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Activity Number: 525
Type: Topic Contributed
Date/Time: Wednesday, August 3, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract #319615
Title: Dynamic Social Networks Based on Movement
Author(s): Henry Scharf* and Mevin Hooten and Bailey Fosdick and Devin Johnson and Josh London and John Durban
Companies: Colorado State University and Colorado State University and Colorado State University and Alaska Fisheries Science Center (NOAA) and Alaska Fisheries Science Center (NOAA) and Southwest Fisheries Science Center
Keywords: dynamic social network ; animal movement ; Orcinus orca ; hidden Markov model ; Gaussian Markov random field
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.


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