Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Joint spatiotemporal modelling of zooplankton and whale abundance in a dynamic marine environmentopen access

Authors
Kang, BokgyeongSchliep, Erin M.Gelfand, Alan E.Clark, Christopher W.Hudak, Christine A.Mayo, Charles A.Schosberg, RyanYack, 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.
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Natural Science > Department of Statistics > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Kang, Bok Gyeong photo

Kang, Bok Gyeong
College of Natural Science (Department of Statistics)
Read more

Altmetrics

Total Views & Downloads

BROWSE