Final Report OCS Study 2000-055

August 2000

Authors:

G. R. Cutter, Jr.

R. J. Diaz

John A. Musick

John Olney, Sr.

Donna M. Bilkovic

Jerome P.-Y. Maa

Sung-Chan Kim

C. Scott Hardaway, Jr.

D. A. Milligan

Rebecca Brindley

Carl H. Hobbs, III

Virginia Institute of Marine Science

Project Manager:

Carl H. Hobbs, III

Virginia Institute of Marine Science

Prepared under MMS contract 1435-01-97-CT-30853 to the

Virginia Institute of Marine Science

College of William & Mary

International Activities and Marine Minerals Division

Environmental Survey of Potential Sand Resource Sites

Offshore Delaware and Maryland 
DISCLAIMER

This report has been reviewed by the Minerals Management Service and approved for

publication. Approval does not signify that the contents necessarily reflect the views and

policies of the Service, nor does mention of trade names or commercial products constitute

endorsement or recommendation for use.
Final Report

Environmental Survey of Potential Sand Resource Sites

By

G. R. Cutter, Jr.

R. J. Diaz

John A. Musick

John Olney, Sr.

Donna M. Bilkovic

Jerome P.-Y. Maa

Sung-Chan Kim

C. Scott Hardaway, Jr.

D. A. Milligan

Rebecca Brindley

Carl H. Hobbs, III

Virginia Institute of Marine Science

Carl H. Hobbs, III, Project Manger

Virginia Institute of Marine Science 

for 

U. S. Department of the Interior

Minerals Management Service

International Activities and Marine Minerals Division

Contract 1435-01-97-CT-30853

OCS Study 2000-055

August 2000
CONTENTS

Technical Summary

Non-Technical Summary

Part 1: Benthic Habitat Mapping and Resource Evaluation of Potential Sand Mining Sites Offshore

Delaware and Maryland

Part 2: Transitory Species (Vertebrate Nekton)

Part 3: Literature Survey of Reproductive Finfish and Ichthyoplankton Present in Proposed Sand

Mining Locations

Part 4: Potential Modifications to Waves Due to Dredging and Other Oceanographic Considerations

Part 5: Maryland-Delaware Shoreline: Long Term Trends versus Short Term Variability
Final Report OCS Study 2000-055

August 2000

Project Manager:

Carl H. Hobbs, III

Virginia Institute of Marine Science

Prepared under MMS contract 1435-01-97-CT-30853 to

the

Virginia Institute of Marine Science

College of William & Mary

International Activities and Marine Minerals Division

Environmental Survey of Potential Sand Resource Sites

Offshore Delaware and Maryland 

Technical Summary
DISCLAIMER

This report has been reviewed by the Minerals Management Service and approved for

publication. Approval does not signify that the contents necessarily reflect the views and

policies of the Service, nor does mention of trade names or commercial products constitute

endorsement or recommendation for use.
Final Report

Environmental Survey of Potential Sand Resource Sites

Technical Summary

Carl H. Hobbs, III, Project Manger

Virginia Institute of Marine Science 

for 

U. S. Department of the Interior

Minerals Management Service

International Activities and Marine Minerals Division

Contract 1435-01-97-CT-30853

OCS Study 2000-055

August 2000
OCS Study: 2000-055

STUDY TITLE: Environmental Survey of Potential Sand Resource Sites Offshore Delaware and Maryland

REPORT TITLE: Environmental Survey of Potential Sand Resource Sites Offshore Delaware and Maryland

CONTRACT NUMBER: MMS 1435-01-97-CT-30853

SPONSORING OCS REGION: International Activities and Marine Minerals Division

APPLICABLE PLANNING AREA: N/A

FISCAL YEARS OF PROJECT FUNDING: 1997, 1998, 1999, 2000

COMPLETION DATE OF REPORT: August 2000

COSTS: FY 1997: $ 4,635, FY 1998: $ 173,482, FY 1999: $ 169,239, FY 2000: $ 36,885

Cumulative Cost: $ 384,241 

PROJECT MANAGER: Carl H. Hobbs, III

AFFILIATION: Virginia Institute of Marine Science, College of William & Mary

ADDRESS: P. O. Box 1346, Gloucester Point, Virginia, 23062

PRINCIPAL INVESTIGATORS: G. R. Cutter, Jr., R. J. Diaz, J. A. Musick, J. Olney, Sr., D. M. Bilkovic,

J. P.-Y. Maa, S. C. Kim, C. S. Hardaway, Jr., D. A. Milligan, R. Brindley, and C.H. Hobbs, III

KEY WORDS: Maryland, Delaware, continental shelf, sand mining, benthic habitats, fisheries, wave

refraction, storm surge, beach nourishment, shoreline, erosion, sand, shoreline.

BACKGROUND: The shore between Ocean City Inlet, Maryland and Cape Henlopen, Delaware is a

burgeoning recreational area that experiences chronic shoreline erosion and retreat. In order to maintain

the economic viability of the region and to limit losses to physical processes various governmental

agencies, local, state, and federal, have taken actions to stabilize the shoreline. Paramount among these

is beach nourishment. Increasingly the sand used in the nourishment projects is likely to be taken from

beneath federal waters. Over the next two decades, the demand for sand from the continental shelf

offshore of Maryland and Delaware could be on the order of ten to twenty millions of cubic meters. 

Consequently the U.S. Department of the Interior, Minerals Management Service initiated the

Environmental Survey of Potential Sand Resource Sites Offshore Delaware and Maryland in order to

assess potential physical and biological environmental consequences of mining sand.

OBJECTIVES: To examine the present conditions of transitory vertebrate nekton including fishes, sea

turtles, and marine mammals, of reproductive finfish and icthyoplankton, benthic fauna and infauna, of

physical oceanographic phenomena such as waves, currents, and storm surge, and of shoreline stability; 

to develop interpretations of how offshore sand mining might affect those conditions; and, where

reasonable, to issue recommendations as to how potential adverse consequences of sand mining could be

minimized.

DESCRIPTION: The study area includes the inner continental shelf generally out to approximately the 20

m isobath and shore of the region between Cape Henlopen, Delaware and Ocean City Inlet, Maryland. The

study was conducted and is presented in five parts: 1) benthic habitat mapping and resource evaluation of

potential sand mining areas, 2) a review of transitory species of fishes, sea turtles, and marine mammals
that visit the study area, 3) a review of reproductive finfish and icthyoplankton that utilize the area, 4) a

study of the potential modifications to waves due to dredging and other oceanographic considerations, and

5) an analysis of the long term trends and short term variability of the Maryland-Delaware shoreline. Part 1

required a substantial field effort including two cruises with collection and subsequent analysis of myriad

samples and photographic or similar images of the bottom. Parts 2 and 3 primarily were reviews of the

appropriate literature. Part 4 involved acquisition and analysis of historical wave and current information,

reformatting of existing bathymetric data, and substantial computer modeling and analysis. Part 5 is a

synthesis and interpretation of a varied suite of generally unpublished data.

SIGNIFICANT CONCLUSIONS: Although there are potentially adverse consequences to sand mining in

the offshore regions of Delaware and Maryland, they likely are not substantial and actions can be taken to

eliminate or minimize them. Obviously dredging the bottom destroys all the organisms that had lived within

the dredged area, but the best sands for beach nourishment have a comparatively low resource value. The

benthic fauna of those areas are likely to recolonize fairly rapidly especially if small “islands” are left

untouched within the otherwise dredged area. Care should be taken to minimize disturbance of the

substrate between the shoals that will be the targets for dredging. The very small size of the areas likely

to be dredged relative to the large geographic ranges of transitory fishes indicates that sand mining would

have very little impact on the fish populations. The species occurrence of fishes in spawning, egg, and

larvae stages is least from October through March and peak in the late spring and summer. The potential

threat to sea turtles can be avoided by mining from mid-November to mid-April when these sub-tropical

animals are absent from the area. Sand mining poses no foreseeable threat to the migratory and highly

mobile marine mammals. Analysis of existing wave conditions demonstrates that modern shoreline

stability is related to areas of concentration and dispersion of wave energy near the zone of breaking

waves. The relatively stable area around the Maryland-Delaware border is one of relatively low waves

whereas the various erosional “hot spots,” especially along Fenwick Island, appear coincident with zones

of wave energy concentration. Wave transformation modeling indicates that removal of 106

 m3 of sand

from the top of Fenwick and Isle of Wight Shoals will result in very small changes from present conditions. 

Removal of 107

 m3 might cause more noticeable changes in the regions between the dredged areas and

the shore. Modeling also predicts that dredging will have an extremely small impact on ambient tidal

currents and potential storm surges. The Maryland-Delaware shore is experiencing increasing pressure

from expanding recreational and residential uses and the associated commercial developments. The form

of the shoreline results from interactions amongst the local geology and stratigraphy, the history of

Holocene sea-level rise, and the contemporary wave climate. Although rising sea level drives a general

marine transgression/shoreline retreat through the area, the rate of retreat and apparent local stability vary

along the shore. Shoreline engineering, most noticeably sand bypassing at Indian River Inlet and repetitive

beach nourishment at several sites, has been employed to control shoreline retreat and enhance the

recreational value and use of the beach. The cumulative impact of the many beach nourishment projects

that already have been performed appears to be more beneficial than any individual project.

STUDY RESULTS: See Significant Conclusions, above.

STUDY PRODUCTS: Virginia Institute of Marine Science, 2000. Environmental Survey of Potential Sand

Resource Sites Offshore Delaware and Maryland. Final Report to the U.S. Department of the Interior,

Minerals Management Service, International Activities and Marine Minerals Division, Herndon, VA. 

Contract No. 1435-01-97-CT-30853. Printed Copy and CD.

The Department of the Interior

As the Nations’s principal conservation agency, the Department of the Interior

has responsibility for most of our nationally owned public lands and natural

resources. This includes fostering sound use of our land and water resources,

protecting our fish, wildlife, and biological diversity, preserving the

environmental and cultural values of our national parks and historic places; and

providing for the enjoyment of life through outdoor recreation. The Department

assesses our energy and mineral resources and works to ensure that their

development is in the best interests of all our people by encouraging stewardship

and citizen participation in their care. The Department also has a major

responsibility for American Indian reservation communities and for people who

live in island territories under U.S. Administration.

The Minerals Management Service Mission

As a bureau of the Department of the Interior, the Minerals Management

Service’s (MMS) primary responsibilities are to manage the mineral resources

located on the Nations’s Outer Continental Shelf (OCS), collect revenue from the

Federal OCS and onshore federal and Indian lands, and distribute those revenues.

Moreover, in working to meet its responsibilities, the Offshore Minerals

Management Program administers the OCS competitive leasing program and

oversees the safe and environmentally sound exploration and production of our

Nation’s offshore natural gas, oil and other mineral resources. The MMS Royalty

Management Program meets its responsibilities by entrusting the efficient, timely

and accurate collection and distribution of revenue from mineral leasing and

production due to Indian tribes and allottees, States and the U. S. Treasury

The MMS strives to fulfill its responsibilities through the general guiding principles of: (1)

being responsive to the public’s concerns and interests by maintaining a dialog with all

potentially affected parties and (2) carrying out its programs with an emphasis on working to

enhance the quality of life for all Americans by lending MMS assistance and expertise to

economic development and environmental protection.
Final Report OCS Study 2000-055

August 2000

Project Manager:

Carl H. Hobbs, III

Virginia Institute of Marine Science

Prepared under MMS contract 1435-01-97-CT-30853 to

the

Virginia Institute of Marine Science

College of William & Mary

International Activities and Marine Minerals Division

Environmental Survey of Potential Sand Resource Sites

Offshore Delaware and Maryland 

Non-Technical Summary
DISCLAIMER

This report has been reviewed by the Minerals Management Service and approved for

publication. Approval does not signify that the contents necessarily reflect the views and

policies of the Service, nor does mention of trade names or commercial products constitute

endorsement or recommendation for use.
Final Report

Environmental Survey of Potential Sand Resource Sites

Offshore Delaware and Maryland

Non-Technical Summary

Carl H. Hobbs, III, Project Manger

Virginia Institute of Marine Science 

for 

U. S. Department of the Interior

Minerals Management Service

International Activities and Marine Minerals Division

Contract 1435-01-97-CT-30853

OCS Study 2000-055

August 2000
1

BACKGROUND

The shore between Ocean City Inlet, Maryland and Cape Henlopen, Delaware is a burgeoning

recreational area that experiences chronic shoreline erosion and retreat. In order to maintain the

economic viability of the region and to limit losses to physical processes, various governmental

agencies, local, state, and federal, have taken actions to stabilize the shoreline. Paramount among these

is beach nourishment. Increasingly the sand used in the nourishment projects is likely to be taken from

beneath federal waters, i.e. from more than 3 nautical miles offshore . Over the next two decades, the

demand for sand from the continental shelf offshore of Maryland and Delaware could be on the order

of ten to twenty millions of cubic meters. Consequently the U.S. Department of the Interior, Minerals

Management Service initiated the Environmental Survey of Potential Sand Resource Sites Offshore

Delaware and Maryland in order to assess potential physical and biological environmental

consequences of mining sand. Three shoals, Fenwick, Weaver, and Isle of Wight, roughly 5 km

offshore are considered likely sights for future sand mining.

OBJECTIVES

To examine the present conditions of transitory vertebrate nekton including fishes, sea turtles,

and marine mammals, of reproductive finfish and icthyoplankton, benthic fauna and infauna, of physical

oceanographic phenomena such as waves, currents, and storm surge, and of shoreline stability; to

develop interpretations of how offshore sand mining might affect those conditions; and, where

reasonable, to issue recommendations as to how potential adverse consequences of sand mining could

be minimized.

DESCRIPTION

The study area includes the inner continental shelf generally out to approximately the 20 m

isobath and shore of the region between Cape Henlopen, Delaware and Ocean City Inlet, Maryland. 

The study was conducted and is presented in five parts: 1) benthic habitat mapping and resource

evaluation of potential sand mining areas, 2) a review of transitory species of fishes, sea turtles, and
2

marine mammals that visit the study area, 3) a review of reproductive finfish and icthyoplankton that

utilize the area, 4) a study of the potential modifications to waves due to dredging and other

oceanographic considerations, and 5) an analysis of the long term trends and short term variability of

the Maryland-Delaware shoreline. Part 1 required a substantial field effort including two cruises with

collection and subsequent analysis of myriad samples and photographic or similar images of the bottom. 

Parts 2 and 3 primarily were reviews of the appropriate literature. Part 4 involved acquisition and

analysis of historical wave and current information, reformatting of existing bathymetric data, and

substantial computer modeling and analysis. Part 5 is a synthesis and interpretation of a varied suite of

generally unpublished data.

Part 1 presents an assessment of the existing community structures, spatial distributions,

substrate dependencies, productivity, and trophic linkages in order to anticipate the consequences of

sandmining upon the biological resources of the area. These subjects should be considered with

respect to the scales and magnitudes of normal environmental stressors and the potential for

interference with these dynamics. 

The primary data on which to base this set of studies were obtained during the course of a

research cruise in 1998 and a second cruise in 1999. Instruments used on either or both cruises inclued

a standard “Young” grab with a 0.044 m2 surface area for sediment samples, a Hulcher model Minnie

Sediment profile camera (SPI), a standard bottom imaging sled which carried video cameras and water

quality sensors, a Burrow-Cutter-Diaz Plowing Sediment Profile Camera System, a 600kHz high

resolution side-scan sonar, and a 2.4 m (8 ft) beam trawl to collect juvenile fish, epibenthos, and

macrobenthos. In addition to analyses of the samples and images, the data were coded for display in a

Geographic Information System (GIS).

Calculated indices reflect that the quality of the benthic habitats is relatively low on the shoals

and relatively high in the valleys between the shoals, although the distribution of microhabitats is more

complex than suggested by that simple statement. Biological associations with individual microhabitats

are functions of substrate (primarily grain size distribution) and energy regime. The characteristics of
3

specific areas may vary through time in response to physical changes in the shoals. Thus anthropogenic

modification of the shoals, as would result from sand mining, would alter the benthic habitats. Also the

season(s) in which sand mining took place would affect recolonization as function of the life history

stage of the benthic organisms. Recruitment of larvae and juvenile stages of animals likely would be

better in spring-summer while recruitment of adults likely would be regulated by factors that affect

passive transport, such as storms.

In order to ensure that the biological assemblage that recolonizes a mined area resembles that

prior to mining, it would be beneficial to avoid total stripping of the surface. By leaving small “islands,”

or refuge patches, within the sand mining area, local resident species would more easily be able to

recolonize the near by disturbed sections resulting in a post-mining assemblage that should be generally

like the earlier condition.

The alteration and recovery of a benthic biological community from a disturbance such as sand

mining likely will be dependent upon waves, currents, and bottom stresses in the period subsequent to

mining. Therefore the consequences of sand mining could be substantially different if a long period of

calm or a major storm followed the dredging.

Parts 2 and 3 should be considered together as they address the major biota of the water of

the study area. The work must be taken in the context of the relatively very small size of the potential

mining area as compared to the inner continental shelf offshore of Maryland and Delaware and as

compared to the inner continental shelf of the entire mid-Atlantic region. There are three broad groups

of vertebrate animals that are to be expected in the area: fishes, sea turtles, and marine mammals. 

Because of the substantial seasonal variation in water temperature, most of the fishes and all of the sea

turtles and marine mammals migrate with the seasons. Warm-temperature, sub-tropical species are

present in the summer and boreal species during the winter.

The area is used by a wide variety of fishes, many of which are valuable to either or both the

commercial or recreational fisheries. Sea herring and Atlantiac mackerel, among others, are common
4

during the winter while croaker, drum, sea trout, menhaden, and large coastal sharks are summer

residents. The area is an important migration corridor for striped bass and bluefish. But, as noted

above, the relatively limited size of potential dredging operations compared to the very large geographic

ranges and populations of the fishes suggests that sand mining would have little effect on the fish

populations.

The consideration of spatial scale also should hold true for the spawning, egg, and larval fishes

within the area. These conditions peak in terms of number of species during the summer months and

fall to a low during the winter.

Of the several species of sea turtles which use the mid-Atlantic Bight, the loggerhead and

Kemp’s ridley are vulnerable to entrapment by hopper dredges. The Kemp’s ridley is the most

endangered of the sea turtles and is the second most abundant sea turtle in the mid-Atlantic during the

summer. As all sea turtles are considered threatened or endangered, the National Marine Fisheries

Service (NMFS) takes an active role in the regulation of dredging activities.

The marine mammals that migrate through the area include boreal harbor porpoise, bottlenose

dolphin, juvenile humpback whales, and right whales. Although the right whale which is vulnerable to

collision with moving ships, all of the marine mammals are highly mobile and migratory and easily can

avoid dredges. 

Part 4 both analyzes a set of existing physical oceanographic aspects and models how

conditions might change following sand mining. The work addressed changes in waves, storm surge,

tidal currents, and bottom stress resulting from dredging on Fenwick and Isle of Wight Shoals. The

wave analyses considered two dredging scenarios: mining of approximately 2 x 106 m3 (two million

cubic meters) from each shoal and a total removal of 20 x 106 m3 (twenty million cubic meters). The

model was run using an unmodified bathymetry to establish base conditions then run again using a postdredging bathymetric scenario.
5

For driving conditions and calibration, the study uses wave data from an wave buoy located

about 40 km offshore of Ocean City maintained by the National Data Buoy Center and from two

nearshore stations maintained by the U.S. Army Corps of Engineers. During 13 years of observations

at the offshore station, the maximum significant wave was 7.6 m with a period of 16.7 s which occurred

during a January storm, or northeaster, and not during a hurricane. Review of the data resulted in

selection of 60 waves from among four wave heights (2, 4, 6, and 8m), five perioids (10, 12, 14, 16,

and 20s) from seven general directions (NNE, NE, ENE, E, ESE, SE, and SSE). Because short

period waves (less than 10s) do not affect the shoals, they were not considered even though they have

a relatively high frequency of occurrence. The bathymetry input to the model was taken from NOAA

sources.

The REF/DIF-1 wave transformation model was selected over several other models following

a comparison of the different model’s strengths and weaknesses. The wave model was calibrated by

comparing conditions synoptically observed at the offshore and inshore wave stations with calculated or

modeled data for the inshore stations using the observed offshore data as input. The variable model

parameter estimating bottom friction was adjusted so that the model’s output most closely resembled

the observed conditions.

In addition to providing base-line information, running the wave transformation model with a

unmodified bathymetric input provided an ability to compare the present distribution of wave energy

with the condition of the shoreline. In general the relatively stable region of the shoreline around the

Maryland-Delaware boundary coincides with an area of diminished wave energy and the more erosive

sections near Ocean City appear related to local concentrations of wave energy.

Comparisons of results from model runs with the unmodified bathymetry with runs in which the

removal of approximately 2 x 106 m3 from each shoal indicates that there would be relatively little

change in the wave environment. However a total mining of 20 x 106 m3 would result in an increase in

wave height in the area between the dredge sites and the shoreline. Evaluation of the impact of this

increase on the shore is difficult.
6

The potential impact of dredging on storm surge was assessed with a standard computer model

(SLOSH – Sea, Lake, and Overland Surges from Hurricanes). The model was run with the

unmodified bathymetry and the bathymetry after the 20 x 106 m3 mining scenario. Using a modeled

category 4 hurricane and two storm tracks, one generally shore parallel, the other shore normal, there

were negligible, almost non existent, difference between the pre and post dredging outputs.

The natural tidal currents in the area are fairly small, approximately 20 cm/s at the surface

decreasing at the bottom to around 5 cm/s except slightly greater, 5-10 cm/s, over the shoals. 

Modeling indicates that the cumulative dredging scenario would result in an increase of approximately

10 percent in the bottom currents. As this translates to an overall increase on the order of 1 cm/s, the

impact of dredging on bottom currents is considered to be very small.

Finally, yet another computer model was used to assess changes in the combined wave and

current generated bottom disturbing forces. Again, the impacts of dredging appear minimal.

Part 5 reviews the recent geologic history of the coast with emphases on changes in shoreline

position and possible influences of works intended to stabilize the shore. The approximately 100 km

long coastal region between Ocean City, Maryland and Cape Henlopen, Delaware is the product the

sea rising across a young, sedimentary substrate. The recently eroded, underlying, and presently

eroding strata were formed in very similar environments as the ocean had moved back and forth across

the coastal plain in response to sea level changes during the Quaternary resulting from global changes in

glaciation. The shoreline is a wave (or storm) dominated, micro-tidal (mean tide range about 1.1m)

system that has experienced approximately 30 cm of sea-level rise over the past century. Although

natural process operating along an open coast tend to straighten the shoreline, the actual form of the

shoreline depends, in part, on the geology of strata both being and recently eroded. Bluffs, dunes,

barrier spits, marshes, and inlet associated areas all respond differently and leave different physical

remnants on the post-erosion, flooded sea floor. Modern “hot spots,” sites of chronically greater

erosion, appear to be related to patterns of wave refraction which is a function of the overall wave

climate and the location of offshore shoals.
7

The jetties at Ocean City Inlet, the southern limit of the study area, and Indian River Inlet have

had substantial local impact since their construction and indicate a spatial change in condition along the

coast. The net longshore current near Ocean City flows southward and has built a substantial fillet of

sand against the north jetty whereas the net drift at Indian River Inlet is toward the north. A permanent

sand-bypassing plant serves to feed the longshore drift to the norht of the inlet. The nodal zone, or

region of current reversal, appears to be around the Delaware-Maryland border. Many sections of the

shore have been modified with sea walls or bulkheads and groins. During the past two decades there

have been several substantial episodes of beach nourishment.

The long term history of the shore is one of retreat. Comparisons of maps and charts from

1850 with modern map, chart, and photographic data document a receding shoreline and a

transgressing sea. The rate of retreat shows both spatial and temporal variability. Analysis of recent

beach profiles suggests that although the actual shoreline (i.e. the intersection of the physical shoreface

and a tidal datum such as mean high water or mean sea level) may be retreating, sand eroded from

landward portions of the beach might be accumulating in the shallow nearshore, especially in the vicinity

of sections that have been nourished. If this is so, even though the sand has been lost from the

accessible, recreational beach, it still is part of the beach-shoreface system and might be serving to

protect the inshore portions from larger waves.

SIGNIFICANT CONCLUSIONS

Although there are potentially adverse consequences to sand mining in the offshore regions of

Delaware and Maryland, they likely are not substantial and actions can be taken to eliminate or

minimize them. Obviously dredging the bottom destroys all the organisms that had lived within the

dredged area, but the best sands for beach nourishment have a comparatively low resource value. The

benthic fauna of those areas are likely to recolonize fairly rapidly especially if small “islands” are left

untouched within the otherwise dredged area. Care should be taken to minimize disturbance of the

substrate between the shoals that will be the targets for dredging. The very small size of the areas likely

to be dredged relative to the large geographic ranges of transitory fishes indicates that sand mining

would have very little impact on the fish populations. The species occurrence of fishes in spawning,
8

egg, and larvae stages is least from October through March and peak in the late spring and summer. 

The potential threat to sea turtles can be avoided by mining from mid-November to mid-April when

these sub-tropical animals are absent from the area. Sand mining poses no foreseeable threat to the

migratory and highly mobile marine mammals. 

Analysis of existing wave conditions demonstrates that modern shoreline stability is related to

areas of concentration and dispersion of wave energy near the zone of breaking waves. The relatively

stable area around the Maryland-Delaware border is one of relatively low waves whereas the various

erosional “hot spots,” especially along Fenwick Island, appear coincident with zones of wave energy

concentration. Wave transformation modeling indicates that removal of 106 m3 of sand from the top of

Fenwick and Isle of Wight Shoals will result in very small changes from present conditions. Removal of

107 m3 might cause more noticeable changes in the regions between the dredged areas and the shore. 

Modeling also predicts that dredging will have an extremely small impact on ambient tidal currents and

potential storm surges. 

The Maryland-Delaware shore is experiencing increasing pressure from expanding recreational

and residential uses and the associated commercial developments. The form of the shoreline results

from interactions amongst the local geology and stratigraphy, the history of Holocene sea-level rise, and

the contemporary wave climate. Although rising sea level drives a general marine

transgression/shoreline retreat through the area, the rate of retreat and apparent local stability vary along

the shore. Shoreline engineering, most noticeably sand bypassing at Indian River Inlet and repetitive

beach nourishment at several sites, has been employed to control shoreline retreat and enhance the

recreational value and use of the beach. The cumulative impact of the many beach nourishment

projects that already have been performed appears to be more beneficial than any individual project.

STUDY PRODUCTS: Virginia Institute of Marine Science, 2000. Environmental Survey of

Potential Sand Resource Sites Offshore Delaware and Maryland. Final Report to the U.S. Department

of the Interior, Minerals Management Service, International Activities and Marine Minerals Division,

Herndon, VA. Contract No. 1435-01-97-CT-30853. Printed Copy and CD.
9
The Department of the Interior

As the Nations’s principal conservation agency, the Department of the Interior

has responsibility for most of our nationally owned public lands and natural

resources. This includes fostering sound use of our land and water resources,

protecting our fish, wildlife, and biological diversity, preserving the

environmental and cultural values of our national parks and historic places; and

providing for the enjoyment of life through outdoor recreation. The

Department assesses our energy and mineral resources and works to ensure that

their development is in the best interests of all our people by encouraging

stewardship and citizen participation in their care. The Department also has a

major responsibility for American Indian reservation communities and for

people who live in island territories under U.S. Administration.

The Minerals Management Service Mission

As a bureau of the Department of the Interior, the Minerals Management Service’s

(MMS) primary responsibilities are to manage the mineral resources located on the

Nations’s Outer Continental Shelf (OCS), collect revenue from the Federal OCS

and onshore federal and Indian lands, and distribute those revenues.

Moreover, in working to meet its responsibilities, the Offshore Minerals

Management Program administers the OCS competitive leasing program and

oversees the safe and environmentally sound exploration and production of our

Nation’s offshore natural gas, oil and other mineral resources. The MMS Royalty

Management Program meets its responsibilities by entrusting the efficient, timely

and accurate collection and distribution of revenue from mineral leasing and

production due to Indian tribes and allottees, States and the U. S. Treasury

The MMS strives to fulfill its responsibilities through the general guiding principles of: (1)

being responsive to the public’s concerns and interests by maintaining a dialog with all

potentially affected parties and (2) carrying out its programs with an emphasis on working to

enhance the quality of life for all Americans by lending MMS assistance and expertise to

economic development and environmental protection.
Final Report OCS Study 2000-055

August 2000

Authors:

G. R. Cutter Jr and R. J. Diaz

Virginia Institute of Marine Science

Project Manager:

Carl H. Hobbs, III

Virginia Institute of Marine Science

Prepared under MMS contract 1435-01-97-CT-30853 to

the

Virginia Institute of Marine Science

College of William & Mary

International Activities and Marine Minerals Division

Environmental Survey of Potential Sand Resource Sites Offshore

Delaware and Maryland 

Part 1: Benthic Habitat Mapping and Resource Evaluation of

Potential Sand Mining Areas, 1998-1999
Final Report

Environmental Survey of Potential Sand Resource Sites

Offshore Delaware and Maryland

Part 1

Benthic Habitat Mapping and Resource Evaluation of Potential Sand Mining Areas

Offshore Maryland and Delaware, 1998-1999

G. R. Cutter, Jr. and R. J. Diaz

Principal Investigators

Virginia Institute of Marine Science

Carl H. Hobbs, III, Project Manger

Virginia Institute of Marine Science 

for 

U. S. Department of the Interior

Minerals Management Service

International Activities and Marine Minerals Division

Contract 1435-01-97-CT-30853

OCS Study 2000-055

August 2000
DISCLAIMER

This report has been reviewed by the Minerals Management Service and approved for

publication. Approval does not signify that the contents necessarily reflect the views and

policies of the Service, nor does mention of trade names or commercial products constitute

endorsement or recommendation for use.
The Department of the Interior

As the Nations’s principal conservation agency, the Department of the Interior

has responsibility for most of our nationally owned public lands and natural

resources. This includes fostering sound use of our land and water resources,

protecting our fish, wildlife, and biological diversity, preserving the

environmental and cultural values of our national parks and historic places; and

providing for the enjoyment of life through outdoor recreation. The

Department assesses our energy and mineral resources and works to ensure that

their development is in the best interests of all our people by encouraging

stewardship and citizen participation in their care. The Department also has a

major responsibility for American Indian reservation communities and for

people who live in island territories under U.S. Administration.

The Minerals Management Service Mission

As a bureau of the Department of the Interior, the Minerals Management Service’s

(MMS) primary responsibilities are to manage the mineral resources located on the

Nations’s Outer Continental Shelf (OCS), collect revenue from the Federal OCS and

onshore federal and Indian lands, and distribute those revenues.

Moreover, in working to meet its responsibilities, the Offshore Minerals

Management Program administers the OCS competitive leasing program and

oversees the safe and environmentally sound exploration and production of our

Nation’s offshore natural gas, oil and other mineral resources. The MMS Royalty

Management Program meets its responsibilities by entrusting the efficient, timely

and accurate collection and distribution of revenue from mineral leasing and

production due to Indian tribes and allottees, States and the U. S. Treasury

The MMS strives to fulfill its responsibilities through the general guiding principles of: (1)

being responsive to the public’s concerns and interests by maintaining a dialog with all

potentially affected parties and (2) carrying out its programs with an emphasis on working to

enhance the quality of life for all Americans by lending MMS assistance and expertise to

economic development and environmental protection.
TABLE OF CONTENTS

CHAPTER 1. INTRODUCTION ................................................. 1

1.1. Overview and Objectives ............................................... 1

1.2. Benthic Resources and Habitats .......................................... 2

1.2.1. Scales of Variation ............................................ 2

1.2.1.1. Spatial .............................................. 2

1.2.1.2. Temporal ............................................ 3

1.2.2. Assessment and Evaluation ...................................... 4

1.2.2.1. Existing Tools ......................................... 4

1.2.2.2. Relevance ........................................... 6

1.2.2.3. Modification and Development ............................ 6

CHAPTER 2 STUDY AREA ..................................................... 9

2.1. Regions of Interest .................................................... 9

2.2. Sample Locations ..................................................... 9

3. MATERIALS AND METHODS ............................................... 11

3.1. Field Methods ...................................................... 11

3.1.1. Vessel ..................................................... 11

3.1.2. Grab ...................................................... 11

3.1.3. SPI ....................................................... 11

3.1.4. Sled ...................................................... 12

3.1.5. SPI-Plow .................................................. 13

3.1.6. Sidescan Sonar .............................................. 13

3.1.7. Trawl ..................................................... 13

3.2. Laboratory Methods ................................................. 14

3.2.1. Grab ...................................................... 14

3.2.2. SPI ....................................................... 14

3.2.3. Sled ...................................................... 14

3.2.4. Video Image Analysis Data ..................................... 16

3.2.4.1. SPI-Plow ........................................... 17

3.2.5. Trawl ..................................................... 17

3.2.6. Sidescan Sonar .............................................. 18

3.2.7. Position Data ............................................... 18

3.2.8. Grab Sample Substrate Data .................................... 18

3.2.9. Grab Sample Biological Data .................................... 18

3.2.9.1. Preliminary Data Treatment ............................. 18

3.2.9.2. Community Analysis ................................... 19

3.2.10. Image Analysis: SPI and Sled Still ............................... 21

3.2.11. Regional Condition Data ...................................... 25

3.2.11.1. NOAA Buoy Data ................................... 25

3.2.12. Habitat Classification and Biological Resource Information ............. 25
ii

3.2.12.1. Indices and Derived Statistics ........................... 25

3.2.12.2. Secondary Production ................................ 27

3.2.12.3. Mapping .......................................... 28

CHAPTER 4. RESULTS ....................................................... 31

4.1. Grab Sample Substrate Data ........................................... 31

4.2. Grab Sample Biological Data ........................................... 31

4.2.1. Biological Data - Descriptive Summaries ........................... 31

4.2.1.1. 1998 .............................................. 31

4.2.1.2. 1999 .............................................. 33

4.2.2. Biological Data - Community Structure and Function .................. 34

4.2.2.1. Diversity and Evenness ................................. 34

4.2.2.2. Numbers of Taxa and Species ........................... 35

4.2.2.3 Cluster Analysis ....................................... 35

4.2.2.4. Dominant Species .................................... 37

4.2.2.5 Dominant Community Groups ............................. 39

4.2.2.6 Life History Attributes .................................. 39

4.3. Fish Trawls ........................................................ 41

4.3.1. Fish Assemblages ............................................ 41

4.3.2. Fish Gut Content ............................................. 43

4.4. SPI Image Analysis Data .............................................. 44

4.4.1. Standard SPI Analysis Data .................................... 44

4.4.2. SPI Grain-size Analysis ........................................ 46

4.4.3. Comparison of SPI and Grab Grain-size Determinations ............... 46

4.5. Sled Still Image Analysis Data .......................................... 47

4.6. Video Image Analysis Data ............................................ 47

4.7. Regional Condition Data ............................................... 47

4.7.1. NOAA Buoy Data ........................................... 47

4.8. Biological Resource Information ......................................... 48

4.8.1. Indices and Derived Statistics: SPI-based BHQ and SBHQ ............ 48

4.9. Habitat Classification and Resource Value Results, Secondary Production .......... 49

4.10. Prediction of Biology Using Substrate and Image Data ....................... 52

4.10.1. Secondary Production ........................................ 52

4.10.2. Community Groups .......................................... 53

CHAPTER 5. DISCUSSION, SUMMARY, AND CONCLUSIONS ..................... 55

5.1. Origins and Fate of Topographic Features ................................. 55

5.2. Benthic Habitat Characteristics .......................................... 55

5.3. Perspectives on Former Organism-sediment Models and a New Model ........... 58

5.4. Evaluating Biological Resource Potential ................................... 60

5.5. Relationship of Secondary Production to Habitat Value ........................ 62

5.6. Assessing Potential Sandmining Impacts ................................... 63

5.7. Minimizing Impacts to Biological Resources ................................ 66
iii

CHAPTER 6. ACKNOWLEDGMENTS ........................................... 67

CHAPTER 7. REFERENCES CITED ............................................. 69
iv

LIST OF FIGURES

Figure 2.1-1: General location of the Maryland/Delaware (MD/DE) study area.

Figure 2.1-2: General location and boundaries of the Indian River (IR) and North Bethany Beach

(NBB) regions of interest (ROI).

Figure 2.1-3: General location and boundaries of the Fenwick Shoal (FS), Weaver Shoal (WS), and

Isle of Wight Shoal (IWS) regions of interest (ROI).

Figure 2.2-1: Location of grab samples in the Maryland and Delaware study area.

Figure 2.2-2: Location of sediment profile camera stations in the Maryland and Delaware study area.

Figure 3.1-1: Young grab used to collect benthic infauna and sediments.

Figure 3.1-2: Sediment profile camera system used to collect images of the sediment-water interface.

Figure 3.1-3: Standard video and still camera sled used to collect images of surface features over broad

spatial scales.

Figure 3.2-1: Relationship among sediment grain size, compaction (Cp), and prism penetration (Pen).

Figure 4.1-1: Percent composition of sediments from the Maryland and Delaware study area.

Figure 4.2-1: Distribution frequency of taxa occurrences for all grabs from the Maryland and Delaware

study area for both May 1998 and June 1999.

Figure 4.2-2: Dendogram of all stations sampled in 1998 and 1999 in the Maryland and Delaware

study area. Cluster groups, indicated by letters, are based on simultaneous double

standardization of data, Bray-Curtis similarity, and flexible sorting. See text for details.

Figure 4.2-3: Species cluster groups based on simultaneous double standardization of data from the

Maryland and Delaware study area, Bray-Curtis similarity, and flexible sorting. See text for

details.

Figure 4.2-4: Dendogram of Fenwick Shoals stations sampled both in 1998 and 1999. Cluster groups

are based on simultaneous double standardization of data, Bray-Curtis similarity, and flexible

sorting. See text for details.

Figure 4.2-5: box plots of community structure statistics from the Maryland and Delaware study area

by cluster analysis station group. Bar is median; box is interquartile range; tails are trimmed

range; and dots are outliers (>2 times the interquartile range).
v

Figure 4.3-1: Locations of bottom trawls around the Fenwick Shoals ROI. Trawls were located in four

benthic habitats.

Figure 4.3-2: Cluster analysis of demersal fish collected May 1999 at four benthic habitat types within

the Maryland and Delaware study area. Based on Bray-Curtis similarity and flexible sorting.

Figure 4.3-3: Cluster analysis of fish data collected May 1999 at four benthic habitat types in the

Maryland and Delaware study area. Based on Bray-Curtis similarity and flexible sorting. See

text for a description of the habitat characteristics. See Figure 4.3-1 for the location of the

habitats.

Figure 4.4-1: SPI (sediment profile images) from selected locations.

Figure 4.4-2: SPI (sediment profile images) from selected locations.

Figure 4.4-3: SPI (sediment profile images) from selected locations.

Figure 4.4-4: SPI (sediment profile images) from selected locations.

Figure 4.4-5: SPI (sediment profile images) from selected locations. 

Figure 4.4-6: Examples of sediment graijn-size determinations from sediment profile images. CS =

coarse sand; FS = fine sand; GR = gravel; MS = medium sand; SH = shell; SI = silt; VCS =

very coarse sand; VFS = very-fine sand.

Figure 4.4-7: Relationship between grain-size determinations from sediment profile images. Y-axis is

grab mean grain-size in mm (MZMM). X-axis is image mean grain-size in mm.

Figure 4.5-1: Surface camera images from selected stations.

Figure 4.5-2: Surface camera images from selected stations.

Figure 4.7-1a: Water temperature (oC) and wave height (m) from NOAA Bouy 44009 for the year

1998.

Figure 4.7-1b: Water temperature (oC) from NOAA buoy 44099 for the 17 to 21 May 1998 cruise.

Figure 4.7-2: Water temperature (oC) and wave height (m) from NOAA Bouy 44009 for the first six

months of 1999.

Figure 4.8-1a: Distribution of Benthic Habitat Quality index values for the Indian River ROI.

Figure 4.8-1b: Distribution of Scaled Benthic Habitat Quality index values for the Indian River ROI.
vi

Figure 4.8-2a: Distribution of Benthic Habitat Quality index values for the Fenwick Shoal ROI.

Figure 4.8-2b: Distribution of Scaled Benthic Habitat Quality index values for the Fenwick Shoal ROI.

Figure 5.2-1: Benthic habitat map of Fenwick Shoal ROA showing SBHQ index surface generated

with inverse distance weighting, secondary production, and surface biological features from

video transects. The presence of Ammodytes spp. From the video is over-lain on the video

transcets.

Figure 5.4-1: Sediment grain-size (mm) map of the Fenwick Shoal ROI derived from sediment profile

images. Surface was generated with inverse weighting interpolation. Coarser sediments tended

to be on the crest of the shoals.

Figure 5.6-1: Sand-mining scenario for the Fenwick Shoal ROI. Benthic recolonization to the mined

area (coarse stippling) would proceed from community groups in the buffer zone (fine stippling)

and from outside the mined area. Letters refer to cluster groups and subgroups.
vii

LIST OF TABLES

Table 4.1-1: Sediment grain-size distribution form grab samples collected in the Maryland and

Delaware study area. Gravel is >2mm. Sand is between 2 and 0.063mm. Silt is between

0.063 and 0.039mm. Clay is <0.0039mm.

Table 4.1-2: Sediment grain-size analysis of sand fraction grab samples collected in the Maryland and

Delaware study area. All statistics are in mm.

Table 4.2-1: Major taxa abundance (individuals/0.04 m2) of infauna collected in the Maryland and

Delaware study area in May 1998 and June 1999.

Table 4.2-2: Proportional contribution of major taxa to total infaunal abundance and species richness

for samples collected in the Maryland and Delaware study area in May 1998 and June 1999.

Table 4.2-3: Percentage of major taxa abundance (individuals/individuals/0.04 m2) of infauna collected

in the Maryland and Delaware study area in May 1998 and June 1999.

Table 4.2-4: Species richness (taxa/0.04 m2) by major taxa for infauna collected in the Maryland and

Delaware study area in May 1998 and June 1999.

Table 4.2-6: Percentage species by major taxa for infauna collected in the Maryland and Delaware

study area in May 1998 and June 1999.

Table 4.2-7: Percentage of biomass by major taxa for infauna collected in the Maryland and Delaware

study area in May 1998 and June 1999.

Table 4.2-8: Infaunal community structure in the Maryland and Delaware study area for May 1998 and

June 1999.

Table 4.2-9: Constancy and fidelity based on nodal analysis of all grab data from the Maryland and

Delaware study area. See Figure 4.2-2 for stations and Figure 4.2-3 for species in each group.

Table 4.2-10: Dominant taxa (present at >20% of stations, 15 of 70 stations) collected in the Maryland

and Delaware study area.

Table 4.2-11: Abundance dominants from the Maryland and Delaware study area. Includes all taxa

that were at least one percent of total abundance in either May 1998 or September 1999 with

and without the polychaete Siophanes bombyx.

Table 4.2-12: Biomass dominants (>0.5 g wet weight/0.04 m2) collected in the Maryland and

Delaware study area. Numerical or occurrence dominant species are also included.

Table 4.2-13: Summary of overall dominants based on occurrence, abundance, and biomass collected
viii

in the Maryland and Delaware study area.

Table 4.2-14: Average abundance of dominant taxa in the Maryland and Delaware study area by

cluster analysis station group (see Figure 4.2-2). Dominance Types are: O = Occurrence

(>20% of stations), A = Abundance (>1% of total abundance for May of September cruise), B

= Biomass (>0.5% g total wet weight). * = average abundance was <1. Blank = taxa did not

occur in the station group.

Table 4.2-15: Life history characteristics and estimate of recruitment potential for dominant species

from the Maryland and Delaware study area.

Table 4.3-1: Fish collected in May 1999 at four major benthic habitat types within the Maryland and

Delaware study area. Data are summed occurrence of species caught in eight trawls from each

habitat.

Table 4.3-2: Invertebrates collected in May 1999 at four major benthic types within the Maryland and

Delaware study area. Data are the summed occurrence of selected species caught in eight

trawls from each habitat. For most species only presence in the trawl was recorded (+).

Table 4.3-3: Summary of gut content by taxa for the three dominant demersal fish species trawled

around Fenwick Shoal. Gut content for all individuals was summed for each species.

Table 4.4-1: Sediment size classes estimated from SPI images collected in the Maryland and Delaware

study area.

Table 4.9-1: Estimated secondary production of macroinfauna for the 1998 grab data from the

Maryland and Delaware study area.

Table 4.9-2: Estimated secondary production of macroinfauna for the 1999 grab data from the

Maryland and Delaware study area.

Table 4.9-3: Averaged annual secondary production of macroinfauna from the Maryland and Delaware

study area.

Table 4.9-4: Summary of gut content by major taxa for the three dominant demersal fish species

trawled around Fenwick Shoal. All counts and weights (g wet) are for total stomachs analyzed. 

Mean Ind. Weight is the average weight (g wet) of an individual food item.

Table 4.9-5: Averaged annual P/B ratios of macroinfauna from the Maryland and Delaware study area.

Table 4.9-6: relationship between P/B ratio and sediment type for the May 1998 grab data. Based on

discriminant analysis of sediment grain-size categories and major taxa P/B ratios. A total of 48

stations were classified; four stations did not have grain-size data. CS = coarse sand; FS = fine

sand; GR = gravel; MU = mud.
ix

Table 4.10-1: Averaged abundance (individuals/m2) of selected taxa (occurring at five or more stations)

by cluster subgrouping for the Maryland and Delaware study area.

Table 5.3-1: Scenarios depicting the effect of season and climatology on infaunal recolonization

trajectory. Faunal characteristics favored by the combinations of season and climate are listed

in each cell of the table.
CHAPTER 1. INTRODUCTION

1.1. Overview and Objectives

The removal of mineral resources, typically mining of sand for beach nourishment, from the

continental shelf poses a threat of direct disturbance to benthic communities and trophically dependent

pelagic species. In order to eventually predict the impacts of sandmining upon biological resources

within a designated project area, it is necessary to first determine existing community structures, spatial

distributions, substrate dependencies, productivity and trophic linkages. The natural dynamics of the

biological assemblages should be assessed with respect to the scales and magnitudes of normal

environmental stressors and the potential for some activities to interfere with these dynamics. For

example, benthic community structure can sometimes be accurately inferred based upon sediment

types, however sediment types is a function of the transport environment controlled by highly variable

wave and current dynamics. The mid-Atlantic continental shelf is annually exposed to summer

hurricanes and winter northeaster storms. Yet, primary topographic features, such as the shoals

offshore Maryland and Delaware, and the predominant sediment distribution patterns persist. 

Persistent biological assemblages there reflect balance with system dynamics. However, apparent

persistence may also result from continual population responses to disturbance, where eradication is

quickly followed by recolonization dependent upon substrate changes and water column conditions

subsequent to disturbance.

