Analyzing Whale Calling through Hawkes Process Modeling
- Authors
- Kang, Bokgyeong; Schliep, Erin M.; Gelfand, Alan E.; Yack, Tina M.; Clark, Christopher W.; Schick, Robert S.
- Issue Date
- Oct-2025
- Publisher
- Taylor & Francis
- Keywords
- Gaussian process; North Atlantic right whales; Random time change theorem; Spatial process; Temporal point patterns
- Citation
- Journal of the American Statistical Association, v.120, no.552, pp 2040 - 2052
- Pages
- 13
- Indexed
- SCIE
SCOPUS
- Journal Title
- Journal of the American Statistical Association
- Volume
- 120
- Number
- 552
- Start Page
- 2040
- End Page
- 2052
- URI
- https://scholarworks.dongguk.edu/handle/sw.dongguk/58679
- DOI
- 10.1080/01621459.2025.2501711
- ISSN
- 0162-1459
1537-274X
- Abstract
- Sound is assumed to be the primary modality of communication among marine mammal species. Analyzing acoustic recordings helps to understand the function of the acoustic signals as well as the possible impact of anthropogenic noise on acoustic behavior. Motivated by a dataset from a network of hydrophones in Cape Cod Bay, Massachusetts, using automatically detected calls in recordings, we study the communication process of the endangered North Atlantic right whale. For right whales, an "upcall" is a known signal used to facilitate communication among individuals. We present novel spatiotemporal excitement modeling consisting of a background process and an excitement process. The background process incorporates the influences of diel patterns and ambient noise on contact calls-initial vocalizations aimed at initiating contact. The excitement process captures potential countercalls-responses excited by previous upcalls. Upcall incidence is found to be clustered in space and time; an upcall seems to excite more upcalls nearer to it in time and space. We find evidence that whales make more upcalls at night, respond to other whales nearby, and are likely to remain quiet in the presence of increased ambient noise. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
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Collections - College of Natural Science > Department of Statistics > 1. Journal Articles

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