Re: Clarification of scope of paleoart->uses

[Mike Taylor <mike@indexdata.com>]

On 16 March 2011 23:47, David Marjanovic <david.marjanovic@gmx.at> wrote:
>>  I'd go further. I think it's important to retain
>>  parsimony-uninformative characters for two reasons.
>>
>>  The obvious one is that, if someone goes on to build on your matrix,
>>  the new taxa they add may make the previously uninformative
>>  characters informative.
>
> That's not a reason to already have them in the matrix.

Sure it is.  If they're in the matrix, they're already scored for the
N taxa, and the person adding taxon N+1 only has to score it once, not
N+1 times.  You've already done the work, so why throw it away?

>>  [...] The autapomorphies in Wilson's list are of two kinds: those
>>  that are homoplastic in the phylogenetic analysis, and which are
>>  therefore autapomorphic only in specific cladograms; and others that
>>  were not included in the analysis. The appendix's introduction
>>  doesn't spell it out, but I am guess that many if not all of these
>>  were discovered as parsimony-uninformative characters.
>
> This, too, is not a reason to keep such characters in the matrix.

"That's not an argument, that's just contradiction."

> Keeping them in has disadvantages. It makes your matrix appear bigger than
> it is (...impressive as it is, of the 720 characters in the supermatrix by
> Sigurdsen & Green [2011] only 335 are informative; no surprise, because they
> only kept those 25 taxa, out of something like 110 or 120, that are
> represented in all three input matrices...) [...]

So state in your abstract how many of the characters are parsimony-informative.

> [...] and it increases the CI. Fine,
> PAUP* will give you the CI with and without parsimony-uninformative
> characters, but it seems to be normal to report the former instead of the
> latter and thus make the trees look more robust than they are. And of
> course, the bigger a matrix, the more opportunities there are for glitches.

A side-question: does anyone pay attention to CI?  (In practice, it
seems to be basically a measure of how small the matrix is.)  If any
number can top the Impact Factor for uninformativeness, it's surely
the CI.