonomy into the arena of

textual criticism, but the biological appr oach upon which he d epend ed w as not

ambitious enough to describe the relationships among the sp ecimens and so his textual

techniques appear to have fallen into d esuetud e.

In 1973, Martin West published a shor t work on textual criticism, Textual

Criticism and Editorial Technique.6

In this work, West explains that compu ters might

theoretically hold some prom ise for stemm a construction, because, und er the best

possible circumstan ces, building a stemm a d emand s only simple logic. Such a stemma

would natu rally be an ad vance over Griffiths taxonomic man uscript associations. West

is, however, skeptical about th e idea and holds ou t some theoretical reservations:

If prov ided w ith suitable prepared transcriptions of the manuscripts,

pu rged of coincidental errors, a comp uter could d raw u p a clumsy and

unselective critical apparatus; and it could in principle wh ere there was

no contamination!work out an un oriented stemma. That m eans ... that

it could w ork out a scheme simply by comparing the variants, without

6West, Martin L. (1973) Textual Criticism and Editorial Technique, Stuttgart.


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regard to w hether they were right or wrong; but this scheme would be

capable of susp ension from an y point [i.e., the schem e could not

distingu ish the subarchetyp es] ... The correct orientation could on ly be

determined by evaluating th e quality of the variants, which no machine is

capable of doing.7

Wests objections w ill be considered in detail later, as they are impor tant to the

present investigation. But it is worth noting for now that even if West had wanted to

test a compu terized construction of a stemm a, there would have been obstacles to his

progress.

First, there wou ld not have been readily available technology for his pu rpose.

But more impor tantly, outside of theoretical comp uter science or mathematical graph

theory, there had not been p ractical research on autom ating the construction of

stemmata when the data for the specimens is inconsistent or underdetermined. That is, if the

variants in a set of manuscripts were completely compatible with a u nique stemma, w e

would need on ly make the right inferences to generate it. In reality though, there is

usually no stemm a which is not inconsistent with at least one locus in the m anuscripts;

conversely, if a degree of latitude is allowed so as to overcome such strict

inconsistencies, we find a multitude of possible stemm ata. These stemmata we mu st

distinguish on the basis of some criterion capable of evaluating the likelihood that each

would give rise to the man uscripts as they exist.

Thus, a variety of difficult problems, theoretical and computational, inhere in the

task of mechanically constructing a stem ma and they are not problems wh ich

classicists were likely to attack on their ow n. Fortuitously, however, developm ent had

simultaneously been taking place within the biological disciplines of taxonomy and

7West, op . cit., pp . 71-2.


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systematics so as to motivate biologists to attempt these same problems. For derivation

of the evolutionary relationships of a grou p of extant specimens w as a key part of the

emergent stud y now called cladistics.

Biologist Willi Hennig h ad begun to d evelop and advocate a strictly phylogenetic

approach to arranging organisms.8Hen nigs view, that the evolutionary relationships of

organisms formed the best found ation for classifying and systematizing them, was an d

remains the object of debate.9Parts of his theory how ever, seem to have been ad opted

or ad apted by increasing numbers of systematists throughou t the 1970s.

The cladistic approach seems intu itively obvious, and G.D.C. Griffiths (along

with many defenders) insisted that it alone had the ad vantage of relying on objective

fact abou t the organisms in qu estion (rather than d eploying the organisms into classes

invented by hu man s). Griffiths writes, [Henn igs method ] provides the only

theoretically sound basis for achieving an objective equivalence between the taxa

assigned to particular categories in a phylogenetic system.10

Unfortunately, what seems

intu itively obvious can also be deceptively fallacious, and cladistics does have a

disingenuou s side. It is wor th p ointing out tw o objections to the system here, largely so

that the reader may see that they do notapp ly to a textual app lication of the theory.

8Hennig m ight be called the father of modern cladistics; his work was d eveloped and debated in v arious

pu blications includ ing (1950) Grundzge einer Theorie der Phylogenetischen Systematik, Berlin.

(1966) Phylogenetic Systematics, Urbana, Illinois.

(1971) Zur Situation der biologischen Systematik, Erlanger Forschungen, R. Siewing ed .,

Erlangen.9

For views on the early intellectual p ositions in t he d ebates, see Ernst Mayr (1976) Evolution and the

Diversity of Life: Selected Essays, Cambridge, Mass., pp. 435-41.10

Griffiths, G.D.C. (1972) The Phylogenetic Classification of Diptera Cyclorrhap ha with Special

Reference to the Structur e of the Male Postabdom en, W. Jun k, N.V., The H ague.


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First, even if we are granted a thorou gh knowledge of the evolutionary

interrelationships of the specimens in qu estion, no m ethod is thereby presented for

determ ining the level of descent at which class divisions shou ld be m ade. We are on ly

shown that, having mad e a choice, we are bound to include and exclud e certain

specimens.

Second, given three organisms, A, B, and C, suppose that A and B are similar in

form, while C differs greatly from both A and B. Sup pose further that A and C are

closer evolut ionarily to one another than either is to B. In th is situat ion wh ich is not

uncommon in naturewe w ould be forced u nd er Hen nigs system either to class A, B

and C all together, or else to class A and C together against B (Figure 1).

Figure 1

Neither of these options appeals to our intuition the way that th e system at first did . For

A and B appearto form a group as against C, and yet this is precisely the classification

which we are proh ibited from making.

These two objections, wh ile hav ing much p ractical import for the classifying of

organisms, will clearly be irrelevant w hen w e come to ap ply this method to

manu scripts. First, we need nt classify manuscripts by name (and if we do, we accept

that classification as our own prod uction); second , we have no sympathy for similarity


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of appearance between man uscripts if we have hard evidence that they are u nrelated in

origin (since it is the or igin that is the object of the textual qu est).

These cladistic meth ods of analysis and classification, even if controversial,

promp ted research into the creation and evaluation of stemmata (or cladograms) from

incomp lete and incompatible data. The cladistic approach d epend s for a starting p oint

on d etermining the evolutionary relationships of the specimensand these

relationships m ust be assembled from lists of variations among the specimens. Hence,

in a sense, biologists set to work on the p roblems which had stood in front of Martin

West.

But debate abou t the p hilosophical und erpinnings of the cladistic method ology

did not subside. In 1977, the methodology attracted a d efender in University of

Michigan classicist and zoologist H. Don Cameron, due to cladistics eviden t similarity

to established techniques in trad itional (i.e., not m echanical) textual criticism.11

Cameron

along with Norman I. Platnick d escribe the d ebate, and situate th emselves in it, thus:

Recent years h ave seen an increasing aw areness and use am ong zoological

systematists of the theory and method s of phylogenetic analysis

(cladistics) developed by H ennig. These methods h ave been w ell

defended by [E.O.] Wiley from the point of view of Popperian

hypothetico -dedu ctive science. Critics, both of the method s themselves

and of their application to classification, have not been silent... The

pu rpose of this paper is to point out a fact overlooked d uring th e

controversy, namely, that method s analogous to those of Hennig are

accepted as the stand ard tools of analysis in two other fields th at resemble

phylogenetic systematics in being p rimarily concerned with constructing

and testing h ypotheses about the interrelationships of taxa connected by

ancestor-descendant sequences.

