Published Online: 19 SEP 2013
Copyright © 2001 John Wiley & Sons, Ltd. All rights reserved.
How to Cite
Ramírez, M. J. 2013. Parsimony Methods. eLS. .
- Published Online: 19 SEP 2013
Parsimony methods of phylogenetic reconstruction use an explicit criterion of optimality, the minimisation of evolutionary transformations. By doing so, parsimony also minimises instances of homoplasy (convergence or parallelisms) and maximises similarity due to a common descent (homology). Tree evaluation is done by an optimisation algorithm that calculates the length of each character over the tree; the global tree length is the sum of the individual character lengths. The Sankoff generalised algorithm allows for the optimisation of characters with any cost matrices; there are faster algorithms specific for unordered and additive characters. A problem with tree searches is the vast number of possible trees. There are exact solutions only for small datasets, but most real datasets require heuristic strategies. Measures of group support show the degree on which the data hold the conclusions, and sensitivity analyses help express the robustness of phylogenetic inferences on changes in parameters and conditions of analysis.
Phylogeny is the theoretical background for the classification of living organisms.
Parsimony methods for phylogenetic reconstruction use the minimisation of evolutionary transformations as the optimality criterion.
The parsimony criterion minimises instances of homoplasy (convergence or parallelisms) and maximises similarity due to common descent.
The optimisation of a character on a tree results in states assigned to hypothetical ancestors, implying the minimum number of transformations.
Tree searching is a complex problem, for which exact solutions are limited to the smaller datasets; most real datasets require heuristic solutions.
Differential transformation costs and character weights can be implemented in parsimony methods.
Measures of support and sensitivity can be used to express the robustness of phylogenetic inferences.
- tree search;
- character weighting;
- group support;
- sensitivity analysis