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Detecting changes in dynamic social networks using multiply-labeled movement data

2022/04/01 by Zaineb L. Boulil, John W. Durban, Boulil, Zaineb L. +9
Earth and Planetary Sciences · Engineering · Environmental Science · #Applications (stat.AP) #FOS: Computer and information sciences #Marine animal studies overview #Methodology (stat.ME) #Underwater Acoustics Research #Underwater Vehicles and Communication Systems

paper · pdf · doi:10.48550/arxiv.2204.00542

openalex publication_date 2022/04/01 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28

Abstract

The social structure of an animal population can often influence movement and inform researchers on a species' behavioral tendencies. Animal social networks can be studied through movement data; however, modern sources of data can have identification issues that result in multiply-labeled individuals. Since all available social movement models rely on unique labels, we extend an existing Bayesian hierarchical movement model in a way that makes use of a latent social network and accommodates multiply-labeled movement data (MLMD). We apply our model to drone-measured movement data from Risso's dolphins (Grampus griseus) and estimate the effects of sonar exposure on the dolphins' social structure. Our proposed framework can be applied to MLMD for various social movement applications.

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