11Platnick, Norm an I. and H . Don Cam eron (1977) Clad istic Method s in Textu al, Lingu istic, and

Phylogenetic An alysis, Systematic Zoology 26:380-85.


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The fields referred to are ... textual criticism ... and ... lingu istic

reconstruction.12

Cameron and Platnick, wr iting for an aud ience of biologists, next summarize the

techniques of textual criticism pu t forth by Pau l Maas.13

Differences of techniqu e

between biological and textual stemmatics wh ich Cameron an d Platnick view as

subord inate to an overarching similarity are described in m oderate d etail.14

The pap er

is intend ed to pr ovide a critique of a situation w ithin a d iscipline of biology, but it

serves also to ind icate that these scholars can recognize and make precise the

correspond ence between stemm a construction an d cladistic analysis.

In a conference conclud ed in 1983, Cameron again p resented his view of textual

criticism. The conference had been organized to investigate the biological and clad istic

metaphor in other intellectual fields.15

Cameron treated stemm atics, but he d id not

discuss stemmata as a metaphor from biology, since, as he points out, the stemmatic

meth ods as used in both fields were d eveloped by classical scholars systematically in

the nineteenth century and ... the origins of the method can be found as early as the

sixteenth century...16

Beyond merely recounting the techniques of Maas, Cameron

explores the distinction as far as it imp acts his cladistics-stemm atics comparison

between vertical or uncontaminated traditions and horizontal transmissions, those

12Platnick, op . cit., p. 380.

13Maas, P. (1958) Textual Criticism, Oxford ; Platnick, op. cit., p. 381-3.

14Platnick, op . cit., p. 384.

15Biological Metaphor O utside Biology (1982) and Interdisciplinary Round -Table on Clad istics and Other

Graph Theoretical Representa tions (1983) symp osia at the University of Pennsylvan ia. Proceedings in

Hoen igswald , Henry M . and Lind a F. Wiener, eds. (1987)Biological M etaphor and Cladistic Classification,

Philadelphia. 16

Camer on, H .D. (1987) The Up side-Down Cladogram: Problems in Manuscript Affiliation, in

Hoenigswald, op. cit.


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full of Byzant ine, and even ancient, ed iting and conjecture.17

In the latter cases,

clad istic method s give little aid. But in the former, he conclud es:

[V]ertical transmission an d uncontaminated text tradition make the

mechanical application of cladistic methods to reconstruct a singlearchetype a workable and successful method, with a claim to being

scientific...18

Thus, Cameron argues that, at least in a vertical textual trad ition, we ought to be able to

use method s from cladistics to derive a stemma an d even an archetype.

At this point, the next move for a textual critic might have app eared obvious:

mate Wests insight abou t mechanical prod uction of stemmata with Cameron s insight

th at clad istics provides the theoretical and algorithmic un derpinn ing for Wests

operation. That is, use cladistic techniques to attack thorny problems of textual

transmission. It is un clear w hy this app roach was not exploited in the 1980s. We might,

however, hypothesize a pau city of tools to supp ort such research.

In the 1980s, three fur ther d evelopm ents came about wh ich m ade th e project

presented herein more practicable.19

One breakthrough was imp roved DNA

sequencing:20

it became possible to put genetic mater ial from var ious species into an

autom ated process and receive, as outp ut, essentially a collation showing every genetic

difference between the samples.21

More abundant data w as now available with w hich

cladistic analysis could work.

17Cameron, op. cit., p. 238.

18ibid.

19It is important to note that none of these three developments sprang fully formed from the head of Zeus

in the 1980s. It is convenient to d escribe them here, as their confluence seems to change the research

environment at the time, but research on DNA sequencing, p arsimony algorithms, and of course

computers had a long prior history.20

In paticular the developm ent of polymerase chain reaction (PCR) dup lication of DNA segments.21

That is, in the sequenced stran ds of DNA.


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The second developm ent of this time period was th e availability of comp uters

sophisticated enou gh to compare and evaluate the thousand s or tens of thousands of

possible cladogram s (stemmata) which might result from comparing large num bers of

species. That is, computers allowed biologists to overcome that challenge which Maas

had identified for textual critics, when he observed that a large num ber of specimens or

witnesses would prod uce an astronomical num ber of possible stemmata.22

The last pre-requisite development was software systems to pu t large quantities

of data (whether from DNA or elsewhere) together w ith the compu ters. Software to

compute likely stemmata involves, at its core, algorithm s which have been top ics in

computer science and mathematics for a ha lf-centur y or more. Hence, strictly speaking,

app ropriate software had probably been in d evelopm ent in research u niversities and

corporate labs for some time. But the ear ly 1980s saw the release of packages d esigned

specifically for cladistics, tailored to the needs of practicing biologists, and read y to ru n

on existing microcompu ters.

The present experimental stud y, described below, is an attem pt to establish a

stemma for the textual trad ition of SallustsDe Coniuratione Catalinae using one such

software package, the freely-distributable Phylogeny Inference Package (or, as henceforth ,

PHYLIP).23

22Maas, op. cit., p. 47: If we have fou r w itnesses, the num ber of possible types of stemma am oun ts to 250,

if we have five, to appr oximately 4,000, and so on in qu asi-geometrical progression. 23

Felsenstein, J. (1993) PHY LIP (Phylogeny Inference Package) version 3.5c, distributed by the author, Dept.

of Genetics, Univ. of Washington , Seattle. See http:/ / evolution.genetics.washington .edu


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Before p roceeding to describe the m ethod and outcome of the experiment, it is

app ropr iate to consider two technical objections wh ich textual critics have put forward

concerning stemm a construction.

The first objection is one of M.L. West, prin ted above. West correctly pointed out

that any stemma derived by algorithm w ould be an unoriented stemma (or, as the

clad ists say, an un rooted cladogram ).24

That is, the algorithm could determine the

branchings of the stemma but could not ascertain w hich branching belongs at the

top(in practice, this amou nts to identifying the nodes representing the subarchetypes).

An un rooted cladogram (Figure 2) can represent several d istinct ro oted versions (Figure

3). Each rooted cladogram can, in turn represent several distinct possible phylogenies

(Figure 4).25

Figure 2. Unrooted cladogram. This cladogram shows

the relationships of the specimens relative to oneanother, but does not indicate their relationship toancestors from which they descend.

Figure 3. Rooted cladograms. Each of these five rooted cladograms is consistent with

the unrooted cladogram above (Figure 2). By postulating the first branching in thedescent, the known relationships specify the remainder of the tree. Note, however, thatthe lenths of branches, and the specimens which might lie on the nodes of the tree, arenot indicated.

24West, op . cit., pp . 71-2.

25Hu mp hries, C.J. and P.H . Williams (1994) Clad ogram s and Trees in Biodiversity, Models in Phylogeny

Reconstruction, Robert W. Scotland, Darrell J. Siebert, and David M. Williams, eds., Oxford, pp. 336-7.


