  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.
