A novel approach to quantifying mammal locomotor repertoires using scoring and cluster analysis

Describing behaviour using qualitative categories is a staple of studies on tetrapod functional morphology, ecology and evolution. However, such categorization has several issues, primarily subjectivity and the loss of important behavioural repertoire information. Here, we propose a novel method for quantifying behaviour, using mammal locomotion as a case study to demonstrate its utility and efficacy. Species are scored from 0 to 4 on their proficiency in five locomotor modes (swimming, climbing, digging, running and aerial movement), then Ward's hierarchical clustering is used on the resulting data to group species into biologically informative categories (the number of which can be optimized using clustering validation methods), thus producing a mathematically defined categorical variable. The method is demonstrated on a dataset of 250 mammal species, representing every extant mammal family. We show that this approach successfully quantifies mammal locomotion, producing both a data matrix that can be used as a set of covariates in multivariate analyses and a categorical variable. This method introduces a replicable technique for quantifying animal behaviour and subsequently deriving a categorical variable, which is highly versatile and can be tailored to analyse a variety of other behaviours and taxonomic groups for widespread use across evolutionary and ecological research.