Disturbances associated with sandmining are not inconsequential because of existing highly

dynamic conditions on the shelf. Sandmining will directly alter topographic features which in turn will

influence how and to what degree water column dynamics will influence the substrate and hence the

biology. Prediction of the short-term responses of the benthic community will be considerably more

difficult than long-term because of asynchronous and variable natural short-term population fluctuations

(Maurer et al., 1976). Long-term responses however can be considered in terms of a spatial problem

in that community structure should eventually reflect substrate components, and primary alterations to

substrates should be limited to the general vicinity of the mined region.

Impacts of sandmining to the biological resources include removal of and extermination of
2

infauna, epifauna, and some benthic fish; alteration of bathymetry by reduction of topographic features;

exposure of buried substrate; and potential dispersion of surrounding biological community constituents.

Regions of potential sandmining activities in U.S. Federal waters off Maryland and Delaware

include the offshore ridges known as Fenwick, Weaver, and Isle of Wight Shoals, and nearshore gravel

and sand sheets east of Indian River Inlet. Sediments in the region are primarily terrigenous quartz

(subarkosic) sands (Milliman, 1972), however grain-sizes from clays to gravels exist in the area. The

offshore ridges are topographic features initially thought by Shepard (1948) to be drowned barrier

islands, but are now believed to be long-term accretional and erosional responses to storm-related

hydraulic regimes in combination with sea-level rise since the last deglaciation (Swift and Field, 1981;

Goff et al., 1999). The ridges apparently form as shore-attached features produced during shoreline

erosion induced by storm generated currents and vortices, and also dependent upon substrates

composed of mixed sands with a coarse component (Swift et al., 1973). 

1.2. Benthic Resources and Habitats

1.2.1. Scales of Variation

1.2.1.1. Spatial

Spatial variability in water depth, topography, substrate characteristics and biological

community attributes occurs at very small (cm) to regional (km) scales. Determinations of distributions,

aerial coverages, and transitions will depend upon the scale(s) at which sampling occurs. Resolving

spatial variations is important to delineation of impacts, however resolving power is inversely

proportional to sampling effort. Therefore, in order to resolve both large and small-scale phenomena

and their variability, the sampling support and design must provide sufficient coverage for both. 

Sampling regionally at very high densities is infeasible, therefore we chose to use varying spatial

supports for our data collection. Point sampling provided large-scale coverage and gross

approximations of habitat distributions, and transect sampling (with point and continuous devices)

provided fine-scale coverage and estimates of rates of small-scale spatial change which could be

combined with the large-scale data in order to better represent intermediate-scales without direct

sampling. This approach utilized the concepts of geostatistics and varied support, reactive sampling
3

techniques in order to maximize the information return for the field efforts.

1.2.1.2. Temporal

Temporal variability operates upon benthic habitats and communities at long and short time

scales. Continental shelf communities vary seasonally but this temporal variation is confounded with

distance off shore and depth (Maurer et al., 1976). The farther offshore and deeper the area the less

pronounced seasonality becomes and presumably interannually variation also declines (Boesch et al.,

1979). However, within our region of interest benthic community structure and function is primarily

associated with substrate type and changes in substrate. Therefore, unless the bottom changes in

substrate or hydrodynamics, variation from temporal change in the communities should be relatively

small, and community structure and function should be relatively predictable, irrespective of when

sampled during the year. Temporal dynamics in substrates and habitats that will have the most effect

upon benthic communities operate in response to forcings with short temporal scales but broad spatial

scales. Stresses incurred by the bottom are primarily associated with major storm events or dredging

activities. Unless storms spawn tornadoes that would proceed along a discrete path across the study

area, the storms' potential to induce bottom change would influence the entire study area. And, unless

climatic dynamics change drastically, seasonal effects in a particular location should be predictable from

year to year. Unpredictable changes that would be expected where there are combined effects induced

by high-energy events occurring in transitional areas. Transitional areas are characterized by high rates

of local variability in terms of physical structure (steep grades) or the induced effects of structural

changes exhibited in water column physics and resultant effects on the substrate (shear, suspension,

sedimentation) and biology (exposure, removal, burial). Thus determinations of rates of spatial changes

in ecosystem components are more important than attempts at detecting seasonal patterns in dynamic

environments, especially if the time series used to detect seasonality is short or the sampling interval

infrequent.

1.2.2. Assessment and Evaluation

1.2.2.1. Existing Tools

The organism sediment index (OSI) was developed (Rhoads and Germano, 1986) in order to
4

provide a means to assess benthic habitat condition using sediment profile image data. Recently,

Nilsson and Rosenberg (1997) developed a similar index, the benthic habitat quality (BHQ) index, also

for applying profile image data to habitat assessment. Standard sediment profile image (SPI) analysis

and the OSI and BHQ indices offer insights into habitat and microhabitats which other techniques do

not (Rhoads and Germano, 1986), and provide complementary data (Bonsdorff et al., 1996) to

standard benthic community and substrate characterization. 

Other survey tools and techniques provide habitat data at scales difficult to resolve by SPI

sampling. Video and interval still imaging sleds, which provide continuous detailed transect data,

equivalent to ROV-gathered data, but at lower cost and cover greater area in less time although with

less directional control. Side-scan sonar devices provide wider swath coverage over transect lines,

revealing substrate configurations and transitions. Additionally, sonar units can be deployed

simultaneously with towed sleds, providing the acoustic swath view of the bottom about to be

encountered by the sled. These techniques provide habitat and microhabitat information important to

understanding biological community data from sediment grab samples and benthic trawl samples. 

Data from benthic-grab sample analysis offer several resource assessment parameters including

abundance, biomass, and diversity. These basic community structure parameters offer intuitively

valuable resource information and have formed the basis of many impact assessments. Recently

community structure parameters have been incorporated into numerical indices designed to measure the

magnitude of response of the benthos to various forms of disturbance. For example, a benthic index of

biotic integrity (B-IBI) was developed by Weisberg et al., (1997) as a means of evaluating benthic

community conditions based upon species' tolerances and sensitivities to environmental conditions and

used data from “reference” areas as a calibration of the index. This approach focuses on community

structure and does not account for energetic or ecosystem level responses, which should be the central

issue in any assessment of potential impacts. To overcome the limited usefulness of community

structure based assessments we have included an energy flow based approach to this study by

estimating the secondary production of the infaunal communities.
5

Secondary production can be derived from the grab data and imparts more efficiency upon any

assessment or characterization of resource value or potential than any of the community structure based

approaches (Diaz and Schaffner, 1990). Secondary production estimates also benefit from

mathematical models that have incorporated many datasets from around the world (for recent

examples see Brey (1990) and Tumbiolo and Downing (1994), meaning that applicability and

comparison are enhanced. The Benthic Resources Assessment Technique (BRAT) developed by Lunz

and Kendall (1987) was an early attempt to add the concept of energy flow to resource assessment

and ascribe resource value to subtidal estuarine and marine bottom habitats. BRAT utilizes data

collected on both infaunal communities via grab and fish assemblages via trawl. Gut content analysis of

the fishes is compared to existing benthic infaunal resources to estimate trophic transfer of benthic

resources. However, BRAT is rather labor intensive and difficult to apply over large areas because

sampling at frequent intervals over a year is needed to characterize energy transfer to transient fishes. In

addition, BRAT relied on standing stock biomass of benthos and did not consider the productivity and

turnover rate of the infauna.

If the goal is to assess benthic biological resources that will support fisheries species, high

priority should be given to assessing resource potential of a bottom to provide fish food and the spatial

distribution of these resources.

Evaluating sand-mining effects based upon benthic biological resources and habitat conditions

requires a combination of the previously described tools and techniques for habitat valuation, and in

addition predictions concerning community and system responses to disturbance events. 

1.2.2.2. Relevance

Both of the profile image-derived indices (the OSI developed for northeast estuarine and

marine bottoms and the BHQ developed for Scandinavian fjords systems) heavily weight the redox

potential discontinuity (RPD) layer depth. Although appropriate for the systems within which these

indices were developed, where variation in RPD layer depth could be related to variation in biological

activity. RPD layer depth is also intimately linked to and correlated with geophysical and geotechnical
6

sediment properties. RPD layer depth is essentially the recent time-averaged depth below the

sediment-water interface to which oxidized water penetrates, either by biogenic flushing induced by

organismal activity (Aller and Aller, 1998) or by physical percolation induced by advection (Ziebis et

al., 1996). Unfortunately, both indices (OSI and BHQ) utilize RPD without compensating for

confounding factors that control permeability and influence RPD, such as sediment grain-size, porosity,

cohesivity, compaction, and sorting. In simple cases, sediment grain-size distributions can serve as a

proxy for the others. As long as the sediments studied are similar, in that grain-size distributions and

geotechnical properties are limited in variability, application of either index is valid. However, neither

the OSI nor BHQ can accommodate the physical processes that structure surficial substrates in our

study region, the inner continental shelf offshore Maryland and Delaware, or the east coast continental

shelf in general. Coarse, highly permeable sediments cover much of the area as does steep topographic

features that are often exposed to strong currents and high turbulence.

1.2.2.3. Modification and Development

Since the OSI and BHQ suffer serious effects from confounded, correlated variables such as

RPD and sediment grain-size, and have limited relevance to most continental shelf habitats, we

developed a new SPI index based upon the BHQ. We used the BHQ index as a base because it relies

upon discretely identifiable sediment and biological features. We do not present the OSI in this report

because successional states were indeterminate for nearly all the images, therefore the OSI was

undefined. Goals for the new index included simplicity, similarity to OSI and BHQ, adjustment for

correlated variables, and accommodation for variables exceeding tool measurement capabilities, and of

course utilization for mid-Atlantic continental shelf environments. We call the index SBHQ for Scaled

Benthic Habitat Quality index. The design of the SBHQ should make it applicable to not only the midAtlantic shelf but to sedimentary environments in general.
9

CHAPTER 2 STUDY AREA

2.1. Regions of Interest

The study area was located on the inner continental shelf in the central portion of the midAtlantic bight (Figure 2.1-1). Minerals Management Service (MMS) specified five regions of interest

(ROI). Two ROI's were located offshore Indian River Inlet, Delaware: the ROI to the north was called

Indian River ROI (IR-ROI) and its southern neighbor was called North Bethany Beach ROI (NBBROI) (Figure 2.1-2). Three ROI's were located on the primary shoal features offshore northern

Maryland and southern Delaware: from north to south, Fenwick Shoal (FS), Weaver Shoal (WS), and

Isle of Wight Shoal (IWS) (Figure 2.1-3). 

For general descriptions in this report, Indian River Regions refers to both the Indian River and

North Bethany Beach ROI's. Likewise, Fenwick Shoals Regions refers to the three shoal ROI's (FSROI, WS-ROI, IWS-ROI). 

2.2. Sample Locations

Sampling in 1998 encompassed all ROI's, and some areas surrounding the ROI's. In 1999,

sampling was concentrated in the FS Regions, primarily where major sedimentological and biological

transitions were identified from the 1998 data. Several different sampling gear were deployed within

and around the ROI's. In May 1998, point samples were acquired from stations on a regular lattice:

SPI at all stations, sediment grabs at randomly selected stations within the lattice. Additional point

samples were taken at intermediate positions along seven of the lattice axes. Transects were sampled

using a towed sled system along three of the higher density point sample lines and also across areas

within the overall lattice where point samples were not acquired. In June 1999 sediment grabs were

acquired at a subset of the same grab stations from 1998 (Figure 2.2-1).

SPI samples were taken at some of the same stations visited in 1998. However, because of the

sea-state, sampling efforts were reallocated, focusing upon collection of biological grab samples at the

same stations as 1998, video sled tows, and high-density point SPI sampling, where SPI images were

acquired at close intervals along transects by drift deployment. The high-density SPI transect samples
10

were acquired over and between much of Fenwick Shoal and Weaver Shoal (Figure 2.2-2).
11

3. MATERIALS AND METHODS

3.1. Field Methods

3.1.1. Vessel

For the 1998 cruise we used the M/V Atlantic Surveyor from Toms Inlet, New Jersey used for

this cruise. Oits overall length was 110 feet and accommodated berthing for 9 nine scientists. For the

1999 cruise we used the UNOLS vessel R/V Cape Henlopen of the University of Delaware its overall

length was 120 feet and accommodated berthing for 12 scientists. On both cruises we conducted 24-

hour operations to make the most efficient use of our shipboard time.

3.1.2. Grab

A Young grab, 0.044 m2 surface area, was deployed to collect sediment grab samples for

substrate and biological community data (Figure 3.1-1). This is the same sampler used by EPA in its

EMAP and MAIA programs. The Young grab is similar to a van Veen grab that has been placed in a

frame to hold it level with the sediment surface while a sample is collected. Grabs without frames tend

to twist and collect uneven samples. Because of its frame, the Young grab functions well in both soft

and hard sediment.

3.1.3. SPI

A Hulcher model Minnie Sediment profile camera was deployed attached to a Benthos profile

camera frame (Figure 3.1-2). Fujichrome 100 ASA professional color slide film was used. Tests were

done frequently onboard in order to ensure camera function and mark stations. In addition, a video

camera was attached to the SPI camera frame in order to both monitor camera operation on bottom

and to provide close-up video images of the sediment surface features and epifauna in front of the

profile prism.

The sediment profile camera was developed to collect data on sediments at and below the

sediment-water interface. Sediment profile cameras provide a unique in situ view of the sediment-water

interface and subsurface sediments yielding both quantitative and qualitative data on the biological,

chemical, and physical character of the sediments. The sediment profile camera is composed of two
12

parts; 1- the camera, encased in a pressure housing, and 2- a 45o prism, with an approximately 15 x 23

cm clear plexiglass face plate and mirror to reflect the image of the sediment up to the camera lens. The

bottom edge of the prism is sharpened to neatly cut through the sediment. The prism is filled with clear

fresh water to prevent hydrostatic pressure from distorting the faceplate as the prism is lowered below

the sea surface. The lens and light source (strobe for still and incandescent bulbs for video) used to

illuminate the sediment are both contained inside the clear water-filled prism. The camera is focused on

the prism faceplate and records sediment features pressed against the faceplate. This configuration

allows the camera to work in complete darkness with image clarity independent of turbidity. For

deployment, the camera and prism are attached to a cradle held by a larger stabilizing frame to insure

the prism enters the sediment at a 90o angle (Figure 3.1-2). The entire cradle and frame assembly is

lowered to the bottom by winch. Once on the bottom a hydraulic piston regulates the rate of descent of

the prism and camera cradle into the bottom. This prevents excessive disturbance of the sedimentwater interface. The profile camera is externally triggered on contact with the bottom. Electronic

circuits in the camera control the exposure timing to allow the prism to penetrate the sediment after

contacting the bottom. Delay times usually range from 1 sec. in soft mud to 15 sec. in hard sand.

3.1.4. Sled

VIMS Standard Bottom Imaging Sled was deployed with video cameras, and water quality

sensors. The sled was towed at <1 knot when possible. This sled system was also deployed with the

SPI-Plow (BCD system, see below) attached after the plow-sled had been damaged (Figure 3.1-3). 

Sled still images had the following dimensions, based upon the camera lens angles and depth above the

bottom. The length of the image was about 43 cm, the width of the image was 30 cm, the area was

about 1300 cm2, or 0.13 The sled was towed at 2 to 3 knots when the vessel was under power

and as low as 0.8 knots when adrift in order to acquire close-up bottom video and water quality data. 

The video camera was set obliquely and about 15 cm from the bottom in order to resolve the smaller

details of the surface and biological structures. The area viewed by the video camera was a trapezoid

about 10 cm along the base line closest to the camera and 40 cm along the other baseline. The field of

view was about 0.2 to 0.4 m2 depending on sled orientation.
13

The camera was mounted so that the focal plane was 40 cm above the plane of the sled

runners, meaning that each image represented an area of 0.13 m2, since the lens had been adjusted for

close-up focus and the lens angles were 20.6E and 28.5E. Normal lens angles are 35E and 50E.

 3.1.5. SPI-Plow

The SPI-Plow, or Burrow-Cutter-Diaz Plowing Sediment Profile Camera System (Cutter and

Diaz, 1998) was deployed, only on the 1999 cruise, to acquire continuous video profile images. It was

towed at 0.1 to 1.5 knots and subbottom video was recorded onboard. However, the sled

superstructure was broken during a tow, was recovered, and the system had to be modified onboard. 

The plow and camera encasement was then transferred to the standard sled.

3.1.6. Sidescan Sonar

A Marine Sonic Technologies Inc. 600 KHz sidescan sonar towfish and digital acquisition unit

were deployed during several of the sled tows and plow drags on the 1999 cruise. Sidescan records

were stored on a PC hard drive, then transferred to removable magnetic disk, and archived on CDROM. 

 3.1.7. Trawl

On the 1999 cruise, Rutgers University researchers deployed an eight-foot beam trawl to

collect juvenile fish, epibenthos, and magabenthos. Four trawls locations were chosen based upon sled

and plow video observations to cover a broad range of sedimentary and biological conditions. Two

physically dominated sandy and gravel/shelly habitats with little evidence of biogenic kstructure were

sampled along the northeastern and northwestern sides of Fenwick Shoal and two more biologically

accommodated Diopatra and Asabellides tube field habitats along the southeastern and southwestern

sides. At each location four trawls were collected during daylight and four trawls during the night. The

trawl was fitted with a meter-wheel to measure the distance trawled so that fish abundance per unit area

could be estimated.

3.2. Laboratory Methods

3.2.1. Grab
14

Samples were rinsed in freshwater over a 500-um sieve and sorted by placing a small amount

of the sample in a plastic dish. All organisms, including fragments of worms, were removed and sorted

to major taxonomic categories such as polychaetes, crustaceans, molluscs, echinoderms, and other. 

After samples were sorted, all organisms were identified and enumerated. Identifications were made at

the lowest practical taxonomic level (LPTL), usually species. After identification and enumeration all

organisms were grouped by LPTL and placed in 2% Formalin until wet weight measurements were

completed.

3.2.2. SPI

Slide images were reviewed on a light table, then digitized using a Polaroid Sprintscan 35Plus

slide scanner. Images were stored as TIFF files, using no compression, and archived on CD-ROM for

later computer image analysis. 

3.2.3. Sled

Still images collected from sled tows using the Benthos Deep-Sea Standard Camera were

processed the same as SPI images (above). Sled video images were transferred from analog to digital

video format. The digital video was then played back at 1/3 speed, and feature determinations, counts,

and classifications were done by an observer every 3 seconds, providing 1 second real-speed interval

data. Substrate configuration, biological feature occurrence, quantity and type were recorded for each

record. Video times were translated to position using DGPS logs recorded onboard. Where position

data were missing due to slowed DGPS data-logging, positions were estimated using an average of the

two nearest neighbors.

Whereposition data between two images or video analysis sequences were too imprecise to

detect a difference, latitude and longitude were adjusted between the two closest reliable points. 

Adjusted latitude and longitude for the sled and plow transects were estimated using one or two

functions. If latitude and longitude values did not change between time intervals, then latitude was

adjusted by a cosine function of heading that incorporated velocity, and longitude was adjusted by a

sine function of heading that incorporated velocity. If one or more successive positions were missing,
15

latitude and longitude values from the previous time step were used with a random value added (on the

order of 0.000001 degrees).

Still images collected at 15 sec and 60 sec intervals with the standard underwater camera

attached to the sled in 1998 were analyzed for features pertinent to substrate and habitat

characterization. Seventy-nine images from the 1-minute interval series and 83 images from the 15-

second interval series were visually analyzed for habitat features. Most features were accounted for in

terms of binary occurrence/absences (1 if present or 0 if absent), and more than one feature may have

been present per set, they were not necessarily mutually exclusive. Counts of features in the table

represent number per the 0.13 m2 image area. 

Several sets of parameters, listed below, were documented and are presented in the CD-ROM

appendix. Maps of the features elucidate the small-scale spatial variation and zonation inherent to the

regions studies (Map Atlas). 

Set 1 - Sediment Type. Presence/Absence of: 

• Silt (SI)

• Very Fine Sand to Fine Sand (VFSFS)

• Medium Sand to Coarse Sand (MSCS)

• Gravel (GR)

• Shell Fragments (SHFR)

• Large Shell Parts or Whole Shells (SHLG)

Set 2 - Bed Type. Presence/Absence or Predominance of: 

• Bedforms; Wave or Current Ripples (Bedf)

• Burrowed 

• Tracked 

• Small Tubes 

• Large Tubes (LgTubes)

• Tube Bed or Mat (TubeBed)
16

Set 3 - Surface Variance. Image brightness pattern properties induced by primary bottom

features:

• Uniform

• Graded

• Split

• Periodic

• Heterogeneous (HETERO)

Set 4 - Roughness. Primary origins of roughness (equivalent contribution by more than one

feature led to some cases where more than one parameter would be attributed responsibility):

• Small Ripples (SMALLRIPP)

• Large Ripples (LARGERIPP)

• Biogenic Structures, e.g. tubes (BIOSTRUCT)

• Evidence of Biogenic Activity, e.g. tracks (BIOACTIV)

• Sediment Grains or Shells (GRAINorSH)

• Unstructured; perhaps heterogeneous, or a surface in transition (UNSTRUCTURED)

Set 5 - Live Biology (LiveBiol); epifauna or structures attributable to certain infauna. Presence

or Absence of: 

• Asabellides oculata tubes (Asabellides)

• Diopatra cuprea tubes (Diopatra)

• Other Polychaete tubes (OtherPoly)

• Crustaceans, typically Cancer crabs (Crust)

• Hermit Crabs or Gastropods; shell inhabitants indiscernible (HermCrabORGast)

• Gastropods; discernible (Gast)

• Bivalves (Biv)

• Echinoderms; sea stars or sand dollars (Echin)

• Ascidaceans; tunicates (Ascidacean)

• Fish

• Other

3.2.4. Video Image Analysis Data

Expanding upon the analysis concept used for the 1998 still images, video acquired from sled

tows during the 1998 and 1999 deployment were visually analyzed at one second intervals. Substrate,

invertebrate fauna/biological features, and fish were classified into categories for each second of video,

played at one-third speed. Substrates were classified in terms of visible physical characteristics: 

Set 1 - Physical Characteristic Classes (SediHabi)

1. Sharp crested ripples, wavelength greater than video field of view, bedform crest
17

straight, no secondary ripples.

2. Sharp crested ripples, wavelength greater than video field of view, bedform crest

straight, secondary ripples asymmetrical.

3. Sharp crested ripples, wavelength greater than video field of view, bedform

asymmetric, secondary ripples asymmetrical.

4. Smooth crested ripples, wavelength greater than video field of view,no secondary

ripples.

5. Smooth crested ripples, wavelength less than video field of view, no secondary ripples.

6. Smooth crested ripples, wavelength less than video field of view, secondary ripples

asymmetrical.

7. Sandy bottom, bedforms not apparent

8. Uneven bottom, likely biogenic; or outcrops. 

Set 2 - Biogenic Structure Classes (BiogHabi)

1. No biology apparent.

2. Occasional single tube or organism.

3. Small patches of tubes or organisms.

4. Large patches or fields of tubes.

5. Dense tube beds or tube mats.

Maps of the physical and biological habitat feature classes can be found in the Map Atlas and on the

CD-ROM as file “9899sledviddata-final.dbf” that can be accessed via the GIS projects.

In addition to habitat classifications, each fish observed in the video from 1999 was reviewed at

slow speed until species, or lowest practical taxonomic level, identification could be determined by K.

Able, Rutgers University. Maps for the dominant benthic fish species collected in trawls, and seen in

video, are presented in the Map Atlas. 

3.2.4.1. SPI-Plow

SPI-Plow videos were reviewed in the lab, transferred to digital videotape, and archived onto

CD-ROM. 

3.2.5. Trawl

Fish and invertebrates collected in trawls were emptied into large container on-deck, sorted to major

taxa, counted and recorded, and preserved in formalin for laboratory processing and identification. In
18

the laboratory, preserved fish and invertebrates were identified to species or lowest practical taxonomic

level, weighed, and measured. Gut content analysis was preformed on the three most abundant fish;

Urophycis regia, Etropus microstomus, and Prionotus carolinus. Guts were removed and stored in

ethanol. Gut contents were sorted to major taxa, and where possible to the generic level. Contents

were enumerated and wet weight biomass determined by major taxa.

3.2.6. Sidescan Sonar

Sidescan records stored on magnetic disk were transferred to CD-ROM for archive along with

the program for reviewing the sonar image records. 

3.2.7. Position Data

Position data for point and transect interval samples are provided in the and continuous transect

logs are provided in digital form on the CD-ROM Appendix.

3.2.8. Grab Sample Substrate Data

Sediment grain-size distributions and sand fraction distributions were determined by VIMS

Analytical Services Laboratory. Sand:silt:clay ratios were using standard techniques and sand size

fractions were measured using a Rapid Sediment Analyzer. 

3.2.9. Grab Sample Biological Data

3.2.9.1. Preliminary Data Treatment

Prior to performing any of the analyses of the 1998 and 1999 benthic data several modifications

to the station by species matrix were made. The purpose of these modifications was to remove bias

that would result in calculation of diversity and similarity indices from either inflated number of taxa,

which in fact likely do not represent different species and represented identification problems, or

species that were not representatively sampled by the Young grab, or species not properly sampled by

the Young grab such as mobile epifauna. First, several non-infaunal mobile taxa were excluded, such

as hermit crabs (genus Paguras) because of their potential ability to avoid capture by grab. Second,

questionable taxa,such as Unidentified Bivalve, were excluded. Taxa in this group were either very
19

small or fragmented individuals. Third, data for some taxa were pooled. Usually this involved pooling

data for a taxon identified to a level higher than species (e.g., genus) with those data for a species within

the higher taxon. This pooling was done only when a single species of the genus was identified. For

example, Lumbrinerides dayi (a polychaete) was the only species of the genus found, so any

polychaetes identified only to the genus Lumbrinerides were treated as if they were L. dayi. In most

cases the species could not be determined on these organisms because they were small, immature

individuals or key taxonomic structures, like antennae or palps, were missing. Fourth, data for some

species were pooled to a higher level taxon, usually genus, because more than one species of a genus

was identified and many small or immature individuals could not be identified beyond the genus level. 

For example, two species of the bivalve genus Astarte were found, castanea and nana, but about

25% of all Astarte could not be speciated. Therefore, A. castenea and A. nana were combined into

the genus Astarte. This fourth data reduction stratagy would bias diversity and similarity indices in the

opposite direction of the first three and was only applied to six genera (Ampelisca, Astarte, Nucula,

Nephtys, Pseudeurythoe and Tellina). Fifth, several species were not consistently sorted from the

samples and were dropped. These included three species of small ascideans (sea squirts or grapes)

that closely resembled grains of sand and one very small polychaete (Spirorbis sp.) that builds a thin

calcarious tube on sand grains. One sorter did not recognize these four species.

 3.2.9.2. Community Analysis

Diversity and community structure calculations were done with the program PRIMER (Carr,

1997). PRIMER (Plymouth Routines in Multivariate Ecological Research) is a series of programs

developed at the Plymouth Marine Laboratory, United Kingdom, for analysis of benthic community

data. Information on PRIMER can be found at http://www1.npm.ac.uk/primer/. Magurran (1991)

describes all of the diversity indices used here. Shannon’s H’ was calculated by using log2: 

H’ = - Ópi(log pi)

Where pi is the proportion of the total counts arising from the ith species. Comparisons of H’ with other

studies must be done with caution, for two reasons; first, H’ can also be calculated using Napierien

logarithms or Log10 and, second, different sized samplers are affected by species-area relationships,

larger grabs collect more species than smaller samplers. 
20

Species richness was expressed at the total number of species in a sample (S) and Margalef’s

index (d): 

d = (S-1) / log N 

which also incorporates that total number of species present for a given number of individuals (N). 

Equitability of species distribution among individuals was expressed using two measures. 

Pielou’s evenness index that expresses how evenly the individuals are distributed among the different

species:

J’ = H’ / log S

where log S represents the maximal possible diversity that would be achieved if all species were equally

abundant in a sample. Simpson’s dominance index (SI) that expresses dominance of individual species

in a sample, essentially the reverse of evenness:

SI = Ópi

2

Cluster analyses were preformed with the program COMPAH96 (currently available on E.

Gallagher’s web page, http://www.es.umb.edu/edgwebp.htm) originally developed by at the Virginia

Institute of Marine Science in the early 1970’s. The sample and species clusters were generated using

flexible sorting with â of –0.25 and Bray-Curtis similarity, also known as Pielou’s (1984) percentage

similarity, calculated from simultaneous standardization of abundance (Boesch 1977):

Y = X / %(sample total* species total)

where Y is the standardized value of abundance (X). Any taxa that was present in three or fewer grabs

was eliminated from the cluster analysis. This resulted in a combined total of 73 of 166 total taxa being

dropped for both years. 

Results of the station and species clusters were compared using nodal analysis, which examines

the original data matrix rearranged into a two-way table based on the cluster defined groups. 

Constancy, a measure of the association of species with stations (Fager 1963), was calculated from the

nodal table based on the proportions of the number of occurrences of species in the station group to the

total possible number of such occurrences (Boesch 1977):
21

Cij = aij / (ni@nj)

where aij is the actual number of occurrences of members of species group i in station group j, ni is the

total number of species in group i, and nj is the number of stations in group j. Constancy will range

form 0.0 when none of the species in a species group occurred in a station group to 1.0 when all of the

species in a species group occurred in all of the stations of a station group. Fidelity, a measure of the

constancy of species in a station group compared to the constancy over all station groups (Fager

1963), was used to indicate the degree to which species prefer station groups (Boesch 1977):

Fij = (aij3nj) / (nj3aij)

where aij and nj are the same as defined for the constancy index. Fidelity is 1.0 when the constancy of

a species group in a station group is equal to its overall constancy, >1.0 when its constancy in a station

group is greater than that overall, and <1.0 when its constancy is less than its overall constancy. Values

of F >2.0 suggest strong preference of species for a station group and values <0.7 suggest avoidance of

these species from the station group in question (Boesch 1977).

3.2.10. Image Analysis: SPI and Sled Still

Digitized images were analyzed visually on computer screen and digitally using NIH Image

(NIH, public domain), Image Pro Plus® (Media Cybernetics), and Adobe® Photoshop® with the

Image Processing Toolkit© (Reindeer Games). Feature counts were made visually and linear and

aireal feature measurements were made digitally, by direct application of measurement tools and by

spatially calibrated grid overlays. A brief description of major image parameters follows:

Prism Penetration - This parameter provided a geotechnical estimate of sediment compaction

with the profile camera prism acting as a dead weight penetrometer. The further the prism entered into

the sediment the softer the sediments, and likely the higher the water content. Penetration was

measured as the distance the sediment moved up the 23-cm length of the faceplate. The weight on the

camera frame was kept constant at 341 kg (750 lbs.) so prism penetration provided a means for

assessing the relative compaction between stations.

Surface Relief - Surface relief or boundary roughness was measured as the difference

between the maximum and minimum distance the prism penetrated and provided qualitative and
22

quantitative data on habitat characteristics which can be used to evaluate existing conditions. This

parameter also estimated small-scale bed roughness, on the order of the prism faceplate width (15 cm). 

The causes of roughness can often be inferred from visual analysis of the film images and video. 

Apparent Color Redox Potential Discontinuity (RPD) Layer - This parameter has been

determined to be an important estimator of benthic habitat quality (Rhoads and Germano 1986, Diaz

and Schaffner 1988), providing an estimate of the depth to which sediments appear to be oxidized. 

The term apparent was used in describing this parameter because no actual measurement was made of

the redox potential. An assumption was made that, given the complexities of iron and sulfate reductionoxidation chemistry, reddish-brown sediment color tones (Diaz and Schaffner 1988), or in black and

white images whiter or lighter areas of the image (Rhoads and Germano 1986), were indications that

the sediments were oxic, or at least are not intensely reducing. This is in accordance with the classical

concept of RPD depth, which associates it with sediment color (Fenchel 1969, Vismann 1991).

The depth of the apparent color RPD was defined as the area of all the pixels in the image

discerned as being oxidized divided by the width of the digitized image. The area of the image with oxic

sediment was obtained by digitally manipulating the image to enhance characteristics associated with

oxic sediment (greenish-brown color tones). The enhanced area was then determined from a density

slice of the image.

The apparent color RPD has been very useful in assessing the quality of estuarine and coastal

embayment habitats for epifauna and infauna from both physical and biological points of view. Rhoads

and Germano (1986), Diaz and Schaffner (1988), Valente et al. (1992), Nilsson and Rosenberg

(1997) and Bonsdorff et al. (1996) all found the depth of the RPD from profile images to be directly

correlated to the quality of the benthic habitat in polyhaline and mesohaline estuarine zones. Controlling

for differences in sediment type, habitats with thinner RPD's (mm's) tend to be associated with some

type of environmental stress. While, habitats with deeper RPD's (cm's) usually have flourishing

epibenthic and infaunal communities.
23

Sediment Grain-size - Grain-size is an important parameter for determining the nature of the

physical forces acting on a habitat and is a major factor in determining benthic community structure

(Rhoads 1974). The sediment type descriptors used for image analysis follow the Wentworth

classification as described in Folk (1974) and represent the major modal class for each image. Grainsize was determined by comparison of collected images with a set of standard images for which mean

grain-size had been determined in the laboratory 

Surface Features - These parameters included a wide variety of features. Each gives a bit of

information on the type of habitat and its quality for supporting benthic species. The presence of certain

surface features is indicative of the overall nature of a habitat. For example, bedforms are always

associated with physically dominated habitats, whereas the presence of worm tubes or feeding pits

would be indicative of a more biologically accommodated habitat (Rhoads and Germano 1986, Diaz

and Schaffner 1988). Surface features were visually evaluated from each slide and compiled by type

and frequency of occurrence.

Subsurface Features - These parameters included a wide variety of features and revealed a

great deal about physical and biological processes influencing the bottom. Surface features were

visually evaluated from each slide and compiled by type and frequency of occurrence.

Successional Stage - Sediment profile data have also been used to estimate successional

stage of the fauna (Rhoads and Germano 1986). Characteristics associated with pioneering or

colonizing (Stage I) assemblages (in the sense of Odum 1969), such as dense aggregations of small

polychaete tubes at the surface and shallow apparent RPD layers, were easily seen in sediment profile

images. Advanced or equilibrium (Stage III) assemblages also have characteristics that were easily

seen in profile images, such as deep apparent RPD layers and subsurface feeding voids. Stage II is

intermediate to I and III, and has characteristics of both (Rhoads and Germano 1986). A group of SPI

parameters are evaluated to determine successional stage (- = not associated with, + = associated with,

+++ = strongly associated):

Successional Stage
24

Parameter I II III

--------------------------------------------------------------

Average RPD (cm) <1 1-3 >2

Max depth RPD (cm) <2 >2 >4

Small Tubes +++ ++ +

Large Tubes - ++ +++

Burrows - ++ +++

Feeding Voids - + +++

Small Infauna +++ ++ +

Large Infauna - + ++

Epifauna + ++ ++

--------------------------------------------------------------

Organism-Sediment Index - Rhoads and Germano (1982, 1986) developed the multiparameter organism-sediment index (OSI), from data provided by the sediment profile images, to

characterize benthic habitat quality. The OSI defines quality of benthic habitats by evaluating images for

depth of the apparent RPD, successional stage of macrofauna, the presence of gas bubbles in the

sediment (an indication of high rates of methanogenisis), and the presence of reduced sediment at the

sediment-water interface. The following parameter ranges and scores are used in the calculation of the

OSI (taken from Rhoads and Germano 1986):

Depth of the apparent color RPD: Estimated successional stage:

0 cm 0 Azoic -4

>0-0.75 1 I 1

0.76-1.50 2 I-II 2

1.51-2.25 3 II 3

2.26-3.00 4 II-III 4

3.01-3.75 5 III 5

>3.75 6 I on III 5

II on III 5

Methane voids present -2

No/Low DO -4

The OSI ranges from -10, poorest quality habitats, to +11, highest quality habitats. The OSI

has been used in estuarine and coastal bay systems to map disturbance gradients (Valente et al., 1992)

and to follow ecosystem recovery after disturbance abatement (Rhoads and Germano 1986). OSI

values >6 are generally associated with habitats that have well developed infaunal communities.

BHQ – The Benthic Habitat Quality index of Nilsson and Rosenberg (1996) was calculated
25

from the SPI data. The BHQ was developed to evaluate benthic habitat quality in Scandinavian fjords

and is based on biogenic structures seen in the SPI images that relate to infaunal successional stage as

described by Pearson and Rosenberg (1978). The BHQ is calculated from three basic groups of data

derived from SPI images: surface structures, subsurface structures, and mean depth of apparent RPD

(see Table 1 in Nilsson and Rosneberg, 1996). The BHQ ranges from 0 to 16 in its original

formulation. Nilsson and Rosenberg (1996) related the BHQ to successional stage as follows:

Successional Stage BHQ

0 <2

I 2 - 4

II 5 - 10

III >10

BHQ values greater than or equal to 5 should then indicate good quality benthic habitat.

3.2.11. Regional Condition Data

3.2.11.1. NOAA Buoy Data

Historical surface water temperatures from 1998 and 1999 were obtained from NOAA buoy

44009, through the website http://www.ndbc.noaa.gov/station_history?$station=44009. 

3.2.12. Habitat Classification and Biological Resource Information

3.2.12.1. Indices and Derived Statistics

Calculation of the OSI index is based upon mean depth of the RPD layer, presence or absence

of methane gas voids, evidence of hypoxia or anoxia, and successional stage (Rhoads and Germano,

1986). The OSI was abandoned because successional stages were indeterminate for most of the SPI

images and because the RPD depths observed exceeded the range provided for calculation of the OSI

was insufficient for most of the sediments sampled. The Benthic Habitat Quality index is based upon

the RPD and upon surface and subsurface biogenic features (Nilsson and Rosenberg, 1997). Although

the RPD scale designated by the BHQ discriminates levels as deep as 5 cm, it did not accommodate

most RPD's observed from the SPI images collected. Since the BHQ is image feature-based, as

opposed to estimation based OSI, it was calculated for the MD/DE SPI database. Although many of

the biological features were sparse, enough were evident to justify use of the BHQ. Since the RPD

parameterization did not support the range of RPD's observed in these shelf sediments, we modified the
26

BHQ to accommodate observed RPD variable ranges in order to provide a more generally applicable

biological resource index based upon sediment profile image data. 

We call the new index SBHQ, for Scaled Benthic Habitat Quality index. It is scaled in the

sense that RPD values are parameterized after adjustment for generalized sediment class, consolidation

and predicted permeability based upon compaction and compressibility. The other parameters

involved in calculation were maintained. For the SBHQ, scaling of the RPD incorporated three related

variables: gross sediment type (determined visually) and sediment compaction (derived from sediment

type), and prism penetration. As part of the index calculation, certain criteria had to be met by

variables involved. Prism penetration was compared to a threshold value determined by the value of

the 75% quartile of all penetrations measured divided by the compaction rating. The compaction rating

was determined from sediment type. Sediment type classes were grouped into the classes mud,

muddy-fine, fine, muddy-coarse, and coarse. Sediment compaction (inversely related to prism

penetration) is usually indicative of compressibility, and varies non-linearly with sediment type, tending

to be low for mud, high for fine to medium sands, and moderate for coarse sediments (Figure 3.2-1).

Because of that, compaction ratings (Cp) were applied as follows: 

Cpmud = 1, Cpmuddy-fine = 1.5, Cpfine = 3, Cpmuddy-coarse = 2.5, and Cpcoarse = 2

Then, the threshold penetration value (z) was set at the overall 75% quartile value divided by Cp, for

each sample based upon each sample's sediment class. For example, if the 75% quartile for

penetration was 10 cm, and the sediments were coarse grained (Cp=2), threshold penetration value

would be 5 cm. If penetration exceeded the threshold value, in this example 5 cm, and if RPD was

determinable, then the SBHQ index was calculated using the discrete feature count categories of the

BHQ and a scaled RPD. Penetration thresholds were applied because RPD depth is related to the

permeability, which is related to sediment compaction, consolidation, and sediment type, and therefore,

without respect to biological effects, should be deeper in certain sediments. If prism penetration was

insufficient in a particular case, then RPD depths could not be assessed quantitatively or qualitatively

(e.g. observed RPD versus ideal RPD), therefore that case would not be considered. 
27

The RPD was scaled using the following logic. If penetration exceeded threshold penetration,

for each compaction rating, and if RPD exceeded 50% of the actual penetration, scaled RPD was

calculated as (1/Cp)*RPD. If RPD was less than 50% of the actual penetration but greater than 10%

of the actual penetration, scaled RPD was calculated as (1/3.3*Cp)*RPD. If RPD was less than 10%

of the actual penetration but greater than zero, scaled RPD was calculated as (1/33*Cp)*RPD. If

RPD was zero, scaled RPD was zero. Scaled RPD values are not restricted to an absolute range, nor

are they categorized, therefore the SBHQ is also not restricted. However, since they are a function of

penetration depth and adjusted by sediment type, scaled RPD values are more applicable across

habitats and ecosystems, potentially facilitating system comparisons. Also, since Scaled RPD is

adjusted for the effects of physical advection of porewater by accounting for sediment class and

compaction, the parameter should reveal where biological influence upon RPD is relevant. 

3.2.12.2. Secondary Production

Estimates of secondary production were made from the grab samples using the model

developed by Tumbiolo and Downing (1994). This model incorporates basic life history information on

the species and the influence of environmental parameters (temperature and depth) to predict the level

of secondary production. Parameters in the model are:

Log P = 0.24 + 0.96Log B – 0.21Log Wm + 0.03 Ts – 0.16Log (Z+1)

were P is annual production in g Dry Weight (DW) m-2 y-1, B is average biomass in g DW m-2, Wm is

the maximum individual body mass in mg DW, Ts is annual mean bottom temperature in oC, and Z is

depth in m. Wm should be interpreted as the maximum size reached by the populations in the study

area and not the maximum size ever recorded. Because most individuals are small and published

maximum body mass for shallow continental shelf fauna are few, we estimated Wm from the size of the

individuals in the grab samples and applied conversion factors to taxonomic groups that most likely

represented the maximum size for shallow shelf fauna in the study area. Polychaete maximum individual

size was estimated to be a factor of five over the mean individual weight, bivalves and gastropods a

factor of two, and all other groups were given a factor of one. 

The wet weight biomass of each species from the grab samples was converted to dry weight
28

mass based on conversion constants in Waters (1977) and Rainer (1982):

Dry Weight = 0.16 Wet Weight

In general, conversion of various units of biomass to energy is possible because production is primarily

a phyisiological process that is similar for a broad range organisms from bacteria to mammals (Banse

and Mosher, 1980). For example, organic carbon is related to other units as follows: 

1 g C = 10 g Live = 12.5 g wet = 2 g Dry = 1.9 g AFDW = 20 g N = 0.004 g ATP = 2.7 Kcal 

For calculation of production, 12 major taxonomic groups were considered. Species and taxa

groups that were not quantitatively surveyed by the garb sampling were not considered, such as

decapods and echinodrems, even though they contributed significantly to overall community production. 

Both sled video and trawl samples indicated decapods and to a lesser extent echinoderms were

common throughout the study area. Thus our secondary production estimates should be considered

total macroinfaunal production.

3.2.12.3. Mapping

3.2.12.3.1. Point Maps

Point feature maps were produced using ESRI ArcView versions 3.1 and 3.2. Separate maps

were produced for the three Fenwick Shoals ROI's and the two Indian River ROI's. Maps are at

1:45000 scale, unless otherwise specified. 

3.2.12.3.2. Spatial Interpolation

IDW techniques - Two-dimensional interpolations of point feature data were mapped using

inverse distance weighted squared technique with ArcView and the Spatial Analyst extension. Fenwick

Shoals (FS) grid surfaces were created using 56 by 36 cell grid, with cell sizes of 0.0024 by 0.0024

degrees, and no correction for projection. Indian River (IR) grid surfaces were composed of 56 by 30

cells, with cell sizes of 0.0024 by 0.0024 degrees, and no correction for projection. Each cell is

approximately 265 m north-south by 210 m east-west.

3.2.12.3.3. Kriging 
29

Maps interpolated using ordinary kriging were produced using ArcView and the AvenueTM

Script MB.View.SpatialKriging (Boeringa, 1998) available from the ESRI website. FS kriged surfaces

were composed of 69 by 36 cells, with cell sizes of 216.13 * 216.13 m, upon a Lambert Conformal

Conic projection. IR data were not kriged.

3.2.12.3.5. Cokriging

 Cokriging estimates were produced using WinGSLIB(c) and GSLIB(c) routines (Statios LLC:

http://www.statios.com) (Deutsch and Journel, 1998). Model parameters used for cokriging varied and

are included with the resultant maps.
31

CHAPTER 4. RESULTS

4.1. Grab Sample Substrate Data

Sediment grain-sizes determined from grab subsamples indicated that the IR regions contained

larger grained sediments than the FS regions (Table 4.1-1). Sediments from 11 of the 14 IR grab

stations for which grain-size analysis was done, consisted of over 10% gravel (grain diameter >2 mm),

and six of those had over 20% gravel (Figure 4.1-1). Only four of the 36 FS grab stations for which

grain-size analysis was done, had sediments composed of over 10% gravel, and all were less than 20%

gravel. Seven of the FS grab stations had slightly muddy sediments (clay + silt > 1%), and four of

those had muddy (>2%) sediments. Whereas, three of the IR grab stations had slightly muddy

sediments, and only one had muddy sediments (Table 4.1-1 and Figure 4.1-1). 

From the sand fraction only, FS grab stations had a mean sand grain-size of 0.42 mm (SD ±

0.19), and IR grab stations had a mean sand grain-size of 0.52 mm (± 0.17) (Table 4.1-2).

4.2. Grab Sample Biological Data

4.2.1. Biological Data - Descriptive Summaries

4.2.1.1. 1998

4.2.1.1.1. Abundances

Among the 52 samples collected in May 1998, a total of 10,634 infaunal individuals

representing 152 taxa were found (1998 data appendix). Infaunal abundance varied about 780-fold,

ranging form 4 to 3,108 individuals/0.04 m2 (90 to 70,600/m2). Lowest abundance occurred at

stations FS10.5D and FS12E, and highest at station FS01G (Table 4.2-1). Mean (±95% confidence

interval, CI) abundance from all samples collected in 1998 was 204 (±129) individuals/0.04 m2 and the

median was 78 (±42) individuals/0.04 m2. The large difference between the mean and median was a

function of the underlying non-normal distribution of abundance and three outlier stations with high

abundance (FS01G, HCS31, and FS04C).

Annelid worms were the most abundant major infaunal taxon among the May 1998 samples followed

by molluscs and crustaceans (Table 4.2-2). Annelids accounted for >75% of the infauna at 19 stations,

with highest percentages, >90%, at three stations (Table 4.2-3). Molluscs were the overall second
32

highest contributors to infaunal abundance and were >50% of the infauna at two stations. Crustaceans,

the third most abundant major taxon, were only relatively important contributors, >50%, at seven

stations with low total infaunal abundance, <40 individuals/0.04 m2. None of the other major taxa was

an important contributor to abundance. At a slightly finer taxonomic scale, oligochaetes were 15% of

the annelids and polychaetes 85%, gastropods were 3% of the molluscs and bivalves 97%, and

amphipods were 60% of the crustaceans.

