Barrier islands and the estuaries behind them house inherently valuable ecosystems, such as the beaches, marshes and waters of the Edwin B. Forsythe National Wildlife Refuge. These dynamic, ever-changing landforms protect coastlines and communities fromstorms, create habitats that are essential for wildlife, and support infrastructure and various recreational and commercial activities.  The ability of barrier island systems to recover and maintain itself in response to hurricanes like Sandy, in addition to persistent coastal change due to sea-level rise, is largely dependent on the availability of sand and the processes that govern the movement of sand along the beach and in/out of tidal inlets.

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USGS scientists are studying the evolution and behavior of the New Jersey barrier island system so they can provide managers with the data and tools necessary to reduce vulnerability and ensure that human activities and development do not interfere with the natural resiliency of the barrier island system and thus, the numerous benefits the islands provide.

In 2018, Alex Nichols, Chuck Worley, Seth Ackerman, and Walter Barnhardt of the USGS Woods Hole Coastal and Marine Science Center, along with a constant flow of visitors and help from Stockton University and various local agencies, conducted high resolution geophysical mapping of the inner continental shelf along the town of Beach Haven and in areas next to the Holgate and Little Beach Island portions of the Forsythe National Wildlife Refuge on Long Beach Island, New Jersey.

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Working off Stockton University’s research vessel (R/V Petrel), from May 8-15, 2018, the team used a multibeam-sonar system to collect data on bathymetry (sea floor depth) and backscatter across an area of 200 sq km (125 sq miles) of submerged State lands.  These data, combined with sediment samples and bottom photos,  can be used to determine the type of materials (i.e., gravel, sand, mud) that make up the seafloor.  A subbottom (seismic-reflection) system was also deployed  to reveal the underlying geologic structure and stratigraphy, or sediment layers, below the sea floor, and was run concurrently with the multibeam sonar throughout most of the survey.  All of these geophysical systems use acoustic (sound) data, measuring the travel time and intensity of sound reflected by the sea floor and the sediment layers beneath it. The survey team collected approximately 740 linear kilometers of bathymetry and backscatter data, and 700 linear kilometers of subbottom data.

From October 22-23, 2018, Wayne Baldwin and Seth Ackerman, joined by students and researchers from Stockton University, collected geological sampling data using the USGS’s mini SEABed Observation and Sampling System (miniSEABOSS) while again aboard the R/V Petrel. The miniSEABOSS is a specially designed shallow-water sampling device used to collect photos, videos, and sediment grab samples all at the same time within a stainless-steel frame that can be deployed off various research vessels. During the two-day survey, the team collected samples and photographs at 70 sites within the survey area. 

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All these data will help the research team characterize the geologic framework of the region and identify the processes controlling the evolution of the coastal system. Ultimately this will inform coastal management and decision making for improving the resilience of coastal habitat and infrastructure. Mapping will support assessments of the formation, quantity, and potential consequences of using offshore sand for beach and dune restoration projects, as well as potential storm impacts that affect barrier island and inlet stability.

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The data release was published in April 2021 and can be downloaded via https://www.sciencebase.gov/catalog/item/5ea2f05c82cefae35a192715.

Ackerman, S.D., Barnhardt, W.A., Worley, C.R., Nichols, A.R., Baldwin, W.E., and Evert, S., 2021, High-resolution geophysical and geological data collected in Little Egg Inlet and offshore the southern end of Long Beach Island, NJ, during USGS Field Activities 2018-001-FA and 2018-049-FA: U.S. Geological Survey data release, https://doi.org/10.5066/P9C3J33K.

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