Darwin-L Message Log 7:3 (March 1994)

Academic Discussion on the History and Theory of the Historical Sciences

This is one message from the Archives of Darwin-L (1993–1997), a professional discussion group on the history and theory of the historical sciences.

Note: Additional publications on evolution and the historical sciences by the Darwin-L list owner are available on SSRN.

<7:3>From BENEDICT@VAX.CS.HSCSYR.EDU  Fri Mar  4 16:05:26 1994

Date: 04 Mar 1994 17:06:23 -0500 (EST)
Subject: cladistics & distance data
To: darwin-l@ukanaix.cc.ukans.edu

  Here's a topic to flame on. I'll pose some questions, give my thinking and
see what bounces back. I don't expect to respond much my self after this

  MAIN QUESTION: Can one estimate cladistic relationships from distance data?
  Background:  Distance data. For those who aren't familiar, distance data are
measures of (dis)similarity that yeild an overall estimate of that property
without measuring any component that contributes to the measure. Examples from
biology include immunological comparisons and DNA-DNA hybridization. The
opposite is character data, data we get by comparing two things, homologizing
the differences and noting how the expression (state) of each homologous
quality (character) varies from beast to beast.  Some comparisons are
intermediate -- we can specify differences but the differences are unordered
and it is hard to compare three or more things which are mutually different.
  Cladistic relationships. I presume this means only that we hypothesize a
branching relationship between entities compared, due to common ancestry.
  Context. E. Mayr and W. Bock (Ibis 1994 issue 1) published a long note
pleading for conservation of the current (e.g., Peters Checklist, AOU 6th
Edition) classification of birds of the world, generally in regard to
alternate proposals by anyone and specifically to the classification proposed
by Chas. Sibley, Jon Ahlquist & Burt Monroe (Auk 1988) and based on DNA-DNA
hybridization measurements done by the first two. I am not interested in
the merits of any specific alternate classification. I am interested in your
reaction to an argument the used: the Sibley-Ahlquist-Monroe classification
can not be cladistic because it's based on distance data and must therefore be
phenetic (and thus is unsuitable for general use).
  My opinion:  Yes.  Many techniques that purport to be cladistic ultimately
use distance values, and character state information, if needed is used only
to estimate distances between taxa and their "intermediates"  A specific
example is the Character Wagner method of James Farris, who derived the
corresponding algorithm (Distance Wagner) for distance wagner.  There are some
techniques that can't use distance data (e.g., character compatibility), but
the opposite isn't true since distances can be obtained from any character
data. Whether the answer a particular method gives is what actually happened
is irrelevant, unless you can show that all distance only methods never
recover the actual history (branching pattern) but character do. So don't
bother us with specific cases where charcters and distances give different
results. I know those can be produced, but I have no idea as to how common
they are in real situations.

  SUBQUESTION: What makes a technique cladistic?
  My opinion: Any alogrithm which produces intermediates that are used in
subsequent steps of that algorithm is cladistic. Thus, even UPGMA is
cladistic. An example of a purely phenetic method is the method of Prim
Networks (Minimum spanning trees) - it produces no intermediates. I have no
doubt that some techniques are more efficient at recovering the real branching
pattern than others, and in a biological context it is clear that some
techniques (e.g., Distance Wagner) were derived with an evolutionary process
in mind while others (e.g., UPGMA) were derived with a mathematical operation
in mind. I'd expect the former to work better, but if the situation agrees
with the assumptions of the technique, that shouldn't matter.

So that's it for now. It's getting late and I've lots of snow to shovel.

Paul DeBenedictis
Educational Communications
S.U.N.Y Health Science Center at Syracuse

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