4.2.1.1.2. Number of Species

The total number of species per sample collected in May 1998 varied about 10-fold, ranging

from 3 to 35 at station FS10.5D and IR04E, respectively (Table 4.2-4). Mean (±95% CI) number of

species from all samples collected in 1998 was 17.8 (±4.4) and the median was 17 (±5) species per

sample.

Among the major taxa collected in May 1998, overall, annelid worms contributed the highest

percentage of species (Table 4.2-5). Annelids accounted for from 0 to 85% of the species collected at

each station. Crustaceans and molluscs accounted for about 22-23% of the species, overall, and 0 to

about 50% of the species on an individual station bases. Within each of their respective major taxa,

polychaetes, amphipods, and bivalves provided the greatest contribution to species numbers.

4.2.1.1.3. Biomass 

Among the 52 samples collected in May 1998, representing 10,634 infaunal individuals, total wet

weight biomass was 214.8 g (1998 biomass data appendix). Biomass varied by over 6,000-fold, ranging

form 14 mg to 88.6 g wet/0.04 m2 (0.3 to 2,000 g wet/m2). Lowest biomass occurred at station

FS10.5D and highest at IR05D (Table 4.2-6). Mean (±95% confidence interval, CI) biomass from all

samples collected in 1998 was 4.1 (±4.5) g wet/0.04 m2 and the median was 0.2 (±0.07) g wet/0.04 m2. 

The large difference between the mean and median was a function of the underlying non-normal

distribution of biomass and outlier stations that contained large individuals, most of which were molluscs,

such as stations IR05D and IR05.5C (Table 4.2-6). 

Molluscs made up most of the biomass in the May 1998 samples accounting for about 87% of
33

the total biomass. The second highest contribution to biomass was from polychaetes, about 6%. 

Gastropods and amphipods were about 3% and 1%, respectively (Table 4.2-7). All other taxa

contributed about 3% of the total biomass.

4.2.1.2. 1999

4.2.1.2.1. Abundances

Among the 20 samples collected in June 1999, a total of 6,145 infaunal individuals representing

108 taxa were found (1999 data appendix). Infaunal abundance varied about 130-fold, ranging form

10 to 1,336 individuals/0.04 m2 (230 to 30,400/m2). Lowest abundance occurred at station FS12E,

and highest at station FS04C (Table 4.2.1). Mean (±95% CI) abundance from all samples collected in

1999 was 307.2 (±162.8) individuals/0.04 m2 and the median was 87.5 (±75.4) individuals/0.04 m2. 

The large difference between the mean and median was a function of the underlying non-normal

distribution of abundance and outlier stations with high density. 

Annelid worms were the most abundant major infaunal taxon among the June 1999 samples

followed by crustaceans and molluscs (Table 4.2-2). Annelids accounted for >80% of the infauna at

four stations, with highest percentage of about 87% at station FS07B2 (Table 4.2-3). Crustaceans and

molluscs were about equal contributors to infaunal abundance and were >50% of the infauna at three

and five stations, respectively. None of the other major taxa was an important contributor to

abundance. At a slightly finer taxonomic scale, oligochaetes were 8% of the annelids and polychaetes

92%, gastropods were 8% of the molluscs and 92% bivalves, and amphipods were 92% of the

crustaceans. 

4.2.1.2.2. Number of Species

The total number of species per sample collected in June 1999 varied about 7-fold, ranging

from 6 to 40 at station FS04E and FS07B2, respectively (Table 4.2-4). Mean (±95% CI) number of

species from all samples collected in 1999 was 20.4 (±4.6) and the median was 18.5 (±6) species per

sample.

Among the major taxa collected in June 1999, overall, annelid worms contributed the highest
34

percentage of species (Table 4.2-2). Annelids accounted for from 0 to 65% of the species collected at

each station. Crustaceans and molluscs accounted for about 20-21% of the species, overall, and <5 to

about 60% of the species at each station (Table 4.2-5). Within each of their respective major taxa,

polychaetes, amphipods, and bivalves typically provided the greatest contribution to species numbers. 

4.2.1.2.3. Biomass

Among the 20 samples collected in June 1999, representing 6,145 infaunal individuals, total wet

weight biomass was 214.8 g (1998 biomass data appendix). Biomass varied by over 6,000-fold,

ranging form 14 mg to 88.6 g wet/0.04 m2 (0.3 to 2,000 g wet/m2). Lowest biomass occurred at station

FS10.5D and highest at IR05D (Table 4.2.6). Mean (±95% confidence interval, CI) biomass from all

samples collected in 1998 was 4.1 (±4.5) g wet/0.04 m2 and the median was 0.2 (±0.07) g wet/0.04 m2. 

The large difference between the mean and median was a function of the underlying non-normal

distribution of biomass and outlier stations that contained large individuals, most of which were molluscs,

such as stations IR05D and IR05.5C (Table 4.2-6). 

Molluscs made up most of the biomass in the June 1999 samples accounting for about 64% of

the total biomass. The second highest contribution to biomass was from polychaetes, about 24%. 

Amphipods and gastropods were 6% and 3%, respectively (Table 4.2-7). Cephalochordates were

about 1% of the biomass and all other taxa contributed about 2% of the total biomass.

4.2.2. Biological Data - Community Structure and Function

4.2.2.1. Diversity and Evenness

As measured by the Shannon index (H’), diversity among individual stations collected in May

1998 varied from 0.44 at station FS01G to 4.00 at station IR04B (Table 4.2-8). Species Richness

(SR), Evenness (J’), and Simpson Dominance (d) among stations ranged widely from about 1.0 to 4.1,

0.1 to 1.0, and 0.1 to 0.9, respectively. As diversity increased, SR (r = 0.78 p = <0.001) and J’(r =

0.51 p = <0.001) also increased while d (r = -0.91 p = <0.001) declined. 

Diversity among individual stations collected in June 1999 varied from 1.70 at station FS10B to

4.00 at station FS08D (Table 4.2.8). Species Richness (SR), Evenness (J’), and Simpson Dominance
35

(d) among stations ranged widely from about 1.0 to 4.1, 0.1 to 1.0, and 0.1 to 0.9, respectively. As in

May 1998, diversity in June 1999 was correlated to SR (r = 0.68 p = 0.001), J’(r = 0.54 p = 0.013),

and d (r = -0.89 p = <0.001). At station FS07B the three replicate grabs, collected to evaluate withinstation variation, had very consistent diversity values (Table 4.2.8).

4.2.2.2. Numbers of Taxa and Species

The total number of taxa from May 1998 and June 1999 included in the analysis of infauna was

166. Criteria for inclusion are described in the methods (Section 3.2.10.1 Preliminary Data

Treatment). In May 1998, 152 taxa were collected and 102 in the June 1999 collections. The

majority of taxa were identified to species level (141 species in the 166 taxa collected). Of the 25 nonspecies taxa 19 were at the generic level and the other 6 at higher taxonomic levels, such as

Oligochaeta. The lower number of taxa in the June 1999 collections reflects the strong species-area

relationship know to exist in marine systems (Sanders, 1968; Hurlbert 1971). In general, the larger the

area sampled the more species encountered, up to an asymptotic level that would characterize the total

species diversity for a system. The distribution of individuals among the species followed the classical

log-normal distribution of species occurrence (Hurlbert, 1971). The majority of species, about 54%,

occurred in fewer than five of the 72 stations occupied (Figure 4.2-1).

4.2.2.3 Cluster Analysis

Cluster analysis of the 1998 and 1999 infaunal data, all 72 grab stations, segregated the stations

into five dissimilar groups, which were subdivided into a total of 11 subgroups (Figure 4.2-2), and

species into six dissimilar groups (Figure 4.2-3).

The basic patterns in both cluster analyses appeared to be controlled by species-habitat or

species-sediment preferences. Year to year difference among the stations was minimal with only a

subset of station group D being exclusively composed of 1999 stations. Subgroup D’ was composed

of five stations from Fenwick Shoals that were all sampled in 1999. An analysis of only the 18 stations

sampled both in 1998 and 1999, using the same methods, yielded four dissimilar groups none of which

were composed of stations from a single year (Figure 4.2-4). For 15 of these 18 stations, both years
36

occurred within the same cluster group indicating a strong qualitative and quantitative similarity of fauna

between years. Stations FS12F and FS13F split years between cluster groups primarily due to

quantitative differences between species present, variation in numbers of the same species. Station

FS08E split years due to qualitative differences with a doubling of species from 1998 to 1999.

Station group A, from the combined analysis of all data (Figure 4.2-2), was the largest group

being composed of 28 stations from the BB, IR, and FS areas. Group A stations had high species

richness (SR) but low total abundance. Median and mean Shannon diversity, H’, was highest for

station group A (Figure 4.2-5). Aposteriori contrast of richness, however, did not find station group A

to be significantly different for other high species richness groups (groups B and E) but did find group A

to be significantly higher than lower richness groups C and D. Three stations from group A (BB04,

FS10B, and IR09C) had low H’ due to low evenness, J’, and a high degree of dominance, d, of a few

taxa such as oligochaetes and Brania wellfleetensis. These three stations consistently appear in Figure

4.2.5 as outliers in the H’, J’, and d boxplots. The last two stations to join group A (FS10B and

FS12B) were weakly associated with other stations in group A and had lower H’, J’, and SR (Table

4.2.8).

Station group B was composed of eight FS stations similar in characteristics to group A, except

group B while having similar total species occurrence had lower SR and H’. Group C was composed

of three FS stations and represented areas with lowest total abundance and species occurrence, SR,

and H’. Group C had high J’ and highest d of all station groups. Station group D was composed of 11

stations (10 from FS and one from IR) with low abundance and intermediate values for most of the

other community structure measures, including numbers of species, SR, H’, and d. The only exception

was station FS04C, which was an outlier with higher abundance and lower J’ (Figure 4.2-5). Average

and median J’ for group D was highest of all station groups. Group D also had the strongest year-toyear difference with subgroup D’ composed of five FS stations from 1999. As explained above, this

yearly signal was caused by within year similarity of stations not sampled both years.

Station group E was composed of 12 stations from FS, IR, and the single HCS station. Group
37

E represented station with highest abundance and species occurrences. Aposteriori contrast of

abundance and species, however, did not find station group E to be significantly different for other

groups A and B which also had high abundance and species totals, but did find group E to be

significantly higher than groups C and D for these parameters. Within group E, five stations from IR

formed a separate subgroup (E’’) that had higher H’ and J’ and much lower total abundance than

group E’ stations. Overall, stations from IR tended to have lower abundance than FS (Table 4.2.2).

Species groups formed primarily around subtle differences in sediment preference. Species

groups I, II, and III tended to be representative of coarse sediment stations while groups IV, V, and VI

represented finer sediment stations. Nodal analysis of the station/species data matrix indicated that

species group I had a high constancy and fidelity to station group B (Table 4.2-9). Species group II

was most associated with station group A. Species group III was not characteristic of any single

station group, but occurred across all station groups with low constancy and fidelity. Species group IV,

which contained the highest abundance species, such as Spiophanes bombyx, was strongly associated

with all of station group E. Both species groups V and VI were highly associated with station group E’’

(Table 4.2-9).

4.2.2.4. Dominant Species

While a total of 166 taxa were collected over both cruises, only 31 taxa occurred at >20% (at

least 15 of 72) of the stations (Table 4.2-10). By comparison, the median number of station

occurrences for a taxa was four and about 28% of the taxa occurred once. Of the top 31 taxa,

oligochaete worms were most widely distributed and occurred at about 80% of the stations. This taxa

represents at least two families of oligochaetes (Tubificidae and Enchytraeidae) and many species. As

a group oligochaetes are diverse on the middle Atlantic continental shelf (Diaz et al., 1987) and difficult

to identify. Two other major taxonomic groups, which could not be speciated, were among the

occurrence dominants; anthozoans or sea anemonies (21%) and nemertiean worms (76%). Thirteen

polychaetes were among the top occurring taxa being found at about 55 to 21% of the stations. Of the

six occurrence dominant bivalves, Tellina spp. was found at 65% of the stations while the rest occurred

at 32 to 21%. Crusteaceans were represented by eight taxa that occurred at from 42 to 21% of the
38

stations. The cephalochordate Branchiostoma caribaeum, or sand lance, occurred at 25% of the

stations.

4.2.2.4.1. By Abundances

Total abundance of taxa was distributed in a similar manner as species with most taxa being

rare. About 52% of all taxa had a total abundance of <10 individuals at all stations, combining both

cruises (Table 4.2-11). One taxon dominated both cruises. The polychaete Spiophanes bombyx

accounted for about 35% of all individuals collected. The next closes taxa was oligochaeta being about

10% of all individuals and no other taxa represented more than 5%. If the overwhelming dominance of

Spiophanes bombyx was removed than three additional taxa were >5% of the individuals (Table 4.2-

11). The top 26 taxa, each being at least 1% of individuals for one cruise, cumulatively represented

86% of all individuals. Of these taxa polychaetes represented 15, bivalves five, and crustaceans four. 

Oligochaeta and Nemertinea were also included.

4.2.2.4.2. By Biomass

Wet weight biomass was dominated by the bivalve Spisula solidissima, the surf clam, which

composed about 65% of the total biomass for both cruises (Table 4.2-12). The next top 23 taxa with

>0.5 g wet wt/0.04 m2 were about 30% of the total biomass. Bivalves were the major contributors to

biomass, eight being in the top 24 taxa, followed by eight polychaetes, two gastropods, nemerteans,

one cephalochordate, three amphipods, and one isopod. The dominant biomass taxa also tended to

have the largest mean individual weights, but there were another 14 taxa that while not dominant (>0.5

g wet wt/0.04 m2) had mean individual weights >20 mg wet wt (Table 4.2-12). This group included

three bivalves, three amphipods, and eight polychaetes.

4.2.2.5 Dominant Community Groups

Ten taxa were considered to be overall dominants within the study site (Table 4.2-13). These

overall dominants were defined as the taxa that appeared on all three lists of dominant taxa (occurrence

Table 4.2-10, abundance Table 4.2-11, and biomass Table 4.2-12). The relationship between
39

dominant taxa (those that occurred at >20% of the stations, were >1% of the individuals for at least

cruise, and had >0.5 g total wet weight biomass) and cluster analysis species groups was primarily

related to sediment grain-size with Nemertina, Astarte spp., Crenella glandula, Mytilus edulis, and

Byblis serrata characteristic of coarser grained sediments that had significant amounts of gravel or

coarse-sands. Asabellides oculata, Spio setosa, Spiophanes bombyx, Tellina spp., and Unciola

irrorata were all associated with finer grained sediments that had significant amounts of fine-sands or

even silts. All of these top dominants were broadly distributed but the coarser sediment dominants had

highest fidelity within cluster station groups A and B, which represented most of the coarse sediment

stations, and finer sediment dominants had highest fidelity within cluster groups D and E (Table 4.2-14). 

Similar preferences for sediment type were seen among other important taxa. The exception

was the Oligochaeta taxon that likely represented a multispecies mix of at least 10 species. Diaz et al.

(1987) found the species richness of oligochaetes to be high on the shallow Middle Atlantic continental

shelf. Oligochaeta was an occurrence and abundance dominant at all cluster station groups, except C

(Table 4.2-14). Group C was composed of three Fenwick Shoals stations (FS02.5 and FS13E from

May and FS03E from September) that were depauperate relative to other station groups. 

4.2.2.6 Life History Attributes

The dominant taxa represented a broad range of life history traits (Appendix B and summarized

in Table 4.2-15). In general, the literature indicates that shallow continental shelf macrobenthic

communities are controlled primarily by sediment grain-size and bottom topography. The life histories

of the dominants reflect this physical processes control of species distributions with many of the taxa

restricted to either coarse sands or fine sands. The feeding type of the majority of the dominants was

either suspension feeders, common in high energy and high particulate habitats, or carnivorous. Deposit

feeders played a less prominent role, a reflection of the lack of fine sediment depositional areas within

the study area. The majority of the dominants had some ability to move being free-burrower like

nemertean worms, tube builders with mobility like the amphipod Ampelisca spp., or mobile surface

dwellers like the cumacean Oxyurostylis smithi. Overall, the predominance of mobile fauna reflects
40

the dynamic nature of shallow continental shelf habitats. 

Dominants could be separated into two basic spawning modes, discrete spawners that have

one or two spawns per year like the surf clam Spisula solidissima and multiple event spawners like the

polychaete Aphelochaeta sp. (Table 4.2-15). Typically the spawning mode matches a species larval

development mode and life span, with annual species (completes life cycle in a year or less) that have

planktonic larvae spawning once during the year. Annual species that brood typically have multiple

spawning events during the year. Longer-lived, greater than one year, species tend to spawn once or

twice a year.

The potential of a species to recolonize an area that has been mined for its sand resources will

primarily be a function of its life history traits. The traits summarized in Table 4.2-15 were evaluated

and a recruitment or recolonization potential was determined for each species. Unfortunately, the life

histories of many species are not well known. Of the 37 taxa and species considered, complete

information the life history table was only found for 16 species. Three categories of recolonization

potential were considered based on season; Year Round (YR), Spring/Summer (SS), and Fall/Winter

(FW). A species was considered to be a good YR colonizer if it had a broad range of sediment

preferences, spawned more than once a year over several seasons, and was an annual. Any species

with good mobility or dispersal was considered a good colonizer. For example, oligochaetes while

small and not able to move long distances on their own can recolonize a habitat as adults any time of the

year by being carried along as part of the bed load transport. Thus storm conditions would aid in the

dispersal of oligochaetes. Poor YR colonizers were considered those species that spawned once per

year and recruited over a single season to a limited range of sediment types. Good SS or FW

colonizers were those species that recruited during spring/summer or fall/winter, respectively, and had

good mobility. A total of 15 species were considered to be good and 18 poor YR colonizers. Of the

18 poor YR colonizers, eight were good SS colonizers and seven good FW colonizers (Table 4.2-15). 

We had insufficient information to categorize four of the dominant taxa. 

Taxonomically the best YR recolonizers were Nemerteans, oligochaetes, gastropods,
41

cumaceans, and cephalochordates, each for a particular life history trait. Amphipods, in general, were

poor YR recolonizers mainly because of limited reproductive periods, which made the five species

good SS colonizers. Anemonies was the only taxonomic group considered to be a poor recolonizer for

any season. Bivalves and polychaetes were split between good and poor YR recolonizers with half of

the bivalves and about one-third of the polychaetes considered good YR colonizers. The three poor

YR bivalve species were considered to be good FW colonizers. Among the poor YR polychaetes,

three were good SS, three were good FW, and three could not be assigned a good recruitment season

(Table 4.2-15).

4.3. Fish Trawls

4.3.1. Fish Assemblages

On the May 1999 cruise a metered beam trawl was used to assess fish use in the four major

habitat types delineated by the June 1998 data. The four habitats were the northeast seaward flank of

the shoal (NE) primarily coarser sands with gravel and shell hash, the northwest shoreward face of the

shoal (NW) primarily medium and fine sands with some shell hash, the southeast seaward trough (SE)

where surface sediments medium and fine sands and dominated by Diopatra tubes, and the southwest

shoreward trough (SW) where surface sediments were finer sands with some silt and dominated by

Asabellides tubes. The first two habitats represent physically dominated bottom with little evidence of

biological control over habitat characteristics. The last two habitats represent biologically dominated

bottom and are named after the predominant biogenic structure present. Both species are polychaetes

that construct large tubes, Diopatra uses fragments of organic debris and shell, Asabellides uses fine

sand. Trawls were conducted during both day and night at two locations within each habitat type

(Figure 4.3-1). Within each habitat four trawls were collected during the day and four at night to

evaluate diurnal patterns of habitat use.

A total of 333 fish representing 20 species were collected at the four habitats along with 35

species of invertebrates (Table 4.3-1, 4.3-2). The most abundant fish was the hake, Urophycis regia,

followed by the Etropus microstomus. Together they were about 70% of the fish caught and common

members of the shallow continental shelf fish assemblages (Able and Fahay, 1998). Cluster analysis of
42

the fishes grouped by habitat and day/night trawls indicated that there were day/night differences in fish

caught in the SW Asabellides tube and NW sand habitats. Two of the five species groups were

associated with the SW Asabellides tube habitat. Species group D being six species mostly associated

with day trawls in the SW Asabellides tube habitat. Group E was four species caught only at night in

the SW Asabellides tube habitat (Figure 4.3-2). Group C was primarily a night time group of three

species mostly associated with the NW sand habitat. Species group B were day/night species from the

NW sand and SE Diopatra habitat. Group A was the numerically dominant species that occurred in

all habitats both day and night (Table 4.3-1, Figure 4.3-2). 

The association of fishes between habitats appeared to be related to sediment grain-size, bed

roughness, and presence of biogenic structure. Cluster analysis indicated that both the NE coarser

sand/gravel and SE Diopatra tube habitats had the similar fish assemblages (Cluster group I, Figure

4.3-3). The NW sand habitat (group II) fish assemblage was most similar to the more dynamic sandy

habitats represented in group I (Figure 4.3-3). The SW Asabellides tube habitat (group III) was the

most dissimilar of the four habitat types. 

The trawl also collected many mobile and sessile invertebrates (Table 4.3-2) that were not 

collected quantitatively by the grab. The most abundant being Pagurus spp., Libinia emarginata, and

Cancer irroratus crabs. Large gastropods Busycon canaliculatum and Polinices spp. were also

collected. Other large collected were the infaunal bivalves Spisula solidissima and Ensis directus that

are know to jump out of the sediment in response to a disturbance. Astarte spp. also a bivalve known

to lie on the sediment surface was collected along with the echinoderms Asterias spp. and

Echinarachnius parma. Overall, crabs were most abundant in the habitats with biogenic structure,

SW Asabellides and SE Diopatra tube habitats, and appeared to be using these habitats as nursery

areas since the most of the individuals were small (<5 cm). Other species were broadly distributed

across all habitats such as Nudibranchs, Pagurus spp., Crangon septemspinosa, and Asterias sp. 

The two species that appeared to prefer the sandy more dynamic habitats were Polinices spp. and

Echinarachnius parma.
43

4.3.2. Fish Gut Content

Gut content was analyzed for the three fish species that were dominants and occurred in all four trawled

areas (Table 4.3.1). Overall, Urophycis regia was the most abundant species collected with 78 guts

from fish that ranged from 43 to 215 mm were examined. A total of 36 guts from Etropus

microstomus that were 42 to 125 mm and 22 guts from Prionotus carolinus that ranged from 47 to

200 mm were also examined. All these fish represented young of the year or year class +1 individuals. 

A total of 45 taxa were identified from the guts of these three fishes (Table 4.3-3). The most numerous

food items were epifaunal or near surface infaunal species in the decapod, amphipod, and mysid

taxonomic groups and accounted for over 90% of all gut items and biomass. Because the fish were all

small, total range of 42 to 215 mm, the average size of the food items was also small being about 3 to 9

mg wet weight. Polychaetes and other soft bodied taxa were not well represented in the gut content

likely because of rapid digestion. Only large individuals were recognizable in the guts. The average wet

weight of the polychaetes found in the guts was 25 mg that was 16 mg larger then the grand average

individual weight (9 mg) of all polychaetes collected in the grab samples.

Based on the gut content analysis, benthic habitats with high numbers of epifauna, particularly

crustaceans, and amphipods would have higher resource value then habitats without epifauna. 

Epifaunal species, those that live on or near the sediment surface, were the most common food item in

the stomachs of the fish examined. The presence of abundant epifauna, such as mysid shrimp and

Crangon septemspinosa, in an area would then attract fishes and provide more resource value relative

to areas with little to no epifauna. Unfortunately, the grab sampler did not quantify the abundance of

many of the mobile epifaunal species, for example mysids or the small shrimp Cragnon septemspinosa

that turned out to be the most abundant species found in the guts. The second most abundant food item

were the aphipods, which quantified with the grab. Most of the aphipods were either surface tube

builders (Ampelisca and Unciola) or shallow free burrowing infaunal species like the haustorids. The

distribution of amphipods around Fenwick Shoal as characterized by grab samples corresponded well

with their occurrence in fish guts.

4.4. SPI Image Analysis Data
44

4.4.1. Standard SPI Analysis Data

Sediment profile image analysis produced measures of sediment type, sediment-water interface

properties, and biogeochemical features, and counts of organisms and biogenic features. These data

formed the bases for many of the benthic habitat maps in the Map appendix and are contained on the

CD-ROM appendix. For all of the SPI parameters, their spatial variability is often more informative

than the descriptive statistics described here, and the maps below have been included to synthesize the

spatial patterns. Example images depicting the range of benthic habitats found can be found in Figures

4.4-1 to 4.4-5.

RPD: In the Fenwick Shoals Regions, 1998 apparent color redox potential discontinuity layer

depth (RPD) averaged 7.8 cm (SD=3.2; SE=0.3). Along the SPI transects in the Fenwick Shoals

Region, 1999 average RPD was 6.1 cm (3.0; 0.3). In the Indian River Regions, 1998 average RPD

was 7.4 cm (3.3; 0.5). See Map appendix. 

The lower average RPD in the FS Regions in 1999 was a result of sampling locations. The

1998 data represent broad coverage with samples taken at approximately 500 to 1000 m spacing. In

1999, SPI images were collected along several transects at short intervals, typically less than 50 m. 

Also, the 1999 transects were focused upon transition zones, where steep gradients in substrate

properties occurred over short distances. 

Prism Penetration: Average prism penetration into the substrate for Fenwick Shoals Regions,

1998 was 8.3 cm (3.2; 0.3). Along the SPI transects in the Fenwick Shoals Region, 1999 average

penetration was 6.8 cm (3.4; 0.3). In the Indian River Regions, 1998 average was 7.2 cm (3.2; 0.4).

Visible Infauna: Infauna were observed in images at 13 stations in Fenwick Shoals Regions,

1998, at three stations along the SPI transects in the Fenwick Shoals Region, 1999, and at three

stations in the Indian River Regions, 1998. Most of the infaunal organisms appeared to be freeburrowing polychaete or nemertean worms and associated with the more physically dominated habitats.
45

Infaunal Feeding Voids: Few voids were observed overall. Voids were present in images at

2 stations in Fenwick Shoals Regions, 1998, at 5 stations along the SPI transects in the Fenwick Shoals

Region, 1999, and at two stations in the Indian River Regions, 1998. The development of voids is very

much related to grain-size in particular the fine (silt-clay) content. Sediments with less than 10% fines

typically do not support void structures (Diaz and Schaffner, 1988).

Fecal Pellets: Fecal pellets were present in images at 18 stations in the Fenwick Shoals

Regions, 1998, at 21 stations along the SPI transects in the Fenwick Shoals Region, 1999, and at

seven stations in the Indian River Regions, 1998. 

Sediment-Water Interface Relief: In the Fenwick Shoals Regions, 1998 sediment-water

interface (SWI) relief averaged 2.3 cm (SD =1.5; SE = 0.1). The origins of SWI relief, or roughness,

generally could be attributed to one or a combination of four factors: bedforms (wave and/or currentinduced sand ripples), sediment grains, or biogenic structures, or shell (whole, fragments, or hash) (See

Map Atlas). In the FS Regions, 1998 SWI relief was dominated by ripples at 126 of 154 SPI stations

analyzed for relief. In the FS Regions, 1998 biogenic features or reworked bedforms were sometimes

apparent, resulting in 33 of 154 stations where biogenic features were independently or co-responsible

for SWI relief, typically in the deeper, muddier areas (See Map Atlas). Sediment grains, generally

pebbles, dominated SWI relief at eight of the 154 stations in the FS Regions, 1998. 

In the Indian River Regions, 1998 average SWI relief was 2.6 cm (SD = 1.4; SE = 0.2). In the

IR Regions SWI relief was dominated by ripples at 35 of the 61 SPI stations analyzed for relief. 

Biogenic roughness dominated at only four of the 61 stations, and grain roughness dominated at 26 of

the 61 station in the IR Regions. 

4.4.2. SPI Grain-size Analysis

Sediment grain-size determinations made using SPI images were performed by visually

classifying sediments into Wentworth size classes, including mixed classes, the range of observed

classes are presented in Figure 4.4-6. Size classes were converted to millimeter size estimates using
46

Folk's (1974) scheme. The median value for the primary sediment class was taken as the initial value,

then adjusted by the number of size steps larger or smaller dependent on the number of size class

differences between the primary and secondary sediment components. For example, a sediment

classification of medium sandy coarse sand (mscs) indicated coarse sand as the primary component and

medium sand as the secondary component. The median size value for coarse sand was 0.71 mm, but

since the secondary component was medium sand, the value from the next lower size step, 0.59 mm,

would be used. Had the secondary component been fine sand, two size classes lower than coarse

sand, the value of 0.5 mm that was two size steps smaller would have been used. The size classes and

converted size estimates as well as more general descriptions of the sediment types and coarseness are

presented in Table (4.4-1). 

4.4.3. Comparison of SPI and Grab Grain-size Determinations

The agreement between grain-size estimates from SPI images and those from grab samples was

good. SPI and grab grain-sizes were compared using paired t-test for stations where only sandy

sediments were found; where zero percent or negligible amounts of clay, silt, or gravel were present. 

Direct comparison was made for samples containing only sandy sediments because the Rapid Sediment

Analyzer (RSA) results characterized the entire grain-size distribution for those sediments only. 

Twenty-seven of the stations met these criteria. A paired t-test between square-root transformed mean

grain-size from the RSA analysis and square-root transformed modal grain-size from SPI

determinations showed no significant difference (p=0.70). 

A linear regression of the square-root transformed data, excluding an outlier from station

FS09D, reveals the relationship for sandy sediments: 

%(Mean Grab RSA Grain-size (mm)) = 0.284 + 0.506*(%(Modal SPI Grain-size (mm)))

(df = 24, R-square = 0.53, p = 0.0001).

The grain-size relationship is presented in Figure 4.4-7, after forcing the line through the origin. When

sorting is accounted for by multiple linear regression of the square-root transformed grain-size data, the

relationship is improved. The model for all grab station samples is:

%(Mean Grab RSA Grain-size (mm)) = 0.906 +0.224*(%(Modal SPI Grain-size (mm)))
47

-0.699*(Sorting Value in mm from RSA, "SIMM")

(R-square = 0.60, p = 0.0001).

4.5. Sled Still Image Analysis Data

Sled still image data were used to augment many of the benthic habitat maps contained in the

Map appendix and also to confirm modeled surface sediment grain-size maps. A range of different

benthic habitats were documented from physically dominated coarse sands (Figure 4.5-1) to

biologically dominated very-fine to fine-sands (Figure 4.5-2). 

4.6. Video Image Analysis Data

Video image data from the 1998 and 1999 sled tows produced detailed information on physical

and biological characteristics of the bottom over broad-scales (> Km). These data were used to

augment many of the benthic habitat maps contained in the Map appendix. The data files from the sled

video analysis are contained in the CD-ROM appendix.

4.7. Regional Condition Data

4.7.1. NOAA Buoy Data

1998: Surface water temperature records from archived data from NOAA buoy 44009, for

1998 are summarized in the graph in Figure 4.7-1 a. Water temperature from the period during the

1998 sampling cruise are presented in Figure 4.7-1 b. Average surface water temperature during the

1998 cruise (May 17-21, 1998) was 14.3 EC (SD=1.5). 

1999: Surface water temperature records from archived data from NOAA buoy 44009, for

1999 are summarized in the graph in Figure. Average surface water temperature during the 1999

cruise (June 12-17, 1999) was 18.8 EC (SD=0.3).

4.8. Biological Resource Information

4.8.1. Indices and Derived Statistics: SPI-based BHQ and SBHQ

Overall BHQ values for 1998 ranged from 1 to 13 and averaged 5.6 (SD= 1.76, CV= 31.19;
48

SD: Standard Deviation, CV: Coefficient of Variation). In the Indian River Regions, 1998 BHQ values

ranged from 1 to 8 and averaged 5.0 (SD= 1.26, CV=25.09). In the Fenwick Shoals Regions, 1998

BHQ values ranged from 1 to 13 and averaged 5.8 (SD= 1.86, CV=31.77). While the overall range

of the SBHQ for 1998 was the same as the BHQ, the averaged SBHQ was consistently lower being

3.2 (SD= 1.81, CV=55.79). In the Indian River Regions, 1998 SBHQ values ranged from 2 to 6 and

averaged 2.8 (SD= 0.91, CV=32.40). 

In the Fenwick Shoals Regions, 1998 SBHQ values ranged from 1 to 13 and averaged 3.4

(SD= 1.99, CV=58.97). For 1999, BHQ values ranged from 1 to 12 and averaged 5.6 (SD= 2.07,

CV=36.66). The 1999 SBHQ values ranged from 1 to 11 and averaged 3.1 (SD= 2.07, CV=65.93)

(Data on CD-ROM). Overall, for combined data from 1998 and 1999, BHQ ranged from 1 to 13 and

averaged 5.6 (SD= 1.88, CV= 33.29). SBHQ ranged from 1 to 13, averaging 3.2 (SD= 1.90, CV=

59.14).

In general, within the IR Regions the BHQ and SBHQ were low and exhibited little spatial

variation (Figures 4.8-1a and 4.8-1b). In the FS Regions both the BHQ and SBHQ were lower on the

shoals, especially in the central area and on northwest faces of the shoals, and were higher in the valleys

between and deeper regions just inshore and offshore from the shoals (Figures 4.8-2a and 4.8-2b). 

This basic pattern of lower BHQ and SBHQ values on the shallow shoals and higher values in the

deeper less physically dynamic areas is related the concentration of biogenic features in the deeper

more protected areas and corresponds with the clines of lower bottom energy reflected in the grain-size

distributions.

4.9. Habitat Classification and Resource Value Results, Secondary Production

To ascribe potential for disturbing ecosystem energy flow we have incorporated an estimate of

secondary production into our assessment of impacts from sand mining on benthic resources. Benthic

habitats are conspicuous sites for focusing and transformation of biological energy and are an integral

part of ecosystem function. Consideration of energy flow is important because, in general, the annual

income and outgo of energy to an ecosystem is in balance. This is most true for physical budgets (i.e.
49

Temperature). For biological budgets, a portion of the biomass of long-lived species is carried over to

the next year, but since this occurs every year there is also a general balance in the biological budgets of

an ecosystem. Lindeman (1942) was one of the first to consider this overall flow and balance of matter

in an energetic sense. Thus in assessing impacts from disturbance, such as sand mining, it is the

energetic transformations that occur between and within portions of an ecosystem through time that are

most important for informed management of resources.

Measurements of biomass or standing stock while important in comparing immediately available

energy are quite inadequate for purposes of predicting rates of predator cropping, yield, or growth. 

For example, without information on production it is not possible to predict if the food supply of benthic

feeding fishes has been impacted. 

The range in infaunal production estimated for all 1998 and 1999 grab stations was 0.2 to

159.0 and 1.3 to 59.1 g DW m-2 y-1, respectively (Tables 4.9-1 and 4.9-2). However, the differences

between years for the 18 stations sampled both years was less pronounced and indicted that broad

scale spatial variation in production related to benthic habitat type was greater than both time and small

scale variation within habitat types. The range for the 18 stations sampled both years was 0.3 to 86.5 g

DW m-2 y-1 for 1998 and 1.3 to 58.0 g DW m-2 y-1 for 1999. Average production at these 18 stations

indicated that overall secondary production in 1999 may have been slightly higher than in 1998 by

about 1 g DW m-2 y-1. While not a significant difference, 14 of the 18 stations may have had higher

productivity in 1999 over 1998. Within station variation in production for both years (0.3 to 29.4 g

DW m-2 y-1) was smaller than between stations for either year. FS02 and FS04 had consistently high

production both years. Other high production stations were FS01G, IR05, IR05.5, and HCS31 in

1998 and FS07B in 1999. Since interannual differences in production did not appear to be significant,

we averaged the two years for the purposes of assessing regional and habitat productivity differences

(Table 4.9-3).

Taxonomically, bivalves had the highest productivity in both years and accounted for 68% of

the total production in 1998 and 49% in 1999. Polychaetes were second most productive with 21%
50

and 33% of the total in 1998 and 1999, respectively. All crustaceans combined accounted for 5% and

13% of the production for both years. The remaining seven taxonomic groups accounted for about 7 to

8% of the production. Since productivity by major taxa for both years was similar the data were

combined and averaged to represent the basic macroinfaunal secondary productivity of the habitats

within the study area. Overall, bivalves were about 65%, polychaetes and other worms about 24%, all

crustacean taxa about 6%, gastropods about 3%, and anemonies and cephalochordates about 1% of

the total secondary production within the study area (Table 4.9-3). 

The high productivity of bivalves was due to rapid growth rates of large numbers of small

young-of-the-year (YOY) individuals. For example, at stations FS02C and FS04C there were

hundreds of small bivalves in the grab samples. Only at stations IR05C and IR05.5C was bivalve

production due to growth of larger (+1 year old) individuals. It is likely that small YOY bivalves are a

significant source of food for many invertebrate (crabs, nemerteans) and fish predators. Settlement of

bivalves into a habitat would contribute significantly to higher habitat value from an energetic standpoint. 

Large bivalves, which were not sampled with the grab, would also contribute significantly to habitat

value both ecologically and commercially. Some larger benthic predators, for example rays, whelks

and starfish, feed directly on these larger bivalves.

Polychaetes and other worms, including oligochaetes, phoronids, sipunculids, and nemerteans,

were the numerically dominant taxonomic groups (75 to 80% of all individuals) but their productivity

was second to bivalves because of their smaller mean individual weight. Stations with highest worm

productivity had large numbers of small individuals, over 25,000 individuals m-2. For example, FS01G

with >76,000 individuals m-2 and HCS31 with >26,000 had the highest worm productivity of 32.6 and

36.1 g DW m-2 y-1, respectively (Table 4.9-3). 

While crustaceans, including amphipods, isopods, mysids, cumaceans, and tanaids, where the

third most productive group they were very important in the diet of bottom feeding fishes (Table 4.9-4). 

In part this was due to the slower digestion of their exoskletons that made crustaceans more

recognizable in the stomach contents. Worms and other soft-bodied fauna tend to be less obvious in
51

stomach content and consequently their importance in the diet of fishes is usually underestimated. 

Habitats high in crustacean numbers would then be more valuable habitat for bottom feeding fishes. In

particular amphipods, which accounted for about 70% of the crustaceans production, were common

prey. Mysids were also important food items but were not quantified by the grab samples.

The dynamic nature secondary production and its contribution to energy flow through an

ecosystem can be expressed by the production to biomass ratio (P/B). The main attraction of the P/B

or turnover ratio is its potential to characterize, for various species and habitats, the magnitude of

production and express productivity independent of the standing stock (biomass). It is called turnover

ratio because the number arrived at seems intuitively to be the number of times the population biomass

turns over for a specific period. The units of P/B are y-1. It expresses the number of times that the

biomass could possibly change for the period studied relative to total biomass produced. In general,

the higher the P/B ratio the higher the energy flow. A lower production but higher P/B taxa could be as

important at the ecosystem level as quantitatively more productive but lower P/B taxa. On average

oligochaetes had the highest P/B ratio of 8.9 (SD = 1.3), which basically means that over the period of

a year the biomass of oligochaetes turns over about 9 times. Lowest P/B ratios were associated with

areas dominated by larger longer-lived individuals such as IR05D and IR05.5C where the bivalve P/B

ratio was 0.4. Overall, the bivalve P/B ratio was 3.9 (2.1) indicating the dominance of small YOY

individuals (Table 4.9-5). 

The magnitude of P/B is related to a combination of both life-history traits of the individual

species and physical environmental conditions. This property of P/B makes it useful for expresses the

relationship between habitat conditions and biological response. Discriminant analysis was used to test

if the pattern in the distribution of P/B ratios corresponded to sediment type and cluster analysis groups,

both of which are the major determinant and expression of benthic habitat conditions. For the sediment

discrimination 48 stations were included, four stations were missing sediment data, and seven sediment

type groups defined that ranged from coarse-sand to muddy-fine-sand. The pattern in the major taxa

P/B ratios closely corresponded to sediment type with 92% of the stations remaining with their original

grain-size group. Misclassification occurred four times among the coarser sediment stations (Table 4.9-
52

6). Of the 16 coarse-sand stations predicted group membership changed for two stations. Station

FS07F was classified to the fine-sand group and BB04 to the gravel-coarse-sand group. One finesand station (FS07F) and one gravel-coarse-sand station (FS10A) were classified to the coarse-sand

group. 

4.10. Prediction of Biology Using Substrate and Image Data

4.10.1. Secondary Production

The relationship between energy flow within an ecosystem and substrate characteristics is not

well understood. Using estimates of secondary production as a surrogate for energy flow we find that

for infauna there is a no linear correlation between the secondary production and substrate grain-size. 

A quadratic model was significant (p = 0.029) and did indicate that secondary production tended to be

higher in sediments with mean Phi >2 (coarse sands) and <1 (fine to very-fine sands):

Log (Production) = 0.98 – 0.98 (Mean Phi) + 0.38 (Mean Phi)2

An examination of the variance structure of the data indicated that the silt content accounted for much

of the variation between production and grain-size and that there was a strong relationship (p = 0.0004)

between secondary production and percent silt:

Log (Production) = 0.38 (Arcsin % silt)

This is consistent with other production studies that found mixed (sand-silt-clay) sediments to have

highest secondary production (see references in Diaz and Schaffner (1990) and Tumbiolo and Downing

(1994)). However, the range in silt content with the study area was small (0 to 4.4%) relative to

published production studies and indicates how important substrate characteristics are in shaping habitat

value on the shallow continental shelf.

Therefore, any disturbance that would increase the silt-clay content of surface sediments would

also likely increase secondary production of the infauna. This in turn would support higher utilization by

demersal feeding fishes.

Spatial interpolation of secondary production to biomass ratios (P:B) were done using ordinary

cokriging accomplished with GSLIB (Deutsch and Journel, 1998), with sediment grain size determined
53

from SPI as the covariate. Two realizations of that procedure are presented in the Map Atlas. The

cokriged interpolations of P:B agree well with the spatial distributions for many of the other habitat

parameters mapped for the FS regions. High P:B estimates occur in the biologically rich, deeper, finer

sediment regions, and also on parts of the shoals. 

4.10.2. Community Groups

If sandmining activities are planned for a certain location, potential impacts can be assessed

based upon the community groups found at the point grab sample locations (Figure 2.2-1 and Table

4.10-1). Each community cluster group and subgroup (A, A', A'',B, B', C, D, D', D'', E, and E')

represents a relatively unique species distribution. At the level of entire regions of interest (ROI), the

following provides a summary of which communities were, and are likely to be, present in each ROI. 

In both IR-ROI and in NBB-ROI, three cluster subgroups, A, A', and E', were present. In FS-ROI,

nine cluster subgroups, A, A', A'', B, B', C, D, D', and D'', were present. In WS-ROI, 4 cluster

subgroups, A', B, D, and D'', were present. In IWS-ROI, 4 cluster subgroups, B, C, D, and D'', were

present. In MKP-SMA-FS (Maa and Kim proposed Sand Mining Area - Fenwick Shoal), two

cluster subgroups, D and D', were present. And in MKP-SMA-IWS (Maa and Kim proposed

Sandmining Area - Isle of Wight Shoal), four cluster subgroups, B, C, D, and D'', were present. 

Determinations of the likely impacted constituents are based upon species composition and relative

distributions by each cluster subgroup (Table 4.10-1).
55

CHAPTER 5. DISCUSSION, SUMMARY, AND CONCLUSIONS

5.1. Origins and Fate of Topographic Features

Although the shoals of interest are apparently storm-derived offshore ridges (Swift and Field,

1981; Goff et al., 1999), their rate and process of formation suggest that they will not aggrade to prior

form if they are subject to major loss of material to mining. The offshore ridges are believed to have

initially originated as shore-attached ridges during a prior of lower sea level. As shoreface transgression

has occurred, hydrodynamic processes, including storm waves and currents, have led to detachment

and segmentation of the offshore ridges from the shore-attached ridges, and slight offshore and

southward migration (Swift and Field, 1981). Since becoming offshore features, the ridge

morphologies have changed, producing mildly concave northwest shoals with slope asymmetries, such

that the seaward slopes are steeper, such as in the case of Fenwick Shoal, where the southeastern face

has the steepest slope in the region (Swift and Field, 1981) (Figure 2.1-3).

5.2. Benthic Habitat Characteristics

Preliminary habitat classifications, or habitat proxies, were made using SPI and sled images. 

The Benthic Habitat Quality (BHQ) index (Nilsson and Rosenberg, 1997) was used as SPI Habitat

Proxy 1. Values of the BHQ ranged from 1 to 13, on a scale of 0 to 15, with mean value of 5.63 (SD

= 1.75) overall, 5.83 (1.85) for the Fenwick Shoals Regions, and 5.04 (1.26) for the Indian River

Regions. The BHQ index did distinguish between gross habitat types, such as physically structured

versus biologically dominated. However, BHQ did not resolve finer scale differences well. The

formulation of the BHQ relies heavily upon the thickness of the RPD layer, and also parameterizes RPD

into specific, discrete value bounded bins, as does the OSI index (Rhoads and Germano, 1986). Both

indices weight the RPD according to values representative of the ranges typically found in the

ecosystems for which they were developed, estuaries and harbors of the northeast U.S. coast and

Swedish fjords. In order to apply the BHQ to shelf habitats we decided to scale the influence of the

RPD and to adjust for correlation between the RPD and properties of the sediment which affect

porewater flow by substituting a variable derived from RPD, sediment grain-size, and sorting.

The OSI was undefined for nearly all cases because it depends upon determination of
56

successional stage for its calculation. Successional stage was indeterminate for most of the images from

this study area. Although some images, like those from the valley to the south of Fenwick Shoal,

resemble successional stage III, and could be classified, images from the shoal stations were often

devoid of indicative features. Therefore, objective classification might apply the label azoic to those

images, which would be misclassification due to sediment characteristics, and limitations in the images

and the index. In the shoal crest sediments, lack of apparent biogenic features does not necessarily

mean a paucity in biological resources. Rather, the biological constituents are adapted to the energetic

conditions that maintain "clean sand" appearance and do not expend energy or resources in futile

attempts to provide lasting structures, which are the bases for construction of both the BHQ and OSI

indices. The biology common to those conditions were very small ascideans (tunicates or sea squirts)

which often were only as large as the sand grains and were attached to the grains, and deep-burrowing

amphipods. The ascideans were rarely visible when small.

The BHQ and the SBHQ would both indicate that benthic habitat quality in the vicinity of FS,

WS, and IWS shoals was relatively low on the shoals and high in the valleys between and deeper water

areas inshore of the shoals. That result is slightly deceiving because the indices are based upon

biological and physical sediment features which are likely to occur and be preserved in finer, more

cohesive sediments. The SBHQ does not indicate simply that habitat quality on the shelf increases with

depth, rather it indicates that the local effects of topographic variability influence habitat conditions such

that organism-sediment interactions are enhanced where finer-grained substrates occur. Because of the

combined effects of topography, hydrodynamics, and climatalogical forcing, particle and sediment

transport and deposition have resulted in patterns of substrate composition and biological community

structure which resemble variations in bathymetric. In addition, scales of bathymetric variation are

important to benthic communities. For example, infaunal communities can vary according to their

position in relation to bedform crests or troughs. However, bedforms are dynamic features, and the

material composing crests does not move all at once, there is rather a gradual diminishment and

reformation of crests as feature migration occurs. It is unlikely that a particular community moves in

unison with a particular crest feature. More reasonable is that the vertical distribution of the local

community constituents reflects microhabitat condition preference. In broader terms, microhabitat
57

associations should be predictable based upon the substrate and energy regime. 