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Figure 4. Phylogenetic Trees. All four of these phylogenetic trees are compatible with asingle cladogram above (Figure 3.ii). Note that schemata involving direct descent areincluded.

Wests objection is legitimate. It shou ld not, though, prevent u s from pu rsuing

autom ated stemma construction, for several reasons. First, the unrooted cladogram is, if

accura te, a great advance over no stemma and an even greater advance over an

incorrect stemma. Second , it may in many cases be tolerably easy to p roperly root th e

cladogram, thus p roducing a traditional stemma, based on our knowledge of the dates

and locales of origin for the various manuscripts. Third, compu ter method s are

par ticularly useful in the frequent circumstance that the collation is not u niquely

compatible with any single prop osed stemm a. In such cases, we shall be happ y to have

an analysis of the entire collation, a most-likely stemma, and a mathematical

justification for excluding many other stemmata.

The second objection is one advanced by Roger David Dawe in stud ies of the

traditions of Aeschylus and Sophocles.26Dawes contention is that there is so much

horizontal transmission in the trad itions for these authors, as ind icated by numerou s

true readings app earing in d ependent m anuscripts though absent in other manu scripts,

as to invalidate th e stemmatic approach.27 Dawe confronts the method ology of Pasquali

26Daw e, R.D. (1964) The Collation and Investigation of Manuscripts of Aeschylus, Cambridge

and (1973) Studies on the Text of Sophocles, 2 vols., Leiden .27

Cameron, op. cit., p. 237.


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and consequently confronts my m ethod, which d erives partly throu gh Pasquali, Maas,

and Westat least in the case of individu al authors such as Aeschylus. He writes:

We believe that th e fact of un ique preservation has been dem onstrated [in

the Aeschylean case]; consequen tly the fault mu st lie with th e theory ofdescent, and we conclud e that the ... stemm a does not after all represent,

even in th e simplest form, the tru e character of the trad ition. ...

It seems clear that the p icture p resented by the manuscripts is one of a

recension so entangled that it is utterly impossible for us to u nravel the

threads.28

Cameron summ arizes the p roblems w hich Dawes assertion p oses to an y method such

as the one employed in the present study:

Dawe d enies radically that archetypes can be reconstructed, but he

necessarily p ays a theoretical pr ice for his conclusion...

If there are no archetypes or stemm ata, and if true readings are uniquely

preserved in any m anuscript regard less of its stemmatic position, we are

then throw n back to a procedur e of evaluating readings which is unaided

by considerations of outgroup comp arison, reconstruction of an

archetype, or to p ush the concept to its logical conclusion, withou t the

consideration of manu script au thority of any kind.29

In order tha t we m ay avoid an imbroglio in Aeschylean Textkritik, we might concede

Dawes assertion to hold true in certain specific textual trad itions. But we need not

suppose that any p articular nu mber of such trad itions invalidates the ded uctive

stemmatic method in general. Hence, in the absence of any argum ent against stemmatic

representat ion of the Sallustian trad ition, we can proceed to an alyze it via the cladistic

approach.

Experimental Procedure

28Daw e, op . cit. 1964, pp. 157-8.

29Cameron, op. cit., pp. 237-8.


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In this stud y, the manu scripts containing the De Coniuratione Catilinae and theDe

Bello Iugurthino were examined, as these two works are found together in one set of

manuscripts. Absent access to a complete collation, an ad apte d collation w as formed by

the following method . Eleven manuscripts were selected from those includ ed in L.D.

Reynold s Oxford text of 1991 (Table 1).

Siglum Manuscript

A Parisinus 16025B BasileensisC Parisinus 6085D Parisinus 10195F Hauniensis Fabricianus

H Berolinensis Phillippsianus 1902K Vaticanus Palatinus 887N Vaticanus Palatinus 889P Parisinus 16024Q Parisinus 5748V Vaticanus 3864

(Florilegium Vaticanum)

Table 1

Beginning at Catilina 1.1, the first 300 loci were selected which contain var iants in

one or m ore of the above eleven man uscripts.30

The adap ted collation was then formed

by listing, for each locus, the grou ps of man uscripts wh ich exhibited the same reading.

The collation then consisted of a sequence of row s such as appear in Table 2.

Locus: Group 1 Group 2 Group 3 Group 4

[rows 1-11]12 ABCDFNP HK V13 C N A BDFHKPV[rows 14-300]

Table 2

30To be more precise, in keeping w ith the biological metaphor, only the latest markings in the

man uscripts wer e collated. Thus, as corrected mar kings were ignored , loci containing variants in ear lier

hands are n ot includ ed in th e 300. The selected loci do, how ever, includ e every varian t in the last hand (at

each locus) of the app rop riate manu script from Catilina 1.1 to 52.35.


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To analyze the collation, the DNAPARS componen t of the PHYLIP package was to be

employed, because it is the only component of PHYLIP which can p rocess mu lti-state

discrete characters (albeit by marking the states with DNA labels).31

DNAPARS is a

program w hich compar es DNA base sequences for a set of specimens and evaluates

various p ossible cladogram s on the basis of a parsimony criterion.

A parsimony criterion favors arrangements of the specimens w hich require the

fewest character state changes in the course of the specimens evolution. For example, a

phylogeny wh ich requires a specimen possessing a DN A sequence of AAA to give rise

to one possessing ACT and , thereafter, requires the specimen p ossessing ACT to give

rise to one possessing the sequence AAA again wou ld not be favored. This prop osed

phylogeny requires two bases to change state (AA to CT) and later to change again

(back to AA), involving four base changes overall. Instead , a parsimon y criterion might

favor an arrangement w here one specimen featur ing the AAA sequence gives rise to the

other w ith the AAA sequence, and the latter gives rise to that possessing the ACT

sequence.32

This latter phylogeny requ ires only a single change of two bases, or two

character state changes overall, and is thus more parsimonious than the former.

Further assump tions involved in the par simony m ethod, and d iffering views

about them, are listed (or references provided) by Felsenstein.33

In order to evaluate the collation using DN APARS, the collation d ata had to be

converted from the form illustrated in Table 2 to a form wh erein manuscrip ts group ed

31See Frequen tly Asked Qu estions, Felsenstein, op. cit.

32This ph ylogeny m ight be favored because one can observe other p ossible phylogenies with only two

character state changes. Such phylogenies would be equally parsimonious with the one given, and hence

wou ld be judged equally d esirable by a p arsimony criterion.33

DNAPARSDNA Parsimony Program (documentation) in Felsenstein, op. cit.


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by a shared reading were each assigned a particular DNA base abbreviation (A, C, G, T,

or -, wh ich ind icates a fifth state to DN APARS). The DNA base label assigned to a

manuscript at a particular locus wou ld correspond to the group in wh ich that

manuscript resided at that locus.

