Is BAMM Flawed? Theoretical and Practical Concerns in the Analysis of Multi-Rate Diversification Models.

ABSTRACT: Bayesian analysis of macroevolutionary mixtures (BAMM) is a statistical framework that uses reversible jump Markov chain Monte Carlo to infer complex macroevolutionary dynamics of diversification and phenotypic evolution on phylogenetic trees. A recent article by Moore et al. (MEA) reported a number of theoretical and practical concerns with BAMM. Major claims from MEA are that (i) BAMM's likelihood function is incorrect, because it does not account for unobserved rate shifts; (ii) the posterior distribution on the number of rate shifts is overly sensitive to the prior; and (iii) diversification rate estimates from BAMM are unreliable. Here, we show that these and other conclusions from MEA are generally incorrect or unjustified. We first demonstrate that MEA's numerical assessment of the

SUBMITTER: Rabosky DL 

PROVIDER: S-EPMC5790138 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature