Data from: How well can we detect lineage-specific diversification-rate shifts? A simulation study of sequential AIC methods

Evolutionary biologists have long been fascinated by the extreme
differences in species numbers across branches of the Tree of Life. This
has motivated the development of statistical methods for detecting shifts
in the rate of lineage diversification across the branches of phylogenic
trees. One of the most frequently used methods, MEDUSA, explores a set of
diversification-rate models, where each model assigns branches of the
phylogeny to a set of diversification-rate categories. Each model is first
fit to the data, and the Akaike information criterion (AIC) is then used
to identify the optimal diversification model. Surprisingly, the
statistical behavior of this popular method is uncharacterized, which is a
concern in light of: (1) the poor performance of the AIC as a means of
choosing among models in other phylogenetic contexts; (2) the ad hoc
algorithm used to visit diversification models, and; (3) errors that we
reveal in the likelihood function used to fit diversification models to
the phylogenetic data. Here, we perform an extensive simulation study
demonstrating that MEDUSA (1) has a high false-discovery rate (on average,
spurious diversification-rate shifts are identified ≈30% of the time), and
(2) provides biased estimates of diversification-rate parameters.
Understanding the statistical behavior of MEDUSA is critical both to
empirical researchers—in order to clarify whether these methods can make
reliable inferences from empirical datasets—and to theoretical
biologists—in order to clarify the specific problems that need to be
solved in order to develop more reliable approaches for detecting shifts
in the rate of lineage diversification.