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

An organism's phenotype often relates to its ecology in a well-characterized manner, enabling prediction of ecology for taxa that lack direct ecological information, such as fossils. Diet is a particularly important component of a species' ecology; however, in order to predict diet it must first be codified, and establishing metrics that effectively summarize dietary variability without excessive information loss remains challenging. We employed a dietary item relative importance coding scheme to derive multivariate dietary classifications for a sample of extant carnivoran mammals, and then used Bayesian multilevel modeling to assess whether these scores could be predicted from a set of dental metrics. There is no ``one size fits all" model for predicting dietary item importance; different topographical features best predict different foods, and model-averaged estimates perform especially well. We also show how models derived from living taxa can be used to provide novel insights into the dietary diversity of extinct carnivoran species. Our approach need not be limited to diet as an ecological trait of interest, to these phenotypic traits, or to carnivorans. Rather, this framework serves as a general approach to predicting multivariate ecology from phenotypic traits.