Computer Science professor Berger-Wolf has received a $900,000 NSF grant "Computational Methods for Understanding Social Interactions in Animal Populations". The interdisciplinary three-year project, lead by UIC, is a collaboration with Daniel Rubenstein, a behavioral ecologist from Princeton University, and Jared Saia, a computer scientist from University of New Mexico.

A zebra with a GPS collar which is used to infer social groups from spatial proximity
Computation has fundamentally changed the way we study nature. Recent breakthroughs in data collection technology, such as GPS and other mobile sensors, are giving biologists access to data about wild populations that are orders of magnitude richer than any previously collected. These data offer the promise of answering some of the big ecological questions about animal populations. Unfortunately, in this domain, our ability to analyze data lags substantially behind our ability to collect it. In particular, interactions among individuals are often modeled as social networks where nodes represent individuals and a link exists if the corresponding individuals have interacted during the observation period. The model is essentially static in that the interactions are aggregated over time and all information about the time and ordering of social interactions is discarded. Such aggregate approach lacks the expressive and computational power to answer the scientific questions.

The goal of the proposed research is to create analytical and computational tools that explicitly address the time and order of social interactions between individuals. The proposed approach combines ideas from social network analysis, Internet computing, distributed computing, and machine learning to solve problems in population biology. The diverse computational tasks of this project include design of algorithmic techniques to identify social entities such as a communities, leaders, and followers, and to use these structures to predict social response patterns to danger or disturbances. Nowhere is the impact of social structure likely to be greater than when species come in contact with predators. Thus, the accuracy and predictive power of the proposed computational tools will be tested by characterizing the social structure of horses and zebras (equids) both before and after human- or predator-induced perturbations to the social network.

Computational Population Biology Lab, UIC

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