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The use and design of prior distributions that reflect prior ignorance have long been controversial in statistics. The use of any prior distribution marks the difference be- tween Bayesian and frequentist schools of thought. Dis- agreements regarding the design of prior distributions to reflect ignorance, and the interpretation of posterior dis- tributions derived from such priors, have resulted in con- flicting Bayesian philosophies. In a recent study, Pickett and Randle (2005) examined uniform prior distribution on tree topologies, which has been commonly used as a reflection of prior ignorance. We demonstrated that if all trees are considered to be equally probable a priori, all clades cannot be (except when n taxa <4). In a combined analysis of 17 empirical studies, nonuniform clade priors were correlated with Bayesian clade support. However, resampling supports (bootstrap and jackknife values) were also correlated with clade priors in these empiri- cal studies. Using contrived data, we demonstrated that the correlation between resampling support and nonuni- form clade priors is due to character conflict. When data sets lacked conflict, correlation disappeared, which fol- lows from well-known properties of the bootstrap and jackknife (Farris, 1996). On the other hand, Bayesian clade support was correlated with nonuniform clade pri- ors, even when there was no character conflict. Brandley et al. have called these findings into question, and al- though we consider their arguments flawed in several ways, they have posed a provocative question; are un- equal clade priors problematic in Bayesian analysis? We address their concerns with our work, and conclude with a consideration of whether the question they pose has been answered.

Are Nonuniform Clade Priors Important in Bayesian Phylogenetic Analysis? A Response to Brandley et al.