SBHQ values should be calculated for archived SPI images or profile image analysis datasets

from various systems in order to compare SBHQ values to BHQ and OSI. Once that has been done,

it should be clear how values of SBHQ relate to particular habitat conditions and generalized states of

ecosystem health, such as those specified by the BHQ and OSI. The SBHQ, should provide a

measure of benthic ecosystem health that allows cross-system comparison, without the limitations of the

indices developed specifically for sheltered systems and based upon a model with limited diagnostic

capability offshore (Maurer et al., 1993). 

When sled transect image data are considered, there is greater support that the deeper regions

surrounding and especially the valley between FS and WS is a more biologically active and productive

area than the shoal crests and northwest faces. However, sled data also shows that fish, filter feeding

epibenthos, and sand dollars are more prevalent on the shoals. In fact, what we observed is a

functional community shift between the subenvironments. Biological community data from grabs and

fish and epibenthic community data from the trawls demonstrate the same. In terms of production and

resource value, none of the individual means of assessment (indices and statistics) provide a complete

diagnostic. Therefore, combining them into a system assessment tool has become one of our priorities. 

Until a suitable combined measure can be reviewed, some of the methods may be applied with

reasonable relevancy if they are used and interpreted with respect to the local environment. Transitions

in the local environment occur over different scales in the vicinity of FS, and the spatial rate of change

appears related also to the rate of change in bathymetry. The steepest faces of FS, WS, and IWS are

to the southeast of each crest. In between FS and WS, bathymetric change is greatest, and habitat

changes are most abrupt. Substrates changed from medium to coarse sand to clayey-silty mud in a

very short distance, (tens of meters), with a limited zone of mixing. Instead, it appears that the transition

follows the predominant, time varying sedimentation scheme which in most cases produces mud beds,

but at times covers them with large, sand bedforms induced by storm transport of lower shoal

sediments. Therefore, the lower parts of the SE face of FS can be found interbedded sand and mud

and large relict bedforms colonized by dense mats of mud-tube-building infaunal polychaetes.
58

The biogenic structure classes identified in video corresponded well with the general spatial

patterns determined by interpolation of point sample data from SPI and grab (Figure 5.2-1). In the

Fenwick Shoal ROI, the regions of dense biological associations at or near the sediment surface

occurred to the southwest and southeast of the shoal. In the southwest, the region was marked by fine

sediments, diverse and numerous epifauna, and infauna which build tubes from fines, such as

Asabellides. To the southeast, often large Diopatra cuprea tubes pervaded the shelly rippled sands. 

On the northeast and northwest shoal flanks, the surface oriented infauna and epifauna was sparser, and

in some instances absent (Map Atlas). 

On the northeast flank of Fenwick Shoal, where the fish Ammodytes occurred, surface

oriented biology and biological features were absent (Figure 5.2-1). The behavior of Ammodytes was

likely responsible, since Ammodytes buries into the surface sediments. It actually dives head-first into

sandy ripples at high speed, as seen in the video records. Where these fish were abundant, the

continued disturbance to the surface layer sediments apparently has affected the distribution of epifauna

and infauna, which build surficial structures. These epifauna and infauna may have been absent solely

because of the physical energy regime, however on other parts of the northeast shoal flank, sparse

epifauna and infaunal tubes were present, whereas Ammodytes were absent. Disturbance was the

likely exclusionary culprit because since Ammodytes feeds upon plankton (K. Able, pers. comm.),

competition for resources with the epifauna and infauna was unlikely.

5.3. Perspectives on Former Organism-sediment Models and a New Model

Organism-sediment interaction models can be useful for classifying habitat conditions based

upon the appearance of the substrate. For example, Pearson and Rosenberg (1978) produced a model

demonstrating how organic enrichment gradients could structure communities, organism-sediment

interactions, and habitat condition. Rhoads and Germano (1986) later produced a model resembling

that of Pearson and Rosenberg, but dealt with how physical disturbance effected community

succession. Both models were developed for application in fjords, estuarine, harbor, and some river

systems, however both lack validity in sandy open coastal systems such as the Atlantic coast of the

U.S. We suggest that in continental shelf environments the transitions in community structure and
59

organism-substrate associations along dynamic energy edges (for example the transition form coarse to

silty-fine sands) would resemble in part both of the former models. However, on the continental shelf,

the structuring factors are principally space and energy regime and propose that a spatial-energetic

benthic community structuring model is more applicable to shelf environments. 

Both prior models of community-state succession, in space or time, are theoretically

appropriate for disturbance cases such as sandmining. However, as previously discussed, a shelf

habitat state may appear similar to late successional stage assemblage in intracoastal waters but may

actually be the functional equivalent of an early stage condition. In addition, water column dynamics

and topographic variability combine to confound the former models primarily because of material

transport effects. For example, community alteration and recovery from disturbance effects on shoals

will likely be highly dependent upon wave, current, and bottom stresses in the period subsequent to

sandmining. Although it is possible to predict loss of and recolonization community structure based

upon existing local populations and their spatial distributions, actual responses will be influenced by

fluxes driven by the direction and magnitude of water mass motion. Therefore, the results of a

sandmining operation could be totally different if a long period of calm followed or if a major storm

followed the dredging. Prediction of recolonization and community development subsequent to

sandmining disturbances therefore follows no simple formula, and may be simple or complicated

dependent upon the occurrence of unpredictable influential conditions and interactions of climatological,

topographic, and biological phenomena.

Several recolonization senarios could be envisioned by considering just season and climatology

subsequent to sandmining activities (Table 5.3-1). The primary effect of season would be related to

temperature, which regulates primary productivity and thus availability new food to the benthos. The

primary effect of climatology would be sediment reworking and transport during storm events. Thus

spring/summer stormless conditions would tend to favor the deposition of organic matter over the mined

area that would tend to initially favor surface deposit feeders and then subsurface feeder. Fall/winter

stormy conditions would tend to favor vigorous reworking and transport of sediments, including

associated epifaunal and infaunal individuals, into the mined area. Fine sediments and organic matter
60

would also be transported out of the area. Summer storms and winter quite periods would be

intermediate in effecting recolonization.

5.4. Evaluating Biological Resource Potential

Community measures such as abundance and biomass, numbers of species (diversity), diversity

indices, and measures of dominance provide good indications of biological resource status for a

particular time and place. Indices of biotic integrity provide a good consolidated measure of community

attributes, however do not directly assess resource potential. Secondary production provides a time

integrated measure of biological resource potential in terms of availability to trophic transfer, can be

modeled using few measurement parameters, and is based upon world-wide data and theory. BRAT

measures are intended to provide energy transfer data, but the technique is difficult to implement for

large regions over time, especially if different subregional habitats exist which have different resident

species as well as transients. 

Production is probably the best means of assessing benthic biological resource potential, when

used in combination with the spatial characteristics of the region of interest. Production essentially

relates the rate of biomass change for a particular taxa or community. Predators feed upon prey

biomass, whereupon energy changes trophic levels, and in turn affect prey production. Production

estimates incorporate information about growth in terms of individuals and populations, including

population dynamics, additions and losses, whereby information concerning the fate of biomass is

unnecessary. Production estimates are robust to organism interactions and system dynamics because

those variable influences are incorporated into the measure. Variations in community structure and

function are important to production, and although those can change in time, certain communities or

community types are characteristic of certain shelf habitats and regions. Therefore habitat

characteristics are important to production because living modes are controlled and structured by

habitat and microhabitat conditions. Spatial variation in habitat characteristics is more important on

annual to decadal scales than seasonal changes in water column conditions because seasonal conditions

recur with similar variability. Unless climatic changes occur rapidly, seasonality in an east-coast shelf

environment will be predictable. Even extreme energetic events such as cyclonic summer and winter
61

storms can be expected, and they impact the region on a spatial scale much larger than the study area. 

Similarly, most weather and the related changes in water column conditions are larger than the scale of

interest. Seasonal climatic conditions do influence habitats, and over long periods, even configure

topography and substrates which help determine habitat distributions. However, the present

distribution of habitats with respect to regional and topographic influences, including the substrate and

the functional biological community, should have the greatest influence upon production and therefore

resource potential. 

Water column conditions induced by climatic events can alter the resource availability, however

their likelihood may be estimated from features evident in the substrate. For example, storms with high

winds and waves most often are easterly in the Fenwick Shoals Region (Maa, pers. comm.). From

SPI and Sled images, surface relief and the largest bedforms, as well as larger grain-sized sediments

were all found just to the west of the crests of the three shoals (FS, WS, IWS). The roughness

configurations and their magnitudes apparently correspond to easterly waves peaking over the shoals. 

Although those features are ephemeral, they likely are maintained until similarly erosive conditions

occur, thus elucidating where and to what extent the substrates were affected by the most recent

disturbance. The larger roughness feature distributions and distributions of relatively larger grain-sized

sediments extended over approximately 10, 8, and 6 square kilometers on FS, WS, and IWS (Figure

5.4-1) based upon IDW interpolations. Because the shoals themselves influence the behavior of the

waves and currents, their lasting presence and configurations influence habitat-structuring conditions

such as those, substrate type and configuration, on scales of influence to large biological populations. 

Therefore, changes to the shoals will influence both habitat distributions and biological resources. The

extent to which either would be affected will depend upon the magnitude and distribution of the changes

to the shoals.

5.5. Relationship of Secondary Production to Habitat Value

Secondary production reflects the main energetic contribution to benthic habitat value that links

primary production and detritus to higher trophic levels. There is, however, no simple connection

between benthic secondary production and fisheries species. Not all benthic production is utilized by
62

or available to fisheries species (Moller et al., 1985, Lunz and Kendall 1987). In addition to the

stocastic element of predation, which allows for a certain level of prey survival, organisms avoid

predators through quick escape responses, burrowing below the sediment surface, and to a lesser

extent in large size. Benthic standing stock biomass at any given time then represents surviving prey

available to be carried over from year to year. The percent biomass carryover tends to be higher in

"mature" communities that exhibit successionally advanced characteristics (Odum 1969, Wolff et al.,

1977, Wolff 1983). This results from the fact that organisms live longer and are larger, which increases

biomass, in "mature" communities. Lower biomass and smaller individual size are characteristics

associated with early successional stage communities or communities under stress. For the shallow

continental shelf the stressor is primarily physical disturbance caused by wave induced sediment

instability.

A portion of the secondary production is also cycled within the benthos by infaunal predators

(Cederwall 1977, Ambrose 1984, McDermatt 1976, Virnstein 1979). Many infaunal species are

predacious (particularly important are nemerteans and many ploychaetes and gastropods) and influence

the energetics of other infaunal species by preying on adult, juvenile, or larval stages (Ambrose 1984,

Commito 1982, Oliver et al., 1982). The production of infaunal predators is then potentially available

to epifaunal predators and may actually be more available then nonpredacious infauna, because of the

free burrowing and surface searching habits associated with a predacious life history (Ambrose 1984).

Overall, areas with higher secondary production would tend to have higher habitat value

particularly if the production is transferred to fisheries species. Base on gut content analysis then areas

with higher levels of crustacean production would have the highest habitat value. Crustaceans were

found to constitute the majority of the food items eaten by demersal feeding fish in the Fenwick Shoals

region.

5.6. Assessing Potential Sandmining Impacts

Our assessment of potential sandmining impacts on biological resources is based upon

observed and interpolated data from offshore Maryland and Delaware (MD/DE). Based upon the

species associations derived from the cluster analysis, certain community groups would be impacted if
63

the ROI's represented regions where sandmining would occur (Figure 5.6-1). A different suite of

species each with their own distribution pattern characterized each of the groups. Since each cluster

subgroup represented a particular species distribution, the magnitude of impact upon particular species

or sets of species by sandmining activities can be estimated directly from the lists in Tables 4.10-1that

provide proportions of species contributions to each group. 

Assuming complete excavation and total removal of organisms within the posed sandmining

ROI's, we predict potential recolonization communities based upon first, simply the occurrences and

proximity of community groups observed in the vicinity of the mined region. First order predicted

recolonization communities are based upon the neighboring community groups, species compositions

and proportional abundances, and the distances between the samples and the border of the proposed

ROI. As such, if we consider a scenario in which MKP-SMA-FS is completely mined to a depth

below the vertical distribution extent of the infauna, on the order of 50 cm, and expect the nearby

(within 1 km of the boundary) communities to provide recruits (Figure 5.6-1), then based upon withingroup abundances the initial recolonizers should be from cluster subgroups A, A', A'', B', C, D, D', and

D''. These are the subgroups within the the 1 km buffer zone. Therefore the initial recolonizers will

likely be some or all of the following dominant taxa, and likely many of the less abundant taxa listed in

Table 4.10-1: 

Oligochaeta (A, A', B', D') Aricidea cerrutii (A, B')

Nemertinea (A, A') Protodorvillea kefersteini (A')

Byblis serrata (A'') Pseudunciola obliquua (A'')

Brania wellfleetensis (A'') Hesionura elongata (B')

Chirodotea coeca (C) Parahaustorius longimerus (C)

Tellina spp. (C, D, D'') Nucula spp. (D)

Asabellides oculata (D) Protohaustorius wigleyi (D')

Mytilus edulis (D') Astarte sp. (D'')

Natica pusilla (D'') 

The effect of seasonal sandmining, either spring/summer or fall/winter, on recolonization

potential would be seen in species that have life history characteristics that would preclude their

availability as recruits (Table 4.2-15). Five of the above listed dominants would likely recruit as well

during any season after a sandmining event (oligochaetes, nemerteans, Protodorvillea, Tellina, and
64

Asabellides). The amphipods (Bybilus, Pseudunciola, and Parahaustorius) would all likely have

better recruitment in the spring/summer than fall/winter. The polychaete Brania and the bivalve Nucula

would both likely do better in the fall/winter. Overall, there would likely be slightly better larval and

juvenile recruitment after spring/summer than after fall/winter sandmining activities. Recruitment by

adults during any season would likely be regulated by factors that affect passive transport, such as

storms. Active transport of mobile species, such as epifaunal mysids or Crangon septemspinosa may

proceed more rapidly during warmer seasons, but would also occur in winter.

If we consider a similar scenario for MKP-SMA-IWS, then the initial recolonizers should be

from cluster subgroups C and D (Tables 4.10-1). For any area to be mined, a disturbancerecolonization scenario can be constructed using the maps and tables provided. This approach ignores

all of the substrate, hydrodynamic, timing, and biological interaction effects that will be important to the

colonization process, however it does provide a reliable, data-based initial prediction. The life history

attributes of the potential recolonizers should be examined to determine if the predicted assemblage is

realistic based upon individual substrate preferences, reproductive timing, frequency and magnitude

(relative to the time of disturbance), adult migration capabilities, and potential competition for resources

with functionally similar species. Some of that information has been documented for some species

(Table 4.2-15), but it can only be inferred for the majority of the species. Additionally, functional

equivalents of occurring species may also be considered as potential recolonizers since community

structure is likely to persist although individual species may differ (Maurer et al., 1976). However the

spatial extent of the disturbance is likely to control how variable the recolonization community will be

relative to the prior assemblage. Therefore, errors in the prediction will increase as the impacted area

grows. 

As an example, we will consider a scenario that removes at least the top meter of sand from all

of the Fenwick Shoals ROI (Figure 5.6-1) and that the grain-size of the sediment surface in the mined

area remains unchanged. The area mined would be approximately 7.7 km2 with a benthic infaunal

community characterized by cluster subgroups D and D’ (Table 4.10-1) in an areal ratio of about 1:2,

respectively. Average infaunal density of the dominant and subdominant species, those included in the
65

cluster analysis, would then be about 1900 individuals/m2, based on data in Table 4.10-1. Average

biomass would be about 3.8 g wet weight/m2, based on data in Table 4.2-6 for stations FS06B,

FS09B, and FS10.5D, the three stations within the ROI mined. For this scenario acute impacts would

be the loss of approximately 150 x 106 infaunal individuals and 300 kg of biomass that would be

removed with the sand resource mined. If we further assume that recolonization would proceed with

the dominant species listed above then a mining operation that ended in time for Spring/Summer

recruiters would favor crustaceans and a Fall/Winter end would favor annelids (Table 4.2-15). After a

single Spring/Summer recruitment season it is likely that some level of benthic resource value for

demersal fishes would return, assuming no change in the character of surface sediments, or possibly

even be enhanced by favorable conditions for crustacean recruitment. After a Fall/Winter recruitment

event benthic resource value would likey not be as high as the Spring/Summer event because annelids

may not be utilized by demersal feeders to the extent that crustaceans are. 

Should the mining operation lead to fining of surface sediments annelids and bivalves would be

favored which might in the long-term reduce resource value for demersal fishes. The accumulation of

fines would be related to the hydrodynamics after mining. It is not certain that fining of surface

sediments will occur. Jutte and VanDolah (1999) found that a year after sand mining of two areas

offshore Hilton Head Island, South Carolina, the silt/clay content of surface sediments increased by

13% and that benthic resources had changed and not recovered to pre-mining community structure. 

However, Jutte et al. (1999) also found that after sand mining of the Cherry Grove borrow area

offshore Myrtle Beach, South Carolina, the surface sediments did not become finer and the infaunal

community recovery occurred after about two years. 

5.7. Minimizing Impacts to Biological Resources 

In order to ensure that the biological assemblage that recolonizes a mined area resembles those

present prior to mining, it would be beneficial to avoid total area removal of surficial substrates. Instead

of mining an extensive continuous region, avoid certain small areas within the sandmining area so that

local resident species remain as likely recolonizers. Retaining small refuges within a sandmining area

should minimize potential alteration of community structure and function, and therefore reduce potential
66

effects upon trophically dependent species. Refuges from mining should be of higher priority when

shoals are to be mined for two main reasons. First, shoal ridge communities differ from mid-shoal and

trough communities; and second, potential recolonizers from similar communities on nearby shoals (if

they exist) will likely suffer high mortality during migration, due to exposure to predators during open

water transit, and therefore have limited success. Whereas, if local mining refuge patches (RP) are

retained, distances and exposure times endured by migrating organisms will be minimized and therefore

recolonization success should be greater. Retaining RP's is analogous to the silvicultural practice of

retaining seed trees for natural regeneration of harvested forests (Zhou, 1998). Although the resultant

recolonization community in the mined area may be different dependent upon whether adult migration or

larval dispersal dominates, RP's should augment similar pre-mining and post-mining communities.

Determinations of impacts of sandmining on mobile fisheries resources is also connected to the

rate and success of benthic recolonization. Many fishes utilize the shallow continental shelf as a nursery

ground (Able and Fahay, 1998) and depending on when their demersal life hsitory stages utilize a

particular area, any impacts could be minimized by insuring that their cover or food base not be

disrupted. For the most part this would primarily mean minimizing impacts to crustaceans and

secondarily to other taxonomic groups. Conversely, any aspect of sandmining that would enhance the

production of crustaceans would likely also improve habitat quality for demersal fishes.
67

CHAPTER 6. ACKNOWLEDGMENTS

We thank the crews of the R/V Atlantic Surveyor and R/V Cape Henlopen for making our data

collection so successful. We also thank the Benthic Ecology Group at VIMS for their assistance in

conducting the fieldwork and laboratory processing of grab samples and profile camera images. 

Special thanks go to Barry Drucker, our contract monitor, for allowing us flexibility in data collection.
69

CHAPTER 7. REFERENCES CITED

Able, K. W. and M. P. Fahay. 1998. The first year in the like of estuarine fishes in the middle Atlantic

Bight. Rutgers University Press New Brunswick, New Jersey, 343 p.

Aller, R. C. and J. Y. Aller. 1998. The effect of biogenic irrigation intensity and solute exchange on

diagenetic reaction rates in marine sediments. Journal of Marine Research 56:905-936.

Ambrose, J. 1984. Role of predatory infauna in structuring marine soft-bottom communities. Mar.

Ecol. Prog. Ser. 17:109-115.

Banse, K. and S. Mosher. 1980. Adult body mass and annual production/biomass relationships of field

populations. Ecol. Monogr. 50:355-379.

Boeringa, M. 1998. MB.View.SpatialKriging Avenue script: interpolates values using the kriging

method. Marco Boeringa, Gemeentewaterleidingen Amsterdam (Amsterdam Water Supply),

Vogelenzangseweg 21, 2114 BA Vogelenzang, the Netherlands, poect8@gw.amsterdam.nl.

Boesch, D. F. 1977. Application of numerical classification in ecological investigations of water

pollution. Report prepared for US EPA Ecological Research Series (EPA-600/3-77-033).

Available as PB269 604 from: National Technical Information Service. U.S. Dept. of

Commerce. Springfield VA 22161.

Boesch, D. F. 1979. Benthic ecological studies: Macrobenthos. Chapter 6. Middle Atlantic outer

continental shelf environmental studies. Final Report to Bureau of Land Management,

Washington, D. C. 

Bonsdorff, E., R.J. Diaz, R. Rosenberg, A. Norkko and G.R. Cutter. 1996. Characterization of softbottom benthic habitats of the Åland Islands, northern Baltic Sea. Marine Ecology Progress

Series 142:235-245.

Brey, T. 1990. Estimating productivity of macrobenthic invertebrates from biomass and mean individual

weight. Meeresforch, 32: 329-343.

Carr, M.R. 1997. PRIMER user manual, Plymouth Routines In Multivariate Ecological Research.

Plymouth Marine Laboratory, Plymouth, England, 44 pp.

Cederwall, H. 1977. Annual macrofauna production of a soft bottom in the northern Baltic proper. pp.

155-164. In: Keegan, B.F.; Ceidigh, P.O.; Boaden, P.J.S. eds. Biology of benthic organsims.

Pergamon Press, Oxford. 

Commito, J. A. 1982. Effects of Lunatia heros predation on the population dynamics of Mya

arenaria and Macoma balthica in Maine, USA. Mar. Biol. 69:187-193.

Cutter, G.R. and R.J. Diaz. 1998. Novel optical remote sensing and ground-truthing of benthic habitat

using the Burrow-Cutter-Diaz plowing sediment profile camera system (BCD sled). Journal of

Shellfish Research, 17: 1443-1444.

Diaz, R.J. and L.C. Schaffner. 1988. Comparison of sediment landscapes in the Chesapeake Bay as

seen by surface and profile imaging. p. 222-240. In: M. P. Lynch and E. C. Krome, eds.

Understanding the estuary; Advances in Chesapeake Bay research. Chesapeake Res. Consort.

Pub. 129, CBP/TRS 24/88.

Diaz, R.J. & Schaffner, L.C. 1990. The functional role of estuarine benthos. In Perspectives in the

Chesapeake Bay, 1990: Advances in estuarine sciences. M. Haire & E. C. Krome (eds.),

Report No. CBP/TRS41/90, Chesapeake Research Consortium, Gloucester Pt., Virginia. pp.

25-56.
70

Diaz, R.J., C. Erseus, and D.F. Boesch, 1987. Distribution and ecology of Middle Atlantic Bight

Oligochaetes. Hydrobiologia 141: 215-225.

Deutsch, C.V., and A.V. Journel. 1998. GSLIB: geostatistical software library and user's guide. New

York, Oxford University Press, 1998.

Fager, E.W. 1963. Communities of organisms. pp. 415-433. In: M. N. Hill (ed.) The sea. WileyInterscience, New York.

Fenchel, T. 1969. The ecology of marine microbenthos. IV. Structure and function of the benthic

ecosystem, its chemical and physical factors and microfauna communities with special reference

to the ciliated Protozoa. Ophelia 6:1-182.

Folk, R.L. 1974. Petrology of sedimentary rocks. Austin, Texas, Hemphill's. 170 pp.

Gaston, G.R. 1987. Benthic polychaeta of the middle Atlantic bight: feeding and distribution. Mar. Ecol.

Prog. Ser., 36: 251-262. 

Goff, J.A., Swift, D.J.P, Duncan, C.S., Mayer, L.A, Hughes-Clarke, J., 1999. High-resolution swath

sonar investigation of sand ridge, dune and ribbon morphology in the offshore environment of

the New Jersey margin. Marine Geology, 161, 307-337.

Hurlbert, S. M. 1971. The non-concept of species diversity: a critique and alternative parameters. 

Ecology 52: 577-586.).

Jutte, P.C. and R.F. VanDolah. 1999. An assessment of benthic infaunal assemblages and sediments

in the Joiner Bank and Gaskin Banks borrow areas for the Hilton Head beach renourishment

project, Final Report – Year 1. to: Town of Hilton Head Island, Hilton Head, South Carolina.

44 p.

Jutte, P.C., R.F. VanDolah, M.V. Levisen, P. Donovan-Ealy, P.T. Gayes and W.E. Baldwin. 1999. 

An environmental monitoring study of the Myrtle Beach renourishment project: physical and

biological assessment of offshore sand borrow sites, Phase I – Cherry Grove borrow area.

Final Report to: U.S. Army Corps of Engineers, Charleston District, Charleston, South

Carolina. 105 p.

Lindeman, R.L. 1942. The trophic-dynamic aspect of ecology. Ecology 23:399-418.

Lunz J.D. and D.R. Kendall. 1987. The Netherlands Meeting on Dredging and Related Technology

10-14 September, 1984, Charleston, SC. Final Report, Mar. 1987. p 84-95.)

Magurran, A.E. 1991. Ecological diversity and its measurement. Academic Press, London.

Maurer, D., P. Kinner, W. Leathem, and L. Watling. 1976. Benthic faunal assemblages off the

Delmarva peninsula. Estuarine and Coastal Mar. Sci., 4: 163-177.

Maurer, D., Robertson, G., Gerlinger, T., 1993. San Pedro Shelf California: testing the PearsonRosenberg model. Marine Environ. Res. 35:303-321.

McDermott, J.J. 1976. Observations on the food and feeding behavior of estuarine nemertean worms

belonging to the order Hoplonemertea. Biol. Bull. 150:57-68.

Milliman, J.D., 1972. Atlantic continental shelf and slope of the united states- petrology of the sand

fraction of sediments, northern New Jersey to southern Florida. Geological survey professional

paper 529-j, 40 pp., 9 plates.

Moller, P., L. Pihl and R. Rosenberg. 1985. Benthic faunal energy flow and biological interaction in

some shallow marine soft bottom habitats. Mar. Ecol. Prog. Ser. 27:109-121.

Nilsson, H.C. and R. Rosenberg. 1997. Benthic habitat quality assessment of an oxygen stressed fjord

by surface and sediment profile images. J.Mar. Syst. 11:249-264.
71

Odum. E.P. 1969. The strategy of ecosystem development: An understanding of ecological succession

provides a basis for resolving man's conflict with nature. Science 164:262-270.

Oliver, J. S., Oakden, J. M.; Slattery, P. N. 1982. Phoxocephalid amphipod crustaceans as predators

on larvae and juveniles in marine soft-bottom communities. Mar. Ecol. Prog. Ser. 7:179-184.

Pearson, T.H. and R. Rosenberg. 1978. Macrobenthic succession in relation to organic enrichment and

pollution of the marine environment. Oceanogr. Mar. Biol. Annu. Rev., 16: 229-311.

Pielou, E.C. 1984. The interpretation of ecological data. A primer on classification and ordination. 

John Wiley, New York.

Rainer, S.F. 1982. Trophic structure and production in the macrobenthos of a temperate Australian

estuary. Estuar. Coast. Shelf Sci. 15:423-441.

Rainer, S.F. 1984. Polychaeta as a representative taxon: their role in function and secondary

production of benthic communities in three estuaries. In: Hutchings, P.A. (ed.). Proc. First

Internat. Polychaete Conf., Sydney. The Linnean Society of New South Wales. p. 370-382.

Rhoads, D.C. 1974. Organism sediment relations on the muddy sea floor. Oceanography and Marine

Biology Annual Review 12:263-300.

Rhoads, D.C. and J.D. Germano. 1982. Characterization of organism-sediment relations using

sediment profile imaging: an efficient method of remote ecological monitoring of the seafloor

(REMOTS system). Mar. Ecol. Prog. Ser. 8:115-128.

Rhoads, D.C. and J.D. Germano. 1986. Interpreting long-term changes in benthic community structure:

a new protocol. Hydrobiologia, 142: 291-308.

Sanders, H. L. 1968. Marine benthic diversity: A comparative study. Amer. Natur. 102: 243-282.).

Shepard, F.P., 1948. Submarine Geology. Harper and Row, New York, 348 pp.

Swift, D.J., Field, M.E., 1981. Evolution of a classic sand ridge field: Maryland sector, North American

inner shelf. Sedimentology, 28, 461-482.

Swift, D.J.P., Duane, D.B., McKinney, T.F., 1973. Ridge and swale topography of the Middle Atlantic

Bight, North America: secular response to the Holocene hydraulic regime. Marine Geology, 15,

227-247.

Tumbiolo, M.L. and J.A. Downing. 1994. Empirical model for the prediction of secondary production

in marine benthic invertebrate populations. Mar. Ecol. Prog. Ser., 114: 165-174.

Valente, R.M., D.C. Rhoads, J.D. Germano and V.J. Cabelli. 1992. Mapping of benthic enrichment

patterns in Narragansett Bay, Rhode Island. Estuaries 15:1-17.

Vismann, B. 1991. Sulfide tolerance: Physiological mechanisms and ecological implications. Ophelia

34:1-27.

Virnstein, R.W. 1979. Predation on estuarine infauna: Response patterns of component species. 

Estuaries 2:69-86.

Waters, T.F. 1977. Secondary production in inland waters. Adv. Ecol. Res. 14:91-164. 

Weisberg, S.B., J.A. Ranasinghe, L.C. Schaffner, R.J. Diaz, D.M. Dauer, and J.B. Frithsen. 1997. An

estuarine benthic index of biotic integrity (B-IBI) for Chesapeake Bay. Estuaries, 20: 149-158.

Wolff, W.J. 1983. A benthic food budget for the Grevelingen Estuary, The Netherlands, and a

consideration of the mechanisms causing high benthic secondary production in estuaries. p.

267-280 In: B.C. Coull (ed.), Ecology of marine benthos. U. of S. Carolina Press, Columbia,

SC.
72

Wolff, W.J., A.J.J. Sandee and L. deWolf. 1977. The development of a benthic ecosystem. 

Hydrobiologia 52:107-115.

Zhou, W. 1998. Optimal natural regeneration of Scots pine with seed trees. Journal of Environmental

Management, 53:263-271. 

Ziebis, W., Huettel, M., Forster, S. 1996. Impact of biogenic sediment topography on oxygen fluxes in

permeable seabeds. Mar. Ecol. Prog. Ser. 140: 227-237. 
FIGURES
Figure 2.1-1: Location of the Maryland and Delaware (MD/DE) study area.
Figure2.1-2: General location and boundaries of the Fenwick Shoal (FS) and

North Bethany Beach regions of interest (ROI).
Figure 2.1-3: General location and boundaries of the Fenwick Shoal (FS),

Weaver Shoal (WS), and Isle of Wight Shoal (IWS) regions of interest.

Figure 2.2-2: Location of sediment profile camera stations in the

Maryland and Delaware study area.
Figure 3.1-1: Young grab used to collect benthic infauna and sediments.
Figure 3.1-2: Sediment profile camera system used to collect images of the sediment-water interface. 

The camera system includes a Hyulcher profile camera, a Benthos still camera, and a Panasonic video

camera within a Benthos deep-sea camera frame. The SPI prism window is in the center of the image.
Figure 3.1-3: Standard video and still camera sled used to collect images of surface features over broad

spatial scales.
Figure 3.2-1: Relationship among sediment grain-size, compaction (Cp), and prism penetration (pen).
0%

20%

40%

60%

80%

100%

% Clay

% Silt

%Sand

 % Gravel

Figure 4.1-1: Grain-size composition of sediments from the Maryland and Delaware study area.
0

5

10

15

20

25

30

35

40

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58

Number of Occurrences

Number of Taxa

Figure 4.2-1: Distribution frequency of taxa occurrences for all stations, both May 1998 and June

1999.
A 5/98 BB02 --------------I

 5/98 FS10B --I I--------I

 5/98 FS13C --I-----------I I-----I

 5/98 IR02D -----------------------I I--I

 5/98 IR09A -----------------------------I I

 5/98 BB04 -----------I I--I

 5/98 IR09C -----------I--------------------I I

 5/98 FS09B --------------I I

 5/98 FS14F --------------I--I I

 5/98 IR05.5 -----------------I--------I I

 6/99 FS13C --------------------------I--I I--------I

 5/98 FS10A -----------------I I--I I I

 6/99 FS09B -----------------I-----------I I I I

 5/98 FS12B -----------------I I I I

 5/98 FS13F -----------------I-----------I I--I I

 5/98 FS12C --------I I I I

 6/99 FS12C --------I--------------I I--I I

 5/98 FS12F --------------------I I-----I I--------I

 6/99 FS11C --------------------I--I I I

 5/98 FS08D -----------------I I I

 5/98 IR02C -----------------I-----I I I

 6/99 FS08D -----------------------I--I I I

 5/98 FS09D --------------------------I--------------I I I--I

 5/98 IR02B --------------------I I I I I

 5/98 IR07C --------------------I--------I I--I I I

 5/98 IR08C -----------------------------I-----------I I I

 6/99 FS10B --------------------I I I

 6/99 FS12B --------------------I--------------------------------I I

B 5/98 FS02C -----------I I-----------------I

 5/98 FS06C -----------I--------------I I I

 6/99 FS06C --------------------------I--------I I I

 5/98 FS06D -----------------I I-----------I I I

 6/99 FS12F -----------------I-----------------I I I I

 5/98 FS11C -----------I I--------I I

 5/98 FS14A -----------I--------I I I

 5/98 FS14E --------------------I--------------------------I I

C 5/98 FS02.5 --------------I I

 5/98 FS13E --------I I--------------------------------------------------I I

 6/99 FS03E --------I-----I I I

D 5/98 FS03E -----------------------I I I

 5/98 FS08E -----------------------I--------------------I I I

 5/98 FS04C --------------------------------------I I I I

 5/98 FS04E -----------------------------I I I--I I I

 6/99 FS06D -----------------------------I-----I I--I I I I I

 5/98 FS12E -----------------------------------I--I I--I I I I

 5/98 FS09C -----------------------------------------I I I--------I

 5/98 FS07F -----------------I I I I

 5/98 FS10.5 -----------------I-----I I-----I I I

 5/98 FS14D -----------I I-----------------I I I I I

 5/98 FS14G -----------I-----------I I I I I I

 5/98 FS08B --------------I I I I I I

 5/98 FS09A --------------I-----------I I-----I I I I

 5/98 FS08C --------------------------I-----------I I I I I

 5/98 FS13A -----------------------------I I--I I-----------I I

 5/98 IR04B -----------------------------I--------I I I

D’ 6/99 FS02C -----------I I I

 6/99 FS08E -----------I--------------------I I I

 6/99 FS04E --------------I I I I

 6/99 FS13F --------------I--I I--------------------I I

 6/99 FS12E -----------------I--------------I I

E 5/98 FS01G -----------------I I

 5/98 HCS31 --------------I I-----------------------I I

 6/99 FS04C --------------I--I I I

 5/98 FS07B -----------------I I-----------I I

 6/99 FS07B2 -----------------I-----I I I I

 6/99 FS07B1 -----I I-----------------I I I

 6/99 FS07B3 -----I-----------------I I I

 5/98 IR04D -----------------I I--------------------I

 5/98 IR05D -----------------I-----------I I

 5/98 IR08B -----------------------------I-----I I

 5/98 IR04E --------------------------I I-----------------I

 5/98 IR07E --------------------------I--------I

Figure 4.2-2: Dendogram of all stations sampled in 1998 and 1999. Cluster groups, indicated by

letters, are based on simultaneous double standardization of data, Bray-Curtis similarity, and

flexible sorting. For details see text.
 Anthozoa --------I

 Leptosynapta tenuis --------I--I

 Travisia parva -----------I--------I

 Crassinella martinic --------------------I-----I

 Mytilus edulis --------------------I I

 Crenella glandula --------------I I I--------I

Astarte spp. --------------I--I I-----I I

 Pleuromeris tridenta -----------I I--I I

 Bushia elegans -----------I-----I I-----------------I

Pseudeurythoe spp. -----------------I I I

 Cyclocardia borealis -----------------I-----------------I I

 I

 Nemertinea --------I I

 Oligochaeta --------I-----I I

 Aricidea (Acmira) ce --------------I-----I I

 Parapionosyllis long --------------I I I

 Tanaissus psammophil --------------I-----I I

 Brania wellfleetensi --------------I I--I I

 Aphelochaeta sp. --------------I--I I I I

 Aricidea (Acmira) ca -----------------I--I I-----------I I

 Streptosyllis pettib -----------------I I I I

 Hemipodus roseus -----------------I-----I I I

 Parougia caeca -----------I I I

 Tharyx acutus -----------I-----------------------I-----I I--------------------I

 Scoloplos rubra --------------I I I I I

 Lyonsia hyalina --------------I--------I I I I I

 Paradoneis sp. B --------------------I I-----I I I I I

 Paradoneis sp. A --------------------I--I I I I I I

 Ampharete finmarchic --------------------I I-----I I-----I I I

 Spisula solidissima --------------------I--------I I I I I

 Pisione remota --------------I I I I I

 Protodorvillea kefer --------------I-----------I I I I I

 Ancistrosyllis hartm --------------------I I--------------I I I I

 Ophelia denticulata --------------------I-----I I I I

 I I I

 Sigalion arenicola --------------------------I I I I

 Natica pusilla -----------------I I-----I I I I

 Branchiostoma cariba -----------------I--------I I I-----I I

 Paraonis pygoenigmat --------------I I I I

 Americhelidium ameri --------------I-----------I I I I

 Protohaustorius wigl --------------------I I-----I I I

 Rhepoxynius hudsoni --------------------I-----I I--------I I I

 Byblis serrata -----------------I I I I I

 Pseudunciola obliquu -----------------I--------------I I I I

 Hesionura elongata -----------------I I I I

 Monticellina baptist -----------------I-----------I I-----I I

 Lumbrinerides dayi -----------------------------I--I I I

 Politolana concharum --------------------I I-----I I I

 Parahaustorius holme --------------------I-----------I I I I

 Scoletoma tenuis --------------------I I--I I

 Bathyporeia parkeri --------------------I--------I I I

 Chiridotea coeca --------------I I--------I I

 Parahaustorius longi --------------I--------------I I

 I

 Sthenelais limicola -----------------I I

 Edotea triloba -----------------I--------------I I

 Spiophanes bombyx --------------------------I I I

 Asabellides oculata --------I I I I

 Unciola irrorata --------I--------------I I-----I I

Nucula spp. --------------I I--I I I

Tellina spp. --------------I--------I I--------I I

 Apoprionospio pygmae -----------------------------I I I I

 Turbonilla interrupt --------------------I I--I I I

 Mysella planulata --------------------I--------I I I

Nephtys spp. -----------I I I

 Caulleriella sp. B -----------I-----------I I-----I I

 Lumbrineris fragilis -----------------I I--------I I I I

 Pseudoleptocuma mino -----------------I-----I I-----I I I I

 Cirolana polita --------------------------------I I I I I

 Paraonis fulgens -----------------------I I--I I I

 Oxyurostylis smithi -----------------I I--------------I I I

 Phoronis sp. -----------------I-----I I--------------I I

 Glycera dibranchiata -----I I I I

 Cyathura burbancki -----I-----------I I I I

 Polycirrus eximius -----------------I--------I I I I

 Sabaco elongatus -----------I I-----------I I I I

Odostomia spp. -----------I--------------I I I I I

 Drilonereis longa --------I I--------I I I

 Sipuncula --------I-----------------------------I I I

 I I

 Phyllodoce arenae --I I I

 Nereis grayi --I--------------I I I

 Pherusa affinis -----------------I--------------I I-----------I

 Notocirrus spiniferu --I I I

Leitoscoloplos spp. --I-----I I-----I I

 Scalibregma inflatum --------I-----------------------I I I

 Glycera americana -----------------I I I

 Nassarius trivittatu -----------------I-----I I--------I I

Notomastus spp. -----------------------I-----I I I I

 Polydora cornuta --------I I--------I I I

 Amastigos caperatus --------I--------------------I I I

 I I

 Spio setosa --------------I I--------------I

 Ensis directus --------------I-----I I

 Mediomastus ambiseta -----I I I

Ampelisca spp. -----I--I I--------------------------I

 Clymenella torquata --I I--I I

 Macroclymene zonalis --I-----I I--------I

 Pitar morrhuanus -----------I

Figure 4.2-3: Species cluster groups based on simultaneous double standardizataion of data

from MD/DEstudy area, Bray Curtis similar, and Flexible sorting. For details see text.
 5/98 FS02C --------------I

 5/98 FS06C --------------I-----------------I

 5/98 FS06D -----------------------I I--------I

 6/99 FS12F -----------------------I--------I I

 6/99 FS06C --------------------------------I I--I

 5/98 FS08D -----------------I I--------I I

 6/99 FS08D -----------------I--------------I I

 6/99 FS02C -----------------I I--------------------I

 6/99 FS08E -----------------I--------------------I I I

 6/99 FS06D --------------------------------------I-----I I

 I

 5/98 FS03E --------------------------------I I

 6/99 FS04E -----------------I I I

 6/99 FS12E -----------------I--I I--I I-----I

 6/99 FS13F --------------------I-----------I I I I

 5/98 FS04E -----------------I I--------------I I I

 5/98 FS08E -----------------I-----------------I I I I

 6/99 FS03E --------------------------------------------I I--------------I I

 5/98 FS12E --------------------------------------------I-----I I

 I

 5/98 FS09B -----------------I I

 6/99 FS13C -----------------I--------------I I

 5/98 FS10B --I I I--I

 5/98 FS13C --I--------------------------I I--------I I I

 5/98 FS12B --------------I I--I I I I

 5/98 FS13F --------------I--------------I I I I

 6/99 FS09B -----------------------I I-----------I I I

 6/99 FS11C -----------------------I--I I I I I

 5/98 FS12F --------------------------I--I I I I I

 5/98 FS11C --------------------I I-----------I I I I

 5/98 FS12C -----------I I--------I I-----------------I I

 6/99 FS12C -----------I--------I I I

 6/99 FS10B -----------------------I I I

 6/99 FS12B -----------------------I-----------------------------I I

 I

 5/98 FS04C --------------------------------------------I I

 6/99 FS04C -----------------------I I I

 5/98 FS07B --------------------I I I-----------------------------I

 6/99 FS07B2 --------------------I I--------------------I

 6/99 FS07B1 -----I I--I

 6/99 FS07B3 -----I--------------I

Figure 4.2-4: Dendogram of Fenwick Shoals stations sampled both in 1998 and 1999. Cluster

groups are based on simultaneous double standardization of data, Bray-Curtis similarity, and

flexible sorting. For details see text.
1

2

3

S

H

A

N

N

O

N

a b c d e

CLUSTER

1000

2000

3000

T

O

T

I

N

D

a b c d e

10

20

T 30

O

T

S

P

C

a b c d e

1

2

3

4

R

I

C

H

N

E

S

S

a b c d e

0.2

0.4

0.6

0.8

E

V

E

N

N

E

S

S

a b c d e

0.2

0.4

0.6

0.8

S

I

M

P

S

O

N

a b c d e

Figure 4.2-5. Box plots of community structure statistics from MD/DE study area by cluster analysis station group. Bar is median, box is

interquartile range, tails are trimmed range, and dots are outliers (>2 times interquartile range).

 Species Group

Ammodytes spp. --------------------------------I

 Etropus microstomus --------------I I A

 Prionotus carolinus --------------I--------I I-----------I

 Urophycis regia -----------------------I--------I I

 Anchoa mitchilli --------------I I--I

 Conger oceanicus --------------I-----------------I B I I

 Centropristis striat --------------------------------I-----------I I

 Ophidion marginatum --------------I I--------------------------I

 Stenotomus crysops --------------I--I C I I

 Scomber scombrus -----------------I-----------------------------I I

 I

 Cynoscion regalis --I I

 Paralichthys dentatu --I--------------------------I I

 Enchelyopus cimbrius -----------I I D I

 Pseudopleuronectes a -----------I-----------I I--------------------------------------------I

 Raja eglantaria --------------I I-----I I

 Raja erincea --------------I--------I I

 I

 Mugil curema --I I

 Prionotus evolans --I--------I E I

 Syngnathus fuscus --I I--------------------------------------------------------------I

 Scophthalmus aquosus -----------I

 Similarity 0.92 0.61 0.23 -.16 -.55 -.94

Figure 4.3-2: Cluster analysis of demersal fish collected May 1999 at four benthic habitat types within

the MD/DE study area. Based on Bray-Curtis similarity and flexible sorting.
 Habitat Day/Night

 Shell&Gravel Day -----------------------------------I 

 Shell&Gravel Night --I I Group I

 Diopatra Day --I---------------------I I-----------I

 Diopatra Night ------------------------I----------I I

 Group I

 Sand Day -----------------------------------I II I--------------------I Sand Night --------

---------------------------I-----------I I 

 I

 Asabellides Day ------------------------I Group III I Asabellides Night -----

-------------------I-------------------------------------------I

 Similarity 0.71 0.57 0.41 0.24 0.08

Figure 4.3-3: Cluster analysis of fish collected May 1999 at four benthic habitat types in the MD/DE

Study area. Based onCurtis-Bray similarity and flexible sorting. See test for a description of the habitat

characteristics. See Figure 4.3-1 for the location of the habitats.
FS11f-2 FS12bc-2

Figure 4.4-1: Selected Sediment Profile Images (SPI).
FS12g-1 FS14a-2

Figure 4.4-2: Selected Sediment Profile Images (SPI).
FS09de-1 FS11f-1

Figure 4.4-3: Selected Sediment Profile Imaages (SPI).
FS09d-1 FS09d-2

Figure 4.4-4: Selected Sediment Profile Images (SPI).
FS14a-2 FS08c-2

Figure 4.4-5: Selected Sediment Profile Images (SPI).
MDDE 1998 SPI: Examples of Sediment Grain Size Determinations

SIVFS

fs09e-2b-598_p.TIF

VFSFS

fs03g-1b-598_p.TIF

FS

fs02d-1b-598_p.TIF

FSMS

fs10g-1a-598_p.TIF

FSMSCS

fs04f-1b-598_p.TIF

MS

fs11b-1b-598_p.TIF

MSCS

fs10a-1b-598_p.TIF

GRCS (MS-CS-GR)

fs03d-1b-598_p.TIF

GRVCS

fs08d-1b-598_p.TIF

SHGRCS(CS-GR-SH)

fs10f-1b-598_p.TIF

Figure 4.4-6: Examples of sediment grain-size determinations from sedimepnt profile images. CS = coarse sand, FS = fine sand, 

GR = gravel, MS = medium sand, SH = shell, SI = Silt, VCS = very coasrse sand, VFS = very fine sand.

