| Abstrakt: |
Simple Summary: Animals interact with their environment via a wide range of behaviors. Thus, exploring the factors that influence the occurrence and outcome of these consequential behaviors is important to understanding how animals interact and are affected by the world around them. Displacements—an aggressive behavior wherein one individual is chased from a resource by another—have implications for social hierarchies and geographic distribution in songbirds. At bird feeders, factors like body size and dominance rank have been shown to mediate these displacement behaviors. However, the role of the physical environment, namely temperature, humidity, and time of day, which may influence an individual's energy needs and thus motivation to displace another individual, has remained understudied. We monitored songbird displacement behaviors using computerized bird feeders, which recorded who ate at the feeder, when, and under what environmental conditions. With these data, we used a machine learning algorithm to identify what social and environmental factors predict the occurrence and outcome of songbird displacement events. We found that the physical environment (i.e., humidity and the time of day) is associated with the occurrence of a displacement event, whereas the social environment (i.e., who's displacing and being displaced) is associated with who's involved in a displacement event. 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. [ABSTRACT FROM AUTHOR] |
