The Annals of Applied Statistics
- Ann. Appl. Stat.
- Volume 10, Number 4 (2016), 2182-2202.
Dynamic social networks based on movement
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.
Ann. Appl. Stat., Volume 10, Number 4 (2016), 2182-2202.
Received: January 2016
Revised: August 2016
First available in Project Euclid: 5 January 2017
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Scharf, Henry R.; Hooten, Mevin B.; Fosdick, Bailey K.; Johnson, Devin S.; London, Josh M.; Durban, John W. Dynamic social networks based on movement. Ann. Appl. Stat. 10 (2016), no. 4, 2182--2202. doi:10.1214/16-AOAS970. https://projecteuclid.org/euclid.aoas/1483606856
- Supplement A: MCMC details. Priors and full-conditionals for the model are presented.
- Supplement B: Animation. Animation of killer whales.
- Supplement C: R code. Code used for simulation in Section 3.