Joint spatiotemporal modelling of zooplankton and whale abundance in a dynamic marine environmentopen access
- Authors
- Kang, Bokgyeong; Schliep, Erin M.; Gelfand, Alan E.; Clark, Christopher W.; Hudak, Christine A.; Mayo, Charles A.; Schosberg, Ryan; Yack, Tina M.; Schick, Robert S.
- Issue Date
- Jan-2026
- Publisher
- Oxford University Press
- Keywords
- data fusion; geostatistical model; hierarchical model; joint species distribution; measurement error; point pattern data
- Citation
- Journal of the Royal Statistical Society Series C: Applied Statistics, v.75, no.1, pp 120 - 138
- Pages
- 19
- Indexed
- SCIE
SCOPUS
- Journal Title
- Journal of the Royal Statistical Society Series C: Applied Statistics
- Volume
- 75
- Number
- 1
- Start Page
- 120
- End Page
- 138
- URI
- https://scholarworks.dongguk.edu/handle/sw.dongguk/58951
- DOI
- 10.1093/jrsssc/qlaf038
- ISSN
- 0035-9254
1467-9876
- Abstract
- North Atlantic right whales are an endangered species. Their entire population is estimated to be approximately 372 individuals, and they are subject to major anthropogenic threats. They feed on zooplankton species whose distribution shifts in a dynamic and warming oceanic environment. Because right whales in turn follow their shifting food resource, it is necessary to jointly study the distribution of whales and their prey. The innovative joint species distribution modelling (JSDM) contribution here is different from anything in the large JDSM literature, reflecting the processes and data we have to work with. Specifically, our JSDM supplies a geostatistical model for the expected amount of zooplankton collected at a site. We require a point pattern model for the intensity of right whale abundance. The two process models are linked through a latent conditional-marginal specification. Furthermore, each species has two data sources informing its respective distribution, necessitating a novel data fusion approach. The result is a complex multi-level model. Through simulation, we demonstrate that our joint specification effectively identifies model unknowns and improves the estimation of species distributions compared to modelling them separately. We then apply our model to real data from Cape Cod Bay, Massachusetts, USA.
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- Appears in
Collections - College of Natural Science > Department of Statistics > 1. Journal Articles

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