FSML: Expanding the Biological/Physical Sampling at Rutgers Marine Field Station at Tuckerton

Project Details

Description

Understanding the movements of fish and marine life in and out of coastal estuaries is a challenge for scientists and critical to understanding their ecology. Solving this problem is a critical issue as observations collected over the last 30 years suggests that the fisheries in the Mid- Atlantic is changing. Observations suggest the number and type of fish have been showing significant changes. The implications and reasons for the changes will require quantitative information that can resolve the dynamics of fish in nature. Traditional sampling techniques will not be able to resolve these dynamics, and as a result scientists will not be able to understand why these changes are occurring. This project will develop network that will continuously track zooplankton, fish, and marine mammals in the Mullica River-Great Bay estuary right at the interface of the ocean. This effort will collect a continuous record in time allowing us to for the first time track the animal movement in and out of the estuary. An advantage studying this estuary is one of the cleanest estuary-coastal inlets in the northeast United States allowing us to resolve dynamics in a pristine estuary.

The Rutgers University Marine Field Station (RUMFS) (http://marine.rutgers.edu/rumfs/) is located in the Mullica River−Great Bay (MRGB) estuary which is part of the Jacques Cousteau National Estuarine Research Reserve (JCNERR) in New Jersey.. The buildings, causeway, and grounds of RUMFS occupy seven acres on a peninsula close to Little Egg Inlet in southern New Jersey. Water is exchanged between the ocean and the estuary mainly through a narrow but deep (15 m) Little Egg Inlet that is directly adjacent (meters) to the RUMFS facility. Unlike most estuaries in the northeastern U.S., the surrounding area, including most of the Pine Barrens watershed, is protected from large-scale human disturbance. As a result, the MRGB estuary is probably the cleanest estuary-coastal inlets on the east coast. This makes RUMFS a unique natural laboratory. Existing RUMFS efforts have been centered around long-term fish time series sampling. These surveys have provided invaluable information about the diversity, ecology, and life histories of fishes in the region; however, the sampling is sparse in space and time relative to ecological dynamics of these systems. Although there is some information about individual species movements in and out of the estuary through tagging/passive acoustic techniques, the available information is limited on the diversity and abundance of fish moving into and out of Great Bay with sufficient resolution to link the physical conditions to the fish ecology. We propose to fill this crucial data gap by acquiring an integrated system that combines a multi-frequency combining a BioSonic multi-frequency array with a SubSea hydrophone. The system will be deployed adjacent to the field station. RUMFS sits adjacent (1.5 meters) to a deep channel (~12 meters depth) that is the major ocean entrance to JC NERR. We propose to deploy the acoustic system in the channel to track the movement of organisms in and out of the estuary. The integrated multi-frequency active/passive acoustics array will complement the one-of-a-kind plankton sampling RUMFS has been conducting on a weekly basis for 30 years. The addition of the proposed acoustic array will strengthen ongoing and newly formed graduate and undergraduate efforts by providing information that is relevant on both the local and global levels in real-time back to campus. The data will be served to a wide range of courses both at Rutgers as well as two community colleges through open access data and visualization software.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

StatusFinished
Effective start/end date9/1/19 → 8/31/21

Funding

  • National Science Foundation: $149,679.00

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