ABSTRACT: The Bering Sea ecosystem has experienced significant climatic and biological shifts over the past 3 decades, including temporal and spatial fluctuations of jellyfish biomass. Jellyfish are important predators and competitors of fish; thus, it is critical to understand the effects of environmental factors on their population dynamics. We explored the effects of ocean bottom temperatures and circulation on jellyfish populations using a non-parametric, nonlinear multiple time series analysis of a 23 yr dataset. The study area was divided into 3 subregions that reflected distinct jellyfish catches and distributions. Aggregations and the influence of temperature and circulation on jellyfish biomass were found to differ in each of the 3 subregions. The northern region biomass was affected by central biomass, mediated by the strength of advection from the central region. In both the northern and central regions, current-year biomass was associated with lag-1 biomass, but was mediated by local bottom temperatures (colder temperatures strengthened the relationship with lag-1 biomass). However, in the central region, this relationship held only for the period after 1997. Prior to 1997, advection from the southern region drove central region biomass, suggesting that the primary source of jellyfish biomass to the central eastern Bering Sea shelf changed, coming from the southern shelf before 1997 and from the central shelf after 1997. The southern jellyfish biomass was affected by only the lag-1 southern jellyfish biomass. Transport from the south may have seeded the central region in the early 1990s, but once established, jellyfish polyp populations near islands in the central region may have supplied the area with medusae in the late 1990s.
KEY WORDS: Scyphomedusae · Biophysical conditions · Climate change · Time series
Full text in pdf format Supplementary material | Cite this article as: Decker MB, Liu H, Ciannelli L, Ladd C, Cheng W, Chan KS
(2013) Linking changes in eastern Bering Sea jellyfish populations to environmental factors via nonlinear time series models. Mar Ecol Prog Ser 494:179-189. https://doi.org/10.3354/meps10545
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