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Journal Article

Numerical Methods for Inferring Evolutionary Trees

Joseph Felsenstein
The Quarterly Review of Biology
Vol. 57, No. 4 (Dec., 1982), pp. 379-404
Stable URL: http://www.jstor.org/stable/2826886
Page Count: 26
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Numerical Methods for Inferring Evolutionary Trees
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Abstract

Despite a century of evolutionary theory, only in the last few decades have clearly defined procedures for inferring phylogenies been stated. For discrete characters whose ancestral states are known, the prescriptions of Henning are well defined, but they are applicable only when there is no incompatibility between different characters. This limitation has led to the elaboration of a number of methods for dealing with such incompatibilities. One category consists of the parsimony methods, which choose that phylogeny on which the fewest changes of character state need be assumed. Another category consists of 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 a different set of implicit assumptions concerning the biology of the characters and the information available from the data. If the methods are considered in a statistical framework as different estimators of an unknow quantity (the phylogeny), these asseumptions are more clearly seen. 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. Although existing statistical models are highly oversimplified and do not reflect the complexity of evolutionary processes, it is by viewing the problem as a statistical one that we can place all these methods in common fremework, within which their behavior and assumptions can be compared. It is essential that we not adopt a single methods as a universal panacea, but that an attempt be made to understand the biological assumptions and statistical behavior of each method.

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