Data and Scripts from: Bayesian prediction of multivariate ecology from phenotypic data yields novel insights into the diets of extant and extinct taxa

Morphology often reflects ecology, enabling the prediction of ecological
roles for taxa that lack direct observations such as fossils. In
comparative analyses, ecological traits, like diet, are often treated as
categorical, which may aid prediction and simplify analyses but ignores
the multivariate nature of ecological niches. Futhermore, methods for
quantifying and predicting multivariate ecology remain rare. Here, we
ranked the relative importance of 13 food items for a sample of 88 extant
carnivoran mammals, and then used Bayesian multilevel modeling to assess
whether those rankings could be predicted from dental morphology and body
size. Traditional diet categories fail to capture the true multivariate
nature of carnivoran diets, but Bayesian regression models derived from
living taxa have good predictive accuracy for importance ranks. Using our
models to predict the importance of individual food items, the
multivariate dietary niche, and the nearest extant analogs for a set of
data-deficient extant and extinct carnivoran species confirms
long-standing ideas for some taxa, but yields new insights about the
fundamental dietary niches of others. Our approach provides a promising
alternative to traditional dietary classifications. Importantly, this
approach need not be limited to diet, but serves as a general framework
for predicting multivariate ecology from phenotypic traits.