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Detecting Internal Inconsistencies in Distance Data
Scott M. Lanyon
Vol. 34, No. 4 (Dec., 1985), pp. 397-403
Stable URL: http://www.jstor.org/stable/2413204
Page Count: 7
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Phylogenetic trees, derived from distance measures, may be of variable reliability due to variance in the quality of the data sets from which they are produced. Such trees, therefore, are of questionable value as a means of summarizing large data sets. To improve our confidence in these trees, a jackknife technique is presented that, in combination with existing consensus techniques, identifies those portions of evolutionary history that are poorly known due to inconsistencies in the data. Such trees more accurately represent the results of a study than do current tree-generating algorithms that obscure areas of uncertainty. The approach is a simple modification of existing tree-generating methods. As an illustration, a biochemical data set is analyzed using this technique.
Systematic Zoology © 1985 Oxford University Press