Data from: Detecting hidden diversification shifts in models of trait-dependent speciation and extinction

The distribution of diversity can vary considerably from clade to clade.
Attempts to understand these patterns often employ state-dependent
speciation and extinction models to determine whether the evolution of a
particular novel trait has increased speciation rates and/or decreased
extinction rates. It is still unclear, however, whether these models are
uncovering important drivers of diversification, or whether they are
simply pointing to more complex patterns involving many unmeasured and
co-distributed factors. Here we describe an extension to the popular
state-dependent speciation and extinction models that specifically
accounts for the presence of unmeasured factors that could impact
diversification rates estimated for the states of any observed trait,
addressing at least one major criticism of BiSSE (Binary State Speciation
and Extinction) methods. Specifically, our model, which we refer to as
HiSSE (Hidden State Speciation and Extinction), assumes that related to
each observed state in the model are “hidden” states that exhibit
potentially distinct diversification dynamics and transition rates than
the observed states in isolation. We also demonstrate how our model can be
used as character-independent diversification models that allow for a
complex diversification process that is independent of the evolution of a
character. Under rigorous simulation tests and when applied to empirical
data, we find that HiSSE performs reasonably well, and can at least detect
net diversification rate differences between observed and hidden states
and detect when diversification rate differences do not correlate with the
observed states. We discuss the remaining issues with state-dependent
speciation and extinction models in general, and the important ways in
which HiSSE provides a more nuanced understanding of trait-dependent
diversification.