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Dynamic social networks based on movement

November 1, 2016

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.

Publication Year 2016
Title Dynamic social networks based on movement
DOI 10.1214/16-AOAS970
Authors Henry Scharf, Mevin Hooten, Bailey K. Fosdick, Devin S. Johnson, Joshua M. London, John W. Durban
Publication Type Article
Publication Subtype Journal Article
Series Title The Annals of Applied Statistics
Index ID 70192622
Record Source USGS Publications Warehouse
USGS Organization Coop Res Unit Seattle