Dynamic social networks based on movement

TL;DR

This paper introduces a Bayesian hierarchical model that infers dynamic social networks among animals from minimally-invasive telemetry data, effectively capturing complex social behaviors and validated with killer whale data.

Contribution

It presents a novel framework for inferring animal social networks from movement data using a dynamic Bayesian model, reducing reliance on costly or invasive data collection methods.

Findings

Model accurately identifies complex social behaviors in simulations.

Inferred networks are consistent with known killer whale ecology.

Framework offers a minimally-invasive alternative for social network analysis.

Abstract

Network modeling techniques provide a means for quantifying social structure in populations of individuals. Data used to define social connectivity are often expensive to collect and based on case-specific, ad hoc criteria. Moreover, in applications involving animal social networks, collection of these data is often opportunistic and can be invasive. Frequently, the social network of interest for a given population is closely related to the way individuals move. Thus telemetry data, which are minimally-invasive and relatively inexpensive to collect, present an alternative source of information. We develop a framework for using telemetry data to infer social relationships among animals. To achieve this, we propose a Bayesian hierarchical model with an underlying dynamic social network controlling movement of individuals via two mechanisms: an attractive effect, and an aligning effect. We demonstrate the model and its ability to accurately identify complex social behavior in simulation, and apply our model to telemetry data arising from killer whales. Using auxiliary information about the study population, we investigate model validity and find the inferred dynamic social network is consistent with killer whale ecology and expert knowledge.