Each row of the collation would yield one DN A base label for each m anu script;

thu s the 300 loci in the collation wou ld prod uced a 300-base DNA strand for each of

the eleven manu scripts. The creation and da ta entry of these 3,300 base labels was

beyond what could easily be accomplished man ually. To perform the task, a custom

app lication p rogram was written (MSS2DNA) which allows th e entry of the collation in

table form, p erforms the translation to sequences of DNA base labels for the various

manuscripts, and m oun ts the results on the Microsoft Wind ows clipboard (Figure 5).34

From the clipboard , the DNA d ata for the various manu scripts was assembled

with a text editor into the file format required by DNAPARS, as documente d by

Felsenstein.35

In order to facilitate comp arison to Reynolds stemmatic work on the

Sallust m anu scripts, and because they represent only p arts of the text, data for

manuscripts V (a florilegium) and Q w ere removed from the d ata file, leaving the nine

manuscripts for which Reynolds had p ublished a stemma. In removing V and Q, some

27 (i.e., 9%) of the loci were ren dered irrelevant, although th ey remain in the set.36

34This program , while not elegant, is pu blicly available (with source code) so that others may

independ ently condu ct investigations or repeat and verify the p resent investigation. The pr ogram,

MSS2DNA, runs on 32-bit Microsoft Wind ows p latforms (Wind ows 95, Wind ows 98, Window s NT) and

may be dow nloaded in archived (ZIP) form at http:/ / homer.bus.miami.edu / ~adbreind/ mss2dna.zip35

Molecular Sequence Program s in Felsenstein, op. cit.36

These data p oints represent loci at wh ich only Q and/ or V differed from the consensus of remaining

ma nu scripts. These sites can be identified from Ap pen dix B, in the table marked steps in each site, as

sites where the table shows 0 steps. That is, the rem aining man uscripts show consensus at the site, so no

character state changes are required for any phylogenetic arrangement of the manuscripts.


18/31

Breindel 18

The completed DNAPARS file app ears in this report as Ap pen dix A: Infile.

The DNAPARS program was th en run, using th is file as its data source.37

Figure 5. MSS2DNA. The columns collect themanuscripts which share a reading at each locus. Thecolumn headings indicate the DNA base labels which willbe attached to the manuscript groups.

DNAPARSprodu ced the outp ut file wh ich app ears in th is report as App end ix B:

Outfile, and which includ es the preliminary phylogenetic tree (Figure 6). DNAPARS

was then run on the inpu t data several more times in order that other possible most

parsimonious trees might be discovered. No other m ost parsimonious trees were found.

37The 386-Windows precompiled PHYLIP executables were used throughout. The program options

selected for DNAPARS were all defaults with the following exceptions: Rand omize ord er was selected,

with a seed of 69 (=4*17+1) and 100 perm utations of the inp ut r ows; terminal typ e was set to (none); inpu t

sequences interleaved was set to No; and all printing options for the output were selected.


19/31

Breindel 19

One most parsimonious tree found:

+--F.Hauniens

+--8+--7 +--D.Par10195! !

+--6 +-----H.Beroline! !

+--------5 +--------K.VatP_887! !! +-----------N.VatP_889

+--4! ! +--C.Par_6085! ! +--3

--1 +--------------2 +--B.Basileen! !

! +-----A.Par16025!+-----------------------P.Par16024

remember: this is an unrooted tree!

Figure 6

In order that the outpu t from this program m ight be comp ared to Reynolds

pu blished stemma for Sallust, and in recognition of Reynolds jud gments abou t the

quality of the textual variants, the tree was re-oriented using th e PHYLIPs RETREE

program. Since manu scripts F, D, H, K, and N formed a monop hyletic group an d

because they had been collected in Reynolds presentation ofthe Sallust stemma, the

nod e representing their common an cestor was selected for the ou tgroup (or

subarchetype). Note that althou gh the tree was re-oriented n o changes were made to

the genealogical relationships inferred betw een the m anuscripts by DNAPARS.38

The

transcript of the RETREE session ap pear s in th is report as Ap pendix C: RETREE

38Re-orientation in effect asserts likely positions for the subarchetyp es. As described above, West had

indicated th at such a step w ould be r equired, and that it should be cond ucted using a critics evaluation

of the variants.


20/31

Breindel 20

Session.39

The session also produ ced as outp ut a new tree file. This tree file was u sed as

inpu t to PHYLIPs DRAWGRAM program, which constructed a grap hical

representation of the stemma (Figure 7).

Figure 7

For the sake of comparison, Reynolds stemma is reprod uced (Figure 8).40

Figure 8

As can be observed from the compu ter-generated tree and Reynolds tree

(Figures 7 and 8), they are nearly identical modu lo inversion. There are, however, tw o

39The progr am op tions selected for RETREE were all defaults w ith the following exception: no gra ph ics

was selected.40

Reynold s, L.D., ed. (1991) C. Sallusti Crispi: Catilina, Iugurtha, Historiarum Fragmenta Selecta, Appendix

Sallustiana, Oxford, p . xi.


21/31

Breindel 21

differences. First, Reynold s associates N an d K more closely with each other than with

H, D, or F, while DNAPARS detected no su ch difference in proximity. Second,

Reynolds associates A more closely with P than with B or C, wh ile DNAPARS indicated

no su ch closer affiliation. This latter d istinction can in fact be attribu ted to d ifferences in

the text being collated, rather than to differences between th e analyses of Reynolds and

DNAPARS (see below).

Analysis

Since several hund red rearrangements of the order of the DNA strand s

produ ced no further most parsimonious trees, it seems reasonable to supp ose that the

manuscript collation d ata specify a unique m ost parsimonious tree.41

The existence of a

un ique most par simon ious tree is itself an ind ication that the p resent method may be

produ ctive, as it obviates the need for a hu man to insert prejudices into the analysis, by

selecting one cladogram from a list of many. The similarity of the resu lts derived

throu gh Reynolds analysis to those derived through the parsimon y analysis can, in

light of the n ovelty of the app roach, only be called stu nning.

This similarity is fur ther strengthened when w e account for one of the two

ind icated differences between the stemmata. As described above (see n. 30), in keeping

with the metaphor of biological evolut ion, only the latest extant m arkings (corrections,

not including d eletions) on each man uscript w ere collated. Thus, wh ere the first and

41This supposition is based on Felsensteins implicit assumption that a relatively small number of

rearran gemen ts of the inpu t data ought to yield mu ltiple most par simonious trees if they exist. Such an

assertion seems mathem atically suspect, considering th e large num ber of possible permu tations of, say,

nine m anu scripts (over 360,000). On this m atter, how ever, I defer to Felsensteins know ledge as a

specialist.


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Breindel 22

second hand s of A differed, the second han d w as read for the collation instead of the

first. Reynolds naturally constructs his stemma indicating the position of the original A

text. But h e notes that Secund a manu s (A2) librum lectionibu s instruxit ex aliquo stirp is

[= B, C] codice petitis. That is, where readings exist in A2, they come from the B-C

branchwhich fact DNAPARS appears to have recognized, in asserting the A -A2

man uscript to d escend both from an ancestor of P and also from a closer ancestor of B

and C. To test this hypoth esis, we w ould merely need to modify the collation to reflect

only A-A1

readings, and th en see where DNAPARS places the manu script.

