Using Machine Learning to Identify Associations between the Environment, Occurrence, and Outcomes of Songbird Displacements at Supplemental Feeders

Abstract

The context and outcome of aggressive interactions between individuals has important fitness consequences. Displacements—an aggressive interaction wherein one individual is chased from a location by another—also have implications for social hierarchy formation and geographic distribution in songbirds. Morphological correlates, like body size, and social correlates, such as dominance rank, have been shown to mediate displacements in songbirds. However, the role of the physical environment, namely temperature, humidity, and time of day, which may influence an individual’s energy needs and thus displacement motivation, has remained understudied. We monitored songbird feeding and displacement behaviors using computerized automated feeders. We observed asymmetric differences across species in displacement involvement. To identify the conditions of the social and physical environment that are associated with the occurrence and outcome of songbird displacements at supplemental feeders, we use the machine learning approach, random forest, which is a novel method to the fields of ornithology and animal behavior. From our random forest models, we found that the attributes of the physical environment (i.e., humidity and the time of day) are associated with the occurrence of a displacement event, whereas the attributes of the social environment (i.e., species of the displacer and displaced individuals) are associated with which species are involved. These results provide context to develop further observational and experimental hypotheses to tease apart the inner workings of these multifactorial behaviors on a larger scale and provide a proof of concept for our analytical methods in the study of avian behavior.