Data from: Robust regression and posterior predictive simulation increase power to detect early bursts of trait evolution

A central prediction of much theory on adaptive radiations is that traits
should evolve rapidly during the early stages of a clade's history
and subsequently slowdown in rate as niches become saturated – a so-called
“Early Burst”. Although a common pattern in the fossil record, evidence
for early bursts of trait evolution in phylogenetic comparative data has
been equivocal at best. We show here that this may not necessarily be due
to the absence of this pattern in nature. Rather, commonly used methods to
infer its presence perform poorly when when the strength of the burst -
the rate at which phenotypic evolution declines - is small, and when some
morphological convergence is present within the clade. We present two
modifications to existing comparative methods that allow greater power to
detect early bursts in simulated datasets. First, we develop posterior
predictive simulation approaches and show that they outperform maximum
likelihood approaches at identifying early bursts at moderate strength.
Second, we use a robust regression procedure that allows for the
identification and down-weighting of convergent taxa, leading to moderate
increases in method performance. We demonstrate the utility and power of
these approach by investigating the evolution of body size in cetaceans.
Model fitting using maximum likelihood is equivocal with regards the mode
of cetacean body size evolution. However, posterior predictive simulation
combined with a robust node height test return low support for Brownian
motion or rate shift models, but not the early burst model. While the jury
is still out on whether early bursts are actually common in nature, our
approach will hopefully facilitate more robust testing of this hypothesis.
We advocate the adoption of similar posterior predictive approaches to
improve the fit and to assess the adequacy of macroevolutionary models in
general.