Hav ing taken the discrepancies into account, it seems that both th e hum an and

the machine-assisted analysis derive results from th e same und erlying p attern among

the man uscript readings. This study, then, preliminarily suggests that the parsimony

analysis technique could substantively advance knowledge of textual tran smission.

Furthermore, the parsimony analysis can indicate the readings likely to ap pear in

the archetype an d subarchetypes, in ord er that th ey most efficiently give rise to the

extant man uscripts. A detailed examination of such archetype reconstruction is beyond

the scope of this study. But ambitious readers shou ld note that Append ix B to this

pap er (i.e., the DNAPARS outp ut) prov ides the readings likely to appear at various

nod es in the cladogram for every locus studied. On Reynolds view of the transmission,

the archetype (his ), ough t to bear the readings given for node 4.

Future Research


23/31

Breindel 23

The futu re p resents a nu mber of immediate challenges and possibilities for the

cladistic analysis of texts using p arsimony techniques. The obvious methods throu gh

which th e procedu re may be tested include examining a variety of texts, as well as

using full collationsin p lace of collations bu ilt from apparatus criticiso as to avoid

dep endence on one editors opin ion of wh at may be viable manuscript read ings.42

If positive results are indicated, p arsimony an alysis might be deployed to assist

the textual critic in determining the relationships of texts, and in reconstructing

archetypes, for n ew pu blications. Perspectives may also be presented for re-evaluating

existing dogm a about traditions wh ich have not been recently examined .43

In the

classroom, the use of graphical interactive parsimony programs, wh ich allow on e to

manipulate stemmata on -screen an d immed iately to observe the consistencies or

inconsistencies thus fostered , may facilitate integration of stemmatics into the stand ard

classics curr iculu m.44

Lastly, literary theorists may w ish to pond er the existence of

deeper metaph ors connecting the enzymes and mutation s of DNA replication with the

correspond ing verbal agents and scribal errors giving rise to many of our textual

variants.

42Readings w hich mu st quite certainly be eliminated h ave no place und er the text, wr ites Maas (p. 23),

thu s giving editors license to omit even from the app. crit. those readings deemed eliminanda.43

We may sup pose that p arsimony an alysis will be effective in evaluating relationships between

man uscripts of texts in m oder n, as well as ancient, languages.44

MacClade (distributed by Sinau er Associates) is one such program. Many cand idates which might be

useful for heavy-du ty analysis as well as pedagogy are described by Felsenstein at

http:/ / evolution.genetics.washington.edu/ phylip/ software.html


24/31

Appendix A: Infile

9 300

P.Par16024AAACCCCCCCAATACCCCCCAACGCCCACACCCAACCACCACCCCCACGACGGAAACCCCCCGCCCCCCACCACACCACACCCCCCACCCAACCCATACCACCAAACACACGCCCCCGCCACACGACGGCACACACCCCACACAACAC-

CTCACCCACAACCCCCCCCACCACCCAACAAACCGCCCAAACCCACACCCCCACCACCCAACCCCCACCCCACACCCCCACAAACCCCCCACCTACCCCCCCCCACCAGCCCCACCAACCAAACCAACCCCCGAACCACCCCCACCCCCCAA.Par16025CCACACCCCACAGCCCCCCCACCGCACCCCCCACCCCCCCCCCCCCCCATCGAACCCGCCACGCCCCCCGCAACCCCCCCCCCCCCCACACTCCCCACCACCCACACCCCCGACCCAAACCAAAAACGGCCCCCCCCCCACACCCCCCCCAC-CCACCCAAAACACCCACCCCCCCACACCCAACCAACAAAACCACCCCCCCCAACACCCCCCACCACCCCCCCACACCCCCCAAACAACCCACCCCCACCCCCCCGCCCCACCCACCACCCCACACCCCGACCCCACCCCGACACCGCB.BasileenAACCCCCCCAAATCACCCCCAGCGCCCACACAACCCCCACACCCCCGCCCCGGAACCCCCCCCCACCACCCCCCCCCCCCCCCCCCCACCATACCCAACGAACAAACCCCCGAACCACACACAACCCGGACACCGCCCCACACCCCCCACCACCCACCCCCAACCCCCACACCCCACCAGACAACCACCCCCCCCACCCCCCCCAACCCCCCCCCCCCAACCCCCCCCCCCCCCCCGCCCACCCCCCCCACCACCCCGCACCACCCACCGCCCCACCCCCCGACCCCCCCCCCACACCGAC.Par_6085AAACCCCCCAAAACACCCCCAGCGCACCCCCAACCCCCCCCCCCCCGCACCGGACCCCCCACACACCACTCAACCCCACACCCCCCCACACTCCCCTACGCCCACACCCCCGCACCACACAAAACCCGGCCACCCCCCCACACCCCCCCCCACCCACCCCCAACCCCCCCCCCCCCCCAGCCACCCACCCCCAACACCCCCCCCACCACCCCCCACCCCAACACACCCCCCCCCCCGCCCCCCCCCCCCACCCCCCCGCCCCACCCACCACCCCACCCCCCGACCCCACCCCGACACCGCN.VatP_889CAACCCAACCCACACCACAAACCGCCCCACCCCCCAAAACACCCCCAAGGCAGCCCCACACAAACCCCCACCACCC

CCCCCAACCCCACCCGCCCCACAAAGAACCCACCCGCCCCCGCCCCCAGCAGGCGGCCGACACCACCCCCCAGCGCCCCCCCCCCCAACCCCCCCCCCCCCCAGACAGCACACACCCAACACCCCCCCAACACCCCCACACCCCACCCCCCCCCCCAACCACAACAACCACCCCCCCCCCAGCCCCCCCAACCACCCCCCCCCCCCCCCACCCCCCGCAGCCGCK.VatP_887ACCCCCCACCCCTCCAACCAAGCGCCCCCCCCCCCAAAACACCACAAAGGAGGCCCCCCCACGACCCCCGCCGCCCCCCCCCCCCACCCCGGACCCCACAAGACCCACCCCACACACGCCACCCGCAGAGGGCCCACACCACCCACCATCCCTCCACCCCCCACCACACACCCCCAACAGCCAGCCCCCCCCCAACCCCACCACAACCCACAAACCCCCCCCCCCCCCCCCCCACCACCCAACACACCCCCACCCAAGCCCCACCACCCCCACCCCCCCCCGCCCACCACCCACCACCGCH.BerolineAACCCACCACACTCCCCCCACGACCCCCACCCCCCCACACGCCCCAAAGCCGGACCCCACCGGCCCCACCACCCCCCCCCCCCCCACACCCTACCCGCCTAGCCCAACCCACCCACCGCCCCCAGCCGGACTCCCCAACCCCCCGCCATCCC-CCACCCCCAACCACACCCCACCACAAGACAGACCCCCCCCCCACCCCCCAAGCCCAAAAACAGACCAACCCCCACCCCCCCCCCCCCAC-GCCCCCCCACCCCCACACCCCCACCAGCCCCACCCACCGCCCCCCCCCAACCTCCGCD.Par10195CACACACCACCATCCACACAATCACCACCCACCCCCAAACGACAAAACGGACCCCCCCCCCAGACAAACACCCACCCCCCACAACACCCCTCAAACGCCAAGCCCCACACAACCAACGCCCCCCGCACCGGGCCACAAACCCCCCCCAGCCCGCCCCACCCACCCCAACAACAACGAAAGCAAGCCCCCCCCCACCCAACCCAACCCAAAAAACCGACCAACCACCACCCCCCCCCCCCCACGCCCACCCCACCACCCCACACCCACCAGCAAACACCCAAGCCCCCCCACCCCCACCAC