Astarte

Ensis 

 and Spisula 

 valves

Figure 4.5-1. Selected surface camera images, coarser environments.
Asabellides oculata

Asabellides

and unid. small tubes

Figure 4.5-2: Selected surface camera images, finer-grained environments.
0

5

10

15

20

25

30

1

0

1

1

98

168

23

7

1

98

335

22

1 4

1

98

502

21

21

1

98

669

20

28

1

98

836

1 9

4

2

98

1003

1 8

1 1

2

98

1170

1 7

1 8

2

98

1337

1 6

25

2

98

1504

1 4

4

3

98

1671

1 3

1 1

3

98

1838

1 2

1 8

3

98

2005

1 1

25

3

98

2172

1 0

1

4

98

2339

9

8

4

98

2506

8

1 5

4

98

2673

7

22

4

98

2840

6

29

4

98

3007

5

7

5

98

3174

4

1 4

5

98

3341

3

21

5

98

3508

2

28

5

98

3675

1

4

6

98

3842

0

1 1

6

98

4009

23

1 7

6

98

4176

22

24

6

98

4343

21

1

7

98

4510

20

8

7

98

4677

1 9

1 5

7

98

4844

1 8

22

7

98

5011

1 7

29

7

98

5178

1 6

5

8

98

5345

1 5

1 2

8

98

5512

1 3

1 9

8

98

5679

1 2

26

8

98

5846

1 1

31

8

98

6013

1 0

7

9

98

6180

9

1 4

9

98

6347

8

21

9

98

6514

7

28

9

98

6681

6

5

1 0

98

6848

5

1 2

1 0

98

7015

4

1 9

1 0

98

7182

3

26

1 0

98

7349

2

3

1 1

98

7516

1

1 0

1 1

98

7683

0

1 7

1 1

98

7850

23

23

1 1

98

8017

22

30

1 1

98

8184

21

7

1 2

98

8351

20

1 4

1 2

98

8518

1 9

21

1 2

98

8685

1 8

28

1 2

98

0

1

2

3

4

5

6

7

8

Surface Water Temp. (C)

Average Wave Height (m) 

1998 NOAA Buoy 44009 Water Temp. (C) and Wave Height (m)

Fugure 4.7-1a: Water temperature (oC) and wave height (m) from NOAA Buoy 44009 for the year

1998.
Surf. Water Temp (C) 

May 17 - 21, 1998, During VIMS MDDE Benthic Cruise 

10

11

12

13

14

15

16

17

18

19

20

3242

0

1 7

5

98

3247

5

1 7

5

98

3252

1 0

1 7

5

98

3257

1 5

1 7

5

98

3262

20

1 7

5

98

3267

1

1 8

5

98

3272

6

1 8

5

98

3277

1 1

1 8

5

98

3282

1 6

1 8

5

98

3287

21

1 8

5

98

3292

2

1 9

5

98

3297

7

1 9

5

98

3302

1 2

1 9

5

98

3307

1 7

1 9

5

98

3312

22

1 9

5

98

3317

3

20

5

98

3322

8

20

5

98

3327

1 3

20

5

98

3332

1 8

20

5

98

3337

23

20

5

98

3342

4

21

5

98

3347

9

21

5

98

3352

1 4

21

5

98

3357

1 9

21

5

98

Surf. Water Temp (C)

Figure 4.7.1-b: Water temperature (oC) data from NOAA Bouy 44009 during the 17 to 21 May,

1998 cruise.
0

5

10

15

20

25

30

0

1

1

99

23

7

1

99

22

1 4

1

99

21

21

1

99

20

28

1

99

1 9

4

2

99

1 8

1

1

2

1 7

1 8

2

99

1 6

25

2

99

1 5

4

3

99

1 4

1

1

3

1 3

1 8

3

99

1 2

25

3

99

1

1

1

4

1 0

8

4

99

9

1 5

4

99

8

22

4

99

7

29

4

99

6

6

5

99

5

1 3

5

99

4

20

5

99

3

27

5

99

2

4

6

99

1

1

1

6

0

1 8

6

99

23

24

6

99

0

1

2

3

4

5

6

7

8

Surface Water Temp. (C)

Average Wave Height (m) 

1999 NOAA Buoy 44009 Water Temp. (C) and Wave Height (m)

Figure 4.7-2: Water temperature (oC) and wave height (m) from NOAA Buoy 44009 for the first six

months of 1999.
Figure 4.8-1a: Distribution of Benthic Habitat Quality index values

for the Indian River ROI.
Figure 4.8-1b: Distribution of Scaled Benthic Habitat Quality index value

for the Indian River ROI.
Figure 4.8-2a: Distribution of Benthic Habitat Quality index values for the Fenwick Shoal ROI.
Figure4.8-2b: Distribution of Scaled Benthic Habitat Quality index values for

the Fenwick Shoal ROI.
Figure 5.2-1: Benthic habitat map of Fenwick Shoals ROI showing SBHQ index surface generated

with inverse distance weighting, secondary production, and surface biological features from vide

transects. The presence of Ammodytes spp. from the video over-lain on the video transects.
Figure 5.4-1: Sediment grain-size (mm) map of

the Fenwick Shoal ROI derived from sediment

profile images. Surface was generated with

inverse distance weighting interpolation. Coarser

sediments tend to be on the crests of the shoals.


TABLES
Table 4.1-1

Sediment grain-size distribution from grab samples collected in the MD/DE study area.

Gravel is >2mm. Sand is between 2 and 0.063 mm. Silt is between 0.063 mm and 0.0039 mm.

Clay is <0.0039 mm.

STN % Gravel %Sand % Silt % Clay

BB04 13.8 85.6 0.1 0.5 

FS01G 0.6 88.7 4.4 6.3 

FS02.5D 0.0 99.6 0.1 0.3 

FS02C 2.7 96.8 0.0 0.5 

FS02C 12.3 86.9 0.1 0.7 

FS03E 0.0 99.2 0.2 0.6 

FS04C 0.0 95.7 2.9 1.4 

FS04E 0.0 99.6 0.0 0.4 

FS06C 14.7 85.0 0.0 0.3 

FS06D 0.3 99.2 0.1 0.4 

FS07B 0.0 91.2 6.0 2.8 

FS07F 0.2 99.0 0.1 0.7 

FS08B 0.0 98.8 0.4 0.8 

FS08C 0.3 97.9 0.7 1.1 

FS08D 2.2 97.5 0.1 0.2 

FS08E 0.0 99.5 0.1 0.4 

FS09A 2.1 96.8 0.3 0.8 

FS09B 0.9 98.1 0.3 0.7 

FS09C 0.0 99.5 0.1 0.4 

FS09D 3.3 94.6 0.7 1.4 

FS10.5D 0.0 99.4 0.1 0.5 

FS10A 2.4 96.7 0.3 0.6 

FS10B 5.5 93.8 0.1 0.6 

FS11C 3.7 96.0 0.1 0.2 

FS12B 16.7 82.8 0.1 0.4 

FS12C 15.2 84.5 0.0 0.3 

FS12E 1.0 98.8 0.0 0.2 

FS12F 2.3 97.3 0.1 0.4 

FS13A 4.5 94.5 0.2 0.8 

FS13C 7.5 91.9 0.1 0.5 

FS13E 0.0 99.1 0.2 0.7 

FS13F 1.5 97.9 0.1 0.5 

FS14A 3.2 96.2 0.1 0.5 

FS14D 1.9 97.5 0.2 0.4 

FS14D 0.0 99.2 0.1 0.7 

FS14E 1.4 98.0 0.1 0.5 

FS14G 0.0 99.1 0.1 0.8 

HCS31 0.0 74.0 7.7 18.3 

IR02B 66.4 33.5 0.1 0.1 

IR02D 18.5 80.6 0.2 0.7 

IR04B 4.5 94.8 0.1 0.6 

IR04D 56.7 42.3 0.3 0.7 

IR04E 61.1 36.1 1.2 1.6 

IR05.5C 4.3 95.1 0.1 0.5 

IR05D 34.1 65.1 0.2 0.6 

IR07C 1.7 97.6 0.1 0.6 

IR08B 21.9 77.3 0.1 0.7 

IR08C 36.0 62.8 0.4 0.8 

IR09A 11.2 88.2 0.1 0.5 

IR09C 16.1 83.2 0.1 0.6 

SBB32 14.7 84.1 0.2 1.0 
Table 4.1-2

Sediment grain-size analysis of sand fraction from grab samples

collected in the MD/DE study area. All statistics are in mm.

------------------------------------------------------------------------------

Station Mean Median Sorting Skewness Kurtosis

BB04 0.47 0.49 0.59 0.94 0.57 

FS01G 0.18 0.18 0.70 1.04 0.74 

FS02.5D 0.37 0.39 0.70 0.82 0.59 

FS03E 0.24 0.24 0.78 0.96 0.77 

FS04C 0.14 0.14 0.67 1.27 0.72 

FS04E 0.37 0.39 0.70 0.82 0.63 

FS06C 1.07 1.11 0.58 0.83 0.13 

FS06D 0.60 0.61 0.66 0.87 0.45 

FS07B.2 0.12 0.11 0.82 1.19 0.88 

FS07F 0.31 0.27 0.53 1.19 0.60 

FS08B 0.26 0.26 0.76 1.12 0.71 

FS08C 0.28 0.29 0.65 0.91 0.69 

FS08D 0.76 0.74 0.68 0.96 0.39 

FS08E 0.32 0.33 0.62 0.88 0.60 

FS09A 0.29 0.28 0.61 1.02 0.63 

FS09B 0.46 0.43 0.57 1.13 0.61 

FS09C 0.37 0.37 0.77 0.92 0.67 

FS09D 0.53 0.86 0.39 0.65 0.66 

FS10.5D 0.35 0.37 0.71 0.79 0.62 

FS10A 0.41 0.40 0.58 1.08 0.61 

FS10B 0.41 0.38 0.55 1.21 0.59 

FS11C 0.65 0.65 0.55 0.92 0.53 

FS12B 0.52 0.47 0.53 1.11 0.56 

FS12C 0.55 0.54 0.55 0.97 0.56 

FS12E 0.33 0.33 0.65 1.02 0.63 

FS12F 0.57 0.57 0.49 0.94 0.46 

FS13A 0.49 0.43 0.54 1.21 0.56 

FS13C 0.52 0.51 0.54 1.00 0.62 

FS13E 0.21 0.21 0.76 0.97 0.77 

FS13F 0.62 0.63 0.49 0.94 0.58 

FS14A 0.48 0.44 0.51 1.06 0.54 

FS14D 0.29 0.29 0.67 0.95 0.68 

FS14E 0.42 0.41 0.61 1.02 0.63 

FS14F 0.44 0.44 0.53 0.98 0.58 

FS14G 0.28 0.28 0.76 1.07 0.75 

HCS31 0.45 0.44 0.56 0.94 0.55 

IR02B 0.97 1.11 0.57 0.77 0.39 

IR02D 0.64 0.60 0.52 1.05 0.52 

IR04B 0.42 0.41 0.65 1.06 0.60 

IR04D 0.27 0.26 0.73 1.08 0.74 

IR04E 0.41 0.41 0.50 0.98 0.58 

IR05.5C 0.61 0.61 0.58 0.96 0.49 

IR05D 0.30 0.29 0.73 1.10 0.71 

IR07C 0.56 0.52 0.62 1.10 0.52 

IR08B 0.51 0.50 0.59 1.05 0.59 

IR08C 0.48 0.47 0.54 1.04 0.51 

IR09A 0.67 0.72 0.55 0.89 0.47 

IR09C 0.52 0.52 0.53 0.93 0.53 

SBB32 0.47 0.44 0.54 1.09 0.57 
Table 4.2-1.

Major taxa abundance (individuals/0.04 m2) of infauna collected off Maryland and Delaware in

May 1998 and June 1999.

---------------------------------------------------------------------------------------------------------------------

May 1998

Anthozoa Annelida Crustacea Phoronida Cephalochordata

Station Nemertinea Bivalvia Sipuncula Echinodermata Totals

BB02 1 20 293 6 28 0 0 0 0 348

BB04 1 14 243 1 12 0 0 0 0 271

FS01G 0 1 3066 13 26 0 2 0 0 3108

FS02.5D 0 1 9 1 37 0 0 0 0 48

FS02C 4 13 53 61 15 0 0 2 2 150

FS03E 0 0 0 0 20 0 0 0 1 21

FS04C 0 0 82 734 56 0 0 0 0 872

FS04E 0 1 1 3 2 0 0 0 0 7

FS06C 19 7 206 166 5 0 0 11 2 416

FS06D 0 0 21 33 3 0 0 0 0 57

FS07B 0 0 283 102 68 0 8 0 0 461

FS07F 0 2 9 13 10 0 0 0 0 34

FS08B 0 3 29 2 42 0 2 0 0 78

FS08C 0 1 98 65 2 2 0 0 0 168

FS08D 5 13 143 4 2 0 0 0 1 168

FS08E 0 0 2 4 3 0 2 0 0 11

FS09A 0 0 56 23 38 0 0 0 1 118

FS09B 0 5 44 6 37 0 0 0 0 92

FS09C 0 0 3 1 12 0 0 0 0 16

FS09D 0 3 72 0 3 0 0 0 0 78

FS10.5D 0 0 1 0 3 0 0 0 0 4

FS10A 0 12 159 0 27 0 0 2 0 200

FS10B 1 13 149 4 14 0 0 0 0 181

FS11C 0 2 81 12 2 0 0 0 0 97

FS12B 0 9 58 1 15 0 0 0 0 83

FS12C 0 5 49 1 3 0 0 0 1 59

FS12E 0 0 1 1 2 0 0 0 0 4

FS12F 0 4 25 2 3 0 0 1 0 35

FS13A 0 3 20 0 6 0 6 0 0 35

FS13C 2 15 153 4 12 0 0 0 1 187

FS13E 0 0 0 2 6 0 0 0 0 8

FS13F 2 38 52 16 15 0 0 0 0 123

FS14A 0 26 233 5 7 0 0 0 0 271

FS14D 0 0 3 2 4 0 0 0 0 9

FS14E 0 1 12 0 3 0 0 0 0 16

FS14F 0 6 50 5 12 0 0 0 0 73

FS14G 0 1 0 2 13 0 0 0 0 16

HCS31 0 11 1123 68 22 0 7 0 0 1231
Table 4.2.1. Continued.

May 1998

Anthozoa Annelida Crustacea Phoronida Cephalochordata

Station Nemertinea Bivalvia Sipuncula Echinodermata Totals

IR02B 0 12 62 16 2 0 0 0 1 93

IR02C 0 21 201 15 6 0 0 0 0 243

IR02D 0 10 119 18 2 0 0 0 0 149

IR04B 0 1 14 4 6 0 0 0 0 25

IR04D 0 0 12 5 4 0 0 0 0 21

IR04E 0 1 257 20 32 2 0 0 0 312

IR05.5C 0 4 47 5 4 0 0 0 0 60

IR05D 0 0 145 6 11 1 2 0 0 165

IR07C 0 5 41 1 8 0 0 1 0 56

IR07E 0 0 19 0 4 2 1 0 0 26

IR08B 0 0 14 2 5 0 0 0 0 21

IR08C 0 4 41 2 14 0 0 0 0 61

IR09A 1 1 29 5 5 0 0 0 0 41

IR09C 0 12 192 2 1 0 0 0 0 207

Totals 36 301 8075 1464 694 7 30 17 10 10634

June 1999

Nemertinea Bivalvia Sipuncula Echinodermata

Anthozoa Annelida Crustacea Phoronida Cephalochordata

FS02C 4 3 1 38 21 0 0 1 0 68

FS03E 0 0 0 2 11 0 0 0 1 14

FS04C 0 3 1136 157 40 0 0 0 0 1336

FS04E 0 0 0 8 4 0 0 0 1 13

FS06C 3 6 73 13 2 0 0 1 3 101

FS06D 0 11 10 10 4 0 0 0 0 35

FS07B1 0 16 1034 187 53 0 0 0 0 1290

FS07B2 0 3 1007 86 39 0 27 0 0 1162

FS07B3 1 2 711 110 181 1 0 0 0 1006

FS08D 7 13 131 13 1 0 0 2 1 168

FS08E 1 1 7 28 6 0 0 0 0 43

FS09B 0 8 159 7 56 0 0 0 0 230

FS10B 0 1 25 7 170 0 0 0 0 203

FS11C 0 4 52 4 5 0 0 0 0 65

FS12B 0 5 20 0 103 0 0 0 2 130

FS12C 1 14 43 7 7 0 0 0 2 74

FS12E 0 1 1 4 3 0 0 0 1 10

FS12F 0 5 6 32 3 0 0 0 1 47

FS13C 0 16 39 13 52 0 0 0 2 122

FS13F 0 1 3 14 9 0 0 0 1 28

Totals 17 113 4458 740 770 1 27 4 15 6145
Table 4.2-2.

Proportional contribution of major taxa to total infaunal abundance and species richness

 for samples collected off Maryland and Delaware in May 1998 and June 1999.

------------------------------------------------------------------------------------------------------------

May 1998

Percentage of Percentage of

Major Taxon Individuals Taxa

Anthozoa 0.3 0.7

Nemertinea 2.8 0.7

Annelida 75.9 50.7

Mollusca 13.8 23.0

Crustacea 6.5 22.4

Sipuncula 0.1 0.7

Phoronida 0.3 0.7

Echinodermata 0.2 0.7

Cephalochordata 0.1 0.7

June 1999

Percentage of Percentage of

Major Taxon Individuals Taxa

Anthozoa 0.3 0.9

Nemertinea 1.8 0.9

Annelida 72.5 52.8

Mollusca 12.0 21.3

Crustacea 12.5 20.4

Sipuncula 0.02 0.9

Phoronida 0.4 0.9

Echinodermata 0.1 0.9

Cephalochordata 0.2 0.9
Table 4.2-3.

Percentage of major taxa abundance (individuals/0.04 m2) of infauna collected in the Maryland

and Delaware study area in May 1998 and June 1999.

May 1998

Anthozoa Annelida Crustacea Phoronida Cephalochordata

Station Nemertinea Bivalvia Sipuncula Echinodermata

BB02 0.3 5.7 84.2 1.7 8.0 0.0 0.0 0.0 0.0

BB04 0.4 5.2 89.7 0.4 4.4 0.0 0.0 0.0 0.0

FS01G 0.0 0.0 98.6 0.4 0.8 0.0 0.1 0.0 0.0

FS02.5D 0.0 2.1 18.8 2.1 77.1 0.0 0.0 0.0 0.0

FS02C 2.7 8.7 35.3 40.7 10.0 0.0 0.0 1.3 1.3

FS03E 0.0 0.0 0.0 0.0 95.2 0.0 0.0 0.0 4.8

FS04C 0.0 0.0 9.4 84.2 6.4 0.0 0.0 0.0 0.0

FS04E 0.0 14.3 14.3 42.9 28.6 0.0 0.0 0.0 0.0

FS06C 4.6 1.7 49.5 39.9 1.2 0.0 0.0 2.6 0.5

FS06D 0.0 0.0 36.8 57.9 5.3 0.0 0.0 0.0 0.0

FS07B 0.0 0.0 61.4 22.1 14.8 0.0 1.7 0.0 0.0

FS07F 0.0 5.9 26.5 38.2 29.4 0.0 0.0 0.0 0.0

FS08B 0.0 3.8 37.2 2.6 53.8 0.0 2.6 0.0 0.0

FS08C 0.0 0.6 58.3 38.7 1.2 1.2 0.0 0.0 0.0

FS08D 3.0 7.7 85.1 2.4 1.2 0.0 0.0 0.0 0.6

FS08E 0.0 0.0 18.2 36.4 27.3 0.0 18.2 0.0 0.0

FS09A 0.0 0.0 47.5 19.5 32.2 0.0 0.0 0.0 0.8

FS09B 0.0 5.4 47.8 6.5 40.2 0.0 0.0 0.0 0.0

FS09C 0.0 0.0 18.8 6.3 75.0 0.0 0.0 0.0 0.0

FS09D 0.0 3.8 92.3 0.0 3.8 0.0 0.0 0.0 0.0

FS10.5D 0.0 0.0 25.0 0.0 75.0 0.0 0.0 0.0 0.0

FS10A 0.0 6.0 79.5 0.0 13.5 0.0 0.0 1.0 0.0

FS10B 0.6 7.2 82.3 2.2 7.7 0.0 0.0 0.0 0.0

FS11C 0.0 2.1 83.5 12.4 2.1 0.0 0.0 0.0 0.0

FS12B 0.0 10.8 69.9 1.2 18.1 0.0 0.0 0.0 0.0

FS12C 0.0 8.5 83.1 1.7 5.1 0.0 0.0 0.0 1.7

FS12E 0.0 0.0 25.0 25.0 50.0 0.0 0.0 0.0 0.0

FS12F 0.0 11.4 71.4 5.7 8.6 0.0 0.0 2.9 0.0

FS13A 0.0 8.6 57.1 0.0 17.1 0.0 17.1 0.0 0.0

FS13C 1.1 8.0 81.8 2.1 6.4 0.0 0.0 0.0 0.5

FS13E 0.0 0.0 0.0 25.0 75.0 0.0 0.0 0.0 0.0

FS13F 1.6 30.9 42.3 13.0 12.2 0.0 0.0 0.0 0.0

FS14A 0.0 9.6 86.0 1.8 2.6 0.0 0.0 0.0 0.0

FS14D 0.0 0.0 33.3 22.2 44.4 0.0 0.0 0.0 0.0

FS14E 0.0 6.3 75.0 0.0 18.8 0.0 0.0 0.0 0.0

FS14F 0.0 8.2 68.5 6.8 16.4 0.0 0.0 0.0 0.0

FS14G 0.0 6.3 0.0 12.5 81.3 0.0 0.0 0.0 0.0

HCS31 0.0 0.9 91.2 5.5 1.8 0.0 0.6 0.0 0.0
Table 4.2-3. Continued.

May 1998

Anthozoa Annelida Crustacea Phoronida Cephalochordata

Station Nemertinea Bivalvia Sipuncula Echinodermata

IR02B 0.0 12.9 66.7 17.2 2.2 0.0 0.0 0.0 1.1

IR02C 0.0 8.6 82.7 6.2 2.5 0.0 0.0 0.0 0.0

IR02D 0.0 6.7 79.9 12.1 1.3 0.0 0.0 0.0 0.0

IR04B 0.0 4.0 56.0 16.0 24.0 0.0 0.0 0.0 0.0

IR04D 0.0 0.0 57.1 23.8 19.0 0.0 0.0 0.0 0.0

IR04E 0.0 0.3 82.4 6.4 10.3 0.6 0.0 0.0 0.0

IR05.5C 0.0 6.7 78.3 8.3 6.7 0.0 0.0 0.0 0.0

IR05D 0.0 0.0 87.9 3.6 6.7 0.6 1.2 0.0 0.0

IR07C 0.0 8.9 73.2 1.8 14.3 0.0 0.0 1.8 0.0

IR07E 0.0 0.0 73.1 0.0 15.4 7.7 3.8 0.0 0.0

IR08B 0.0 0.0 66.7 9.5 23.8 0.0 0.0 0.0 0.0

IR08C 0.0 6.6 67.2 3.3 23.0 0.0 0.0 0.0 0.0

IR09A 2.4 2.4 70.7 12.2 12.2 0.0 0.0 0.0 0.0

IR09C 0.0 5.8 92.8 1.0 0.5 0.0 0.0 0.0 0.0

June 1999

Anthozoa Annelida Crustacea Phoronida Cephalochordata

Station Nemertinea Bivalvia Sipuncula Echinodermata

FS02C 5.9 4.4 1.5 55.9 30.9 0.0 0.0 1.5 0.0

FS03E 0.0 0.0 0.0 14.3 78.6 0.0 0.0 0.0 7.1

FS04C 0.0 0.2 85.0 11.8 3.0 0.0 0.0 0.0 0.0

FS04E 0.0 0.0 0.0 61.5 30.8 0.0 0.0 0.0 7.7

FS06C 3.0 5.9 72.3 12.9 2.0 0.0 0.0 1.0 3.0

FS06D 0.0 31.4 28.6 28.6 11.4 0.0 0.0 0.0 0.0

FS07B1 0.0 1.2 80.2 14.5 4.1 0.0 0.0 0.0 0.0

FS07B2 0.0 0.3 86.7 7.4 3.4 0.0 2.3 0.0 0.0

FS07B3 0.1 0.2 70.7 10.9 18.0 0.1 0.0 0.0 0.0

FS08D 4.2 7.7 78.0 7.7 0.6 0.0 0.0 1.2 0.6

FS08E 2.3 2.3 16.3 65.1 14.0 0.0 0.0 0.0 0.0

FS09B 0.0 3.5 69.1 3.0 24.3 0.0 0.0 0.0 0.0

FS10B 0.0 0.5 12.3 3.4 83.7 0.0 0.0 0.0 0.0

FS11C 0.0 6.2 80.0 6.2 7.7 0.0 0.0 0.0 0.0

FS12B 0.0 3.8 15.4 0.0 79.2 0.0 0.0 0.0 1.5

FS12C 1.4 18.9 58.1 9.5 9.5 0.0 0.0 0.0 2.7

FS12E 0.0 10.0 10.0 40.0 30.0 0.0 0.0 0.0 10.0

FS12F 0.0 10.6 12.8 68.1 6.4 0.0 0.0 0.0 2.1

FS13C 0.0 13.1 32.0 10.7 42.6 0.0 0.0 0.0 1.6

FS13F 0.0 3.6 10.7 50.0 32.1 0.0 0.0 0.0 3.6
Table 4.2-4

Species richness (taxons/0.04 m2) by major taxa for infauna collected in the Maryland and

 Delaware study area in May 1998 and June 1999.

---------------------------------------------------------------------------------------------------------------------

May 1998

Nemertinea Bivalvia Sipuncula Echinodermata Total

Anthozoa Annelida Crustacea Phoronida Cephalochordata

SBB32 1 1 18 6 4 0 0 0 0 30

BB04 1 1 11 1 3 0 0 0 0 17

FS01G 0 1 15 2 5 0 1 0 0 24

FS02.5D 0 1 3 1 5 0 0 0 0 10

FS02C 1 1 7 7 7 0 0 1 1 25

FS03E 0 0 0 0 6 0 0 0 1 7

FS04C 0 0 10 7 4 0 0 0 0 21

FS04E 0 1 1 3 2 0 0 0 0 7

FS06C 1 1 11 11 4 0 0 1 1 30

FS06D 0 0 4 6 3 0 0 0 0 13

FS07B 0 0 10 11 6 0 1 0 0 28

FS07F 0 1 5 1 5 0 0 0 0 12

FS08B 0 1 10 1 10 0 1 0 0 23

FS08C 0 1 14 8 2 1 0 0 0 26

FS08D 1 1 10 2 2 0 0 0 1 17

FS08E 0 0 2 1 3 0 1 0 0 7

FS09A 0 0 11 4 7 0 0 0 1 23

FS09B 0 1 9 5 9 0 0 0 0 24

FS09C 0 0 2 1 3 0 0 0 0 6

FS09D 0 1 17 0 2 0 0 0 0 20

FS10.5D 0 0 1 0 2 0 0 0 0 3

FS10A 0 1 18 0 6 0 0 1 0 26

FS10B 1 1 18 2 7 0 0 0 0 29

FS11C 0 1 10 6 1 0 0 0 0 18

FS12B 0 1 10 1 3 0 0 0 0 15

FS12C 0 1 11 1 2 0 0 0 1 16

FS12E 0 0 1 1 2 0 0 0 0 4

FS12F 0 1 6 2 2 0 0 1 0 12

FS13A 0 1 11 0 3 0 1 0 0 16

FS13C 1 1 16 4 4 0 0 0 1 27

FS13E 0 0 0 1 4 0 0 0 0 5

FS13F 1 1 7 2 6 0 0 0 0 17

FS14A 0 1 15 2 4 0 0 0 0 22

FS14D 0 0 3 1 2 0 0 0 0 6

FS14E 0 1 5 0 2 0 0 0 0 8

FS14F 0 1 9 2 5 0 0 0 0 17

FS14G 0 1 0 1 6 0 0 0 0 8

HCS31 0 1 17 5 6 0 1 0 0 30
Table 4.2.4. Continued.

May 1998

Nemertinea Bivalvia Sipuncula Echinodermata Total

Anthozoa Annelida Crustacea Phoronida Cephalochordata

IR02B 0 1 18 3 2 0 0 0 1 25

IR02C 0 1 13 4 2 0 0 0 0 20

IR02D 0 1 15 7 2 0 0 0 0 25

IR04B 0 1 11 3 3 0 0 0 0 18

IR04D 0 0 8 2 4 0 0 0 0 14

IR04E 0 1 22 7 4 1 0 0 0 35

IR05.5C 0 1 10 4 4 0 0 0 0 19

IR05D 0 0 13 6 5 1 1 0 0 26

IR07C 0 1 12 1 4 0 0 1 0 19

IR07E 0 0 9 0 2 1 1 0 0 13

IR08B 0 0 7 2 4 0 0 0 0 13

IR08C 0 1 11 2 3 0 0 0 0 17

IR09A 1 1 8 4 3 0 0 0 0 17

IR09C 0 1 10 2 1 0 0 0 0 14

Totals 1 1 77 35 34 1 1 1 1 152

June 1999

Nemertinea Bivalvia Sipuncula Echinodermata Total

Anthozoa Annelida Crustacea Phoronida Cephalochordata

FS02C 1 1 1 7 3 0 0 1 0 14

FS03E 0 0 0 1 5 0 0 0 1 7

FS04C 0 1 16 6 4 0 0 0 0 27

FS04E 0 0 0 2 3 0 0 0 1 6

FS06C 1 1 8 6 2 0 0 1 1 20

FS06D 0 1 7 5 4 0 0 0 0 17

FS07B1 0 1 20 9 5 0 0 0 0 35

FS07B2 0 1 24 11 3 0 1 0 0 40

FS07B3 1 1 21 8 5 1 0 0 0 37

FS08D 1 1 17 5 1 0 0 1 1 27

FS08E 1 1 4 6 3 0 0 0 0 15

FS09B 0 1 14 3 13 0 0 0 0 31

FS10B 0 1 10 3 7 0 0 0 0 21

FS11C 0 1 9 2 3 0 0 0 0 15

FS12B 0 1 11 0 4 0 0 0 1 17

FS12C 1 1 13 2 4 0 0 0 1 22

FS12E 0 1 1 2 3 0 0 0 1 8

FS12F 0 1 4 5 2 0 0 0 1 13

FS13C 0 1 9 6 7 0 0 0 1 24

FS13F 0 1 3 3 4 0 0 0 1 12

Totals 1 1 57 23 22 1 1 1 1 108
Table 4.2-5

Percentage species by major taxa for infauna collected off Maryland and Delaware in May 1998

and June 1999.

---------------------------------------------------------------------------------------------------------------------

May 1998

Station Nemertinea Mollusca Sipuncula Echinodermata

Anthozoa Annelida Crustacea Phoronida Cephalochordata

BB02 3.3 3.3 60.0 20.0 13.3 0.0 0.0 0.0 0.0

BB04 5.9 5.9 64.7 5.9 17.6 0.0 0.0 0.0 0.0

FS01G 0.0 4.2 62.5 8.3 20.8 0.0 4.2 0.0 0.0

FS02.5D 0.0 10.0 30.0 10.0 50.0 0.0 0.0 0.0 0.0

FS02C 4.0 4.0 28.0 28.0 28.0 0.0 0.0 4.0 4.0

FS03E 0.0 0.0 0.0 0.0 85.7 0.0 0.0 0.0 14.3

FS04C 0.0 0.0 47.6 33.3 19.0 0.0 0.0 0.0 0.0

FS04E 0.0 14.3 14.3 42.9 28.6 0.0 0.0 0.0 0.0

FS06C 3.3 3.3 36.7 36.7 13.3 0.0 0.0 3.3 3.3

FS06D 0.0 0.0 30.8 46.2 23.1 0.0 0.0 0.0 0.0

FS07B 0.0 0.0 35.7 39.3 21.4 0.0 3.6 0.0 0.0

FS07F 0.0 8.3 41.7 8.3 41.7 0.0 0.0 0.0 0.0

FS08B 0.0 4.3 43.5 4.3 43.5 0.0 4.3 0.0 0.0

FS08C 0.0 3.8 53.8 30.8 7.7 3.8 0.0 0.0 0.0

FS08D 5.9 5.9 58.8 11.8 11.8 0.0 0.0 0.0 5.9

FS08E 0.0 0.0 28.6 14.3 42.9 0.0 14.3 0.0 0.0

FS09A 0.0 0.0 47.8 17.4 30.4 0.0 0.0 0.0 4.3

FS09B 0.0 4.2 37.5 20.8 37.5 0.0 0.0 0.0 0.0

FS09C 0.0 0.0 33.3 16.7 50.0 0.0 0.0 0.0 0.0

FS09D 0.0 5.0 85.0 0.0 10.0 0.0 0.0 0.0 0.0

FS10.5D 0.0 0.0 33.3 0.0 66.7 0.0 0.0 0.0 0.0

FS10A 0.0 3.8 69.2 0.0 23.1 0.0 0.0 3.8 0.0

FS10B 3.4 3.4 62.1 6.9 24.1 0.0 0.0 0.0 0.0

FS11C 0.0 5.6 55.6 33.3 5.6 0.0 0.0 0.0 0.0

FS12B 0.0 6.7 66.7 6.7 20.0 0.0 0.0 0.0 0.0

FS12C 0.0 6.3 68.8 6.3 12.5 0.0 0.0 0.0 6.3

FS12E 0.0 0.0 25.0 25.0 50.0 0.0 0.0 0.0 0.0

FS12F 0.0 8.3 50.0 16.7 16.7 0.0 0.0 8.3 0.0

FS13A 0.0 6.3 68.8 0.0 18.8 0.0 6.3 0.0 0.0

FS13C 3.7 3.7 59.3 14.8 14.8 0.0 0.0 0.0 3.7

FS13E 0.0 0.0 0.0 20.0 80.0 0.0 0.0 0.0 0.0

FS13F 5.9 5.9 41.2 11.8 35.3 0.0 0.0 0.0 0.0

FS14A 0.0 4.5 68.2 9.1 18.2 0.0 0.0 0.0 0.0

FS14D 0.0 0.0 50.0 16.7 33.3 0.0 0.0 0.0 0.0

FS14E 0.0 12.5 62.5 0.0 25.0 0.0 0.0 0.0 0.0

FS14F 0.0 5.9 52.9 11.8 29.4 0.0 0.0 0.0 0.0

FS14G 0.0 12.5 0.0 12.5 75.0 0.0 0.0 0.0 0.0

HCS31 0.0 3.3 56.7 16.7 20.0 0.0 3.3 0.0 0.0
Table 4.2.5. Continued.

May 1998

Station Nemertinea Bivalvia Sipuncula Echinodermata

Anthozoa Annelida Crustacea Phoronida Cephalochordata

IR02B 0.0 4.0 72.0 12.0 8.0 0.0 0.0 0.0 4.0

IR02C 0.0 5.0 65.0 20.0 10.0 0.0 0.0 0.0 0.0

IR02D 0.0 4.0 60.0 28.0 8.0 0.0 0.0 0.0 0.0

IR04B 0.0 5.6 61.1 16.7 16.7 0.0 0.0 0.0 0.0

IR04D 0.0 0.0 57.1 14.3 28.6 0.0 0.0 0.0 0.0

IR04E 0.0 2.9 62.9 20.0 11.4 2.9 0.0 0.0 0.0

IR05.5C 0.0 5.3 52.6 21.1 21.1 0.0 0.0 0.0 0.0

IR05D 0.0 0.0 50.0 23.1 19.2 3.8 3.8 0.0 0.0

IR07C 0.0 5.3 63.2 5.3 21.1 0.0 0.0 5.3 0.0

IR07E 0.0 0.0 69.2 0.0 15.4 7.7 7.7 0.0 0.0

IR08B 0.0 0.0 53.8 15.4 30.8 0.0 0.0 0.0 0.0

IR08C 0.0 5.9 64.7 11.8 17.6 0.0 0.0 0.0 0.0

IR09A 5.9 5.9 47.1 23.5 17.6 0.0 0.0 0.0 0.0

IR09C 0.0 7.1 71.4 14.3 7.1 0.0 0.0 0.0 0.0

June 1999

Station Nemertinea Mollusca Sipuncula Echinodermata

Anthozoa Annelida Crustacea Phoronida Cephalochordata

FS02C 7.1 7.1 7.1 50.0 21.4 0.0 0.0 7.1 0.0

FS03E 0.0 0.0 0.0 14.3 71.4 0.0 0.0 0.0 14.3

FS04C 0.0 3.7 59.3 22.2 14.8 0.0 0.0 0.0 0.0

FS04E 0.0 0.0 0.0 33.3 50.0 0.0 0.0 0.0 16.7

FS06C 5.0 5.0 40.0 30.0 10.0 0.0 0.0 5.0 5.0

FS06D 0.0 5.9 41.2 29.4 23.5 0.0 0.0 0.0 0.0

FS07B1 0.0 2.9 57.1 25.7 14.3 0.0 0.0 0.0 0.0

FS07B2 0.0 2.5 60.0 27.5 7.5 0.0 2.5 0.0 0.0

FS07B3 2.7 2.7 56.8 21.6 13.5 2.7 0.0 0.0 0.0

FS08D 3.7 3.7 63.0 18.5 3.7 0.0 0.0 3.7 3.7

FS08E 6.7 6.7 26.7 40.0 20.0 0.0 0.0 0.0 0.0

FS09B 0.0 3.2 45.2 9.7 41.9 0.0 0.0 0.0 0.0

FS10B 0.0 4.8 47.6 14.3 33.3 0.0 0.0 0.0 0.0

FS11C 0.0 6.7 60.0 13.3 20.0 0.0 0.0 0.0 0.0

FS12B 0.0 5.9 64.7 0.0 23.5 0.0 0.0 0.0 5.9

FS12C 4.5 4.5 59.1 9.1 18.2 0.0 0.0 0.0 4.5

FS12E 0.0 12.5 12.5 25.0 37.5 0.0 0.0 0.0 12.5

FS12F 0.0 7.7 30.8 38.5 15.4 0.0 0.0 0.0 7.7

FS13C 0.0 4.2 37.5 25.0 29.2 0.0 0.0 0.0 4.2

FS13F 0.0 8.3 25.0 25.0 33.3 0.0 0.0 0.0 8.3
Table 4.2.6.

Biomass (mg wet/0.04 m2) by major taxa for infauna collected off Maryland and Delaware in May 1998

and June 1999.

--------------------------------------------------------------------------------------------------------------------------------------------

May 1998

Anthozoa Polychaetes Gastropods Isopods Other Crustaceans Phoronida Cephalochordata

Station Nemertinea Oligochaetes Bivalves Amphipods Sipuncula Echinodermata Total

BB02 1 5 80 9 0 14 0.5 8 0.5 0 0 0 0 118

BB04 0.5 2 54.5 15 0 1 0 17 2 0 0 0 0 92

FS01G 0 5 2387.5 0.5 4893 1 62 17 0.5 0 0.5 0 0 7367

FS02.5D 0 0.5 4.5 0 0 0.5 32.5 87 0.5 0 0 0 0 125.5

FS02C 2 679 5 1 1 6798 282 69 1 0 0 7 123 7968

FS03E 0 0 0 0 0 0 18 117 0.5 0 0 0 6 141.5

FS04C 0 0 645 4 1143 8176 9 4 0 0 0 0 0 9981

FS04E 0 1 0 0.5 3 4.5 0 6.5 0 0 0 0 0 15.5

FS06C 7 7 291.5 30 28 700.5 0.5 1.5 0.5 0 0 4 285 1355.5

FS06D 0 0 2.5 2 1 502 6.5 18 0 0 0 0 0 532

FS07B 0 0 715 7 48.5 155.5 16 173.5 2 0 0.5 0 0 1118

FS07F 0 1 4.5 0.5 0 445 0.5 26.5 0 0 0 0 0 478

FS08B 0 2 348.5 0 0 0.5 1.5 16 5 0 0.5 0 0 374

FS08C 0 1 322.5 1 46 719 0 1 0 3 0 0 0 1093.5

FS08D 1 6 34.5 8 0 120 0.5 1 0 0 0 0 10 181

FS08E 0 0 0.5 0.5 0 0.5 4 52 0 0 0.5 0 0 58

FS09A 0 0 25.5 2 0 11 0 114 1 0 0 0 111 264.5

FS09B 0 3 15 2 0 3.5 13 27 3 0 0 0 0 66.5

FS09C 0 0 0.5 0.5 0 1 42 54 0 0 0 0 0 98

FS09D 0 5 113.5 3 0 0 0 2 0 0 0 0 0 123.5

FS10.5D 0 0 1 0 0 0 4 9 0 0 0 0 0 14

FS10A 0 3 113.5 0.5 0 0 0 52.5 0.5 0 0 13 0 183

FS10B 0.5 8 35.5 5 0 4 0 77 2 0 0 0 0 132

FS11C 0 4 32.5 3 3 68 0 124 0 0 0 0 0 234.5

FS12B 0 15 119.5 1 0 0.5 0 0.5 1 0 0 0 0 137.5

FS12C 0 1 5 3 0 1 0 2 0.5 0 0 0 6 18.5

FS12E 0 0 0.5 0 0 39 0 12 0 0 0 0 0 51.5

FS12F 0 3 25.5 2 0 90 0.5 115 0 0 0 0.5 0 236.5

FS13A 0 3 15.5 0.5 0 0 0 0 7.5 0 3 0 0 29.5

FS13C 2 5 596 3 5 2.5 0 19 1 0 0 0 9 642.5

FS13E 0 0 0 0 0 25 147 13.5 0 0 0 0 0 185.5

FS13F 0.5 10 3 4 0 1 8.5 9.5 1 0 0 0 0 37.5

FS14A 0 8 119 14 0 48 9 14 4 0 0 0 0 216

FS14D 0 0 1.5 0 0 30 0 12 0.5 0 0 0 0 44

FS14E 0 0.5 2.5 0.5 0 0 87 11 0 0 0 0 0 101.5

FS14F 0 20 23.5 3 0 1 0 1.5 4 0 0 0 0 53

FS14G 0 0.5 0 0 0 55 4 43 1 0 0 0 0 103.5
Table 4.2-6 Continued

May 1998

Anthozoa Polychaetes Gastropods Isopods Other Crustaceans Phoronida Cephalochordata

Station Nemertinea Oligochaetes Bivalves Amphipods Sipuncula Echinodermata Total

HCS31 0 182 3633.5 2 0 1459 6 36 4 0 30 0 0 5352.5

IR02B 0 6 101 2 0.5 4.5 0 0.5 0.5 0 0 0 18 133

IR02C 0 9 83 7 0 56.5 0.5 0.5 0 0 0 0 0 156.5

IR02D 0 5 147.5 2 0 1438.5 0 1 1 0 0 0 0 1595

IR04B 0 1 210 0.5 0 47 0 24 1 0 0 0 0 283.5

IR04D 0 0 154 0.5 0.5 0.5 3 4 2 0 0 0 0 164.5

IR04E 0 0.5 906.5 6 71 3 11 82 0 1 0 0 0 1081

IR05_5C 0 50 97.5 2 0 78386 86 20 0 0 0 0 0 78641.5

IR05D 0 0 425 0.5 0.5 88136 11 1.5 0.5 0.5 0.5 0 0 88576

IR07C 0 1 1108 0.5 0 3 0 11 1 0 0 4 0 1128.5

IR07E 0 0 22.5 0.5 0 0 0 1 0.5 15 0.5 0 0 40

IR08B 0 0 9.5 0.5 0 55 67 11 0.5 0 0 0 0 143.5

IR08C 0 0.5 105 0 541 0.5 0 13.5 0 0 0 0 0 660.5

IR09A 30 2 46 0.5 0 3.5 0.5 2.5 0 0 0 0 0 85

IR09C 0 6 23 18 0 2741.5 0 0 12 0 0 0 0 2800.5

June 1999

Anthozoa Polychaetes Gastropods Isopods Other Crustaceans Phoronida Cephalochordata

Station Nemertinea Oligochaetes Bivalves Amphipods Sipuncula Echinodermata Total

FS02C 0.5 89 67 0 11 12855 2 41 0 0 0 72 0 13137.5

FS03E 0 0 0 0 0 71 0.5 17 0.5 0 0 0 330 419

FS04C 0 23 2650 1 2 3457 0 74 0 0 0 0 0 6207

FS04E 0 0 0 0 8 49 0 49 0 0 0 0 10 116

FS06C 0.5 8 28.5 1 0 60 6.5 0 0 0 0 12 13 129.5

FS06D 0 0.5 4 0.5 0 69 0.5 79 0 0 0 0 0 153.5

FS07B1 0 8 1817 23 342 3406 0 582.5 14 0 0 0 0 6192.5

FS07B2 0 4 1554 7 502 259.5 0 235 6 0 19 0 0 2586.5

FS07B3 6 1 2204.5 13 40 1155 0 436.5 1 0.5 0 0 0 3857.5

FS08D 18 12 233 6 9 186.5 1 0 0 0 0 2 5 472.5

FS08E 0.5 1 4 0.5 14 227.5 1 19 0 0 0 0 0 267.5

FS09B 0 15 55.5 28 3 166 9 146 5 0 0 0 0 427.5

FS10B 0 4 188 0.5 0 849 0 402 16.5 0 0 0 0 1460

FS11C 0 4 47 4 0 14.5 98 13.5 0 0 0 0 0 181

FS12B 0 5 22 0.5 0 0 0 165 1 0 0 0 21 214.5

FS12C 1 6 12 2 0 224 0 12 1 0 0 0 16 274

FS12E 0 0.5 0.5 0 3 95 7 9.5 0 0 0 0 9 124.5

FS12F 0 3 8 0.5 6.5 170.5 0 33 0 0 0 0 10 231.5

FS13C 0 8 19 1 14 549.5 0 128 3 0 0 0 40 762.5

FS13F 0 0.5 164.5 0 4 133 0 14.5 0 0 0 0 40 356.5
Table 4.2-7

Percentage of biomass by major taxa for infauna collected off Maryland and Delaware in

May 1998 and June 1999.

