Numerical methods for inferring evolutionary trees
Despite a century of evolutionary theory, it has only been in the last few decades that clearly defined procedures for inferring phylogenies were stated. For discrete characters whose ancestral states are known, the prescriptions of Hennig are well-defined, but their applicability only when there is no incompatibility between different characters has led to the elaboration of a number of methods for dealing with incompatibilities. One category is the parsimony methods, which choose that phylogeny on which the fewest changes of character state need be assumed. Another is the compatibility methods, which choose that phylogeny which is perfectly compatible with the largest number of characters, irrespective of how many changes need be assumed in other characters. Other approaches include the use of phenetic clustering algorithms and methods fitting trees to similarity or distance matrices. Each method has different implicit assumptions concerning the biology of the characters and the information available in the data. Standard statistical approaches such as maximum likelihood can be used to obtain methods whose properties are known, and for which one can determine the amount of uncertainty in the resulting estimates of the phylogeny. It is by viewing the problem as a statistical one that we can place all these methods in a common framework, within which their behavior and assumptions can be compared. It is essential that an attempt be made to understand the biological assumptions and statistical behavior of each method.
- Research Organization:
- Washington Univ., Seattle (USA). Dept. of Genetics
- OSTI ID:
- 5944011
- Report Number(s):
- DOE/EV/71005-61; ON: DE82002295
- Country of Publication:
- United States
- Language:
- English
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