Data for: Diversification models conflate likelihood and prior, and cannot be compared using conventional model-comparison tools

Time-calibrated phylogenetic trees are a tremendously powerful tool for
studying evolutionary, eco-logical, and epidemiological phenomena. Such
trees are predominantly inferred in a Bayesian framework, with the
phylogeny itself treated as a parameter with a prior distribution (a “tree
prior”). However, we show that the tree “parameter” consists, in part, of
data, in the form of taxon samples. Treating the tree as a parameter fails
to account for these data and compromises our ability to compare among
models using standard techniques (e.g., marginal likelihoods estimated
using path-sampling and stepping-stone sampling algorithms). Since
accuracy of the inferred phylogeny strongly depends on how well the tree
prior approximates the true diversification process that gave rise to the
tree, the inability to accurately compare competing tree priors has broad
implica- tions for applications based on time-calibrated trees. We outline
potential remedies to this problem, and provide guidance for researchers
interested in assessing the fit of tree models.