F.HauniensCACACACCCACATCCAAACCAGCAACACCCACCCCCAACAAAACAAACGGACCCCCATCCCACACAAACACACACACCCCACAACCCACCTGAACCGGCAAGCCCCCCACCACAAACGCACCCCGCCAGTGTCCGCACACCCCACCCAGCCCGCCCCACCCACACAAACACCAAAGAAAGCAAGCCCCACCCCACGCAACACAACCCCAAAACCCCAACACCCACCACCCCCCCCCCACCACGCCCACCACAACACCCCACCCCCACCAGCCAAGACCAAAGCCACCCCACCCCC-ACAC


25/31

Breindel 25

Appendix B: Outfile

DNA parsimony algorithm, version 3.572c

Name Sequences---- ---------

P.Par16024 AAACCCCCCC AATACCCCCC AACGCCCACA CCCAACCACC ACCCCCACGA CGGAAACCCCA.Par16025 CC..A....A C.GC...... .C...A.C.C ..ACC..C.. C.....C.AT ..A.CC.G..B.Basileen ..C......A ...CA..... .G........ .AACC..CA. ......G.CC .....C....C.Par_6085 .........A ..ACA..... .G...A.C.C .AACC..C.. C.....G.AC ....CC....N.VatP_889 C.....AA.. C.C...A.AA .C.....CAC ...CCAA.A. .......A.G .A.CCC.A.AK.VatP_887 .CC....A.. CC.C.AA..A .G.....C.C ...CCAA.A. ...A.A.A.G A..CCC....H.Beroline ..C..A..A. .C.C.....A CGAC...CAC ...CC.ACA. G....A.A.C ....CC..A.D.Par10195 C.CA.A..A. C..C.A.A.A .T.A..AC.C A..CC.A.A. GA.AAA...G ACCCCC....F.Hauniens C.CA.A...A C..C.AAA.. .G.AA.AC.C A..CC.A..A .AA.AA...G ACCCCCAT..

P.Par16024 CCGCCCCCCA CCACACCACA CCCCCCACCC AACCCATACC ACCAAACACA CGCCCCCGCCA.Par16025 A........G .A..C..C.C ......CA.A CT...CAC.A C...C..C.C ..A...AAA.B.Basileen ..C.A..A.C ..C.C..C.C ......CA.. .TA..CA..G .A.....C.C ..AA..ACA.C.Par_6085 A.A.A..A.T .A..C..... ......CA.A CT...C...G C...C..C.C ...A..ACA.N.VatP_889 .AAA...... ....C..C.C .AA...CA.. CG...CACAA .GA.CC...C ..........

K.VatP_887 A..A.....G ..G.C..C.C .....AC... GGA..CC..A .GACCCAC.C .A.A.A....H.Beroline .G.....A.C A.C.C..C.C .....ACA.. CTA..CGC.T .G.CC.AC.C AC..A.....D.Par10195 .A.A.AAA.. ..CAC..C.C A.AA.AC... TCAAACGC.A .G.CCCACAC AA..AA....F.Hauniens .ACA.AAA.. .ACACA.C.C A.AA..CA.. TGAA.CGG.A .G.CCC.CAC .A.AAA...A

P.Par16024 ACACGACGGC ACACACCCCA CACAACAC-C TCACCCACAA CCCCCCCCAC CACCCAACAAA.Par16025 CA.AA..... C.C.C..... ...CC.C.C. A.-..AC.C. AAA.A..... .C...C...CB.Basileen ...ACC...A CAC.G..... ...CC.C.A. CAC..AC.CC .AA....... AC....C..GC.Par_6085 .A.ACC.... CAC.C..... ...CC.C.C. CAC..AC.CC .AA.....C. .C...CC..GN.VatP_889 C.CA.CA... GGC.GA.A.C AC.CC.CAG. G.C...C.CC ..AA....C. .C...CC..GK.VatP_887 ..C..CA.AG GGC.CA.A.C AC.C..CAT. C.T..AC.CC ..A..A.ACA .C.......GH.Beroline C.CA.C...A CTC.C.AA.C .C.CG.CAT. C.-..AC.CC .AA..A.AC. .CA...CA.GD.Par10195 C.C..CACCG GGC...AAAC .C.CC.CAG. C.G...CACC .A....AACA ACAA.G.A.GF.Hauniens C.C..C.A.T GTC.G.A.AC .C..C.CAG. C.G...CACC .A.A.AAACA .CAAAG.A.G

P.Par16024 ACCGCCCAAA CCCACACCCC CACCACCCAA CCCCCACCCC ACACCCCCAC AAACCCCCCAA.Par16025 C.AA..A.C. AAAC...... .C...A.ACC ....AC.A.. C.C..A.AC. CCC.AAA.A.B.Basileen ..AA..ACCC ...C...... .C...A..CC .....C..AA C.C.....C. CCC...G..CC.Par_6085 C.AC..ACCC ..A....... .C.....ACC ....AC...A ....A...C. CCC...G..CN.VatP_889 ..A..A..C. ....ACA... .C...A.ACC ...A...... ..C.....C. CC.A..A.A.K.VatP_887 C.A....CCC ....AC...A .CA..A..C. .AAA.C.... C.C.....C. CCCA..A..CH.Beroline ..A.A..CCC ...C...... .CAAG..... AAA.AGA..A ..C...A.C. CCC......CD.Par10195 CAA....CCC .....C.AA. .CAAC..A.. AAA..GA..A ..CA..A.C. CCC......CF.Hauniens CAA....C.C .....G.AA. ACAAC..... AA...CAA.A C.CA..A.C. CCC....A.C

P.Par16024 CCTACCCCCC CCCACCAGCC CCACCAACCA AACCAACCCC CGAACCACCC CCACCCCCCAA.Par16025 ..C......A ...C..C... .....C.... CC...CA... ...C..CA.. ..GA.A..GCB.Basileen A.CC.....A ..AC..C..A .....C...G CC...C.... ...C..C... ..CA.A..G.C.Par_6085 ..CC.....A ...C..C... .....C.... CC...C.... ...C..CA.. ..GA.A..GCN.VatP_889 .AAC.A.... ...C...... ..C....... CC..CC.... .CCC.AC... ..G.AG..GC

K.VatP_887 AAC..A.... .A.C.A.... ......C..C C...CC.... ..CC.AC.A. .....A..GCH.Beroline A.-G...... .A.C..CA.A ..C...C.AG CC...C..A. ..CC..C... .A...T..GCD.Par10195 A.GC..A... .A.CA.CC.A .AC...C.AG C.AAC....A A.CC..C..A ..C..A..ACF.Hauniens A.GC..A..A .AACA.CC.A ..C...C.AG CCAAG...AA A.CCA.C..A ..C..-A.AC


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Breindel 26

One most parsimonious tree found:

+--F.Hauniens+--8

+--7 +--D.Par10195! !+--6 +-----H.Beroline! !

+--------5 +--------K.VatP_887! !! +-----------N.VatP_889

+--4! ! +--C.Par_6085! ! +--3

--1 +--------------2 +--B.Basileen! !! +-----A.Par16025!+-----------------------P.Par16024

remember: this is an unrooted tree!

requires a total of 500.000

steps in each site:0 1 2 3 4 5 6 7 8 9

*-----------------------------------------0! 3 2 2 1 1 1 1 2 210! 2 3 2 3 2 1 2 3 1 120! 2 1 4 1 2 1 2 1 2 230! 2 1 1 1 1 1 2 1 2 340! 1 4 1 1 2 1 1 2 2 250! 4 2 2 2 2 2 1 1 3 1

60! 1 3 3 4 2 1 1 1 2 070! 5 1 3 3 1 1 1 0 2 080! 2 1 1 2 1 0 2 1 3 090! 2 4 4 2 1 1 1 4 4 1100! 3 2 2 2 1 2 2 2 2 1110! 1 2 2 2 3 1 2 1 2 1120! 1 3 2 1 3 2 2 3 2 2130! 4 3 4 1 0 5 2 1 2 1140! 1 2 1 0 2 3 0 1 1 5150! 0 3 1 5 0 0 3 1 1 1160! 2 1 2 2 2 1 2 1 1 2170! 2 2 1 1 1 1 4 3 1 0180! 2 4 1 1 2 1 1 1 2 2190! 2 1 1 2 3 2 3 1 1 1200! 1 1 1 1 1 2 3 0 4 2

210! 2 1 1 2 2 3 4 1 2 1220! 2 4 0 2 1 1 1 1 1 1230! 0 1 1 2 2 1 1 3 1 2240! 2 2 2 4 4 0 2 1 0 0250! 2 0 1 2 1 1 1 2 2 0260! 2 0 1 2 0 0 1 1 0 1270! 3 1 3 1 1 3 2 1 0 2280! 1 1 1 1 1 1 2 1 2 1290! 1 0 1 4 1 1 4 1 0 2300! 2


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Breindel 27

From To Any Steps? State at upper node( . means same as in the node below it on tree)

1 AAACCCCCCC MATMCCCCCC AVCGCCCMCM CCCMMCCACC1 4 maybe .......... .......... .S.....C.C ...CC.....4 5 yes .......M.. C.....M..A .......... .....MA.A.

5 6 yes ..C....... .M.C.M.... .G........ ..........6 7 yes .....A.CM. .......... ...V...... .....C....7 8 yes C..A...... .A...A.A.. ...A..A... A.........8 F.Hauniens yes ........CA ......A..C ....A..... ........CA8 D.Par10195 yes ........A. ......C... .T........ ..........7 H.Beroline yes ........A. AC...CC... C.AC....A. .......C..6 K.VatP_887 yes .C.....A.. .C...AA... .......... .....A....5 N.VatP_889 yes C.....AA.. ..CA..A.A. .C......A. .....A....4 2 yes .........A ...C...... .....M.... ..A....C..2 3 yes .......... A...A..... .G........ .A........3 C.Par_6085 yes .......... ..A....... .....A.... ..........3 B.Basileen yes ..C....... .......... .....C.A.A ........A.2 A.Par16025 yes CC..A..... C.G....... .C...A.... ..........1 P.Par16024 maybe .......... A..A...... .A.....A.A ...AA.....

1 ACCCCCACGN CGGAMMCCCC CCGCCCCCCA CCACMCCMCM1 4 maybe .......... ....CC.... .......... ....C..C.C4 5 yes .......A.G ...C...... .M.A...... ..........5 6 yes .....A.... M......... .......... ..V.......6 7 yes R......... .......... .V.....A.. ..C.......7 8 yes .A..A..C.. ACC....... .A...AA... ...A......8 F.Hauniens yes A.A....... ......AT.. ..C....... .A...A....8 D.Par10195 yes G..A...... .......... .......... ..........7 H.Beroline yes G........C C..A....A. .G.C.....C A.........6 K.VatP_887 yes ...A...... A......... AC.......G ..G.......5 N.VatP_889 yes .......... .A.....A.A .AA....... ..........4 2 yes M.....V.A. .......... M........N .M........2 3 yes ......G..C .......... ..V.A..A.. ..........3 C.Par_6085 yes C......... .......... A.A......T .A.....A.A3 B.Basileen yes A.......C. ....A..... C.C......C .CC.......

2 A.Par16025 yes C.....C..T ..A....G.. A........G .A........1 P.Par16024 maybe .........A ....AA.... .......... ....A..A.A

1 CCCCCCMMCC MDCCCMWMCM ACCAMACACM CGCCCCCGCC1 4 maybe ......CA.. C....CA..A ....C..M.C ..........4 5 yes .......... .G........ .GM..C.... ..........5 6 yes .....A.... ..A...V... ...C..AC.. .A...M....6 7 yes .......... ......GC.. ..C....... M...A.....7 8 yes A.AA...... T..A...... ........A. .....A....8 F.Hauniens yes .....C.... .......G.. ......C... C..A.....A8 D.Par10195 yes .......C.. .C..A..... .......... A.........7 H.Beroline yes .......... .T.......T .....A.... AC...C....6 K.VatP_887 yes .......C.. G.....CA.. ..A....... ...A.A....5 N.VatP_889 yes .AA....... .......CA. ..A....A.. ..........4 2 yes .........M .T........ M......C.. ..M...AVA.

2 3 yes .......... .......A.G .......... ...A...C..3 C.Par_6085 yes .........A ......T... C......... ..C.......3 B.Basileen yes .........C A.A....... AA..A..... ..A.......2 A.Par16025 maybe .........A .......C.. C......... ..A....A..1 P.Par16024 maybe ......AC.. AA...ATA.C ....A....A ..........

1 MCAMGMCGGC VCMCMCCCCA CACMMCMC?C YC?CCMMCMM1 4 maybe .......... ..C.C..... ...CC.C... C.?...C.C.4 5 yes ..C..CM... GG...M.A.C MC.....A.. .........C5 6 yes .........G .......... ........K. ..?.......6 7 yes C......... .K...CA... C......... ..........


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Breindel 28

7 8 yes ...C...V.. ....V...A. ........G. ..G..C.A..8 F.Hauniens yes ......CA.T .T..G..C.. ...A...... ..........8 D.Par10195 yes ......ACC. .G..A..... .......... ..........7 H.Beroline yes ...A..C..A CT........ ....G...T. ..-..A....6 K.VatP_887 yes A..C..A.A. .....A.... A...A...T. ..T..A....5 N.VatP_889 yes C..A..A... ....GA.... A.......G. G.C..C....4 2 maybe .M.AV..... C......... ........C. .....A....

