A multi-scale approach to studying interspecies interactions in animal collectives

Coordinated motion in animals often results in mesmerizing patterns and plays a key role in predator avoidance or disease transmission. To study collective movement, we can now use advanced image-based analysis software and algorithms and quantify behavior in greater detail than ever before. In my research, I use computer vision and machine learning algorithms to analyse phenomena at different scales, from detailed individual behavior to emergent collective patterns. Starting in the lab, during my PhD I developed experiments and quantitative behavioral analyses to investigate how parasitism affects the individual and social behavior of guppies. However, to understand how the environment shapes behavior and how behavior affects individual fitness, we need to study animals within their natural habitats. Therefore, for my postdoctoral research, I am using drone imaging to analyze collective predator-prey interactions of blacktip reef sharks and bait fish schools in their natural habitat. This approach allows me to explore questions about coordinated intra- and inter-species hunting behavior, as well as collective evasion strategies of the schooling prey. With this talk, I will share insights from my work on how I use imaging technologies to bridge the gap between laboratory studies and field observations. By integrating detailed behavioral analyses at both individual and collective levels, we can gain a deeper understanding of the complex dynamics that govern animal behavior.