Kitching IJ, Forey PL, Humphries CJ, Williams DM, 1998. Cladistics second edition - the theory and practice of parsimony analysis. The Systematics Association Publication No. 11. Oxford Science Publications.
..., in my previous post I mentioned that I also ran into a section that I find hard to agree with. The chapter on support values opens with the following:
Page 118: The study of phylogeny is an historical science, concerned with the discovery of historical singularities. Consequently, we do not consider phylogenetic inference per se to be fundamentally a statistical question, open to discoverable and objectively definable confidence limits. Hence, we are in diametric opposition to those who would include such a standard statistical framework as part of cladistic theory and practice.I can only repeat in slightly different words what I wrote some time ago about the same question in the context of biogeographic studies. I find it hard to draw a line between historical science and non-historical science, not least because, to take just one example, any physical experiment, be it ever so reproducible, turns into a singular historical event a split second after it has been conducted.
To me there is really no big difference. We always infer what is most likely to have happened in individual instances in the past and then draw more general conclusions from those instances, no matter whether it is history or social science, archeology or engineering, paleobotany or (extant) plant taxonomy, evolutionary biology or population genetics.
I assume that a big part of the difference in perspective here is about what organismal characters people are thinking of. Reading through the cladistics textbook, the focus is pretty much always on morphology. Reading through works that introduce likelihood or Bayesian phylogenetics, in other words probabilistic and model-based evolutionary analysis, the focus is pretty much always on nucleotide sequence data, with protein sequence data coming a distant second.
It makes sense to me that somebody who thinks predominantly in terms of trait shifts like the evolution of bird feathers from scales or of angiosperm gynoecia from ovules sitting nakedly on a stalk would have reason to favour parsimony analysis. In fact I myself, despite frequently using likelihood and Bayesian phylogenetics for sequence data, would still have to be counted among those who are highly sceptical whether the Mk model works better with morphological traits than parsimony.
These kinds of characters have very low homoplasy, at least if scored correctly; and where they do show homoplasy, I would say that is due to a scoring error that can be rectified (e.g. if double fertilisation has evolved independently in angiosperms and gnetophytes then the two should be scored as separate character states). And it just so happens that parsimony analysis is a better tool for the data the less homoplasy there is. What is more, it seems a bit odd to try and apply the same model to all morphological characters, given how vastly different they are.
It also makes a lot of sense to me that somebody who thinks predominantly in terms of trait shifts like an A in the DNA sequence turning into T would see reason to favour analyses using models of sequence evolution. As Prof. Bromham pointed out during her talk I heard a few weeks ago, if that A has changed into a T in two parallel instances and then all the A-carrying individuals died out there is no way in which we can ever find evidence for that.
In other words, in the case of our four letter soup of DNA sequence characters homoplasy is not a scoring error to be discovered by looking closer but a hard fact of life that we cannot rid ourselves of (except to the degree that we can choose slower-evolving markers). And it just so happens that parsimony analysis is a worse tool for the data the more homoplasy there is, while the right model-based approach can deal with that. (Or at least somewhat better - obviously, once homoplasy is so rampant that all signal is lost no phylogenetic method will work, and likelihood analysis has also been shown to suffer from long branch attraction.) What is more, it seems logical to apply the same model to all DNA sequence characters, given that they are equivalent nucleotides along a chain.
So when I call myself a cladist, what I mean is not that I prefer parsimony analysis for all data, but that I acknowledge Willi Hennig's legacy, the idea that systematists should classify consistently by relatedness.
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