---------------------------------------------------------------------------------------------------------------------

May 1998

Anthozoa Polychaetes Gastropods Isopods Other Crustaceans Phoronida Cephalochordata

Station Nemertinea Oligochaetes Bivalves Amphipods Sipuncula Echinodermata

BB02 0.8 4.2 67.8 7.6 0.0 11.9 0.4 6.8 0.4 0.0 0.0 0.0 0.0

BB04 0.5 2.2 59.2 16.3 0.0 1.1 0.0 18.5 2.2 0.0 0.0 0.0 0.0

FS01G 0.0 0.1 32.4 0.0 66.4 0.0 0.8 0.2 0.0 0.0 0.0 0.0 0.0

FS02.5D 0.0 0.4 3.6 0.0 0.0 0.4 25.9 69.3 0.4 0.0 0.0 0.0 0.0

FS02C 0.0 8.5 0.1 0.0 0.0 85.3 3.5 0.9 0.0 0.0 0.0 0.1 1.5

FS03E 0.0 0.0 0.0 0.0 0.0 0.0 12.7 82.7 0.4 0.0 0.0 0.0 4.2

FS04C 0.0 0.0 6.5 0.0 11.5 81.9 0.1 0.0 0.0 0.0 0.0 0.0 0.0

FS04E 0.0 6.5 0.0 3.2 19.4 29.0 0.0 41.9 0.0 0.0 0.0 0.0 0.0

FS06C 0.5 0.5 21.5 2.2 2.1 51.7 0.0 0.1 0.0 0.0 0.0 0.3 21.0

FS06D 0.0 0.0 0.5 0.4 0.2 94.4 1.2 3.4 0.0 0.0 0.0 0.0 0.0

FS07B 0.0 0.0 64.0 0.6 4.3 13.9 1.4 15.5 0.2 0.0 0.0 0.0 0.0

FS07F 0.0 0.2 0.9 0.1 0.0 93.1 0.1 5.5 0.0 0.0 0.0 0.0 0.0

FS08B 0.0 0.5 93.2 0.0 0.0 0.1 0.4 4.3 1.3 0.0 0.1 0.0 0.0

FS08C 0.0 0.1 29.5 0.1 4.2 65.8 0.0 0.1 0.0 0.3 0.0 0.0 0.0

FS08D 0.6 3.3 19.1 4.4 0.0 66.3 0.3 0.6 0.0 0.0 0.0 0.0 5.5

FS08E 0.0 0.0 0.9 0.9 0.0 0.9 6.9 89.7 0.0 0.0 0.9 0.0 0.0

FS09A 0.0 0.0 9.6 0.8 0.0 4.2 0.0 43.1 0.4 0.0 0.0 0.0 42.0

FS09B 0.0 4.5 22.6 3.0 0.0 5.3 19.5 40.6 4.5 0.0 0.0 0.0 0.0

FS09C 0.0 0.0 0.5 0.5 0.0 1.0 42.9 55.1 0.0 0.0 0.0 0.0 0.0

FS09D 0.0 4.0 91.9 2.4 0.0 0.0 0.0 1.6 0.0 0.0 0.0 0.0 0.0

FS10.5D 0.0 0.0 7.1 0.0 0.0 0.0 28.6 64.3 0.0 0.0 0.0 0.0 0.0

FS10A 0.0 1.6 62.0 0.3 0.0 0.0 0.0 28.7 0.3 0.0 0.0 7.1 0.0

FS10B 0.4 6.1 26.9 3.8 0.0 3.0 0.0 58.3 1.5 0.0 0.0 0.0 0.0

FS11C 0.0 1.7 13.9 1.3 1.3 29.0 0.0 52.9 0.0 0.0 0.0 0.0 0.0

FS12B 0.0 10.9 86.9 0.7 0.0 0.4 0.0 0.4 0.7 0.0 0.0 0.0 0.0

FS12C 0.0 5.4 27.0 16.2 0.0 5.4 0.0 10.8 2.7 0.0 0.0 0.0 32.4

FS12E 0.0 0.0 1.0 0.0 0.0 75.7 0.0 23.3 0.0 0.0 0.0 0.0 0.0

FS12F 0.0 1.3 10.8 0.8 0.0 38.1 0.2 48.6 0.0 0.0 0.0 0.2 0.0

FS13A 0.0 10.2 52.5 1.7 0.0 0.0 0.0 0.0 25.4 0.0 10.2 0.0 0.0

FS13C 0.3 0.8 92.8 0.5 0.8 0.4 0.0 3.0 0.2 0.0 0.0 0.0 1.4

FS13E 0.0 0.0 0.0 0.0 0.0 13.5 79.2 7.3 0.0 0.0 0.0 0.0 0.0

FS13F 1.3 26.7 8.0 10.7 0.0 2.7 22.7 25.3 2.7 0.0 0.0 0.0 0.0

FS14A 0.0 3.7 55.1 6.5 0.0 22.2 4.2 6.5 1.9 0.0 0.0 0.0 0.0

FS14D 0.0 0.0 3.4 0.0 0.0 68.2 0.0 27.3 1.1 0.0 0.0 0.0 0.0

FS14E 0.0 0.5 2.5 0.5 0.0 0.0 85.7 10.8 0.0 0.0 0.0 0.0 0.0

FS14F 0.0 37.7 44.3 5.7 0.0 1.9 0.0 2.8 7.5 0.0 0.0 0.0 0.0

FS14G 0.0 0.5 0.0 0.0 0.0 53.1 3.9 41.5 1.0 0.0 0.0 0.0 0.0
Table 4.2-7 Continued

May 1998

Anthozoa Polychaetes Gastropods Isopods Other Crustaceans Phoronida Cephalochordata

Station Nemertinea Oligochaetes Bivalves Amphipods Sipuncula Echinodermata

HCS31 0.0 3.4 67.9 0.0 0.0 27.3 0.1 0.7 0.1 0.0 0.6 0.0 0.0

IR02B 0.0 4.5 75.9 1.5 0.4 3.4 0.0 0.4 0.4 0.0 0.0 0.0 13.5

IR02C 0.0 5.8 53.0 4.5 0.0 36.1 0.3 0.3 0.0 0.0 0.0 0.0 0.0

IR02D 0.0 0.3 9.2 0.1 0.0 90.2 0.0 0.1 0.1 0.0 0.0 0.0 0.0

IR04B 0.0 0.4 74.1 0.2 0.0 16.6 0.0 8.5 0.4 0.0 0.0 0.0 0.0

IR04D 0.0 0.0 93.6 0.3 0.3 0.3 1.8 2.4 1.2 0.0 0.0 0.0 0.0

IR04E 0.0 0.0 83.9 0.6 6.6 0.3 1.0 7.6 0.0 0.1 0.0 0.0 0.0

IR05_5C 0.0 0.1 0.1 0.0 0.0 99.7 0.1 0.0 0.0 0.0 0.0 0.0 0.0

IR05D 0.0 0.0 0.5 0.0 0.0 99.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0

IR07C 0.0 0.1 98.2 0.0 0.0 0.3 0.0 1.0 0.1 0.0 0.0 0.4 0.0

IR07E 0.0 0.0 56.3 1.3 0.0 0.0 0.0 2.5 1.3 37.5 1.3 0.0 0.0

IR08B 0.0 0.0 6.6 0.3 0.0 38.3 46.7 7.7 0.3 0.0 0.0 0.0 0.0

IR08C 0.0 0.1 15.9 0.0 81.9 0.1 0.0 2.0 0.0 0.0 0.0 0.0 0.0

IR09A 35.3 2.4 54.1 0.6 0.0 4.1 0.6 2.9 0.0 0.0 0.0 0.0 0.0

IR09C 0.0 0.2 0.8 0.6 0.0 97.9 0.0 0.0 0.4 0.0 0.0 0.0 0.0

June 1999

Anthozoa Polychaetes Gastropods Isopods Other Crustaceans Phoronida Cephalochordata

Station Nemertinea Oligochaetes Bivalves Amphipods Sipuncula Echinodermata

FS02C 0.0 0.7 0.5 0.0 0.1 97.8 0.0 0.3 0.0 0.0 0.0 0.5 0.0

FS03E 0.0 0.0 0.0 0.0 0.0 16.9 0.1 4.1 0.1 0.0 0.0 0.0 78.8

FS04C 0.0 0.4 42.7 0.0 0.0 55.7 0.0 1.2 0.0 0.0 0.0 0.0 0.0

FS04E 0.0 0.0 0.0 0.0 6.9 42.2 0.0 42.2 0.0 0.0 0.0 0.0 8.6

FS06C 0.4 6.2 22.0 0.8 0.0 46.3 5.0 0.0 0.0 0.0 0.0 9.3 10.0

FS06D 0.0 0.3 2.6 0.3 0.0 45.0 0.3 51.5 0.0 0.0 0.0 0.0 0.0

FS07B1 0.0 0.1 29.3 0.4 5.5 55.0 0.0 9.4 0.2 0.0 0.0 0.0 0.0

FS07B2 0.0 0.2 60.1 0.3 19.4 10.0 0.0 9.1 0.2 0.0 0.7 0.0 0.0

FS07B3 0.2 0.0 57.1 0.3 1.0 29.9 0.0 11.3 0.0 0.0 0.0 0.0 0.0

FS08D 3.8 2.5 49.3 1.3 1.9 39.5 0.2 0.0 0.0 0.0 0.0 0.4 1.1

FS08E 0.2 0.4 1.5 0.2 5.2 85.0 0.4 7.1 0.0 0.0 0.0 0.0 0.0

FS09B 0.0 3.5 13.0 6.5 0.7 38.8 2.1 34.2 1.2 0.0 0.0 0.0 0.0

FS10B 0.0 0.3 12.9 0.0 0.0 58.2 0.0 27.5 1.1 0.0 0.0 0.0 0.0

FS11C 0.0 2.2 26.0 2.2 0.0 8.0 54.1 7.5 0.0 0.0 0.0 0.0 0.0

FS12B 0.0 2.3 10.3 0.2 0.0 0.0 0.0 76.9 0.5 0.0 0.0 0.0 9.8

FS12C 0.4 2.2 4.4 0.7 0.0 81.8 0.0 4.4 0.4 0.0 0.0 0.0 5.8

FS12E 0.0 0.4 0.4 0.0 2.4 76.3 5.6 7.6 0.0 0.0 0.0 0.0 7.2

FS12F 0.0 1.3 3.5 0.2 2.8 73.7 0.0 14.3 0.0 0.0 0.0 0.0 4.3

FS13C 0.0 1.0 2.5 0.1 1.8 72.1 0.0 16.8 0.4 0.0 0.0 0.0 5.2

FS13F 0.0 0.1 46.1 0.0 1.1 37.3 0.0 4.1 0.0 0.0 0.0 0.0 11.2
Table 4.2.8.

Community Structure

-----------------------------------------------------------------------------------------------------------

May 1998

Total Total Species Shannon Simpson

Station Species Individuals Richness Diversity Evenness Dominance

BB02 30 348 3.43 3.65 0.74 0.11

BB04 17 271 1.98 2.31 0.57 0.37

FS01G 24 3108 1.98 0.44 0.10 0.91

FS02.5D 10 48 1.61 2.35 0.71 0.29

FS02C 25 150 3.32 3.72 0.80 0.12

FS03E 7 21 1.37 2.33 0.83 0.26

FS04C 21 872 2.05 2.03 0.46 0.37

FS04E 7 7 2.14 2.81 1.00 0.14

FS06C 30 416 3.33 3.47 0.71 0.15

FS06D 13 57 2.06 3.07 0.83 0.16

FS07B 28 461 3.05 3.75 0.78 0.10

FS07F 12 34 2.16 2.90 0.81 0.20

FS08B 23 78 3.50 3.89 0.86 0.10

FS08C 26 168 3.38 3.65 0.78 0.12

FS08D 17 168 2.16 2.81 0.69 0.20

FS08E 7 11 1.73 2.55 0.91 0.21

FS09A 23 118 3.20 3.55 0.79 0.12

FS09B 24 92 3.53 3.64 0.79 0.12

FS09C 6 16 1.25 1.97 0.76 0.36

FS09D 20 78 3.02 3.15 0.73 0.20

FS10.5D 3 4 1.00 1.50 0.95 0.38

FS10A 26 200 3.27 3.16 0.67 0.23

FS10B 29 181 3.73 3.76 0.77 0.12

FS11C 18 97 2.58 3.20 0.77 0.16

FS12B 15 83 2.20 3.54 0.91 0.10

FS12C 16 59 2.55 3.10 0.78 0.17

FS12E 4 4 1.50 2.00 1.00 0.25

FS12F 12 35 2.14 3.08 0.86 0.15

FS13A 16 35 2.92 3.73 0.93 0.09

FS13C 27 187 3.45 3.65 0.77 0.13

FS13E 5 8 1.33 2.16 0.93 0.25

FS13F 17 123 2.30 3.20 0.78 0.16

FS14A 22 271 2.60 3.00 0.67 0.18

FS14D 6 9 1.58 2.42 0.94 0.21

FS14E 8 16 1.75 2.38 0.79 0.29

FS14F 17 73 2.58 3.25 0.80 0.15

FS14G 8 16 1.75 2.38 0.79 0.29

HCS31 30 1231 2.82 1.82 0.37 0.53

IR02B 25 93 3.67 3.78 0.81 0.11

IR02C 20 243 2.40 3.45 0.80 0.12

IR02D 25 149 3.32 3.60 0.78 0.14

IR04B 18 25 3.66 4.00 0.96 0.07

IR04D 14 21 2.96 3.65 0.96 0.09

IR04E 35 312 4.10 3.79 0.74 0.11

IR05.5C 19 60 3.05 3.38 0.80 0.15

IR05D 26 165 3.39 2.78 0.59 0.33

IR07C 19 56 3.10 3.60 0.85 0.11

IR07E 13 26 2.55 3.47 0.94 0.10

IR08B 13 21 2.73 3.42 0.93 0.12
Table 4.2-8 Continued

Total Total Species Shannon Simpson

Station Species Individuals Richness Diversity Evenness Dominance

IR08C 17 61 2.70 3.48 0.85 0.12

IR09A 17 41 2.99 3.34 0.82 0.16

IR09C 14 207 1.69 1.56 0.41 0.57

Community structrue 1999

Total Total Species Shannon Simpson

Station Species Individuals Richness Diversity Evenness Dominance

FS02C 14 68 2.14 2.92 0.77 0.20

FS03E 7 14 1.58 2.41 0.86 0.25

FS04C 27 1336 2.50 1.90 0.40 0.51

FS04E 6 13 1.35 2.29 0.89 0.24

FS06C 20 101 2.85 2.97 0.69 0.27

FS06D 17 35 3.12 3.51 0.86 0.14

FS07B1 35 1290 3.29 3.15 0.61 0.19

FS07B2 40 1162 3.83 3.17 0.60 0.21

FS07B3 37 1006 3.61 3.84 0.74 0.10

FS08D 27 168 3.52 4.00 0.84 0.09

FS08E 15 43 2.58 3.29 0.84 0.14

FS09B 31 230 3.82 3.73 0.75 0.15

FS10B 21 203 2.61 1.70 0.39 0.60

FS11C 15 65 2.32 3.52 0.90 0.11

FS12B 17 130 2.28 2.58 0.63 0.27

FS12C 22 74 3.38 3.79 0.85 0.10

FS12E 8 10 2.11 2.85 0.95 0.16

FS12F 13 47 2.16 2.63 0.71 0.29

FS13C 24 122 3.32 3.69 0.81 0.11

FS13F 12 28 2.29 2.99 0.84 0.18
Table 4.2.9

Constancy and fidelity based on nodal analysis of all grab data.

See Figure 4.2.x for stations and species in each group.

------------------------------------------------------------------------------------------------------------

Constancy

Species Station Group

Group A B C D E

I 0.2 0.4 0.0 0.1 0.1

II 0.5 0.3 0.0 0.1 0.2

III 0.2 0.3 0.2 0.2 0.1

IV 0.1 0.1 0.1 0.2 0.4

V 0.0 0.0 0.0 0.0 0.3

VI 0.1 0.0 0.0 0.0 0.5

Fidelity

Species Station Group

Group A B C D E

I 1.0 2.8 0.0 0.6 0.7

II 1.7 1.0 0.1 0.4 0.7

III 1.2 1.5 1.1 0.9 0.4

IV 0.7 0.4 0.6 0.9 2.4

V 0.4 0.1 0.0 0.4 4.1

VI 0.5 0.3 0.0 0.4 4.1
Table 4.2-10

Dominant taxa (present at >20% of the stations, 15 of 70 stations) collected

in the MD/DE study area.

---------------------------------------------------------------------------------------------------------------------

Taxa MajorTaxa Total Occurences Total Abundance

Oligochaeta Annelida 58 1610

Nemertinea Nemertinea 55 414

Tellina spp. Bivalvia 47 732

Spiophanes bombyx Annelida 39 5902

Aricidea (Acmira) cerrutii Annelida 37 426

Aricidea (Acmira) catherinae Annelida 32 244

Parapionosyllis longicirrata Annelida 30 109

Unciola irrorata Crustacea 30 279

Protohaustorius wigleyi Crustacea 29 110

Aphelochaeta sp. Annelida 25 165

Brania wellfleetensis Annelida 23 222

Crenella glandula Bivalvia 23 128

Tanaissus psammophilus Crustacea 22 101

Hesionura elongata Annelida 21 115

Hemipodus roseus Annelida 21 54

Astarte spp. Bivalvia 21 166

Nephtys spp. Annelida 20 53

Asabellides oculata Annelida 20 342

Branchiostoma caribaeum Cephalochordata 18 25

Mytilus edulis Bivalvia 17 130

Chiridotea coeca Crustacea 17 57

Byblis serrata Crustacea 17 250

Pseudunciola obliquua Crustacea 17 231

Spio setosa Annelida 16 788

Caulleriella sp. B Annelida 16 84

Pseudoleptocuma minor Crustacea 16 25

Anthozoa Cnidaria 15 53

Streptosyllis pettiboneae Annelida 15 54

Spisula solidissima Bivalvia 15 27

Lyonsia hyalina Bivalvia 15 27

Oxyurostylis smithi Crustacea 15 19
Table 4.2-11

Abundance dominants from MD/DE. Includes all taxa that were at least one percent

of the total abundance in either May 1998 or September 1999 with and without the

polychaete Spiophanes bombyx.

--------------------------------------------------------------------------------------------------------------------------------------------

Percent of Percentages

Major Taxa Abundance Total Abundance No S. bombyx

Taxa Taxa 98&99 98 99 98&99 98 99 98&99 98 99

Annelida Spiophanes bombyx 5902 4240 1662 35.2 39.9 27.0 . . .

Annelida Oligochaeta 1610 1218 392 9.6 11.5 6.4 14.8 9.7 2.6

Annelida Spio setosa 788 73 715 4.7 0.7 11.6 7.2 0.6 4.7

Bivalvia Tellina spp. 732 388 344 4.4 3.6 5.6 6.7 3.1 2.3

Bivalvia Nucula spp. 624 550 74 3.7 5.2 1.2 5.7 4.4 0.5

Annelida Apoprionospio pygmaea 478 50 428 2.8 0.5 7.0 4.4 0.4 2.8

Annelida Aricidea (Acmira) cerrutii 426 388 38 2.5 3.6 0.6 3.9 3.1 0.3

Nemertinea Nemertinea 414 301 113 2.5 2.8 1.8 3.8 2.4 0.7

Annelida Asabellides oculata 342 242 100 2.0 2.3 1.6 3.1 1.9 0.7

Annelida Mediomastus ambiseta 326 132 194 1.9 1.2 3.2 3.0 1.1 1.3

Annelida Macroclymene zonalis 281 2 279 1.7 0.0 4.5 2.6 0.0 1.8

Crustacea Unciola irrorata 279 125 154 1.7 1.2 2.5 2.6 1.0 1.0

Crustacea Byblis serrata 250 15 235 1.5 0.1 3.8 2.3 0.1 1.6

Annelida Aricidea (Acmira) catherinae 244 149 95 1.5 1.4 1.5 2.2 1.2 0.6

Crustacea Pseudunciola obliquua 231 73 158 1.4 0.7 2.6 2.1 0.6 1.0

Annelida Brania wellfleetensis 222 193 29 1.3 1.8 0.5 2.0 1.5 0.2

Bivalvia Astarte spp. 166 122 44 1.0 1.1 0.7 1.5 1.0 0.3

Annelida Aphelochaeta sp. 165 137 28 1.0 1.3 0.5 1.5 1.1 0.2

Annelida Paradoneis sp. B 133 133 0 0.8 1.3 0.0 1.2 1.1 0.0

Bivalvia Mytilus edulis 130 121 9 0.8 1.1 0.1 1.2 1.0 0.1

Bivalvia Crenella glandula 128 79 49 0.8 0.7 0.8 1.2 0.6 0.3

Annelida Hesionura elongata 115 100 15 0.7 0.9 0.2 1.1 0.8 0.1

Crustacea Protohaustorius wigleyi 110 88 22 0.7 0.8 0.4 1.0 0.7 0.1

Annelida Protodorvillea kefersteini 110 85 25 0.7 0.8 0.4 1.0 0.7 0.2

Annelida Parapionosyllis longicirrata 109 99 10 0.6 0.9 0.2 1.0 0.8 0.1

Annelida Clymenella torquata 104 1 103 0.6 0.0 1.7 1.0 0.0 0.7
Table 4.2-12

Biomass dominants (>0.5 g wet weight/0.04 m2) collected in the MD/DE study area.

Numerical or occurrence dominant species are also included.

--------------------------------------------------------------------------------------------------------------------------------------------

Total Percent Mean Individual

Taxa Biomass Taxa Biomass Biomass

Major (g wet wt/0.04 m2) (%) (mg/individual)

Taxa Taxa 98&99 98 99 98&99 98 99 98&99 98 99

Spisula solidissima Bivalvia 166.2 165.8 0.5 65.9 77.2 1.3 6157.1 7206.9 121.0

Astarte spp. Bivalvia 21.0 7.3 13.7 8.3 3.4 36.5 126.8 60.2 311.6

Nucula spp. Bivalvia 11.3 9.6 1.7 4.5 4.5 4.5 18.1 17.4 23.1

Spiophanes bombyx Polychaeta 7.2 4.5 2.7 2.9 2.1 7.1 1.2 1.1 1.6

Busycon canaliculata Gastropoda 4.9 4.9 .* 1.9 2.3 . 4893.0 4893.0 .

Ensis directus Bivalvia 4.7 0.1 4.6 1.9 0.0 12.2 53.3 25.8 54.6

Pitar morrhuanus Bivalvia 4.5 4.5 0.0 1.8 2.1 0.1 104.8 1118.6 0.8

Asabellides oculata Polychaeta 4.3 2.6 1.6 1.7 1.2 4.3 12.4 10.9 16.0

Tellina spp. Bivalvia 3.9 0.9 3.0 1.6 0.4 8.1 5.4 2.3 8.8

Nassarius trivittatus Gastropoda 2.6 1.8 0.8 1.0 0.8 2.1 257.0 254.7 262.3

Lumbrineris fragilis Polychaeta 1.7 1.7 . 0.7 0.8 . 191.8 191.8 .

Nemertinea Nemertinea 1.3 1.1 0.2 0.5 0.5 0.5 3.0 3.5 1.7

Branchiostoma caribaeum Cephalochordata 1.1 0.6 0.5 0.4 0.3 1.3 42.5 56.8 32.9

Spio setosa Polychaeta 1.0 0.5 0.6 0.4 0.2 1.5 1.3 6.4 0.8

Ampelisca spp. Amphipoda 1.0 0.2 0.9 0.4 0.1 2.3 10.2 10.9 10.1

Crenella glandula Bivalvia 0.9 0.6 0.3 0.4 0.3 0.7 7.0 7.9 5.5

Byblis serrata Amphipoda 0.9 0.1 0.7 0.3 0.1 1.9 3.5 9.9 3.1

Politolana concharum Isopoda 0.8 0.7 0.1 0.3 0.3 0.3 30.7 30.9 29.5

Mytilus edulis Bivalvia 0.7 0.7 0.0 0.3 0.3 0.0 5.8 6.1 1.0

Unciola irrorata Amphipoda 0.7 0.2 0.5 0.3 0.1 1.3 2.4 1.4 3.2

Glycera americana Polychaeta 0.6 0.5 0.2 0.3 0.2 0.4 20.4 19.0 26.3

Notocirrus spiniferus Polychaeta 0.6 0.1 0.5 0.2 0.0 1.3 97.7 40.5 126.3

Sigalion arenicola Polychaeta 0.5 0.3 0.3 0.2 0.1 0.7 78.1 67.5 92.3

Macroclymene zonalis Polychaeta 0.5 0.0 0.5 0.2 0.0 1.4 1.9 7.0 1.9

Subdominant Species:

Acanthohaustorius bousfieldi Amphipoda 0.1 . 0.1 0.0 . 0.2 31.5 . 31.5

Parahaustorius holmesi Amphipoda 0.3 0.2 0.1 0.1 0.1 0.2 28.5 32.4 21.8

Parahaustorius attenuatus Amphipoda 0.1 0.1 . 0.1 0.1 . 26.0 26.0 .

Periploma papyratium Bivalvia 0.3 0.3 . 0.1 0.1 . 262.0 262.0 .

Pandora trilineata Bivalvia 0.2 0.2 . 0.1 0.1 . 224.0 224.0 .

Siliqua costata Bivalvia 0.1 0.1 . 0.0 0.0 . 59.0 59.0 .

Scoletoma acicularum Polychaeta 0.3 0.0 0.3 0.1 0.0 0.7 72.8 9.0 264.0

Leitoscoloplos spp. Polychaeta 0.4 0.0 0.4 0.2 0.0 1.2 72.5 0.5 108.5

Onuphis eremita Polychaeta 0.1 0.1 . 0.0 0.0 . 58.0 58.0 .

Glycera dibranchiata Polychaeta 0.4 0.1 0.3 0.2 0.0 0.9 43.1 12.6 165.0

Ophelia denticulata Polychaeta 0.5 0.5 0.0 0.2 0.2 0.0 25.2 159.2 0.1

Sthenelais limicola Polychaeta 0.2 0.2 0.0 0.1 0.1 0.0 22.5 24.1 8.0

Lumbrinerides dayi Polychaeta 0.3 0.0 0.3 0.1 0.0 0.8 21.6 3.5 61.5

Scoletoma tenuis Polychaeta 0.2 0.0 0.2 0.1 0.0 0.4 20.7 5.4 33.0
Table 4.2-13

Summary of overall dominants based on occurrence, abundance, and biomass

collected in the MD/DE study area.

--------------------------------------------------------------------------------------------------------------------------------------------

Occurrences Abundance Biomass Mean Individual

MajorTaxa Taxa Number % ind./0.04 m2 % g wet/0.04 m2 % Weight (mg)

Nemertiena Nemertinea 55 76 414 2 1.3 0.5 3.0

Polychaeta Asabellides oculata 20 28 342 2 4.3 1.7 12.4

Polychaeta Spio setosa 16 22 788 5 1.0 0.4 1.3

Polychaeta Spiophanes bombyx 39 54 5902 35 7.2 2.9 1.2

Bivalvia Astarte spp. 21 29 166 1 21.0 8.3 126.8

Bivalvia Crenella glandula 23 32 128 1 0.9 0.4 7.0

Bivalvia Mytilus edulis 17 24 130 1 0.7 0.3 5.8

Bivalvia Tellina spp. 47 65 732 4 3.9 1.6 5.4

Amphipoda Byblis serrata 17 24 250 1 0.9 0.3 3.5

Amphipoda Unciola irrorata 30 42 279 2 0.7 0.3 2.4
Table 4.2-14.

Average abundance of dominant taxa (those that were important contributors to either occurrence, abundance, or

biomass) by cluster analysis station group (see Fig. 4.2-2). Dominance Type are: O = ccurrence (>20% of stations),

A = Abundance (>1% of total abundance for May or September cruise), B = Biomass (>0.5 g total wet weight).

* = average abundance was <1. Blank = taxa did not occur in station group.

Average Abundance (individuals/0.04 m2)

Dominance Major Cluster Analysis Station Group

Type Taxa Species or Taxa A A' A'' B B' C D D' D'' E E'

O,A,B Nemertinea Nemertinea 11 10 3 6 10 * 2 1 1 5 *

O Cnidaria Anthozoa 1 2 5 1 *

O,A Oligochaeta Oligochaeta 38 25 2 38 26 4 5 1 42 16

O,A Polychaeta Aphelochaeta sp. 8 1 1 1 * * 2 *

A Polychaeta Apoprionospio pygmaea * * 68

O,A Polychaeta Aricidea (Acmira) catherinae 4 10 1 2 1 10 2

O,A Polychaeta Aricidea (Acmira) cerrutii 12 6 3 * 34 * 2 2 1

O,A,B Polychaeta Asabellides oculata * * 2 6 2 33 8

O,A Polychaeta Brania wellfleetensis 10 3 5 * * *

O Polychaeta Caulleriella sp. B 1 * * * 4 3 2

A Polychaeta Clymenella torquata * * 15

O Polychaeta Hemipodus roseus 1 3 2 * 3 *

O,A Polychaeta Hesionura elongata 1 1 1 3 24 * *

B Polychaeta Lumbrineris fragilis * 1 *

A,B Polychaeta Macroclymene zonalis * * 40

A Polychaeta Mediomastus ambiseta * * 1 44 3

O Polychaeta Nephtys spp. * * 2 * 2 3 *

B Polychaeta Notocirrus spiniferus 1

A Polychaeta Paradoneis sp. B 7 * * *

O,A Polychaeta Parapionosyllis longicirrata 4 3 1 1 1 * * 2

A Polychaeta Protodorvillea kefersteini 1 11 1 1

B Polychaeta Sigalion arenicola * * * *

O,A,B Polychaeta Spio setosa * 3 1 104 7

O,A,B Polychaeta Spiophanes bombyx 7 2 1 1 * 1 1 4 * 797 26

O Polychaeta Streptosyllis pettiboneae 2 1 2 * 3 *

B Gastropoda Busycon canaliculata *

B Gastropoda Nassarius trivittatus * * * 1 *

O,A,B Bivalvia Astarte spp. 1 2 1 17 * 7 1

O,A,B Bivalvia Crenella glandula 1 1 16 3 * 1 2 *

B Bivalvia Ensis directus 2 * 12 *

O Bivalvia Lyonsia hyalina 1 * * * 1

O,A,B Bivalvia Mytilus edulis * 2 10 * * 4 * 2 1

A,B Bivalvia Nucula spp. * 67 2 19

B Bivalvia Pitar morrhuanus * 6 *

O,A,B Bivalvia Tellina spp. 2 1 2 1 1 2 36 4 5 52 1

O,B Bivalvia Spisula solidissima 1 2 * * 1 *

O Tanaidacea Tanaissus psammophilus 5 * 4 1 * * *

O Cumacea Oxyurostylis smithi * * * * 1 1 *

O Cumacea Pseudoleptocuma minor 1 * 1 1 1 *

O Isopoda Chiridotea coeca * * 1 1 8 * * 3

B Isopoda Politolana concharum * 1 * 1 1 * * *

B Amphipoda Ampelisca spp. * * 14 *

O,A,B Amphipoda Byblis serrata 2 * 100 * * 1

O,A Amphipoda Protohaustorius wigleyi 1 * * 1 * 7 3 * 1

O,A Amphipoda Pseudunciola obliquua 5 1 28 1 11

O,A,B Amphipoda Unciola irrorata 1 2 3 * * 2 * 1 29 6

O,B Cephalochordata Branchiostoma caribaeum * * 1 2 * * * 1
Table 4.2.15. Life history attribute summary for dominant taxa from the inner continental shelf off MD and DE.

Major Group Species Name Preferred Substrate Feeding Mode Mobility Size (cm) Spawns/Year Larval Mode Spawning Times LifeSpan

Year Round 

Recruitment 

Potential

Spring/SummerRecrui

tment Potential

Fall/Winter 

Recruitment Potential

Amphipoda Ampelisca spp. Medium to Coarse Sand Suspension Tube Builder <1 Twice Brooding Spring/Summer Annual Poor Good Poor

Amphipoda Byblis serrata Medium to Coarse Sand Suspension Tube Builder <1 Multiple Events Brooding Late Spring/Summer Annual Poor Good Poor

Amphipoda Protohaustorius wigleyi Fine Sand Suspension Burrower <1 Multiple Events Brooding Late Spring/Summer Annual Poor Good Poor

Amphipoda Pseudounciola obliquua Medium to Coarse Sand Suspension Tube Builder <1 Multiple Events Brooding Late Spring/Summer Annual Poor Good Poor

Amphipoda Unciola irrorata Coarse to medium Sand Deposit/Suspension Lives in tubes of other organisms <1 Once Brooding Spring/Early Summer Annual Poor Good Poor

Bivalvia Astarte spp. Muddy Fine Sand Deposit/Suspension Limited Mobility 3 Once

Lecithotrophic (eggs attached to 

substratum) Fall 20 years Poor Poor Good

Bivalvia Crenella glandula Fine Sand Suspension Sessile 2 ? ? ? >1 ? ? ?

Bivalvia Ensis directus Medium to Fine Sand, Muddy Sand Suspension Limited Mobility 24 Multiple Events Planktonic ? >1 Good Good Good

Bivalvia Mytilus edulis Hard Substrates, Coarse Sand, Gravel Suspension Sessile 8 Once or Twice Planktonic Late Fall/Winter 7 years Poor Poor Good

Bivalvia Nucula proxima Muddy Deposit Limited Mobility <1 ? Planktonic Late Summer/Early Fall >1 Poor Poor Good

Bivalvia Pitar morrhuanus Coarse to medium Sand Suspension Sessile 4 ? Planktonic ? >7 years ? ? ?

Bivalvia Spisula solidissima Coarse Sand Suspension Limited Mobility 18 Twice Planktonic Late Summer/Fall

20 to 35 

Years Good Good Good

Bivalvia Tellina agilis Medium to Fine Sand, Muddy Sand Surface Deposit Limited Mobility 2 Twice Planktonic Spring/Fall 2 Years Good Good Good

Cephalochordata Branchiostoma caribaeum Coarse to Fine Silty Sand Suspension Mobile 5 ? Planktonic ? ? Good Good Good

Cnidaria Anthozoa Coarse to Fine Sand Carnivore/Suspension Sessile 15 ? Asexual/Planktonic ? Annual? Poor Poor Poor

Cumacean Oxyurostylis smithi Fine Sand Suspension Burrower/Limited Moblity <1 Continuous Brooding Early Winter Annual Good Good Good

Cumacean Pseudoleptocuma minor Fine Sand Suspension Burrower/Limited Moblity <1 Continuous Brooding ? Annual Good Good Good

Gastropoda Busycon canaliculata Coarse to Muddy Fine Sand Carnivore Mobile 19 Once Direct Development ? > 5 years Good Good Good

Gastropoda Nassarius trivittatus Coarse to Fine Sand Scavenger Mobile 2 ? Direct Development ? >1 Good Good Good

Isopoda Chirodotea coeca Coarse to medium Sand ? Limited Mobility 2 ? Brooding ? ? ? ? ?

Isopoda Politolana concharum ? ? Limited Mobility <1 ? Brooding Winter/Early Spring ? Good Good Good

Nemertinea Nemertinea Coarse to Muds Carnivore Burrower 20 ? Direct Development/Planktonic ? Annual? Good Good Good

Oligochaeta Oligochaeta Coarse to Fine Sand, Muds Deposit Burrower/Interstitial <1 Continuous Direct Development Spring/Summer/Fall Annual Good Good Good

Polychaeta Aphelochaeta sp. ? Surface Deposit Tube Builder 2 Multiple Events Lecithotrophic eggs Spring/Summer ? Poor Good Poor

Polychaeta Aricidea spp. Muddy, Silty-Fine Sand Subsurface Deposit Burrower <1 ? Brooding ? ? Poor ? ?

Polychaeta Asabellides oculata Sand, Silty Sand Surface Deposit Tube Builder 1 Once Brooding Winter, Early Spring Annual Good Good Good

Polychaeta Brania wellfleetensis Muddy, Muddy Sandy Deposit Burrower 1 Once Brooding Fall ? Poor Poor Good

Polychaeta Hemipodus roseus Coarse Sand Carnivore Burrower 1 Once Planktonic ? ? Poor ? ?

Polychaeta Hesionura elongata Medium to Coarse Sand Carnivore Burrower 1 Once Planktonic ? ? Poor ? ?

Polychaeta Mediomastus ambiseta Muddy Fine Sand Deposit Tube Builder 3 Once Planktonic, Non-Feeding Late summer/fall Annual Poor Poor Good

Polychaeta Nephtys spp. Coarse to Very Fine Sand Carnivore/Omnivore Burrower 8 Twice Planktonic Spring/Fall 4 Years Good Good Good

Polychaeta Paradoneis sp. B. Clean Sand, Muddy Sand Subsurface Deposit Burrower <1 ? Direct Development ? ? ? ? ?

Polychaeta Parapionosyllis longicirrata Muddy Sand, Shells ? Burrower <1 ? Brooding Fall ? Poor Poor Good

Polychaeta Protodorvillea kefersteini Coarse to Fine Sand Carnivore Burrower <1 Once Direct Development Summer/Late Fall ? Good Good Good

Polychaeta Spio Setosa Muddy Fine Sand Deposit/Suspension Tube Builder 1 Twice Broods Spring/Planktonic Fall Spring/Fall Annual Good Good Good

Polychaeta Spiophanes bombyx Fine Sand, Muddy Deposit/Suspension Tube Builder 1 Once Planktonic Late Summer Annual Poor Good Poor

Polychaeta Streptosyllis pettiboneae Medium Fine Sand Carnivore ? <1 Once Brooding Spring/Early Summer Annual Poor Good Poor
Table 4.3-1

Fish collected in May 1999 at the four major benthic habitat types.

Data are the summed occurrence of species caught in eight trawls from each habitat.

---------------------------------------------------------------------------------------------------------------------------------

Habitat

Fish NE* NW SE SW Total

Ammodytes spp. 9 1 0 0 10

Anchoa mitchilli 0 1 1 0 2

Centropristis striata 0 0 1 0 1

Conger oceanicus 0 1 0 1 2

Cynoscion regalis 0 0 0 1 1

Enchelyopus cimbrius 0 0 0 6 6

Etropus microstomus 22 23 30 10 85

Mugil curema 0 0 0 1 1

Ophidion marginatum 0 3 1 0 4

Paralichthys dentatus 0 0 0 1 1

Prionotus carolinus 4 14 8 0 26

Prionotus evolans 0 0 0 1 1

Pseudopleuronectes americanus 0 0 1 4 5

Raja eglantaria 0 2 2 15 19

Raja erincea 0 0 0 6 6

Scomber scombrus 0 1 0 0 1

Scophthalmus aquosus 0 0 0 2 2

Stenotomus crysops 0 4 0 0 4

Syngnathus fuscus 0 0 0 1 1

Urophycis regia 21 2 37 95 155

* NE – Northeast seaward flank of shoal

 NW – Northwest shoreward face of shoal,

 SE – Southeast seaward trough, surface dominated by Diopatra tubes

 SW – Southwest shoreward trough, surface dominated by Asabellides tubes
Table 4.3-2

Invertebrates collected in May 1999 at the four major benthic habitat types.

Data are the summed occurrence of selected species caught in eight trawls from each habitat.

For most species only presence in the trawl was recorded (+).

--------------------------------------------------------------------------------------------------------------------------------------------

Benthic Habitat Type

Invertebrate Taxa Shell&Gravel Diopatra Sand Asabellides Total

Hydroids +

Molluscs + + + +

Loliginidae +

Nudibranchs + + + +

Littorina spp. + +

Busycon canaliculatum + + +

Polinices spp. + + +

Astarte spp. + +

Ensis directus +

Spisula solidissima +

Crustaceans + + + +

Amphipods + +

Isopoda + +

Limulus polyphemus 0 1 0 8 9

Euceramus sp. 1 1

Pagurus spp. + + + +

Crangon septemspinosa + + + +

Crab, Unknown 0 3 0 9 12

Libinia emarginata 8 93 8 92 201

Cancer irroratus 2 70 9 136 217

Ovalipes ocellatus 1 0 0 0 1

Dissidactylus mellitae 1 0 0 0 1

Pinnixa lunzi 0 0 1 0 1

Pinnixa sp. 0 2 0 0 2

Hexanpanobeus angustifrons 0 0 0 3 3

Rhithropanopeus harrisii 0 3 1 3 7

Echinoderms + + + +

Asterias spp. + + + +

 Echinarachnius parma + + +
Table 4.3-3

Summary of gut content by taxa for the three dominant demersal fish species trawled around

Fenwick Shoals. Gut content of all individual fish was summed for each species.

--------------------------------------------------------------------------------------------------------------------------------------------

Fish Species

Major E. microstomus P. carolinus U. Regia

Taxa Species Number of guts 36 22 80 Total

Nemerteans Nemertinea Abundance 1 3 4

Wet Weight (g) 0.012 0.006 0.018

Polychaetes Ampharetidae Abundance 5 5

Wet Weight (g) 0.241 0.241

Nepthys sp. Abundance 2 2

Wet Weight (g) 0.033 0.033

Polychaeta Abundance 2 5 7

Wet Weight (g) 0.023 0.107 0.130

Spio setosa Abundance 1 1

Wet Weight (g) 0.019 0.019

Spionidae Abundance 1 1

Wet Weight (g) 0.006 0.006

Bivalves Bivalvia Abundance 2 2

Wet Weight (g) 0.025 0.025

Ensis directus Abundance 2 2

Wet Weight (g) 0.069 0.069

Gastropods Nudibranch Abundance 1 1

Wet Weight (g) 0.001 0.001

Cumacean Cumacea Abundance 1 1

Wet Weight (g) 0.001 0.001

Cyclapsis varians Abundance 1 1

Wet Weight (g) 0.003 0.003

Oxyurostylus smithi Abundance 3 6 9

Wet Weight (g) 0.002 0.009 0.011

Pseudoleptocuma minor Abundance 5 18 1 24

Wet Weight (g) 0.009 0.029 0.002 0.040

Mysids Mysidacea Abundance 4 4

Wet Weight (g) 0.009 0.009

Neomysis americana Abundance 9 2 203 214

Wet Weight (g) 0.016 0.003 0.527 0.546

Isopods Politolana concharum Abundance 41 41

Wet Weight (g) 0.735 0.735

Amphipods Ampelisca sp. Abundance 20 12 33 65

Wet Weight (g) 0.324 0.085 0.268 0.677

Amphipoda Abundance 3 2 5

Wet Weight (g) 0.068 0.001 0.069

Byblis serrata Abundance 12 44 50 106

Wet Weight (g) 0.056 0.334 0.448 0.838

Corophium sp. Abundance 3 3

Wet Weight (g) 0.006 0.006

Erichthonius rubricornis Abundance 1 1

Wet Weight (g) 0.006 0.006

Haustoriidae Abundance 4 1 5

Wet Weight (g) 0.007 0.007 0.014

Listriella barnardi Abundance 2 2

Wet Weight (g) 0.001 0.001
Table 4.3-3 Continued.

Major Fish Species

Taxa Species E. microstomus P. carolinus U. Regia Total

Melita dentata Abundance 1 1 2

Wet Weight (g) 0.001 0.002 0.003

Melita sp. Abundance 1 1

Wet Weight (g) 0.002 0.002

Microdeutopus anomalus Abundance 1 1

Wet Weight (g) 0.001 0.001

Monoculodes intermedius Abundance 1 1

Wet Weight (g) 0.004 0.004

Monoculodes sp. Abundance 1 1

Wet Weight (g) 0.001 0.001

Photidae Abundance 1 1

Wet Weight (g) 0.005 0.005

Pseudounciola obliquua Abundance 1 2 2 5

Wet Weight (g) 0.001 0.003 0.011 0.015

Rhepoxyinus hudsoni Abundance 1 1

Wet Weight (g) 0.001 0.001

Synchelidium americanum Abundance 4 1 5

Wet Weight (g) 0.002 0.001 0.003

Unciola irrorata Abundance 62 35 142 239

Wet Weight (g) 0.055 0.118 0.657 0.829

Decapods Cancer irroratus Abundance 8 48 3 59

Wet Weight (g) 0.036 0.242 0.012 0.290

Crab megalopae Abundance 17 27 37 81

Wet Weight (g) 0.043 0.058 0.075 0.176

Cragnon septemspinosa Abundance 42 38 2097 2177

Wet Weight (g) 0.105 0.768 11.230 12.102

Decapoda Abundance 1 1

Wet Weight (g) 0.006 0.006

Euceramus praelongus Abundance 1 1

Wet Weight (g) 0.150 0.150

Hermit crab megalopae Abundance 1 1

Wet Weight (g) 0.001 0.001

Pagarus longicarpus Abundance 2 2

Wet Weight (g) 0.182 0.182

Pagarus sp. Abundance 2 1 3

Wet Weight (g) 0.032 0.031 0.063

Spider Crab Abundance 1 1

Wet Weight (g) 0.086 0.086

Unknown Shrimp Abundance 2 2

Wet Weight (g) 0.002 0.002

Echinoderms Holothroidae Abundance 1 1

Wet Weight (g) 0.003 0.003

Cephalochordate Branchiostoma caribieum Abundance 1 1

Wet Weight (g) 0.046 0.046

Total Abundance 202 249 2642 3093

Total Wet Weight (g) 1.068 1.830 14.568 17.465
Table 4.4-1.

Sediment size classes estimated from SPI images collected in the MD/DE study area.

