Mark Huber Publications

The Ancestral Distance test: A topdown approach to detect correlated evolution in large lineages with missing character data and incomplete phylogenies
D. Hearn and M. Huber, Systematic Biology, vol. 55 no. 5 (October, 2006), pp. 803817.

Abstract: The ancestral distance test is introduced to detect correlated evolution between two binary traits in large phylogenies that may lack resolved subclades, branch lengths, and/or comparative data. We define the ancestral distance as the time separating a randomly sampled taxon from its most recent ancestor (MRA) with extant descendants that have an independent trait. The sampled taxon either has (target sample) or lacks (nontarget sample) a dependent trait. Modeled as a Markov process, we show that the distribution of ancestral distances for the target sample is identical to that of the nontarget sample when characters are uncorrelated, whereas ancestral distances are smaller on average for the target sample when characters are correlated. Simulations suggest that the ancestral distance can be estimated using the time, total branch length, taxonomic rank, or number of speciation events between a sampled taxon and the MRA. These results are shown to be robust to deviations from Markov assumptions. A Monte Carlo technique estimates P-values when fully resolved phylogenies with branch lengths are available, and we evaluate the Monte Carlo approach using a data set with known correlation. Measures of relatedness were found to provide a robust means to test hypotheses of correlated character evolution.

Keywords: Ancestral distance; character correlation test; homeosis; Markov model; Monte Carlo simulation; phylogeny; Poisson process; rate heterogeneity; taxonomic rank; Yule tree

This site supported by NSF CAREER grant DMS-05-48153. Last update: 04 December 2009. Note: All downloads provided solely for use within the restrictions of the Fair Use Act, and all copyrights remain with their respective owners.