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

Author

Listed:
  • Zaineb L. Boulil

    (San Diego State University)

  • John W. Durban

    (Southall Environmental Associates)

  • Holly Fearnbach

    (SR3 SeaLife Response, Rehabilitation and Research)

  • Trevor W. Joyce

    (Environmental Assessment Services)

  • Samantha G. M. Leander

    (Southall Environmental Associates)

  • Henry R. Scharf

    (San Diego State University)

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. Supplementary materials accompanying this paper appear online.

Suggested Citation

  • Zaineb L. Boulil & John W. Durban & Holly Fearnbach & Trevor W. Joyce & Samantha G. M. Leander & Henry R. Scharf, 2023. "Detecting Changes in Dynamic Social Networks Using Multiply-Labeled Movement Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(2), pages 243-259, June.
    • Handle: RePEc:spr:jagbes:v:28:y:2023:i:2:d:10.1007_s13253-022-00522-1
    DOI: 10.1007/s13253-022-00522-1
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    References listed on IDEAS

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    1. Henry R. Scharf & Mevin B. Hooten & Devin S. Johnson & John W. Durban, 2018. "Process convolution approaches for modeling interacting trajectories," Environmetrics, John Wiley & Sons, Ltd., vol. 29(3), May.
    2. Iain D. Couzin & Jens Krause & Nigel R. Franks & Simon A. Levin, 2005. "Effective leadership and decision-making in animal groups on the move," Nature, Nature, vol. 433(7025), pages 513-516, February.
    3. P. G. Blackwell, 2003. "Bayesian inference for Markov processes with diffusion and discrete components," Biometrika, Biometrika Trust, vol. 90(3), pages 613-627, September.
    4. Mu Niu & Paul G. Blackwell & Anna Skarin, 2016. "Modeling interdependent animal movement in continuous time," Biometrics, The International Biometric Society, vol. 72(2), pages 315-324, June.
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