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.

The dataset contains dental topographic metrics (aDNE, OPCr, RFI, and RLGA) and body mass estimates for 113 living and extinct carnivorans (Order Carnivora). It also contains ordinal dietary rankings (1-4, with 1 being none in the diet and 4 being a primary food item) for 13 food items for 88 of the extant species. We use these dietary rankings to generate predictive models of the multivariate diet, then estimate the multivariate diets for fossil and understudied extant taxa. Running the models yields outputs which are then used to generate predictions. The data also contains a phylogenetic tree of Carnivora used in the models.

The Dryad repository contains all of the input and output data from this project. The README.md file details each of the data objects (mostly csv files). As each of the outputs is called as an input in downstream analyses, we included these.

The Zenodo repo linked to this Dryad repo contains the entirety of the GitHub repo for this project. It is written in such a way where, once all the packages have been installed, it can be downloaded and run in its entirety to reproduce all of the results from this project. It contains the scripts, input data, plotting commands, and its own highly detailed README.md file.

The Zenodo repo also contains the Supplemental Figures in .pdf format.