2 3 yes A...CC.... .A........ .......... .AC......C3 C.Par_6085 maybe .A........ .......... .......... ..........3 B.Basileen yes .C.......A ....G..... ........A. ..........2 A.Par16025 yes CA..AA.... .......... .......... A.-......A1 P.Par16024 maybe A..C.A.... A.A.A..... ...AA.A.-. T.A..CA.AA

1 CCMCCCCCAC CMCCCMACAR MCMGCCCAMA CCCACACCCC1 4 maybe ..A....... .C.......G ..A.....C. ..........4 5 yes ........C. .......... .......... ....MC....5 6 yes .....A.A.M .....A.... .......C.C ..........6 7 yes .A........ ..A....A.. .......... ....C.....7 8 yes ..C...A..A ...A.G.... CA........ .......AA.8 F.Hauniens yes ...A...... ....A..... ........A. .....G....8 D.Par10195 yes .....C.... A......... .......... ..........7 H.Beroline yes .........C ......C... A...A..... ...C.A....

6 K.VatP_887 yes .........A .......... C......... ....A....A5 N.VatP_889 yes ...A...... .....CC... A....A.... ....A.A...4 2 yes .A........ .......... ...A..A... ..MM......2 3 yes .......... ......C... .......C.C ..........3 C.Par_6085 yes ........C. .....C.... C..C...... ..AA......3 B.Basileen yes .......... A....A.... A......... ..CC......2 A.Par16025 yes A...A..... .....C...C C......... AAAC......1 P.Par16024 maybe ..C....... .A...A...A A.C.....A. ..........

1 CMCCAMCMMM CCCCCMCCCC ACMCCCCCMC MMACCCMCCA1 4 maybe .C...A..C. .......... ..C.....C. CCM...A...4 5 maybe .......... ...M...... .......... ...M......5 6 yes ..A....C.A .AA..V.... .......... ..C......C6 7 yes ...AVC..A. A..C.SA..A ......A... ...C..C...7 8 yes ....C..... .......... ...A...... ..........

8 F.Hauniens yes A......... ..C..C.A.. C......... .......A..8 D.Par10195 yes .......A.. .....G.... .......... ..........7 H.Beroline yes ....G..... ....AG.... .......... ..........6 K.VatP_887 yes .......... ...A.C.... C......... ...A......5 N.VatP_889 yes .......A.C ...A.A.... .......... ..AA....A.4 2 maybe .........C ....MC.... M......... ..C.......2 3 yes .......... .........A .......... ......G..C3 C.Par_6085 yes .....C.A.. ....A..... A.A.A..... ..........3 B.Basileen yes .......C.. ....C...A. C......... ..........2 A.Par16025 yes .......A.. ....A..A.. C....A.A.. ....AA..A.1 P.Par16024 maybe .A...C.CAA .....A.... ..A.....A. AA....C...

1 CCYMCCCCCC CCCMCCAGCC CCACCAACCA MMCCAMCCCC1 4 maybe ..C....... ...C...... .......... CC...C....4 5 yes .M...M.... .......... ..M....... ....C.....

5 6 yes A......... .A........ ......C..V ..........6 7 yes .C?V.C.... ......CV.A ..C.....AG ........M.7 8 yes ..GC..A... ....A..C.. .......... ..AA.A...A8 F.Hauniens yes .........A ..A....... .......... ....G...A.8 D.Par10195 yes .......... .......... .A........ .A......C.7 H.Beroline yes ..-G...... .......A.. .......... ....A...A.6 K.VatP_887 yes .A.A.A.... .....A.... ..A......C .A........5 N.VatP_889 yes .AAC.A.... .......... ..C....... ..........4 2 yes .........A ......C... .....C.... ..........2 3 maybe ...C...... .......... .......... ..........3 C.Par_6085 no .......... .......... .......... ..........


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3 B.Basileen yes A......... ..A......A .........G ..........2 A.Par16025 yes ...A...... .......... .......... ......A...1 P.Par16024 maybe ..TA...... ...A...... .......... AA...A....

1 CGAMCCMCCC CCRCCMCCSM1 4 maybe ...C..C... .....A..GC4 5 yes ..C..M.... ..........

5 6 maybe .......... ..A.......6 7 maybe .....C.... ..........7 8 yes A........A ..C.....A.8 F.Hauniens yes ....A..... .....-A...8 D.Par10195 no .......... ..........7 H.Beroline yes .......... .A...T....6 K.VatP_887 yes .....A..A. ..........5 N.VatP_889 yes .C...A.... ..G.AG....4 2 yes .......M.. ..GA......2 3 no .......... ..........3 C.Par_6085 maybe .......A.. ..........3 B.Basileen yes .......C.. ..C......A2 A.Par16025 maybe .......A.. ..........1 P.Par16024 maybe ...A..A... ..A..C..CA


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Appendix C: RETREE Session

Tree Rearrangement, version 3.572c

Settings for this run:U Initial tree (arbitrary, user, specify)? User tree from tree file

N Use the Nexus format to write out trees? No0 Graphics type (IBM PC, VT52, ANSI)? ANSIW Width of terminal screen, of plotting area? 80, 80L Number of lines on screen? 24

Are these settings correct? (type Y or the letter for one to change)0

Tree Rearrangement, version 3.572c

Settings for this run:U Initial tree (arbitrary, user, specify)? User tree from tree fileN Use the Nexus format to write out trees? No0 Graphics type (IBM PC, VT52, ANSI)? (none)

W Width of terminal screen, of plotting area? 80, 80L Number of lines on screen? 24

Are these settings correct? (type Y or the letter for one to change)y

Reading tree file ...

retree: can't read intreePlease enter a new filename>treefile

,>>1:F.Hauniens,>15

,>14 `>>2:D.Par10195! !

,>13 `>>>>>3:H.Beroline! !

,>>>>>>>12 `>>>>>>>>4:K.VatP 887! !! `>>>>>>>>>>>5:N.VatP 889

,>11! ! ,>>6:C.Par 6085! ! ,>17

-10 `>>>>>>>>>>>>>16 `>>7:B.Basileen! !! `>>>>>8:A.Par16025!`>>>>>>>>>>>>>>>>>>>>>>>9:P.Par16024

NEXT? (Options: R . U W O T F B N H J K L C + ? X Q) (? for Help) oWhich node should be the new outgroup? 12

,>>1:F.Hauniens,>15

,>14 `>>2:D.Par10195! !

,>13 `>>>>>3:H.Beroline! !


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,>>>>>>>>>>12 `>>>>>>>>4:K.VatP 887! !! `>>>>>>>>>>>5:N.VatP 889!

-10 ,>>6:C.Par 6085! ,>17! ,>16 `>>7:B.Basileen

! ! !`>>>>>>>>>>>>>11 `>>>>>8:A.Par16025!`>>>>>>>>9:P.Par16024

NEXT? (Options: R . U W O T F B N H J K L C + ? X Q) (? for Help) wEnter R if the tree is to be rootedOR enter U if the tree is to be unrooted: r

Tree written to file

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