MEPS 329:99-113 (2007)  -  doi:10.3354/meps329099

Predicting the distribution of the scyphomedusa Chrysaora quinquecirrha in Chesapeake Bay

M. B. Decker1,*, C. W. Brown2, R. R. Hood3, J. E. Purcell4, T. F. Gross5, J. C. Matanoski3,6, R. O. Bannon7, E. M. Setzler-Hamilton8,†

1Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut 06520, USA
2National Oceanic and Atmospheric Administration, Cooperative Institute of Climate Studies, University of Maryland, College Park, Maryland 20742-2465, USA
3Horn Point Laboratory, University of Maryland Center for Environmental Science, PO Box 775, Cambridge, Maryland 21613, USA
4Shannon Point Marine Center, 1900 Shannon Point Road, Anacortes, Washington 98221, USA
5Chesapeake Bay Research Consortium, 645 Contees Wharf Rd., Edgewater, Maryland 21037, USA
6New Mexico State University-Alamogordo, 2400 North Scenic Drive, Alamogordo, New Mexico 88310, USA
7University of Rhode Island, Graduate School of Oceanography, Box 200, South Ferry Road, Narragansett, Rhode Island 02882, USA
8Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science, PO Box 38, Solomons, Maryland 20688, USA
*Email: . †Deceased

ABSTRACT: Jellyfish blooms are important events controlling plankton dynamics in coastal waters worldwide, yet factors that influence bloom development are not well understood. We used the scyphomedusa Chrysaora quinquecirrha as a model to examine physical factors that control jellyfish populations and to develop an ecological forecasting system. Over 700 in situ observations collected from Chesapeake Bay and its tributaries during 1987–2000 were used to develop habitat models that predict the probability of occurrence and the likely concentration of medusae as a function of sea-surface temperature and salinity. Medusae were found within a relatively narrow range of temperature (26 to 30°C) and salinity (10 to 16). Regression analyses reveal that a combination of temperature and salinity is a significant predictor of medusa occurrence. Assessments of the predictive performance of these models using medusae and environmental data collected at independent survey sites (n = 354) indicated that model-predicted medusa occurrence and concentration correspond well with observations. Our models can be forced with near-real time and retrospective estimates of temperature and salinity to generate probability of occurrence maps of C. quinquecirrha medusa presence and abundance in order to better understand how this top predator varies in space and time, and how this species could potentially affect energy flow through the Chesapeake Bay system.

KEY WORDS: Gelatinous zooplankton · Scyphozoa · Jellyfish · Temperature · Salinity · Climate · Predictive model · Forecasting · Nowcasting

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