Inferring leader-follower behavior from presence data in the marine environment: A case study on Reef Manta Rays

Social interactions are fundamental in animal groups, including humans, and can take various forms, such as competition, cooperation, or kinship. Understanding these interactions in marine environments has been historically challenging due to data collection difficulties. However, advancements in acoustic telemetry now enable the remote analysis of such behaviors. This study proposes a method to derive leader-follower networks from presence data collected by a single acoustic receiver at a specific location.Using the Kolmogorov-Smirnov distance, the method analyzes lag times between consecutive presences of individuals to infer directed relationships. Tested on simulated data, it was then applied to detection data from acoustically tagged reef manta rays (Mobula alfredi) frequenting a known site. Results revealed temporal patterns, including circadian rhythms and burst-like behavior with power-law distributed time gaps between presences.The inferred leader-follower network highlighted key behavioral patterns when compared to an appropriate random null model: females followed males more often than expected, males showed stronger but fewer associations with specific females, and smaller individuals were less consistent in following others than larger ones. These findings align with ecological insights, revealing structured social interactions and providing a novel framework for studying marine animal behavior through network theory.Author summary: Understanding social structures in animal populations is critical for advancing both ecological theory and conservation strategies. This study introduces a novel approach to infer leader-follower interactions among animals using only presence data, demonstrated through reef manta rays (Mobula alfredi). By leveraging statistical methods based on the Kolmogorov-Smirnov distance, the methodology identifies directional interactions from observed temporal patterns in presence data. The approach is validated using synthetic datasets and applied to field data, uncovering directional patterns in manta ray behavior, including sex- and size-dependent following dynamics.