A generalized permutation model for the analysis of cross-species data

Many fields of biology employ cross-species comparisons. However, because s pecies descend with modification from common ancestors, and rates of evolut ion may vary among branches of an evolutionary tree, problems of nonindepen dence and nonidentical distributions may occur in comparative data sets. Se veral phylogenetically based statistical methods have been developed to dea l with these issues, but two are most commonly used. Independent contrasts attempts to transform the data to meet the i.i.d, assumption of conventiona l statistical methods. Monte Carlo computer simulations attempt to produce phylogenetically informed null distributions of test statistics. A disadvan tage of the former is its ultimate reliance on conventional distributional assumptions, whereas the latter may require excessive information on biolog ical parameters that are rarely known. We propose a phylogenetic permutatio n method that is akin to the simulation approach but requires less biologic al input information. We show that the conventional, equally likely (EL) ra ndomization model is a special case of our phylogenetic permutations (PP). An application of the method is presented to test the correlation between t wo traits with cross-species data.