--------------------------------------------------------------------------------------------------------------------------------------------

Grain Size Size

Year Station (mm) Phi Sorting Interface Class Class Type Grade

98 BB01 2.00 -1.0 csgr GR GR COARSE

98 BB02 2.00 csgr GR GR COARSE

98 BB03 2.00 csgr GR GR COARSE

98 BB04 1.00 grcs VCS SAND COARSE

98 BB05 0.71 msgrcs CS SAND COARSE

98 BB06 1.00 grcs VCS SAND COARSE

98 BB07 1.00 grcs VCS SAND COARSE

98 FS01.5D 0.30 fsms MS SAND MED

98 FS01A 0.18 2.5 W P fs FS SAND FINE

98 FS01B 0.59 2.0 P P mscs CS SAND COARSE

98 FS01C 0.18 2.5 VW P fs FS SAND FINE

98 FS01D 0.30 3.0 VW P fsms MS SAND MED

98 FS01E 0.30 2.5 W P fsms MS SAND MED

98 FS01F 0.30 2.5 M P fsms MS SAND MED

98 FS01G 0.84 1.5 P P grmscs CS SAND COARSE

98 FS02.5D 0.59 1.5 W P mscs CS SAND COARSE

98 FS02A 0.18 3.0 VW P fs FS SAND FINE

98 FS02B 0.59 2.0 M P mscs CS SAND COARSE

98 FS02C 0.35 1.5 M P ms MS SAND MED

98 FS02D 0.18 2.5 W P fs FS SAND FINE

98 FS02E 0.30 2.5 W P fsms MS SAND MED

98 FS02F 1.00 0.0 P P shgrcs VCS SAND COARSE

98 FS02G 0.59 1.0 P P mscs CS SAND COARSE

98 FS03.5D 2.00 -1.0 VP P csgr GR GR COARSE

98 FS03A 0.35 2.0 M P ms MS SAND MED

98 FS03B 0.15 3.0 VW B vfsfs FS SAND FINE

98 FS03C 0.59 1.5 M P mscs CS SAND COARSE

98 FS03CD 1.19 1.0 M P csvcs VCS SAND COARSE

98 FS03D 1.68 0.0 P P grvcs VCS SAND COARSE

98 FS03DE 0.30 2.0 W P fsms MS SAND MED

98 FS03E 0.15 3.0 VW P vfsfs FS SAND FINE

98 FS03F 0.59 M-P P shgrms-cs CS SAND COARSE

98 FS03G 0.15 3.0 W P vfsfs FS SAND FINE

98 FS04.5D 1.00 -1.0 P P grcs VCS SAND COARSE

98 FS04A 0.07 3.0 W B sivfs VFS SAND FINE

98 FS04B 0.09 3.0 VW B vfs VFS SAND FINE

98 FS04C 0.09 3.0 W B vfs VFS SAND FINE

98 FS04D 0.59 1.0 M P mscs CS SAND COARSE

98 FS04E 0.30 1.0 M P fsms MS SAND MED

98 FS04F 1.5 P P cs/fs SAND MED

98 FS04G 0.09 2.5 W P vfs VFS SAND FINE

98 FS05.5D 0.35 1.5 W P ms MS SAND MED

98 FS05A 0.42 1.0 M-P P csms MS SAND MED

98 FS05B 0.18 2.5 W B fs FS SAND FINE

98 FS05C 0.42 1.5 P-M P csms MS SAND MED

98 FS05D 0.42 1.5 M-W P csms MS SAND MED

98 FS05E 0.84 1.0 M P vcscs CS SAND COARSE
Table 4.4-1. Continued.

Grain Size Size

Year Station (mm) Phi Sorting Interface Class Class Type Grade

98 FS05F 0.21 2.0 VW P msfs FS SAND FINE

98 FS05G 4.0 M/VW I ms/si MIXSM FINE

98 FS06.5D 0.84 1.0 M P grms-cs CS SAND COARSE

98 FS06A 1.00 -1.0 P-M P shgrcs VCS SAND COARSE

98 FS06B 2.0 M-W P ms/sicl MIXSM FINE

98 FS06C 1.68 -1.0 M P grvcs VCS SAND COARSE

98 FS06D 0.42 0.0 M-W P csms MS SAND MED

98 FS06E 0.15 3.0 W B vfsfs FS SAND FINE

98 FS06F 0.30 2.0 M-W P/I fsms MS SAND MED

98 FS06G 0.42 0.0 M P csms MS SAND MED

98 FS07.5D 1.00 0.5 P-M P grcs VCS SAND COARSE

98 FS07A 0.15 3.0 W B/I vfsfs FS SAND FINE

98 FS07AB 0.11 3.0 W-VW B/I fsvfs VFS SAND FINE

98 FS07B 0.11 3.0 VW B/I fsvfs VFS SAND FINE

98 FS07BC 0.11 W-VW B fsvfs VFS SAND FINE

98 FS07C 1.00 0.5 P-M P grcs VCS SAND COARSE

98 FS07CD 1.00 0.5 P P grcs VCS SAND COARSE

98 FS07D 0.84 0.5 M P vcscs CS SAND COARSE

98 FS07DE 0.50 1.0 M-W P vcsms MSCS SAND COARSE

98 FS07E 0.11 2.5 W-VW P fsvfs VFS SAND FINE

98 FS07EF 3.5 W B fsvfs/si MUD FINE

98 FS07F 0.30 2.0 M-W P fsms MS SAND MED

98 FS07FG 1.00 -1.0 M P grcs VCS SAND COARSE

98 FS07G 0.42 1.0 W-VW P csms MS SAND MED

98 FS08.5D 0.59 1.0 M P mscs CS SAND COARSE

98 FS08A 0.30 2.0 M-W I fsms MS SAND MED

98 FS08B 0.18 1.5 W I fs FS SAND FINE

98 FS08C 1.41 0.5 VP-P P grcs-vcs VCS SAND COARSE

98 FS08D 1.00 -1.0 P-M P grcs VCS SAND COARSE

98 FS08E 0.42 1.5 W P csms MS SAND MED

98 FS08F 0.35 1.5 M-W P ms MS SAND MED

98 FS08G 0.0 P/M I shgr/ms MIXGS CF

98 FS09.5D 0.21 2.0 W-VW I msfs FS SAND FINE

98 FS09A 0.30 2.0 M I fsms MS SAND MED

98 FS09AB 0.50 1.5 P-M P shms-cs MSCS SAND COARSE

98 FS09B 0.50 0.0 M I shms-cs MSCS SAND COARSE

98 FS09BC 1.00 -1.0 M-W P grcs VCS SAND COARSE

98 FS09C 0.59 1.0 W-VW P mscs CS SAND COARSE

98 FS09CD 0.30 2.0 W-VW P fsms MS SAND MED

98 FS09D 0.15 3.0 W-VW B vfsfs FS SAND FINE

98 FS09DE 0.15 3.0 VW B vfsfs FS SAND FINE

98 FS09E 0.06 4.0 W-VW B clsivfs SIFS MUD FINE

98 FS09EF 0.21 2.0 M-W P msfs FS SAND FINE

98 FS09F 0.25 2.0 P-M P fs-cs MS SAND MED

98 FS09FG 1.19 1.0 P P grms-vcs VCS SAND COARSE

98 FS09G 0.42 1.5 M-W P csms MS SAND MED

98 FS10.5D 0.42 1.5 M P csms MS SAND MED
Table 4.4-1. Continued.

Grain Size Size

Year Station (mm) Phi Sorting Interface Class Class Type Grade

98 FS10A 0.59 1.0 P P mscs CS SAND COARSE

98 FS10B 0.59 0.0 P P shgrms-cs CS SAND COARSE

98 FS10C 0.84 1.0 P P grms-cs CS SAND COARSE

98 FS10D 0.30 2.0 W P fsms MS SAND MED

98 FS10E 0.84 1.5 P-M P grms-cs CS SAND COARSE

98 FS10F 0.59 -1.0 P-M P mscs CS SAND COARSE

98 FS10G 0.30 2.0 M P fsms MS SAND MED

98 FS11.5D 0.59 1.0 M P mscs CS SAND COARSE

98 FS11A 0.18 2.5 M B shfs FS SAND FINE

98 FS11AB 0.42 1.0 M P csms MS SAND MED

98 FS11B 0.35 1.5 P-M P ms MS SAND MED

98 FS11BC 0.84 1.5 P P grms-cs CS SAND COARSE

98 FS11C 0.84 1.0 P-M P grms-cs CS SAND COARSE

98 FS11CD 0.50 1.5 M P vcsms MSCS SAND COARSE

98 FS11D 0.35 1.5 M-W P ms MS SAND MED

98 FS11DE 0.30 2.0 M-W P fsms MS SAND MED

98 FS11E 1.00 -1.0 P P grcs VCS SAND COARSE

98 FS11EF 0.59 1.0 M P mscs CS SAND COARSE

98 FS11F 0.21 2.5 W-VW P msfs FS SAND FINE

98 FS11FG 0.07 4.0 W B sivfs VFS SAND FINE

98 FS11G 0.18 2.5 VW P fs FS SAND FINE

98 FS12.5D 0.30 2.5 M P fsms MS SAND MED

98 FS12A 0.35 2.0 P-M P ms MS SAND MED

98 FS12B 0.71 1.5 M P ms-vcs CS SAND COARSE

98 FS12BC 1.00 -0.5 VP-P P grcs VCS SAND COARSE

98 FS12C 0.59 2.0 M P mscs CS SAND COARSE

98 FS12CD 1.00 1.0 P-M P grcs VCS SAND COARSE

98 FS12D 0.59 2.0 M-W P mscs CS SAND COARSE

98 FS12DE 0.30 W P fsms MS SAND MED

98 FS12E 0.30 M-W P fsms MS SAND MED

98 FS12EF 0.71 1.0 M P ms-vcs CS SAND COARSE

98 FS12F 0.84 1.0 P-M P grms-cs CS SAND COARSE

98 FS12FG 0.42 2.0 M P csms MS SAND MED

98 FS12G 0.09 3.5 W B vfs VFS SAND FINE

98 FS12GG 0.71 1.0 ms-vcs CS SAND COARSE

98 FS13.5D 0.71 1.0 P P cs CS SAND COARSE

98 FS13A 0.35 1.0 ms MS SAND MED

98 FS13B 0.5 M P ms-cs SAND COARSE

98 FS13C 1.00 -1.0 VP-P P shgrcs VCS SAND COARSE

98 FS13D 1.0 P-M P cs/ms SAND MED

98 FS13E 0.42 M P csms MS SAND MED

98 FS13F 0.42 M P csms MS SAND MED

98 FS13G 0.30 M-W P fsms MS SAND MED

98 FS14A 0.30 1.0 M-W P fsms MS SAND MED

98 FS14B 0.84 1.5 VP-P I grms-cs CS SAND COARSE

98 FS14C 0.35 M-W P ms MS SAND MED

98 FS14D 0.35 1.5 M-W P ms MS SAND MED

98 FS14E 0.30 VW P fsms MS SAND MED

98 FS14F 0.84 1.0 P-M P grms-cs CS SAND COARSE

98 FS14G 0.30 2.0 M-W P fsms MS SAND MED
Table 4.4-1. Continued.

Grain Size Size

Year Station (mm) Phi Sorting Interface Class Class Type Grade

98 HCS31 fssi/cl MUD FINE

98 IR01C 1.68 grvcs VCS SAND COARSE

98 IR01E

98 IR02.5C 2.00 csgr GR GR COARSE

98 IR02B 2.38 vcsgr GR GR COARSE

98 IR02C 0.71 cs CS SAND COARSE

98 IR02D 1.68 grvcs VCS SAND COARSE

98 IR02E 1.68 grvcs VCS SAND COARSE

98 IR03.5C 0.59 mscs CS SAND COARSE

98 IR03B gr(si) MIXGM CF

98 IR03C 2.38 vcsgr GR GR COARSE

98 IR03D gr/cs GR COARSE

98 IR03E 2.38 vcsgr GR GR COARSE

98 IR04.5C 2.38 vcsgr GR GR COARSE

98 IR04B 1.00 grcs VCS SAND COARSE

98 IR04BC 1.68 grvcs VCS SAND COARSE

98 IR04C 2.83 gr GR GR COARSE

98 IR04CD 1.68 grvcs VCS SAND COARSE

98 IR04D 2.00 csgr GR GR COARSE

98 IR04DE 1.68 grvcs VCS SAND COARSE

98 IR04E 1.00 grcs VCS SAND COARSE

98 IR05.5C 1.00 grcs VCS SAND COARSE

98 IR05B 2.00 csgr GR GR COARSE

98 IR05C 1.00 grcs VCS SAND COARSE

98 IR05D 0.35 ms MS SAND MED

98 IR05E

98 IR06.5B gr/sics MIXGM CF

98 IR06.5C 0.59 mscs CS SAND COARSE

98 IR06.5D 1.00 grcs VCS SAND COARSE

98 IR06B 2.38 vcsgr GR GR COARSE

98 IR06C gr(si) MIXGM CF

98 IR06D gr/csvcs GR COARSE

98 IR06E 0.30 shfsms MS SAND MED

98 IR07.5B 0.59 mscs CS SAND COARSE

98 IR07.5C 0.59 mscs CS SAND COARSE

98 IR07.5D 2.38 vcsgr GR GR COARSE

98 IR07B gr/mscs GR COARSE

98 IR07C 2.83 gr GR GR COARSE

98 IR07D 0.59 mscs CS SAND COARSE

98 IR07E gr/csvcs GR COARSE

98 IR08.5B 1.00 grcs VCS SAND COARSE

98 IR08.5C 2.00 csgr GR GR COARSE

98 IR08.5D gr(si) GR COARSE

98 IR08A 2.00 csgr GR GR COARSE

98 IR08AB gr/csvcs GR COARSE

98 IR08B 1.00 grcs VCS SAND COARSE

98 IR08BC 1.41 grcsvcs VCS SAND COARSE

98 IR08C 0.71 cs CS SAND COARSE
Table 4.4-1. Continued.

Grain Size Size

Year Station (mm) Phi Sorting Interface Class Class Type Grade

98 IR08CD 0.71 cs CS SAND COARSE

98 IR08D 0.84 vcscs CS SAND COARSE

98 IR09A 1.00 vcsgrcs VCS SAND COARSE

98 IR09B 0.84 grvcscs CS SAND COARSE

98 IR09C 0.84 grvcscs CS SAND COARSE

98 IR09D vcs(si) MIXGM CF

98 SBB04 0.21 msfs FS SAND FINE

98 SBB09 2.00 csgr GR GR COARSE

98 SBB10 0.42 csms MS SAND MED

98 SBB32 0.84 vcscs CS SAND COARSE
Table 4.9-1

Estimated secondary production of macroinfaunal for 1998 grab data.

Nemertieans Polychaete Gastropods Isopods Crustaceans Phoronids

Station Anemonies Oligochaeta Bivalves Amphipods Sipunculids Cepholo. Total

BB02 0.16 0.02 1.47 0.32 0.23 0.01 0.23 0.02 2.46

BB04 0.07 0.90 0.55 0.02 0.24 0.07 1.78

FS01G 0.08 32.65 0.01 12.56 0.04 0.97 0.33 0.02 0.02 46.60

FS02.5D 0.01 0.09 0.01 0.67 1.15 0.01 1.93

FS02C 0.91 0.02 0.11 0.05 0.02 37.85 2.65 0.80 0.03 1.06 42.57

FS03E 0.29 1.54 0.02 0.10 1.94

FS04C 0.33 5.30 0.14 7.11 73.55 0.28 0.12 86.51

FS04E 0.02 0.01 0.05 0.08 0.12 0.26

FS06C 0.16 0.02 3.09 0.89 0.33 8.52 0.02 0.04 0.01 1.99 14.89

FS06D 0.02 0.05 0.07 0.02 4.72 0.12 0.22 5.20

FS07B 7.65 0.25 0.63 2.45 0.30 2.79 0.05 0.02 14.14

FS07F 0.03 0.08 0.02 3.57 0.01 0.46 4.14

FS08B 0.05 2.90 0.01 0.04 0.42 0.10 0.02 3.49

FS08C 0.02 3.40 0.04 0.38 7.14 0.03 0.07 11.06

FS08D 0.16 0.03 0.65 0.27 1.04 0.01 0.02 0.14 2.14

FS08E 0.02 0.01 0.01 0.01 0.02 0.07 0.56 0.02 0.69

FS09A 0.41 0.08 0.25 1.85 0.02 0.85 3.47

FS09B 0.08 0.28 0.07 0.08 0.20 0.52 0.11 1.25

FS09C 0.01 0.02 0.02 0.65 0.62 1.32

FS09D 0.10 1.34 0.12 0.05 1.51

FS10.5D 0.02 0.07 0.15 0.24

FS10A 0.10 1.77 0.02 0.94 0.02 2.75

FS10B 0.20 0.01 0.69 0.18 0.08 1.09 0.05 2.09

FS11C 0.09 0.58 0.10 0.05 0.84 1.07 2.64

FS12B 0.12 1.46 0.04 0.01 0.02 0.04 1.57

FS12C 0.03 0.02 0.12 0.10 0.02 0.04 0.02 0.10 0.40

FS12E 0.01 0.01 0.34 0.19 0.53

FS12F 0.08 0.36 0.06 0.73 0.01 1.01 2.18

FS13A 0.07 0.26 0.01 0.16 0.08 0.52

FS13C 0.15 0.05 5.83 0.12 0.07 0.05 0.32 0.04 0.13 6.56

FS13E 0.28 1.21 0.23 1.72

FS13F 0.30 0.02 0.08 0.13 0.04 0.14 0.17 0.04 0.61

FS14A 0.23 1.88 0.42 0.55 0.15 0.21 0.09 3.30

FS14D 0.03 0.32 0.20 0.01 0.57

FS14E 0.01 0.06 0.01 0.71 0.17 0.95

FS14F 0.34 0.39 0.11 0.03 0.04 0.11 0.68

FS14G 0.01 0.50 0.07 0.68 0.03 1.28

HCS31 2.04 36.13 0.07 12.30 0.15 0.59 0.09 0.48 49.81

IR02B 0.16 1.24 0.08 0.01 0.12 0.01 0.01 0.22 1.70

IR02C 0.24 1.40 0.24 0.78 0.02 0.02 2.46

IR02D 0.13 2.02 0.06 9.20 0.02 0.02 11.33

IR04B 0.02 1.65 0.02 0.52 0.36 0.03 2.57

IR04D 1.28 0.01 0.01 0.02 0.06 0.08 0.04 1.51

IR04E 0.01 8.85 0.23 0.89 0.08 0.20 1.36 0.03 11.63

IR05.5C 0.62 1.12 0.08 141.05 0.81 0.27 143.33

IR05D 4.68 0.02 0.01 154.01 0.20 0.05 0.01 0.01 0.02 159.02

IR07C 0.03 7.38 0.01 0.05 0.22 0.03 7.69

IR07E 0.32 0.02 0.03 0.01 0.22 0.01 0.62

IR08B 0.15 0.02 0.50 0.67 0.15 0.02 1.51

IR08C 0.02 1.27 2.41 0.01 0.30 3.99

IR09A 0.04 0.32 0.62 0.02 0.08 0.02 0.06 0.79

IR09C 0.16 0.40 0.63 9.41 0.16 10.59

Total 7.5 0.5 142.4 5.8 24.6 471.5 10.8 22.2 1.5 0.3 0.7 4.6 692.3

% Total 1.1 0.1 20.6 0.8 3.5 68.1 1.6 3.2 0.2 0.0 0.1 0.7 100.0
Table 4.9-2

Estimated secondary production for 1999

-----------------------------------------------------------------------------------------------------------------------------------------------------------

Nemertieans Polychaete Gastropods Isopods Crustaceans Phoronids

Station Anemonies Oligochaeta Bivalves Amphipods Sipunculids Cepholo. Total

FS02C 5.66 0.06 0.41 0.20 52.95 0.07 0.61 54.25

FS03E 0.61 0.01 0.33 0.01 1.92 2.89

FS04C 28.60 0.04 0.04 27.92 1.36 57.96

FS04E 0.13 0.56 0.62 0.14 1.44

FS06C 0.17 0.20 0.53 0.04 0.79 0.12 0.21 1.69

FS06D 0.08 0.02 0.83 0.01 0.83 1.77

FS07B1 0.21 20.76 0.68 2.15 28.62 6.72 0.21 59.14

FS07B2 0.09 18.50 0.25 3.77 3.44 3.18 0.11 0.45 29.70

FS07B3 0.03 0.10 22.24 0.40 0.43 11.35 7.06 0.02 0.01 41.51

FS08D 0.27 0.33 2.81 0.19 0.15 1.72 0.02 0.08 4.97

FS08E 0.07 0.02 0.23 2.38 0.02 0.32 3.05

FS09B 0.29 0.90 0.76 0.05 1.45 0.16 2.34 0.12 5.78

FS10B 0.07 1.74 0.02 5.08 6.52 0.29 13.64

FS11C 0.08 0.69 0.12 0.21 0.77 0.23 2.03

FS12B 0.30 0.33 0.01 2.99 0.04 0.28 3.66

FS12C 0.17 0.23 0.07 1.87 0.21 0.03 0.23 2.65

FS12E 0.01 0.05 0.82 0.11 0.16 0.13 1.27

FS12F 0.08 0.12 0.01 0.12 2.02 0.43 0.14 2.85

FS13C 0.21 0.28 0.05 0.23 3.67 2.05 0.10 0.46 6.83

FS13F 0.01 1.02 0.08 1.39 0.29 0.40 3.18

Total 7.6 0.7 99.3 2.7 7.6 147.7 1.3 36.2 0.9 0.0 0.5 4.0 308.6

% Total 2.5 0.2 32.2 0.9 2.5 47.9 0.4 11.7 0.3 0.0 0.1 1.3 100.0
Table 4.9-3

Average annual secondary production of macroinfaunal for 1998 grab data production.

--------------------------------------------------------------------------------------------------------------------------------------------

Nemertieans Gastropods Oligochaeta Isopods Crustaceans Sipunculids Total

Station Anemonies Bivalves Polychaete Amphipods Phoronids Cepholochord.

BB02 0.16 0.02 0.23 0.32 1.47 0.01 0.23 0.02 2.46

BB04 0.07 0.02 0.55 0.90 0.24 0.07 1.85

FS01G 0.08 12.56 0.04 0.01 32.65 0.97 0.33 0.02 0.02 46.68

FS02.5D 0.01 0.01 0.09 0.67 1.15 0.01 1.95

FS02C 3.28 0.04 0.11 45.40 0.05 0.26 1.36 0.70 0.03 1.06 52.30

FS03E 0.61 0.15 0.93 0.02 1.01 2.72

FS04C 0.33 3.58 50.74 0.09 16.95 0.28 0.74 72.70

FS04E 0.02 0.09 0.32 0.01 0.37 0.14 0.95

FS06C 0.17 0.11 0.33 4.66 0.46 1.81 0.07 0.04 0.01 1.10 8.75

FS06D 0.02 0.02 2.77 0.04 0.07 0.07 0.52 3.52

FS07B 0.11 0.10 1.37 8.46 0.35 14.08 0.30 4.22 0.08 0.24 0.01 29.31

FS07F 0.03 3.57 0.02 0.08 0.01 0.46 4.17

FS08B 0.05 0.01 2.90 0.04 0.42 0.10 0.02 3.54

FS08C 0.02 0.38 7.14 0.04 3.40 0.03 0.07 11.08

FS08D 0.22 0.18 0.15 1.38 0.23 1.73 0.02 0.02 0.11 4.04

FS08E 0.02 0.01 0.23 1.20 0.02 0.04 0.05 0.44 0.02 2.03

FS09A 0.25 0.08 0.41 1.85 0.02 0.85 3.47

FS09B 0.19 0.05 0.76 0.42 0.59 0.18 1.43 0.11 3.73

FS09C 0.02 0.02 0.01 0.65 0.62 1.32

FS09D 0.10 0.12 1.34 0.05 1.62

FS10.5D 0.02 0.07 0.15 0.24

FS10A 0.10 0.02 1.77 0.94 0.02 2.84

FS10B 0.14 0.01 2.58 0.10 1.22 3.80 0.17 8.01

FS11C 0.09 0.05 0.53 0.11 0.64 0.77 0.65 2.83

FS12B 0.21 0.01 0.03 0.90 1.51 0.04 0.28 2.97

FS12C 0.10 0.02 0.95 0.09 0.18 0.13 0.02 0.16 1.65

FS12E 0.01 0.05 0.58 0.01 0.11 0.17 0.13 1.06

FS12F 0.08 0.12 1.38 0.04 0.24 0.01 0.72 0.14 2.73

FS13A 0.07 0.01 0.26 0.16 0.08 0.59

FS13C 0.18 0.05 0.15 1.86 0.08 3.06 1.18 0.07 0.29 6.92

FS13E 0.28 1.21 0.23 1.72

FS13F 0.16 0.02 0.08 0.71 0.13 0.55 0.14 0.23 0.04 0.40 2.46

FS14A 0.23 0.55 0.42 1.88 0.15 0.21 0.09 3.54

FS14D 0.32 0.03 0.20 0.01 0.57

FS14E 0.01 0.01 0.06 0.71 0.17 0.97

FS14F 0.34 0.03 0.11 0.39 0.04 0.11 1.02

FS14G 0.01 0.50 0.07 0.68 0.03 1.29

HCS31 2.04 12.30 0.07 36.13 0.15 0.59 0.09 0.48 51.85

IR02B 0.16 0.01 0.12 0.08 1.24 0.01 0.01 0.22 1.86

IR02C 0.24 0.78 0.24 1.40 0.02 0.02 2.71

IR02D 0.13 9.20 0.06 2.02 0.02 0.02 11.47

IR04B 0.02 0.52 0.02 1.65 0.36 0.03 2.59

IR04D 0.01 0.02 0.01 1.28 0.06 0.08 0.04 1.51

IR04E 0.01 0.89 0.08 0.23 8.85 0.20 1.36 0.03 11.64

IR05.5C 0.62 141.05 0.08 1.12 0.81 0.27 143.95

IR05D 0.01 154.01 0.02 4.68 0.20 0.05 0.01 0.02 0.01 159.02

IR07C 0.03 0.05 0.01 7.38 0.22 0.03 7.73

IR07E 0.02 0.32 0.03 0.01 0.01 0.22 0.62

IR08B 0.50 0.02 0.15 0.67 0.15 0.02 1.51

IR08C 0.02 2.41 0.01 1.27 0.30 4.01

IR09A 0.04 0.32 0.08 0.02 0.62 0.02 0.06 1.15

IR09C 0.16 9.41 0.63 0.40 0.16 10.76

Total 10.1 0.9 22.6 466.0 5.5 158.5 10.2 29.4 1.7 0.9 0.3 5.9 712.0

% Total 1.4 0.1 3.2 65.4 0.8 22.3 1.4 4.1 0.2 0.1 0.05 0.8 100.0
Table 4.9-4

Gut content by major taxonomic group for fishes trawled at the four habitat areas around

Fenwick Shoal. Areas around Fenwick Shoal are: NE – northeast seaward flank of shoal,

NW – northwest shoreward face of shoal, SE – southeast seaward trough, surface dominated

by Diopatra tubes, and SW – southwest shoreward trough, surface dominated by Asabellides

tubes.

------------------------------------------------------------------------------------------------------------------

Fish Fish Length Major Total Wet Weight

Area* Species (mm) Group Number (g)

 NE Etropus microstomus 52.4 Amphipod 2 0.001

Decapod 2 0.002

Mysid 1 0.003

Total 0.006

57.7 Amphipod 14 0.007

Decapod 7 0.009

Total 0.016

64.8 Amphipod 4 0.003

Decapod 1 0.001

Total 0.004

50.7 Empty

55.0 Empty

56.0 Empty

62.0 Empty

62.5 Empty

65.2 Empty

71.7 Empty

82.2 Empty

Prionotus carolinus 55.5 Amphipod 10 0.034

Decapod 2 0.007

Total 0.041

55.2 Empty

Urophycis regia 45.0 Mysid 4 0.005

Total 0.005

48.1 Mysid 1 0.006

Nemertean 1 0.001

Total 0.007

56.0 Mysid 1 0.004

Total 0.004

57.8 Mysid 1 0.001

Total 0.001

61.8 Decapod 1 0.001

Total 0.001

62.6 Amphipod 2 0.006

Decapod 1 0.003

Mysid 2 0.005

Total 0.014

65.0 Amphipod 1 0.008

Decapod 6 0.012

Mysid 4 0.014

Total 0.034

67.1 Amphipod 3 0.013

Cumacean 1 0.002

Decapod 7 0.017

Total 0.032
Table 4.9-4. Continued.

Fish Fish Length Major Total Wet Weight

Area* Species (mm) Group Number (g)

 NE U. regia 70.3 Amphipod 3 0.016

Total 0.016

71.2 Amphipod 2 0.003

Polychaete 1 0.006

Total 0.009

72.2 Decapod 1 0.004

Mysid 1 0.001

Total 0.005

76.3 Amphipod 2 0.011

Total 0.011

Amphipod 1 0.005

Decapod 2 0.023

Mysid 2 0.006

Total 0.034

78.7 Decapod 2 0.003

Mysid 2 0.009

Total 0.012

96.3 Amphipod 1 0.001

Decapod 6 0.046

Mysid 3 0.013

Total 0.060

34.0 Empty

73.4 Empty

 NW E. microstomus 41.6 Amphipod 3 0.002

Cumacean 1 0.003

Decapod 1 0.003

Total 0.008

49.8 Empty

51.2 Empty

54.4 Empty

54.5 Empty

55.0 Empty

56.2 Empty

60.8 Empty

61.5 Empty

67.4 Empty

71.5 Empty

P. carolinus 47.0 Decapod 1 0.001

Total 0.001

51.5 Amphipod 1 0.001

Total 0.001

56.6 Amphipod 1 0.003

Total 0.003

62.1 Amphipod 2 0.010

Decapod 4 0.009

Mysid 1 0.001

62.1 Unknown . 0.004

Total 0.024

65.6 Amphipod 3 0.031

Total 0.031
Table 4.9-4. Continued.

Fish Fish Length Major Total Wet Weight

Area* Species (mm) Group Number (g)

 NW P. carolinus 74.8 Amphipod 1 0.008

Decapod 4 0.021

Total 0.029

200.0 Amphipod 45 0.371

Bivalve 1 0.015

Cumacean 1 0.003

Decapod 15 0.035

Total 0.424

52.5 Empty

57.0 Empty

66.3 Empty

66.5 Empty

U. Regia 67.5 Decapod 1 0.002

Mysid 20 0.056

Total 0.058

68.3 Mysid 12 0.038

Unknown . 0.004

Total 0.042

 SE E. microstomus 68.5 Amphipod 5 0.002

Cumacean 5 0.006

Decapod 13 0.011

Unknown . 0.001

Total 0.019

69.8 Amphipod 1 0.001

Decapod 4 0.011

Polychaete 1 0.009

Total 0.021

72.8 Amphipod 3 0.002

Cephalochordate 1 0.046

Decapod 10 0.020

Mysid 1 0.005

Unknown . 0.001

Total 0.074

77.5 Amphipod 2 0.005

Decapod 2 0.019

Mysid 1 0.003

Total 0.027

86.0 Amphipod 25 0.020

Decapod 19 0.071

Gastropod 1 0.001

Mysid 6 0.005

Total 0.097

98.0 Amphipod 9 0.060

Cumacean 1 0.001

Decapod 2 0.002

Polychaete 3 0.049

Total 0.111
Table 4.9-4. Continued.

Fish Fish Length Major Total Wet Weight

Area* Species (mm) Group Number (g)

 SE E. microstomus 103.8 Amphipod 13 0.130

Decapod 3 0.013

Polychaete 1 0.019

Total 0.162

105.0 Amphipod 10 0.064

Decapod 1 0.004

Echinoderm 1 0.003

Nemertean 1 0.012

Polychaete 1 0.044

Total 0.127

117.3 Amphipod 3 0.053

Cumacean 1 0.001

Polychaete 1 0.065

Unknown . 0.008

Total 0.127

124.9 Amphipod 5 0.089

Decapod 6 0.057

Polychaete 2 0.123

Unknown . 0.048

Total 0.317

P. carolinus 50.3 Decapod 2 0.002

Mysid 1 0.002

Total 0.004

56.5 Decapod 2 0.005

Total 0.005

66.0 Amphipod 2 0.002

Decapod 5 0.008

Unknown . 0.008

Total 0.017

67.5 Amphipod 4 0.007

Cumacean 8 0.009

Decapod 7 0.026

Total 0.042

130.0 Amphipod 6 0.028

Cumacean 13 0.025

Decapod 6 0.017

Unknown . 0.033

Total 0.103

140.0 Amphipod 1 0.001

Decapod 7 0.130

Total 0.131

200.1 Amphipod 18 0.073

Bivalve 1 0.054

Cumacean 2 0.003

Decapod 23 0.165

Unknown . 0.041

Total 0.336

200.2 Amphipod 11 0.050

Cumacean 2 0.002

Decapod 26 0.226

Total 0.278
Table 4.9-4. Continued.

Fish Fish Length Major Total Wet Weight

Area* Species (mm) Group Number (g)

 SE U. Regia 42.6 Decapod 1 0.001

Total 0.001

50.3 Amphipod 1 0.002

Total 0.002

50.7 Amphipod 1 0.006

Mysid 8 0.023

Total 0.029

51.1 Mysid 4 0.007

Total 0.007

57.3 Amphipod 1 0.001

Total 0.001

59.1 Decapod 1 0.003

Mysid 1 0.006

Total 0.009

64.5 Decapod 2 0.001

Mysid 1 0.003

Total 0.004

68.0 Amphipod 20 0.186

Decapod 84 0.340

Mysid 16 0.042

Total 0.568

72.5 Decapod 1 0.020

Total 0.020

76.6 Decapod 3 0.017

Total 0.017

77.0 Amphipod 1 0.010

Decapod 2 0.013

Mysid 4 0.014

Total 0.037

84.5 Amphipod 2 0.016

Decapod 8 0.035

Mysid 2 0.018

Total 0.069

107.0 Amphipod 4 0.003

Decapod 6 0.059

Polychaete 1 0.072

Total 0.134

110.2 Decapod 1 0.011

Total 0.011

119.0 Amphipod 2 0.002

Decapod 40 0.115

Mysid 13 0.016

Total 0.133

155.2 Decapod 6 0.226

Isopod 7 0.023

Total 0.249
Table 4.9-4. Continued.

Fish Fish Length Major Total Wet Weight

Area* Species (mm) Group Number (g)

 SW E. microstomus 90.0 Amphipod 1 0.004

Polychaete 1 0.007

Total 0.011

68.0 Empty

118.0 Empty

120.0 Empty

P. carolinus 160.0 Decapod 10 0.447

Total 0.447

U. Regia 46.0 Decapod 1 0.008

Total 0.008

Decapod 21 0.019

48.0 Mysid 1 0.002

Total 0.021

50.0 Decapod 9 0.019

Mysid 2 0.006

Total 0.025

59.0 Decapod 8 0.013

Mysid 8 0.019

Total 0.032

60.0 Decapod 16 0.050

Total 0.050

67.0 Amphipod 3 0.013

Decapod 40 0.116

Total 0.129

67.6 Amphipod 1 0.005

Total 0.005

69.0 Decapod 2 0.001

Mysid 6 0.007

Total 0.008

70.0 Decapod 4 0.065

Mysid 2 0.003

Total 0.068

70.1 Amphipod 2 0.006

Decapod 22 0.050

Total 0.056

72.0 Amphipod 2 0.001

Decapod 1 0.005

Mysid 1 0.001

Nemertean 1 0.001

Total 0.007

73.0 Amphipod 2 0.004

Decapod 3 0.009

Mysid 10 0.038

Total 0.051

75.0 Decapod 6 0.006

Mysid 1 0.002

Unknown . 0.013

Total 0.021
Table 4.9-4. Continued.

Fish Fish Length Major Total Wet Weight

Area* Species (mm) Group Number (g)

 SW U. Regia 75.1 Amphipod 2 0.004

Decapod 46 0.108

Mysid 2 0.009

Total 0.121

80.0 Amphipod 1 0.001

Decapod 5 0.036

Mysid 3 0.004

Total 0.041

80.1 Decapod 21 0.036

Mysid 3 0.010

Total 0.046

82.0 Mysid 6 0.020

Unknown . 0.008

Total 0.028

82.3 Amphipod 6 0.013

Decapod 128 0.411

Mysid 3 0.007

Unknown . 0.008

Total 0.439

83.0 Amphipod 3 0.009

Decapod 24 0.107

Mysid 4 0.005

Total 0.121

85.0 Decapod 14 0.027

Unknown . 0.019

Total 0.046

85.1 Decapod 1 0.008

Total 0.008

106.5 Amphipod 6 0.018

Decapod 97 0.351

Mysid 6 0.025

Total 0.394

110.0 Amphipod 2 0.002

Decapod 26 0.076

Total 0.078

110.1 Amphipod 2 0.003

Decapod 16 0.135

Total 0.138

120.0 Decapod 9 0.016

Mysid 4 0.009

Unknown . 0.012

Total 0.037

145.0 Amphipod 1 0.012

Decapod 40 0.155

Total 0.167

150.0 Amphipod 1 0.004

Decapod 12 0.185

Total 0.189
Table 4.9-4. Continued.

Fish Fish Length Major Total Wet Weight

Area* Species (mm) Group Number (g)

SW U. Regia 150.1 Amphipod 9 0.063

Decapod 85 0.377

Isopod 1 0.003

Mysid 18 0.036

Total 0.479

160.0 Amphipod 46 0.403

Decapod 110 0.588

Isopod 6 0.023

Mysid 13 0.023

Total 1.037

160.1 Amphipod 10 0.095

Decapod 193 1.070

Total 1.165

165.0 Amphipod 3 0.028

Decapod 26 0.042

Isopod 1 0.002

Mysid 1 0.001

Total 0.073

170.0 Amphipod 20 0.077

Decapod 53 0.573

Isopod 2 0.044

Total 0.694

170.1 Decapod 111 0.755

Isopod 2 0.057

Total 0.812

170.2 Amphipod 13 0.058

Decapod 169 1.015

Isopod 3 0.070

Polychaete 1 0.004

Total 1.147

170.3 Amphipod 5 0.018

Decapod 4 0.058

Polychaete 1 0.002

Total 0.078

175.0 Amphipod 8 0.050

Decapod 112 0.664

Isopod 1 0.035

Mysid 3 0.008

Total 0.757

180.0 Amphipod 1 0.009

Decapod 14 0.041

Mysid 1 0.001

Nemertean 1 0.005

Unknown . 0.129

Total 0.185

180.1 Amphipod 10 0.053

Decapod 192 1.250

Isopod 4 0.028

Polychaete 1 0.003

Total 1.334
Table 4.9-4. Continued.

Fish Fish Length Major Total Wet Weight

Area* Species (mm) Group Number (g)

SW U. Regia 185.0 Amphipod 15 0.061

Decapod 194 0.847

Isopod 5 0.106

Mysid 2 0.002

Total 1.016

200.0 Amphipod 6 0.029

Decapod 111 0.913

Isopod 4 0.070

Mysid 5 0.012

Polychaete 1 0.026

Total 1.050

205.0 Bivalve 1 0.005

Decapod 10 0.398

Isopod 4 0.209

Total 0.612

210.0 Amphipod 12 0.088

Bivalve 1 0.020

Decapod 4 0.032

Total 0.140

215.0 Decapod 1 0.150

Isopod 1 0.065

Unknown . 0.197

Total 0.412

65.1 Empty

73.0 Empty

88.4 Empty
Table 4.9-5

Annual P/B ratios of macroinfauna from the MD/DE study area

Nemertieans Oligochaeta Gastropods Isopods Crustaceans Sipunculids

Station Anemonies Polychaete Bivalves Amphipods Phoronids Cepholochordates

BB02 7.8 6.2 8.9 4.6 4.0 7.4 7.3 8.5

BB04 9.1 9.1 4.1 5.4 3.5 8.5

FS01G 4.2 7.4 3.4 0.6 9.0 3.9 4.9 8.5 8.5

FS02.5D 7.4 4.8 6.4 5.2 3.3 7.4

FS02C 2.3 8.4 13.3 3.4 5.4 1.2 5.7 3.3 7.8 2.2

FS03E 2.1 5.7 4.1 8.0 2.7

FS04C 3.6 9.6 2.4 3.4 2.1 7.9 6.1

FS04E 6.2 7.4 4.0 3.6 3.8 3.5

FS06C 5.6 8.2 8.2 3.6 2.9 3.2 6.5 6.5 7.4 2.9

FS06D 12.2 8.8 5.1 5.4 2.7 5.9 2.8

FS07B 6.4 4.0 8.4 2.7 2.6 3.3 4.7 3.7 5.7 8.7 7.4

FS07F 7.2 8.5 4.6 2.0 7.4 4.3

FS08B 6.6 2.1 7.4 7.1 6.6 4.8 8.5

FS08C 6.2 11.1 2.6 2.1 2.5 7.2 5.5

FS08D 6.3 6.6 8.2 3.9 4.1 2.2 6.8 6.2 3.8

FS08E 6.2 7.4 8.3 4.7 4.2 5.6 5.3 3.4 8.5

FS09A 10.3 4.1 5.7 4.1 6.2 1.9

FS09B 5.7 7.9 4.3 4.1 3.9 4.1 4.4 7.5

FS09C 8.5 5.3 5.4 3.9 2.9

FS09D 5.2 9.7 3.0 6.6

FS10.5D 4.4 4.4 4.1

FS10A 8.0 11.1 3.9 4.5 9.9

FS10B 5.4 7.4 8.7 3.6 3.3 3.8 5.2

FS11C 5.5 7.9 4.1 4.1 3.4 2.0 3.2

FS12B 5.4 8.4 3.4 6.4 7.8 9.3 3.4

FS12C 7.8 6.2 8.6 5.5 3.7 4.8 8.2 3.8

FS12E 7.4 5.3 4.1 2.2 3.8 4.0 3.6

FS12F 6.5 7.7 3.6 4.7 2.5 7.4 2.7 3.5

FS13A 5.9 7.4 4.1 5.5 6.9

FS13C 7.0 6.0 11.1 3.1 3.8 3.5 4.1 8.6 3.2

FS13E 2.8 2.1 4.3

FS13F 7.4 8.5 8.4 4.2 4.8 6.1 4.2 4.8 10.1 2.5

FS14A 7.3 7.6 4.0 2.9 4.1 3.7 5.5

FS14D 5.0 2.7 4.2 7.4

FS14E 7.4 7.4 5.8 2.0 3.9

FS14F 4.3 8.9 4.1 7.5 6.5 7.1

FS14G 7.4 2.3 4.4 3.9 7.2

HCS31 2.8 9.3 2.5 2.1 6.1 4.1 5.9 4.0

IR02B 6.7 9.7 3.1 6.4 6.5 7.4 7.4 3.0

IR02C 6.8 8.6 4.2 3.5 9.9 8.5

IR02D 6.7 8.1 3.4 1.6 6.2 6.2

IR04B 6.2 8.5 2.0 2.7 3.8 7.2

IR04D 7.4 2.1 7.4 8.0 4.7 5.1 5.2

IR04E 7.4 9.5 2.4 3.1 6.3 4.6 4.2 7.2

IR05.5C 3.1 9.5 2.9 0.4 2.4 3.4

IR05D 10.4 2.8 6.4 0.4 4.6 8.2 7.4 8.5 7.4

IR07C 8.7 7.4 1.7 4.1 5.0 7.2

IR07E 10.4 3.5 7.8 7.4 7.4 3.6

IR08B 10.4 4.0 2.3 2.5 3.4 8.5

IR08C 9.9 3.0 1.1 6.4 5.6

IR09A 5.2 2.7 9.9 3.3 5.5 8.5 6.2

IR09C 6.7 8.7 4.3 0.9 3.3

Average 6.5 6.5 8.9 3.7 4.0 3.9 5.2 4.9 7.2 7.6 6.2 3.1

SD 1.8 1.8 1.3 1.0 1.7 2.1 2.0 1.6 1.5 1.6 1.6 0.6

Min 2.3 2.7 7.4 1.7 0.6 0.4 2.0 2.7 3.3 4.0 3.6 1.9

Max 12.2 8.5 13.3 5.8 7.4 9.0 9.9 8.5 10.1 8.7 7.4 3.8

Median 6.5 6.6 8.6 3.7 4.1 3.3 4.7 4.3 7.4 8.5 7.2 3.2
Table 4.9-6

Relationship between P/B ratio and sediment type for the May 1998 grab data. Based on

discriminant analysis of sediment grain-size categories and major taxa P/B ratios. A

total of 48 stations were classified, four stations did not have grain-size data.

CS = coarse sand, FS = fine sand, GR = gravel, MU = mud.

-----------------------------------------------------------------------------------------------------------

Original

Sediment Predicted Sediment Group Membership

Group CS FS GRCS GRFS GRMUCS MUCS MUFS Total

CS Stations 14 1 1 0 0 0 0 16

% 87.5 6.2 6.2 0 0 0 0 100

FS Stations 1 10 0 0 0 0 0 11

% 9.1 90.9 0 0 0 0 0 100

GRCS Stations 1 0 12 0 0 0 0 13

% 7.7 0 92.3 0 0 0 0 100

GRFS Stations 0 0 0 2 0 0 0 2

% 0 0 0 100 0 0 0 100

GRMUCS Stations 0 0 0 0 1 0 0 1

% 0 0 0 0 100 0 0 100

MUCS Stations 0 0 0 0 0 2 0 2

% 0 0 0 0 0 100 0 100

MUFS Stations 0 0 0 0 0 0 3 3

% 0 0 0 0 0 0 100 100

Total Stations 16 11 13 2 1 2 3 48

% 33.3 22.9 27.1 4.2 2.1 4.2 6.3 100
Table 4.10-1

Average abundance (individuals/m2) of selected taxa (occurring at five or more stations) by

cluster subgrouping for the MD/DE study area.

Cluster Analysis Subgroups

A A' A'' B B' C D D' D'' E E'

CNIDARIA

Anthozoa 13 43 0 130 0 0 0 0 25 4 0

NEMERTINEA

Nemertinea 286 254 75 155 242 8 43 31 30 129 5

ANNELIDA

Oligochaeta 960 632 38 950 650 0 111 128 15 1054 395

Spiophanes bombyx 172 61 25 15 8 33 14 108 5 19918 650

Aricidea (Acmira) cerrutii 299 146 75 10 858 0 7 56 0 57 35

Aricidea (Acmira) catherinae 108 246 13 0 42 0 0 22 0 261 45

Parapionosyllis longicirrata 94 71 25 20 33 0 0 6 0 4 40

Aphelochaeta sp. 188 25 13 0 33 0 4 8 0 46 5

Brania wellfleetensis 260 71 125 0 8 0 0 3 0 0 5

Hesionura elongata 24 21 13 80 600 0 0 6 5 0 0

Hemipodus roseus 31 68 38 5 67 0 0 3 0 0 0

Nephtys spp. 6 11 50 0 0 0 4 50 0 75 10

Asabellides oculata 3 7 38 0 0 0 150 56 0 832 200

Spio setosa 8 79 13 0 0 0 0 0 0 2589 170

Caulleriella sp. B 24 4 0 0 8 0 4 94 0 64 60

Streptosyllis pettiboneae 43 21 50 5 75 0 0 8 0 0 0

Glycera americana 13 21 0 20 0 0 0 6 0 32 0

Protodorvillea kefersteini 29 282 13 30 0 0 0 0 0 0 0

Parougia caeca 14 82 0 0 8 0 0 0 0 0 0

Mediomastus ambiseta 0 4 0 5 0 0 14 0 0 1093 70

Tharyx acutus 25 57 0 0 0 0 0 0 0 121 35

Ampharete finmarchica 13 7 0 10 0 0 0 8 5 11 15

Pisione remota 14 125 0 85 75 0 0 0 0 0 0

Paradoneis sp. B 175 4 0 5 8 0 0 0 0 0 0

Paradoneis sp. A 24 25 0 20 0 0 0 6 0 0 0

Lumbrinerides dayi 14 0 0 0 17 0 0 3 0 4 0

Polycirrus eximius 0 11 0 15 0 0 0 0 0 0 330

Sigalion arenicola 4 0 0 0 0 0 0 3 10 4 0

Glycera dibranchiata 0 4 0 0 0 0 4 3 0 7 25

Amastigos caperatus 0 4 0 0 0 0 0 14 0 111 0

Macroclymene zonalis 0 0 0 5 0 0 7 0 0 993 0

Sthenelais limicola 3 0 0 0 0 0 18 0 0 11 0

Lumbrineris fragilis 0 4 0 0 0 0 0 17 0 0 10

Monticellina baptisteae 39 0 0 0 217 0 0 0 0 0 5

Travisia parva 3 7 0 100 0 0 0 3 0 0 5

Sabaco elongatus 0 0 0 0 0 0 0 0 0 32 35

Clymenella torquata 0 4 0 0 0 0 4 0 0 364 0

Ancistrosyllis hartmanae 0 25 0 0 0 0 0 0 0 4 0

Paraonis fulgens 10 0 25 5 0 8 0 6 0 0 0

Polydora cornuta 0 0 0 0 0 0 0 3 0 282 0

Apoprionospio pygmaea 0 0 0 0 0 0 7 0 5 1696 0

Notomastus spp. 0 7 0 0 0 0 0 3 0 7 0

GASTROPODA

Natica pusilla 10 14 0 30 8 0 0 0 90 11 0

Nassarius trivittatus 0 4 0 0 0 0 7 3 0 18 5

Turbonilla interrupta 0 0 0 0 0 0 4 0 0 46 35
Table 4.10-1. Continued.

