Bayesian multilevel modeling disentangles phylogenetic and ecological signal in three-dimensional geometric morphometric analysis of the bovid astragalus

Abstract By linking form and function within a particular ecological context, ecomorphology provides a method for reconstructing unknown aspects of the ecology of fossil species. To test how differences in morphology can separate among ecological categories, canonical variates analysis (CVA) is typically employed. However, whether CVA is the most appropriate method for ecomorphology is questionable, as its underlying assumptions, particularly regarding sample size, are often unmet in high-dimensional biological datasets. Moreover, CVA cannot account for the influence of phylogeny. Therefore, alternative statistical methods, which can appropriately handle limited sample sizes and phylogenetic covariance are needed to improve ecomorphological inferences. To achieve this, we employ Bayesian ordinal regression models that discriminate among habitat categories using 3D landmark data of extant African Bovidae astragali, both with and without a phylogenetic parameter. Without a phylogenetic parameter, classification success rates of each model either meet or exceed those of CVAs using the same variables. Including a phylogenetic grouping variable generates nearly perfect predictions of habitat category when species identity and phylogenetic attributions are known, but poor predictions when unknown. Although consideration of similarity due to phylogenetic covariation is critical in evolutionary studies, our results indicate that separating the effect of phylogeny from morphological variables can be detrimental to group membership prediction for taxonomically indeterminate specimens. Therefore, whether to include a phylogenetic effect is contingent upon the research question and the study system. Our work indicates that a Bayesian modeling framework is a robust tool for investigating ecomorphology, with promising future applications for paleoecological studies.