Data from: How important is it to consider lineage diversification heterogeneity in macroevolutionary studies? lessons from the lizard family Liolaemidae

Macroevolutionary and biogeographical studies commonly apply multiple
models to test state-dependent diversification. These models track the
association between states of interest along a phylogeny, although many of
them do not consider whether different clades might be evolving under
different evolutionary drivers. Yet, they are still commonly applied to
empirical studies without careful consideration of possible lineage
diversification heterogeneity along the phylogenetic tree. A recent
biogeographic study has suggested that orogenic uplift of the southern
Andes has acted as a species pump, driving diversification of the lizard
family Liolaemidae (307 described species), native to temperate southern
South America. Here, we argue against the Andean uplift as main driver of
evolution in this group. We show that there is a clear pattern of
heterogeneous diver- sification in the Liolaemidae, which biases state-
and environment-dependent analyses in, respectively, the GeoSSE and RPANDA
programs. We show here that there are two shifts to accelerated speciation
rates involving two clades that have both been classified as having
“Andean” distri- butions. We incorporated the Geographic Hidden-State
Speciation and Extinction model (GeoHiSSE) to accommo- date unrelated
diversification shifts, and also re-analyzed the data in RPANDA program
after splitting biologically distinct clades for separate analyses, as
well as including a more appropriate set of models. We demonstrate that
the “Andean uplift” hypothesis is not supported when the het- erogeneous
diversification histories among these lizards is considered. We use the
Liolaemidae as an ideal system to demonstrate potential risks of ignoring
clade-specific dif- ferences in diversification patterns in
macroevolutionary studies. We also implemented simulations to
show that, in agreement with previous findings, the HiSSE approach can
effectively and substantially reduce the level of distribution-dependent
models receiving the highest AIC weights in such scenarios. However, we
still find a relatively high rate (15%) of distribution-dependent models
receiving the highest AIC weights, and provide recommendations re- lated
to the set of models included in the analyses that reduce these rates by
half. Finally, we demonstrate that trees including clades following
different dependent-drivers affect RPANDA analyses by producing different
outcomes, ranging from partially correct models to completely mis- leading
results. We provide recommendations for the implementation of both
programs.