Detecting Changes in Dynamic Social Networks Using Multiply-Labeled Movement Data

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