Open Access
December 2016 Dynamic social networks based on movement
Henry R. Scharf, Mevin B. Hooten, Bailey K. Fosdick, Devin S. Johnson, Josh M. London, John W. Durban
Ann. Appl. Stat. 10(4): 2182-2202 (December 2016). DOI: 10.1214/16-AOAS970


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.


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Henry R. Scharf. Mevin B. Hooten. Bailey K. Fosdick. Devin S. Johnson. Josh M. London. John W. Durban. "Dynamic social networks based on movement." Ann. Appl. Stat. 10 (4) 2182 - 2202, December 2016.


Received: 1 January 2016; Revised: 1 August 2016; Published: December 2016
First available in Project Euclid: 5 January 2017

zbMATH: 06688773
MathSciNet: MR3592053
Digital Object Identifier: 10.1214/16-AOAS970

Keywords: animal movement , Dynamic social network , Gaussian Markov random field , Hidden Markov model , Orcinus orca

Rights: Copyright © 2016 Institute of Mathematical Statistics

Vol.10 • No. 4 • December 2016
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