Inferring Leader-Follower Behavior from Presence Data in the Marine Environment: A Case Study on Reef Manta Rays

TL;DR

This paper introduces a novel method to infer leader-follower relationships among marine animals using presence data from acoustic telemetry, revealing structured social behaviors in reef manta rays and advancing marine behavioral analysis.

Contribution

The study presents a new approach to derive directed social networks from presence data, enabling analysis of marine animal interactions with minimal data collection.

Findings

Revealed circadian and burst-like activity patterns in reef manta rays.

Identified gender-based differences in following behavior.

Demonstrated the method's effectiveness on real and simulated data.

Abstract

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 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 (\textit{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: females followed males more often than expected, males showed stronger but fewer associations with specific females, and smaller individuals followed others less consistently 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.