Agent-based simulation for reconstructing social structure by observing collective movements with special reference to single-file movement

Abstract

Understanding social organization is fundamental for the analysis of animal societies. In this study, animal single-file movement data—serialized order movements generated by simple bottom-up rules of collective movements—are informative and effective observations for the reconstruction of animal social structures using agent-based models. For simulation, artificial 2-dimensional spatial distributions were prepared with the simple assumption of clustered structures of a group. Animals in the group are either independent or dependent agents. Independent agents distribute spatially independently each one another, while dependent agents distribute depending on the distribution of independent agents. Artificial agent spatial distributions aim to represent clustered structures of agent locations—a coupling of “core” or “keystone” subjects and “subordinate” or “follower” subjects. Collective movements were simulated following two simple rules, 1) initiators of the movement are randomly chosen, and 2) the next moving agent is always the nearest neighbor of the last moving agents, generating “single-file movement” data. Finally, social networks were visualized, and clustered structures reconstructed using a recent major social network analysis (SNA) algorithm, the Louvain algorithm, for rapid unfolding of communities in large networks. Simulations revealed possible reconstruction of clustered social structures using relatively minor observations of single-file movement, suggesting possible application of single-file movement observations for SNA use in field investigations of wild animals.

Using an agent based model to inform sampling design for animal social network analysis

“…It is possible to develop testable predictions and theoretical frameworks for studying an animal population ( van der Vaart and Verbrugge, 2008 ; Franks et al, 2008 ; Bridge et al, 2017 ). The ABM approach has practical uses in conservation and management activities, such as simulating the effects of management interventions and habitat modification on social network structure, stability, and resilience to guide decision-making processes Marshall and Duthie (2022) ; Koda et al (2020) . It can also assist forecast ecological outcomes and population-level effects of social network dynamics.…”