Significance Tests of Consensus Indices

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Abstract
Monte Carlo methods were used to examine the sampling distribution of eight consensus indices based on either of the following two models: all bifurcating trees are equally likely; or all trees (including both bifurcating and multifurcating trees) are equally likely. Ten different consensus-tree methods were applied before computing consensus indices. The strictness of a consensus-tree method and the recognition ratio of a consensus index are the two main factors determining the consensus-index distribution. The former factor influences the mean index values; the latter changes the shape of the distribution curves. We furnish significance test tables for consensus trees or indices based on randomly generated trees permitting multifurcations. These tables can be used to test whether a given consensus tree or consensus-index value obtained from data on real organisms differs significantly from what one would expect if one were computing such quantities from randomly sampled trees.