NDLI: Abundance and patchiness of Chrysaora quinquecirrha medusae from a high-frequency time series in the Choptank River, Chesapeake Bay, USA

Content Provider	Springer Nature Link
Author	Tay, Jacqueline Hood, Raleigh R.
Copyright Year	2016
Abstract	Despite strong control over marine plankton dynamics and negative impacts on human activities, jellyfish are not well quantified due primarily to sampling difficulties with nets. Therefore, some of the longest records of jellyfish are visual shore-based surveys. As surface counting is inexpensive and simple, it is of interest to determine what can be learned from such records as well as the usefulness of the method. We analyzed a 4-year high-frequency time series of Chrysaora quinquecirrha medusa counts collected using three sampling methods in the Choptank River, Chesapeake Bay. Medusa abundance was modeled by change points and was highly correlated between the sampling methods. The remaining signal was random, and indices of aggregation [fit to the Poisson distribution, Taylor’s Power Law (TPL), and Morisita’s Index] indicated that medusae were aggregated. TPL suggested that patches grew in the number of individuals as abundance increased. Additionally, a simple conceptualization of where the time series sampled in space revealed that the upper bound of patch size was on the order of kilometers. Our results enhance the knowledge of local C. quinquecirrha abundance and patchiness, alluding to processes that generate these patterns. This study also provides direction for improving population monitoring from visual shore-based surveys.
Starting Page	227
Ending Page	242
Page Count	16
File Format	PDF
ISSN	00188158
Journal	Hydrobiologia
Volume Number	792
Issue Number	1
e-ISSN	15735117
Language	English
Publisher	Springer International Publishing
Publisher Date	2016-12-07
Publisher Place	Cham
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Abundance Aggregation Patchiness Jellyfish Gelatinous zooplankton Time series Freshwater & Marine Ecology Ecology Zoology
Content Type	Text
Resource Type	Article
Subject	Aquatic Science

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