Parsimony analysis of unaligned sequence data: maximization of homology and minimization of homoplasy, not minimization of operationally defined total cost or minimization of equally weighted transformations

摘要

Wheeler (2012) stated that minimization of ad hoc hypotheses as emphasized by Farris (1983) always leads to a preference for trivial optimizations when analysing unaligned sequence data, leaving no basis for tree choice. That is not correct. Farris's framework can be expressed as maximization of homology, a formulation that has been used to overcome the problems with inapplicables (it leads to the notion of subcharacters as a quantity to be co-minimized in parsimony analysis) and that is known not to lead to a preference for trivial optimizations when analysing unaligned sequence data. Maximization of homology, in turn, can be formulated as a minimization of ad hoc hypotheses of homoplasy in the sense of Farris, as shown here. These issues are not just theoretical but have empirical relevance. It is therefore also discussed how maximization of homology can be approximated under various weighting schemes in heuristic tree alignment programs, such as POY, that do not take into account subcharacters. Empirical analyses that use the so-called 3221 cost set (gap opening cost three, transversion and transition costs two, and gap extension cost one), the cost set that is known to be an optimal approximation under equally weighted homology in POY, are briefly reviewed. From a theoretical point of view, maximization of homology provides the general framework to understand such cost sets in terms that are biologically relevant and meaningful. Whether or not embedded in a sensitivity analysis, this is not the case for minimization of a cost that is defined in operational terms only. Neither is it the case for minimization of equally weighted transformations, a known problem that is not addressed by Kluge and Grant's (2006) proposal to invoke the anti-superfluity principle as a rationale for this minimization.