Cladistic analysis of molecular and morphological data
- 1 May 1994
- journal article
- research article
- Published by Wiley in American Journal of Physical Anthropology
- Vol. 94 (1), 143-156
- https://doi.org/10.1002/ajpa.1330940111
Abstract
Considerable progress has been made recently in phylogenetic reconstruction in a number of groups of organisms. This progress coincides with two major advances in systematics: new sources have been found for potentially informative characters (i. e., molecular data) and (more importantly) new approaches have been developed for extracting historical information from oldornew characters (i. e., Hennigian phylogenetic systematics or cladistics). The basic assumptions of cladistics (the existence and splitting of lineages marked by discrete, heritable, and independent characters, transformation of which occurs at a rate slower than divergence of lineages) are discussed and defended. Molecular characters are potentially greater in quantity than (and usually independent of) more traditional morphological characters, yet their great simplicity (i. e., fewer potential character states; problems with determining homology), and difficulty of sufficient sampling (particularly from fossils) can lead to special difficulties. Expectations of the phylogenetic behavior of different types of data are investigated from a theoretical standpoint, based primarily on variation in the central parameter λ (branch length in terms of expected number of character changes per segment of a tree), which also leads to possibilities for character and character state weighting. Also considered are prospects for representing diverse yet clearly monophyletic clades in larger‐scale cladistic analyses, e. g., the exemplar method vs. “compartmentalization” (a new approach involving substituting an inferred “archetype” for a large clade accepted as monophyletic based on previous analyses). It is concluded that parsimony is to be preferred for synthetic, “total evidence” analyses because it appears to be a robust method, is applicable to all types of data, and has an explicit and interpretable evolutionary basis.This publication has 52 references indexed in Scilit:
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