A STATISTICAL FRAMEWORK TO TEST THE CONSENSUS AMONG ADDITIVE TREES (CLADOGRAMS)

A statistical framework to test the consensus of dendrograms (=phenograms of some authors) is extended to the comparison of cladograms (additive trees). Additive trees can be compared through their associated path-length matrices. The procedure calls for a decomposition of additive trees into ultrametric and star components that are independently permuted and summed together to obtain randomized path-length matrices. This triple permutation test eval? uates the null hypothesis that the trees under comparison are no more similar than random additive trees with a random topology, random labels, and randomized branch lengths. Along with the global test, the integrated approach rationalizes the simultaneous use of tests involving each component separately. The method is applied to kangaroo phylogenies to measure the congruence among trees derived from different character sets. (Additive trees; cladograms; con? sensus; dendrograms; kangaroo; Macropodidae; permutation test; statistical test; star trees.)

[1]  A. Dobson Unrooted trees for numerical taxonomy , 1974, Journal of Applied Probability.

[2]  P. Legendre,et al.  A statistical framework to test the consensus of two nested classifications , 1990 .

[3]  James S. Farris,et al.  The Information Content of the Phylogenetic System , 1979 .

[4]  R. Sokal,et al.  Significance tests of Consensus Indices , 1986 .

[5]  F. Rohlf Consensus indices for comparing classifications , 1982 .

[6]  J. Kirsch The comparative serology of marsupialia, and a classification of marsupials , 1978 .

[7]  R. Sibson Order Invariant Methods for Data Analysis , 1972 .

[8]  G. Sharman,et al.  An evaluation of electrophoresis as a taxonomic method using comparative data from the macropodidae (Marsupialia) , 1973 .

[9]  A. Tversky,et al.  Spatial versus tree representations of proximity data , 1982 .

[10]  R. Sokal,et al.  THE COMPARISON OF DENDROGRAMS BY OBJECTIVE METHODS , 1962 .

[11]  W. DeSarbo,et al.  The representation of three-way proximity data by single and multiple tree structure models , 1984 .

[12]  François-Joseph Lapointe,et al.  Statistical Significance of the Matrix Correlation Coefficient for Comparing Independent Phylogenetic Trees , 1992 .

[13]  S. Hakimi,et al.  The distance matrix of a graph and its tree realization , 1972 .

[14]  Distance à centre , 1985 .

[15]  J. Gower,et al.  Metric and Euclidean properties of dissimilarity coefficients , 1986 .

[16]  N. X. Luong,et al.  Représentations arborées de mesures de dissimilarité , 1986 .

[17]  J. Hartigan REPRESENTATION OF SIMILARITY MATRICES BY TREES , 1967 .

[18]  N. Mantel The detection of disease clustering and a generalized regression approach. , 1967, Cancer research.

[19]  P. Buneman A Note on the Metric Properties of Trees , 1974 .

[20]  Gildas Brossier,et al.  Approximation des dissimilarités par des arbres additifs , 1985 .

[21]  L. Klotz,et al.  A practical method for calculating evolutionary trees from sequence data. , 1981, Journal of theoretical biology.

[22]  Roderic D. M. Page,et al.  QUANTITATIVE CLADISTIC BIOGEOGRAPHY: CONSTRUCTING AND COMPARING AREA CLADOGRAMS , 1988 .

[23]  L. Klotz,et al.  Calculation of evolutionary trees from sequence data. , 1979, Proceedings of the National Academy of Sciences of the United States of America.

[24]  W. A. Beyer,et al.  Additive evolutionary trees. , 1977, Journal of theoretical biology.

[25]  W. DeSarbo,et al.  Optimal variable weighting for hierarchical clustering: An alternating least-squares algorithm , 1985 .

[26]  J. Carroll,et al.  Spatial, non-spatial and hybrid models for scaling , 1976 .

[27]  J. Farris On Comparing the Shapes of Taxonomic Trees , 1973 .

[28]  S. C. Johnson Hierarchical clustering schemes , 1967, Psychometrika.

[29]  George W. Furnas,et al.  Metric family portraits , 1989 .