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DOI: http://dx.doi.org/10.7551/978-0-262-32621-6-ch054
Pages 336-343
First published 30 July 2014

Inferring Social Structure of Animal Groups From Tracking Data

Brian Hrolenok, Hanuma Teja Maddali, Michael Novitzky, Tucker Balch and Kim Wallen

Abstract (Excerpt)

Inferring the social structures of animal groups from their observed behavior is a non-trivial task usually handled by direct observation. Recent advances in sensing and tracking technology have enabled the collection of dense spatial data over long periods of time automatically. The qualitative differences between sparse hand-coded data and dense tracking data necessitate a new approach to inferring the social structure of the observed animals. We present a framework for using agent-based simulations to guide our approach to inferring social structure from tracking data collected from a small group of rhesus macaques over a period of three months. As part of this framework, we describe a version of the DOMWORLD model of dominance interactions in rhesus macaques that has been modified to include association preference, and adapted to more closely match the environment where the monkeys were housed. An exploration of simulation results reveals important characteristics of the tracking data. The inferred social structures of the tracked monkeys are also presented.