Data from: Detecting the dependence of diversification on multiple traits from phylogenetic trees and trait data

Species diversification may be determined by many different variables,
including the traits of the diversifying lineages. The State-dependent
Speciation and Extinction (SSE) framework contains methods to detect the
dependence of diversification on these traits. For the analysis of traits
with multiple states, MuSSE (Multiple-States dependent Speciation and
Extinction) was developed. However, MuSSE and other state-dependent
speciation and extinction models have been shown to yield false positives,
because they cannot separate differential diversification rates from
dependence of diversification on the observed traits. The recently
introduced method HiSSE (Hidden- State dependent Speciation and
Extinction) resolves this problem by allowing a hidden state to affect
diversification rates. Unfortunately, HiSSE does not allow traits with
more than two states, and, perhaps more interestingly, the simultaneous
action of multiple traits on diversification. Here, we introduce an R
package (SecSSE: Several examined and concealed States-dependent
Speciation and Extinction) that combines the features of HiSSE and MuSSE
to simultaneously infer state-dependent diversification across two or more
examined (observed) traits or states while accounting for the role of a
possible concealed (hidden) trait. Moreover, SecSSE also has improved
functionality compared to its two 'parents'. First, it allows
for an observed trait being in two or more states simultaneously, which is
useful for example when a taxon is a generalist or when the exact state is
not precisely known. Second, it provides the correct likelihood when
conditioned on non-extinction, which has been incorrectly implemented in
HiSSE and other SSE models. To illustrate our method we apply SecSSE to 7
previous studies that used MuSSE, and find that in 5 out of 7 cases, the
conclusions drawn based on MuSSE were premature. We test with simulations
whether SecSSE sacrifices statistical power to avoid the high type I error
problem of MuSSE, but we find that this is not the case: for the majority
of simulations where the observed traits affect diversification, SecSSE
detects this.