Cluster Analysis Subgroups

A A' A'' B B' C D D' D'' E E'

BIVALVIA

Tellina spp. 43 14 38 20 25 42 896 97 120 1311 25

Crenella glandula 14 29 0 410 67 0 4 0 20 50 5

Astarte spp. 29 46 13 425 0 0 0 3 185 25 0

Mytilus edulis 6 50 0 250 8 0 4 111 5 57 15

Spisula solidissima 13 0 0 45 8 0 4 14 0 0 5

Lyonsia hyalina 19 4 0 5 0 0 0 3 0 29 0

Nucula spp. 3 0 0 0 0 0 1682 53 0 471 0

Ensis directus 0 0 38 0 0 0 0 3 0 296 5

Pleuromeris tridentata 1 0 0 215 25 0 0 6 0 4 0

Pitar morrhuanus 0 7 0 0 0 0 0 0 0 143 5

Crassinella martinicensis 1 0 0 25 0 0 0 3 0 0 5

Bushia elegans 1 7 0 45 0 0 0 0 0 0 0

CRUSTACEA

Unciola irrorata 15 46 63 10 0 8 39 8 15 718 145

Protohaustorius wigleyi 21 7 0 5 33 0 11 175 85 7 15

Tanaissus psammophilus 115 7 100 15 0 8 7 0 0 0 10

Chiridotea coeca 7 4 0 35 17 208 4 3 70 0 0

Byblis serrata 57 4 2488 5 0 0 0 11 15 0 0

Pseudunciola obliquua 117 18 700 0 0 0 0 19 0 282 0

Pseudoleptocuma minor 14 0 0 5 17 0 0 14 0 18 10

Oxyurostylis smithi 4 4 0 0 8 8 0 19 0 18 5

Politolana concharum 3 0 0 35 8 25 32 6 5 0 5

Rhepoxynius hudsoni 15 4 13 0 0 0 0 28 0 4 0

Ampelisca spp. 0 0 0 0 0 0 0 3 5 346 10

Americhelidium americanum 11 4 0 0 0 0 0 3 5 0 0

Edotea triloba 1 18 0 0 0 8 150 0 0 104 10

Parahaustorius holmesi 0 0 0 20 17 0 7 3 10 0 0

Parahaustorius longimerus 17 0 0 0 0 158 0 0 0 0 0

Bathyporeia parkeri 13 0 0 0 0 17 0 3 0 4 5

PHORONIDA

Phoronis sp. 0 0 0 0 0 0 7 22 0 157 15

SIPUNCULA

Sipuncula 0 0 0 0 0 0 0 6 0 4 25

ECHINODERMATA

Leptosynapta tenuis 4 11 0 70 0 0 0 0 5 0 0

CEPHALOCHORDATA

Branchiostoma caribaeum 8 11 25 40 0 8 4 3 15 0 0
Table 5.3-1.

Scenarios depicting the effect of season and climatology on infaunal recolonization trajectory.

Faunal characteristics favored by the combinations season and climate are listed in each cell of

the table.

Season When Sandmining is Conducted

Climate immediately after

mining

Spring/Summer Fall/Winter

Stormy/Energetic • Transport of small to large

individuals into and out of

mined area

• Dispersal of organic matter

and fine sediments

• Dispersal of individuals form

mass recruitment events

• Lower potential for shift in

community structure

• Recolonization rate

intermeidate

• Production lowered

• Transport of small to large

individuals into and out of

mined area

• Dispersal of organic matter

and fine sediments

• Physical and physiological

stress highest, sensitive life

history stages eliminated

• Recolonization slowed to

lowest rate

• High potential for delay of

community structure

recovery

• Production at lowest point

Calm/Quiescent • Deposition of water column

primary production

• Fine sediments accumulate in

mined area

• Recruitment of warm water

larval forms favored

• Surface and subsurface

deposit feeders favored

• Species that queue on fine

sediments favored

• Recolonization proceeds at

highest rate

• Highest potential for shift in

community structure

• Extended quiescence may

lead to hypoxia, regionally or

within mined pit

• Highest production

• Fines accumulate in mined

area

• Recruitment of cold water

larval forms favored

• Recolonization rate

intermeidate

• High potential for shift in

community structure

• Pulse of high production
Final Report OCS Study 2000-055

January 1999

Author:

John A. Musick

Virginia Institute of Marine Science

Project Manager:

Carl H. Hobbs, III

Virginia Institute of Marine Science

Prepared under MMS contract 1435-01-97-CT-30853 to

the

Virginia Institute of Marine Science

College of William & Mary

International Activities and Marine Minerals Division

Environmental Survey of Potential Sand Resource Sites

Offshore Delaware and Maryland 

Part 2: Transitory Species (Vertebrate Nekton)
DISCLAIMER

This report has been reviewed by the Minerals Management Service and approved for publication. 

Approval does not signify that the contents necessarily reflect the views and policies of the Service, nor

does mention of trade names or commercial products constitute endorsement or recommendation for

use.
Final Report

Environmental Survey of Potential Sand Resource Sites

Offshore Delaware and Maryland

Part 2

Transitory Species (Vertebrate Nekton)

John A. Musick, Principal Investigator

Virginia Institute of Marine Science

Carl H. Hobbs, III, Project Manger

Virginia Institute of Marine Science 

for 

U. S. Department of the Interior

Minerals Management Service

International Activities and Marine Minerals Division

Contract 1435-01-97-CT-30853

OCS Study 2000-055

December 1998
INTRODUCTION AND DISCUSSION

Transitory Species (Vertebrate Nekton)

Three major groups of transitory vertebrate nekton species are to be expected in the

proposed mining area: fishes, sea turtles, and marine mammals. The coastal area off Delaware

has one of the most extreme seasonal ranges of sea temperature in the world. Consequently,

most of the fishes and all of the sea turtles and marine mammals migrate seasonally, with boreal

species present in winter and warm-temperate/sub-tropical species present in summer (Musick

et al., 1986).

Fishes

The coastal region wherein the mining site resides provides habitat for a wide variety of

demersal and pelagic fishes with highest diversity in September and lowest diversity in late

winter (February/March) (Colvocoresses and Musick, 1984; Phoel, 1985; Musick et al.,

1986). Only a small percentage of species are resident year-round. Rather most establish

seasonal residency. Thus, the area is an important foraging and spawning ground for a wide

variety of fishes. In winter, the fauna is dominated by broad species such as sea herring

(Clupea harengus), Atlantic mackerel (Scomber scombrus), hakes (Urophycis, Merluccius),

monkfish (Lophius americanus), and spiny dogfish (Squalus acanthias), (Musick, 1974;

Armstrong et al, 1992; Phoel, 1985; Nammack et al, 1985). In summer the fauna is

dominated by warm temperate and sub-tropical species such as summer flounder

(Paralichthys dentatus), croakers, drums, and sea trouts (Sciaenids), menhaden (Brevoortia

tyrannus), and large coastal sharks (Carcharhinidae) (Desfosse et al., 1990; Musick et al.,

1993). In spring and fall the area is an important migration corridor for striped bass (Morone

saxatilis) and bluefish (Pomatomus saltatrix). Many of the dominant species noted above

are extremely valuable and important to recreational or commercial fisheries or both.

Much of the information needed to provide a detailed assessment of the fish

communities at the proposed mining site can be gleaned from the existing literature and data
2

base at the Virginia Institute of Marine Science and at the National Marine Fisheries

Service laboratory in Woods Hole, Massachusetts.

Sea Turtles

Sea turtles are sub-tropical animals that occur in the mid-Atlantic Bight

seasonally (Musick and Limpus, 1996). The loggerhead sea turtle (Caretta caretta) is

by far the most abundant species nesting regularly as far north as southern Virginia and

occasionally on Assateague Island (Lutcavage and Musick, 1985; Musick, 1988). 

Loggerheads spend the winter south of Cape Hatteras and migrate north in spring

entering Virginia waters as early as mid-April in some years but usually in mid-May. 

As many as 10,000 mostly juvenile loggerheads enter Chesapeake Bay in the summer

and the coastal waters from about Cape May, New Jersey south represent an

important seasonal developmental area for the species (Keinath et al., 1988). The

species occurs regularly each summer as far north as Cape Cod ( Keinath and Musick,

1981a and Shoop and Kenny, 1992). 

The second most abundant sea turtle in the mid-Atlantic Bight in summer is the

Kemp’s ridley (Lepidochys kempi), the most endangered sea turtle on earth (Keinath

and Musick, 1991b). Kemp’s ridley is represented mostly by juvenile individuals in the

mid-Atlantic and occurs regularly as far north as southern New England (Musick,

1988). This species forages for decapod crustaceans usually over shallow estuarine

flats in summer and reaches its maximum abundance along the coast in autumn when it

leaves the estuaries and migrates south to overwinter south of Cape Hatteras (Musick

and Limpus, 1996). Although seaonal distribution and abundance for the species are

well known for Chesapeake Bay, southern Virginia, and North Carolina, little specific

information is available for the area between Cape Charles, Virginia and Long Island,

New York but it probably reaches peak abundance during the autumn migration in

September and October.
3

The leatherback sea turtle (Dermochelys coriacea) is the third most abundant sea

turtle in the mid-Atlantic Bight (Keinath and Musick, 1991c). This species exhibits

gigantothemy (it is warm-blooded) and migrates into the Bight earlier than the other species,

usually in April (Barnard et al., 1989; Musick, unpublished data). It appears that most of the

population migrates north into the Gulf of Maine for the summer (Shoop and Kenny, 1992), but

some individuals remain as far south as Virginia (Musick, 1988). The green sea turtle

(Chelonia mydas) occurs occasionally in summer in estuarine waters of the mid-Atlantic Bight

(Keinath and Musick, 1991d), but occurs too infrequently there to be of any consideration for

this project. The hawksbill (Eretmochelys imbricata) occurs even less frequently (Keinath

and Musick, 1991e). All of the sea turtles are classified as threatened or endangered under the

U.S. Endangered Species Act, and they are completely protected by the U. S. Fish and

Wildlife Service, and National Marine Fisheries Service ( Federal Register, 1978; Terwilliger

and Musick, 1995; Anonymous, 1998). 

Of the species which occur regularly in the mid-Atlantic Bight, the loggerhead and

Kemp’s ridley are highly vulnerable to entrainment and mortality by hopper dredging (Moein et

al., 1994). Thus, dredging operations (under the control of the U.S. Army Corps of Engineers)

have come under close scrutiny and stringent regulation by the NMFS. Incidental take limits of

sea turtles during dredging operations are defined by the NMFS through consultations

authorized under Section 7 of the Endangered Species Act. The most recent consultation in the

mid-Atlantic was for emergency work done to stabilize the beaches on Assateague Island in

April, 1998. For the project NMFS allowed a take of one Kemp’s ridley, one green turtle,

and two loggerheads (Conant, personal communication).

Marine Mammals

Marine mammals are highly migratory and seasonal in the mid-Atlantic Bight (Shoop

and Kenny, 1992; Terwilliger and Musick, 1995). The marine mammal fauna off Delmarva is

dominated by the boreal harbor porpoise (Phocoena phocoena) in winter and by the

bottlenose dolphin (Tursiops truncatus) in summer (Kenney, 1990; Keinath et al., 1994;
4

Wang et al., 1994. Several other cetacean species are transient seasonally through the area. 

Of note are juvenile humpback whales (Megaptera novaeangliae) that have recently begun to

overwinter between Cape Hatteras, North Carolina and Virginia (Swingle et al., 1993; Wiley

et al., 1993) and right whales (Eubalaena glacialis) which briefly pass through the area in fall

(peak in December) on the way to their calving grounds off southern Georgia and northern

Florida. The right whales must return north with their calves in the spring (peak in April) on the

way to their summer foraging area off northern New England and Nova Scotia (Winn et al.,

1986). There have been no recent sightings of right whales in Delaware Bay where the species

was historically hunted. The endangered right whale is vulnerable to collision with moving ships,

but their tenure in the dredging area is brief. 

RECOMMENDATIONS

Fishes: Future sand mining (dredging) may result in local displacement of mobile fish

assemblages and limited mortality of some of the benthic species during the dredging operation. 

However the limited geographic size of likely dredging areas relative to the very large

geographic ranges and estimated population sizes of the fishes involved would suggest that sand

mining would have very little effect on fish populations.

Sea Turtles: Endangered and threatened sea turtles are particularly vulnerable to

entrapment by dredges. However, sea turtles are tropical to subtropical animals and may be

avoided by restricting dredging operations to the colder portion of the year, November 15 -

April 15.

Marine Mammals: All marine mammals in the study area are migratory and highly

mobile and easily can avoid dredges. Sand mining poses no foreseeable threat to the marine

mammal population. 
5

Literature Cited

Anonymous, 1988. Endangered Species Act of 1973, as amended through the 100th

Congress. U. S. Fish and Wildlife Service, U. S. Department of Interior, Washington,

D. C.: 45 pp.

Armstrong, M. P., J.A. Musick and J. A. Colvocoresses. 1996. Food and ontogenetic shifts

in feeding of the goosefish, Lophius americanus. J. Northw. Atl. Fish. Sci., (18):99-

103.

Colvocoresses, J. A. and J. A. Musick. 1984. Species association and community structure of

middle Atlantic Bight continental shelf demersal fishes. Fish. Bull., 82 (2):295-313.

Conant, T. 1998, personal communication. NMFS, Office of Protected Resources, Silver

Spring, MD

Desfosse, J. C., J. A. Musick, A. D. Estes, and P. Lyons. 1990. Stock identification of

summer flounder (Paralichthys dentatus) in the southern mid-Atlantic Bight. Final

Report to Virginia Marine Resources Commission, WB-86-01-02.

Federal Register, July 28, 1978. Listing and Protecting Loggerhead Sea Turtles as

“Threatened Species” and Populations of Green and Olive Ridley Sea Turtles as

“Threatened Species” or “Endangered Species.” Federal Register 43:144:32800-

32811.

Keinath, J. A. and J. A. Musick. 1991a. Atlantic loggerhead turtle. pp. 445-448. In: 

K.Terwilliger (coordinator), Endangered and Threatened Species in Virginia. 

McDonald Woodward Pub. Co., Blacksburg, VA.

Keinath, J. A. and J. A. Musick. 1991b. Kemp’s ridley turtle. p. 451-453. In: K.

Terwilliger (coordinator), Endangered and Threatened Species in Virginia. McDonald

Woodward Pub. Co., Blacksburg, VA.

Keinath, J. A. and J. A. Musick. 1991c. Atlantic hawksbill turtle. p. 450-451. In: K.

Terwilliger (coordinator), Endangered and Threatened Species in Virginia. McDonald

Woodward Pub. Co., Blacksburg, VA.

Keinath, J. A. and J. A. Musick. 1991d. Atlantic green turtle. pp. 448-450. In: K.

Terwilliger (coordinator), Endangered and Threatened Species in Virginia. McDonald

Woodward Pub. Co., Blacksburg, VA.

Keinath, J. A. and J. A. Musick. 1991f. Atlantic leatherback turtle. pp. 453-455. In: K.

Terwilliger (coordinator), Endangered and Threatened Species in Virginia. McDonald

Woodward Pub. Co., Blacksburg, VA.

Keinath, J. A., J. A. Musick, and D. E. Barnard. 1995. Abundance and distribution of sea

turtles off North Carolina. U.S. Dept. of Interior, Minerals Management Service, Gulf

of Mexico OCS Region. Contract Report 19-35-0001-30590: 158pp.

Keinath, J. A., J. A. Musick and R. A. Byles. 1988. Aspects of the biology of Virginia's sea

turtles: 1979-1986. VA J. Sci., 38:329-336.

Lutcavage, M. and J. A. Musick. 1985. Aspects of the biology of sea turtles in Chesapeake

Bay and nearby coastal waters. Copeia, 2:449-456.

Moein, S. E., J. A. Musick, J. A. Keinath, D. E. Barnard, and M. Lenhardt. 1994. Evaluation

of seismic sources for repelling sea turtles from hopper dredges. Final Report

submitted to the U. S. Army Corps of Engineers Waterways Experiment Station,
6

Vicksburg, MS: 33 pp.

Musick, J. A. 1974. Seasonal distribution of the sibling hakes, Urophycis chuss and U.

tenuis (Pisces, Gadidae) in New England. Nat. Mar. Fish. Ser. Fish. Bull.,

72:481-495.

Musick, J. A. 1988. The Sea Turtles of Virginia with Notes on Identification and Natural

History. VA Inst. Mar. Sci., Educational Series 24, 22 pp, Gloucester Pt., VA.

Musick, J. A., J. A. Colvocoresses, E. J. Foell. 1986. Seasonality and distribution, availability

and composition of fish assemblages in Chesapeake Bight. Chapter 21, In: A. Yanez

y. Arancibia (ed.), Fish Community Ecology in Estuaries and Coastal Lagoons: 

Towards an Ecosystem Integration. Univ. Mexico Press., Mexico City.

Musick, J. A. and C. J. Limpus. 1996. Habitat utilization and migration in juvenile sea turtles. 

Chapter 6, pp. 139-165. In: Biology of Sea Turtles. P. Lutz and J. A. Musick (eds.), 

CRC Press: 412 pp.

Nammack, M. F., J. A. Musick, and J. A. Colvocoresses. 1985. Life history of spiny dogfish

off the northeastern United States. Trans. Amer. Fish. Soc., 114:367-376.

Phoel, W. C. 1985. Community strubture of demersal fishes on the inshore U.S. Atlantic

continental shelf: Cape Anna, Massachusetts to Cape Fear, North Carolina. Ph.D.

dissertation presented to the School of Marine Science, Virginia Institute of Marine

Science, College of William & Mary, 95 pp.

Shoop, C. R. and R. D. Kenny. 1992. Seasonal distribution and abundance of loggerhead

and leatherback sea turtles in wates of the northeastern United States. Herp.

Monographs, 6: 43-67 pp.
The Department of the Interior

As the Nations’s principal conservation agency, the Department of the Interior has

responsibility for most of our nationally owned public lands and natural resources. 

This includes fostering sound use of our land and water resources, protecting our fish,

wildlife, and biological diversity, preserving the environmental and cultural values of

our national parks and historic places; and providing for the enjoyment of life

through outdoor recreation. The Department assesses our energy and mineral

resources and works to ensure that their development is in the best interests of all our

people by encouraging stewardship and citizen participation in their care. The

Department also has a major responsibility for American Indian reservation communities

and for people who live in island territories under U.S. Administration.

The Minerals Management Service Mission

As a bureau of the Department of the Interior, the Minerals Management Service’s

(MMS) primary responsibilities are to manage the mineral resources located on the

Nations’s Outer Continental Shelf (OCS), collect revenue from the Federal OCS and

onshore federal and Indian lands, and distribute those revenues.

Moreover, in working to meet its responsibilities, the Offshore Minerals

Management Program administers the OCS competitive leasing program and

oversees the safe and environmentally sound exploration and production of our Nation’s

offshore natural gas, oil and other mineral resources. The MMS Royalty Management

Program meets its responsibilities by entrusting the efficient, timely and accurate collection

and distribution of revenue from mineral leasing and production due to Indian tribes and

allottees, States and the U. S. Treasury

The MMS strives to fulfill its responsibilities through the general guiding principles of: (1)

being responsive to the public’s concerns and interests by maintaining a dialog with all

potentially affected parties and (2) carrying out its programs with an emphasis on working to

enhance the quality of life for all Americans by lending MMS assistance and expertise to

economic development and environmental protection.
Final Report OCS Study 2000-055

November 1998

Authors:

John Olney, Sr. and Donna Marie Bilkovic

Virginia Institute of Marine Science

Project Manager:

Carl H. Hobbs, III

Virginia Institute of Marine Science

Prepared under MMS contract 1435-01-97-CT-30853 to

the

Virginia Institute of Marine Science

College of William & Mary

International Activities and Marine Minerals Division

Environmental Survey of Potential Sand Resource Sites

Offshore Delaware and Maryland 

Part 3: Literature Survey of Reproductive Finfish and

Ichthyoplankton Present in Proposed Sand Mining Locations
DISCLAIMER

This report has been reviewed by the Minerals Management Service and approved for

publication. Approval does not signify that the contents necessarily reflect the views and

policies of the Service, nor does mention of trade names or commercial products constitute

endorsement or recommendation for use.
Final Report

Environmental Survey of Potential Sand Resource Sites

Offshore Delaware and Maryland

Part 3

Literature Survey of Reproductive Finfish and Ichthyoplankton Present in 

Proposed Sand Mining Locations within the Middle Atlantic Bight

John Olney, Sr., Principal Investigator and 

Donna Marie Bilkovic

Virginia Institute of Marine Science

Carl H. Hobbs, III, Project Manager

Virginia Institute of Marine Science

for

U. S. Department of the Interior

Mineral Management Service

International Activities and Marine Minerals Division

Contract 1435-01-97-CT-30853

OCS Study 2000-055

November 1998
Introduction

Ichthyofauna of the Middle Atlantic Bight (the region between Cape Cod and Cape

Hatteras) is dynamic and highly variable due to seasonal and climatic changes, varying life

history strategies, hydrographic phenomena, fishing pressure, and natural cycles of abundance. 

While there are distinct faunal assemblages in the boreal waters north of Cape Cod and in the

warm waters south of Cape Hatteras, there are few endemic fish species in the variable Middle

Atlantic Bight (MAB) waters. However, the fauna is diverse since numerous species, including

commercially and recreationally important species, migrate seasonally through this region to

spawn. This fauna is composed of both northern and southern fish populations that undergo

extensive migrations as they follow temperature isotherms. Thus the MAB is a valuable

migratory path as well as spawning area for numerous species. 

Grosslein and Azarovitz (1982) noted that all year "significant quantities of fish larvae"

can be found throughout the MAB. This may be due to the large number of spawning species,

extensive dispersal of eggs and larvae, and spawning periods of long duration, as well as to the

continuous influx/outflux of northern and southern species. Warm water species such as,

bluefish (Pomatomus saltatrix), and weakfish (Cynoscion regalis) enter the region as

temperatures rise in the spring and summer while cold water species such as, Atlantic cod

(Gadus morhua), Atlantic herring (Clupea harengus harengus), and American shad (Alosa

sapidissima)) migrate north. Similarly, as fall approaches, warm water species such as summer

flounder (Paralichthys dentatus), butterfish (Peprilus triacanthus), and black sea bass

(Centropristis striata) may migrate offshore toward deeper waters and then move southward,

while cold water species move south into the MAB (Grosslein and Azarovitz, 1982). It is also

possible for a pelagic species such as Scomber scombrus to have both a southern and a

northern contingent which spawn within the Bight during different periods, or in the case of

Brevoortia tyrannus, spawning episodes during migrations into and out of the Bight. 

In the following report, available data on reproductive finfish species located within the
2

MAB are summarized with emphasis placed on economically important species previously

observed within or near proposed mining sites. As coastal finfish often have ichthyofauna that

utilize estuarine environments, estuarine residents may have ichthyofauna transported by

estuarine plumes into coastal assemblages. Thus, the report includes some estuarine resident

species, that may have ichthyoplankton interspersed with coastal fauna near the mining sites.

Distribution Data

Information was compiled on 36 fish species that utilize the potential mining locations or

immediate surrounding areas for spawning or nursery grounds (Tables 1 and 2). Table 1

depicts reported periods of spawning and egg and larval presence of fish species that utilize

portions of the MAB within the vicinity of the proposed mining areas. Species are categorized

by spawning mode, either pelagic or benthic, and by general spawning location, including

offshore (areas greater than 27 m in depth), inshore (areas less than 27 m in depth), tributaries,

estuaries, and bays. Table 2 summarizes the data by spawning season. Relative abundance

within the MAB also is depicted based on literature cited; “frequent” implies regular spawning

judged by the presence of eggs and larvae in the study area, and “infrequent” implies infrequent

use based on lack of observations of eggs/larvae or spawning adults in the region. Important

data sources include trawl and plankton surveys from the National Marine Fisheries Service

summarized by Grosslein and Azarovitz (1982), ichthyoplankton surveys in nearshore and bay

regions summarized by Wang and Kernehan (1979), ichthyoplankton surveys made in the

1950s and 1960s from the National Marine Fisheries Service summarized by Colton et al.,

(1979), neuston and bongo sampling of both inshore and offshore stations off the coast of

Virginia and New Jersey (Comyns and Grant, 1993); as well as additional studies cited within

the tables. Often spawning periods and locations were determined based on the presence of

eggs and/or prolarval stages. Commercial and recreational landings data were obtained from

The National Marine Fisheries Service (NMFS) and are reported for the Mid-Atlantic region

which consists of the following states: Delaware, Maryland, New York, New Jersey, and

Virginia. 
3

Results

In general, fish that spawn in the MAB broadcast pelagic eggs (31 species in this

study). Thus, eggs and larvae have the potential to be dispersed throughout the region and into

habitats different than the spawning grounds. Often, offshore spawners have larvae that are

transported with currents to inshore or estuarine nursery grounds. Five benthic spawners were

included in this study: Clupea harengus harengus, Fundulus heteroclitus, Ammodytes spp.,

Menidia menidia, and Pseudopleuronectes americanus. Although these species have

benthic eggs, often larvae exhibit a dispersive pelagic stage. 

Spawning and egg/larval populations vary seasonally within the Bight (Table 2). The

majority of the species present have a spawning period that includes spring and/or summer (29

species). Approximately ten species have a spawning period in the study area during the

winter: Urophysis regia, Leiostomus xanthurus, Micropogon undulatus, Paralichthys

dentatus, Physis chesteri, P. americanus, Urophysis floridiana, Urophysis cirrata, Gadus

morhua and Ammodytes spp. Of these species only U. floridiana and cirrata spawn

primarily in the winter. The other eight species have overlap with other seasons. Some species

are present and may spawn throughout at least three seasons, including Physis chesteri, and

Pseudopleuronectes americanus. 

Commercial and Recreational Species within the Mid-Atlantic Bight

Commercially and recreationally important species that utilize the MAB and the

proposed mining areas during spawning or in early life stages include, but are not limited to

bluefish, summer flounder, Atlantic mackerel, Atlantic Butterfish, scup, and black sea bass. 

These species are all managed under Fishery Management Plans (FMPs).

Bluefish (Pomatomus saltatrix)

Within the bight, bluefish is one of the most important recreational species. Its

commercial value has increased since the 1960s and 1970s. Among sport fish, bluefish ranked

first in the MAB from 1979-1989 with catches occurring inshore and offshore (Pottern, 1989). 

Recreational landings historically exceed commercial landings in the mid-Atlantic region (Figure
4

1). However, combined landings, which peaked in 1980, have declined steadily since that

time, and the stock has been considered overharvested (O'Reilly and Austin, 1996). 

Along the eastern U.S. coast, bluefish have two major spawning aggregations: a

southern Atlantic stock which spawns in the spring and a mid-Atlantic stock which spawns in

the summer. Both of these stocks utilize the MAB at differing life stages and times. The

summer spawning contingent arrives and most spawn offshore between Cape Cod and Cape

Hatteras from June through August. Subsequently, eggs and larvae remain offshore, and,

typically, juveniles remain offshore as well until the onset of cooling water induces southern

migrations. Some juveniles from the summer spawn will migrate into coastal and bay regions

for the early portion of fall. Additionally, there is the potential for inshore and lower estuary

spawning to occur, as ripe females, eggs, and larvae were observed in lower Chesapeake Bay,

off Indian River Inlet and Ocean City, Maryland, respectively (Wang and Kernehan, 1979).

The spring spawning contingent spawns south of Cape Hatteras and juveniles eventually will

migrate into mid-Atlantic bays and estuaries which are used as nursery areas until fall (June

through September) (Pottern et al., 1989; Grosslein and Azarovitz, 1982; Wang and

Kernehan, 1979). 

Essential Fish Habitat (EFH) as described in the draft fishery Management Plan (FMP)

for egg and larval bluefish includes pelagic waters found over the Continental Shelf (from the

coast out to the limits of the EEZ), from Montauk Point, New York to Cape Hatteras, North

Carolina that encompass the highest 90% of the area where eggs or larvae were collected in the

National Marine Fisheries Service (NMFS) Marine Resources Monitoring, Assessment, and

Prediction (MARMAP) ichthyoplankton survey (MAFMC, July 1998). Eggs typically are

found at mid-shelf depths, whereas larvae most commonly are observed above 49 ft (15 m). 

EFH of juveniles includes pelagic waters found over the Continental Shelf (from the coast out to

the limits of the EEZ), from Nantucket Island, Massachusetts to Cape Hatteras, North

Carolina that encompass the highest 90% of the area where juveniles were collected in

MARMAP ichthyoplankton survey. Additionally, juvenile bluefish are in “Mid-Atlantic estuaries
5

from May through October within the ‘mixing’ and ‘seawater zones” (MAFMC, July 1998). 

Adult EFH is over the Continental shelf (from the coast out to the limits of EEZ) in waters

greater than 66 ft (20 m), from Cape Cod Bay, Massachusetts south to Cape Hatteras, in the

highest 90% of the area where adult bluefish were collected in the NEFSC trawl survey. From

April through October, adult bluefish are located within Mid-Atlantic estuaries (MAFMC, July

1998). Eggs, larvae, juveniles, and adults have EFH that may overlap the borrow sites. 

Designated estuaries that are EFH for juveniles and adults include Delaware Bay, Delaware

Inland Bay, and Chincoteague Bay based on NOAA’s Estuarine Living Marine Resources

(ELMR) data. 

Summer flounder (Paralichthys dentatus)

Summer flounder are important both commercially and recreationally in the MAB. 

There is a significant offshore commercial fishery that occurs during the spring inshore migration

and fall offshore migration and continues during the winter. During the summer, commercial and

recreational fisheries are concentrated in coastal and estuarine waters. Recreational landings

typically exceed the commercial landings in the mid-Atlantic region. Steep declines in both

recreational and commercial landings in 1989 were followed by slight increases in recreational

landings, while commercial landings remained constant (Figure 2). O'Reilly and Austin (1996)

attributed the declines to overfishing and year-class failure. Currently, summer flounder are

managed under Amendment 10 of the Summer Flounder, Scup, and Black Sea Bass Fishery

Management Plan, and the stocks are considered over exploited (NEFSC, 1997).

In the MAB, summer flounder spawning occurs during the fall and winter months

(September through February) offshore and the larvae and young become demersal and

migrate inshore to estuaries. In the winter and spring, adult summer flounder move into inshore

and estuary waters (Pottern et al., 1989; Grosslein and Azarovitz, 1982; Wang and Kernehan,

1979). 
6

0 

10000000 

20000000 

30000000 

40000000 

50000000 

Landings (lbs)

 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 

Year

Bluefish: Recreational Bluefish: Commercial

Figure 1: Commercial and recreational landings of bluefish in the Mid-Atlantic Bight

Essential Fish Habitat (EFH) as described in the draft Fishery Management Plan

(FMP) for summer flounder includes pelagic waters found over the Continental Shelf (from the

coast out to the limits of the EEZ), from the Gulf of Maine to Cape Hatteras, North Carolina

that encompass the highest 90% of the area where summer flounder (eggs and larvae) were

collected in the (MARMAP) ichthyoplankton survey (MAFMC, August 1998). For juveniles

and adults, the EFH is the demersal waters (near bottom-waters utilized by demersal fish) over

the Continental Shelf (from the coast out to the limits of the EEZ), from the Gulf of Maine to

Cape Hatteras, North Carolina that encompass the highest 90% of the area where summer

flounder were collected in the MARMAP ichthyoplankton survey. Using this definition and the

MARMAP surveys, the borrow sites are within the designated EFH for this species for all life

stages. Additionally, estuaries designated as EFH for summer flounder include Delaware Inland

Bays, Delaware Bay, and Chincoteague Bay based on ELMR data. 
7

0 

2000000 

4000000 

6000000 

8000000 

10000000 

12000000 

14000000 

Landings (lbs)

 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 

Year

Summer Flounder: Recreational Summer Flounder: Commercial

Figure 2: Commercial and recreational landings of summer flounder in the Mid-Atlantic Bight

Atlantic Mackerel (Scomber scombrus)

The Atlantic mackerel fishery has strong recreational and commercial

components. Historically, the recreational component has exceeded commercial catch

and foreign commercial catches had dominated landings. Controls on foreign catch in the

1970s reduced that proportion of the landings. Since 1987, recreational landings have

remained lower than commercial landings in the mid-Atlantic region (Figure 3). 

Landings closely follow abundance fluctuations; peaks in abundance in the late 1960s led

to peaks in landings as well. Fluctuations in year-class are postulated to be related to

larval survival which may be influenced by several factors including water temperature,

zooplankton abundance, and currents (Grosslein and Azarovitz, 1982). 

Southern populations of Atlantic mackerel from between Long Island and

Chesapeake Bay migrate inshore and/or north beginning in March and April and spawn

progressively in a northward direction during the spring and summer. In the mid-
9

0 

5000000 

10000000 

15000000 

20000000 

Landings (lbs)

 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 

Year

Atlantic Mackerel: Recreational Atlantic Mackerel: Commercial

Figure 3: Commercial and recreational landings of Atlantic Mackerel in the Mid-Atlantic Bight.

Atlantic, spawning occurs from mid-April to June and the adult population begins to migrate

and temporarily intermingles with the northern population which subsequently migrates north to

spawn in June and July. During the fall, the southern population migrates south once again

(Grosslein and Azarovitz, 1982). EFH as described in the FMP for Atlantic mackerel includes

pelagic waters found over the Continental Shelf (from the coast out to the limits of the EEZ),

from Maine to Cape Hatteras, North Carolina that encompass the highest 75% of the area

where Atlantic mackerel eggs, larvae or juveniles were collected in the MARMAP

ichthyoplankton survey (MAFMC, August 1998). Using this definition, the borrow sites would

not be considered EFH for eggs or larvae of this species. However, juvenile and adult

mackerel designated EFH overlap with the borrow sites. Estuaries that are designated EFH for

mackerel include Delaware Bay and Delaware Inland Bays based on ELMR data.
10

Butterfish (Peprilus triacanthus)

The only fishery for butterfish in the mid-Atlantic Bight is the domestic commercial

fishery, since the foreign fishery was phased out in 1986 and there is no reported recreational

fishery (NEFSC, 1997). Commercial landings in the Bight have fluctuated between one and

two million pounds since 1987 (Figure 4).

The butterfish currently is managed under Amendment 8 of the Atlantic Mackerel, Squid and

Butterfish Fishery Management Plan and is defined as an under-exploited fishery.

Adult butterfish migrate inshore following seasonal changes in temperature during the

spring and summer and remain inshore until colder temperatures occur in October and

November (Wang and Kernehan, 1979). Wang and Kernehan (1979) also reported spawning

to occur typically inshore from June to August. Larvae and young may remain a few miles

offshore or move further inshore. Grosslein and Azarovitz (1982) reported spawning from

April through December inshore and in estuaries. Essential Fish Habitat (EFH) as described in

the draft Fishery Management Plan (FMP) for Butterfish includes pelagic waters found over the

Continental Shelf (from the coast out to the limits of the EEZ), from the Gulf of Maine through

Cape Hatteras, North Carolina that encompass the highest 75% of the area where butterfish

eggs, larvae or juveniles were collected in the MARMAP ichthyoplankton survey. EFH is also

the ‘mixing’ and/or ‘seawater’ portions of all estuaries where butterfish are ‘common,’

abundant,’ or ‘highly abundant’ on the Atlantic coast, from Passamaquaddy Bay, Maine to

James River, Virginia” (MAFMC, August 1998). Based on MARMAP surveys, larvae,

juveniles, and adults are found within the Delaware Bay and Chincoteague Bay, and juveniles

and adults also appear to be found in the Delaware Inland Bays. Likewise, eggs, larvae,

juveniles, and adults are noted to have 75% of their catch within the Maryland-Delaware study

area indicating an overlap with designated EFH for this species.
10

1000000 

1200000 

1400000 

1600000 

1800000 

2000000 

Landings (lbs)

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Year

Butterfish: Commercial

Figure 4: Commercial landings of butterfish in the Mid-Atlantic Bight

Scup (Stenotomus chrysops)

Commercial landings historically have exceeded or outweighed recreationally

landings in the mid-Atlantic region. Since 1986 there have been declines in both

commercial and recreational landings with marked declines in 1995 through 1997 

(Figure 5). The fishery currently is managed under Amendment 8 of the Summer

Flounder, Scup, and Black Sea Bass Fishery Management Plan, and the Middle Atlantic

bight stock is considered overexploited (NEFSC, 1997).

In the winter, this species is located primarily offshore in deep waters from

Hudson Canyon to Cape Hatteras. Migrations occur during the spring inshore and during

the autumn offshore. Spawning occurs in the summer (May through August) rarely

southof New Jersey. Scup eggs and larvae or spawning adults have rarely been observed

south of New Jersey (Eklund and Targett, 1990; Grosslein and Azarovitz, 1982). 

Juveniles, which are bottom dwelling, have been reported offshore, inshore, and in bays

and estuaries from May through November from New England to Chesapeake Bay 
11

0 

1000000 

2000000 

3000000 

4000000 

5000000 

6000000 

7000000 

8000000 Landings (lbs)

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Year

Scup: Recreational Scup: Commercial

Figure 5: Commercial and recreational landings of scup in the Mid-Atlantic Bight

 (Grosslein and Azarovitz, 1982). Estuaries designated as EFH for scup are areas where

scup eggs and larvae were identified as common, abundant, or highly abundant in the

ELMR data base for the ‘mixing’ and ‘seawater’ salinity zones. EFH for juveniles and

adults is the demersal waters over the Continental Shelf (from the coast out to the limits

of the EEZ), from the Gulf of Maine to Cape Hatteras, North Carolina that encompass the

highest 90% of the area where scup were collected in the MARMAP ichthyoplankton

survey (MAFMC, August 1998). Using this definition and MARMAP surveys, EFH for

juvenile and adults overlaps borrow sites during the Spring and Fall. Juveniles and adults

also commonly appear to be found in Delaware Bay, and Delaware Inland Bays, and,

rarely, in the Chincoteague Bay according to ELMR data.

Black Sea Bass (Centropristis striata)

Within the middle Atlantic states, recreational landings are comparable to or

greater than those from the commercial fishery. Within the past 10 years commercial

landings have remained level, while there has been some fluctuation in the recreational
12

fishery (Figure 6). Currently, black sea bass are managed under Amendment 9 of the

Summer Flounder, Scup, and Black Sea Bass Fishery Management Plan, and the stocks

are considered overexploited (NEFSC, 1997).

Two stocks of black sea bass are recognized: northern and southern. The

northern stock (north of Cape Hatteras) is found offshore in deep waters during the winter

and then migrates inshore to spawn, typically from April through November in the MidAtlantic Bight waters. Peaks in larval abundance occur from July through November in

the Bight (Able et al., 1995). Although the larvae are planktonic, the young black sea

bass quickly become demersal and use estuaries and the inner continental shelf as nursery

grounds (Able et al., 1995; Grosslein and Azarovitz, 1982). When juveniles settle, they

typically become associated with bottom structure, such as peat and shell accumulations. 

Migrations offshore begin as water temperature cools in the fall (Able et al., 1995). 

For black sea bass eggs, “EFH is the estuaries where eggs were identified as

‘common,’ abundant,’ or ‘highly abundant’ in the ELMR database for the ‘mixing’ and/or

‘seawater’ salinity zones” (MAFMC, August 1998). EFH for larval black sea bass is the

pelagic waters found over the Continental Shelf (from the coast out to the limits of the

EEZ), from the Gulf of Maine to Cape Hatteras, North Carolina that encompass the

highest 90% of the catch where larvae were collected in the MARMAP ichthyoplankton

survey. EFH for juveniles and adults is the demersal waters over the Continental Shelf

(from the coast out to the limits of the EEZ), from the Gulf of Maine to Cape Hatteras,

North Carolina that encompass the highest 90% of the area where black sea bass were

collected in the MARMAP ichthyoplankton survey (MAFMC, August 1998) . Juveniles 

and adults appear to be present in Chincoteague Bay, Delaware Bay, and Delaware 

Inland Bays based on ELMR data. Based on MARMAP surveys, the designated EFH

pelagic and demersal waters for larvae, juveniles and adults overlap with the borrow sites.

 
13

0 

1000000 

2000000 

3000000 

4000000 

5000000 

6000000 

7000000 Landings (lbs)

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Year

Black Sea Bass: Recreational Black Sea Bass: Commercial

Figure 6: Commercial and recreational landings of Black Sea Bass in the Mid-Atlantic Bight

Summary

Of the 36 species listed in this survey, approximately 22 species have the potential

to be influenced by inshore sand-mining activity depending on the time of the

disturbance. These species either directly utilize inshore areas for spawning and nursery

habitats or transverse these areas during spawning migrations, on a frequent basis. Three

additional species have the potential to use the study region, but are infrequently observed

within the targeted reach of the MAB (Clupea harengus harengus, Stenotomus chrysops,

Engraulis eurystole). Menidia menidia is believed to spawn only in the bays/estuaries of

the Bight, but nearshore areas can be influenced by estuarine outflows (Olney and

Wagner, in prep.). Seven species of hakes (Urophysis , Physis and Merluccius spp.)

have larval stages present in the MAB year-round. However, temporal patterns of

occurrence and distributions are not well understood due to difficulties in distinguishing
14

larvae of those species. Typically, it is believed that spawning and larval growth occurs

offshore, however, there is some evidence of inshore spawning (Comyns and Grant,

1993). Species that are believed to be located primarily offshore include Hippoglossina

oblonga, Gadus morhua, and Limanda feruginea. 

Potential use of the proposed mining area by finfish varies. Some species may use

the area in more than one capacity, (i.e., a primary nursery area for larvae or young, a

spawning and nursery area, or as a fall migration path for young and adults). Species that

historically have been observed using the targeted inshore areas as nursery grounds

include Lopholatilis chamaeleonticeps, Pomatomus saltatrix, Leiostomus xanthurus,

Microposgon undulatus, Menticirrhus saxatilis, Paralichthys dentatus, Brevoortia

tyrannus, Lophius americanus, Prionotus carolinus, and Scomber scombrus. Species that

also may use the area as spawning grounds include Anchoa mitchilli, Anchoa hespetus,

Centropristis striata, Cynoscion regalis, Tautoga onitis, Peprilus triacanthus,

Scopthalumus aquosus, Fundulus heteroclitus, Ammodytes, and Pseudopleuronectes

americanus. Many of the above species as well as Alosa sapidissima and Morone

saxatilis, which are freshwater spawners, subsequently traverse the inshore areas during

fall migrations offshore. 

This survey is not an exhaustive survey of all species that may be present at some

time in the MAB. Instead, it relies on published literature which may have introduced

some bias due to sampling location or time. Extensive ichthyoplankton surveys in the

area occurred in the 1960s and 1970s; thus natural cycles of abundance may have altered

species composition or occurrence since that time. In order to completely assess potential

impacts of sand mining on finfish reproduction, ichthyoplankton sampling is

recommended. Sampling is necessary to determine changes in species composition and

use, that could not be elucidated from published literature. In addition, a more accurate

description of relative abundance of species in the area, including economically important

ones, will result from a more current ichthyoplankton survey.
15

Literature Cited

Able, K.W., M.P. Fahay and G.R. Shepherd. 1995. Early life history of black sea bass,

Centropristis striata, in the mid-Atlantic Bight and a New Jersey estuary. Fish.

Bull. 93: 429-445. 

Able, K. W. , R. E. Matheson, W. W. Morse, M. P. Fahay and G. Shepard. 1990. Patterns

of summer flounder (Paralichthys dentatus) early life history of the Mid-Atlantic

Bight and New Jersey Estuaries. Fish. Bull. U.S. 88:1-12.

Abram, B. J. 1985. Species profiles: life histories and environmental requirements of

coastal fishes and invertebrates (Mid-Atlantic) -mummichog and striped killifish.

U.S. Fish Wildl. Serv. Biol. Rep. 82 (11.40).

Chittenden, M. E. Jr. 1976. Present and historical spawning grounds and nurseries of

American shad, Alosa sapidissima, in the Delaware River. Fish. Bull. U.S.

74:343-352.

Colton, J. B. Jr., W. G. Smith, A. W. Kendall Jr., P. L. Berrien and M. P. Fahay. 1979.

Principal spawning areas and times of marine fishes, Cape Sable to Cape Hatteras.

Fish. Bull. 76(4): 911-915.

Comyns, B. H. and G. C. Grant. 1993. Identification and distribution of Urophycis and

Phycis (Pisces, Gadidae) larvae and pelagic juveniles in the U.S. middle Atlantic

Bight. Fish.Bull. U.S. 91:210-223.

Eklund, A. M and Targett, T. E., 1990. Reproductive seasonality of fishes inhabiting

hard bottom areas in the Middle Atlantic Bight. Copeia. 1990(4): 1180-1184.

Fahay, M. P., 1974. Occurrence of silver hake, Merluccius bilinearis eggs and larvae

along the middle Atlantic Continental Shelf during 1966. NOAA Fish. Bull.

72(3): 813-834. 

Fay, C. W., R. J. Neves, and G. B. Pardue. 1983. Species profiles: life histories and

environmental requirements of coastal fishes and invertebrates (Mid-Atlantic) -

striped bass. U.S. Fish Wildl. Serv. Biol. Rep. 82 (11.8)

Fay, C. W., R. J. Neves, and G. B. Pardue. 1983. Species profiles: life histories and

environmental requirements of coastal fishes and invertebrates (Mid-Atlantic)

-alewife / blueback herring. U.S. Fish Wildl. Serv. Biol. Rep. 82 (11.9)

Fay, C. W., R. J. Neves, and G. B. Pardue. 1983. Species profiles: life histories and

environmental requirements of coastal fishes and invertebrates (Mid-Atlantic) -

Atlantic silverside. U.S. Fish Wildl. Serv. Biol. Rep. 82 (11.10)

Grimes, B. H., M. T. Huish, J. H. Kerby, D. Moran. 1898. Species profiles: life histories

and environmental requirements of coastal fishes and invertebrates (Mid-Atlantic)

- summer and winter flounder. U.S. Fish Wildl. Serv. Biol. Rep. 82 (11.112).

Grosslein, M. D., and T. Azarovitz (eds.), 1982. Ecology of the middle Atlantic Bight

fish and shellfish Monograph 15. Fish Distribution. MESA New York Bight Atlas. 

New York Sea Grant Inst., Albany, N.Y.

Hare, J. A. and R. K. Cowen. 1993. Ecological and evolutionary implications of the larval

transport and reproductive strategy of bluefish Pomatomus saltatrix. Mar. Ecol.

Prog. Ser. 98(1-2): 1-16.

Kendall, A. W. Jr. 1976. Patterns of larval fish distributions in the Middle Atlantic bight.

Pages 126-145 In: B. Manowitz (ed.) Proceedings of Conference 'Effects of

Energy-Related Activities on the Atlantic Continental

Shelf '. Brookhaven National Laboratory, Upton N.Y. 
16

Kendall, A. W. Jr. and L.A. Walford. 1979. Sources and distribution of bluefish 

Pomatomus saltatrix, larvae and juveniles off the east coast of the United States.

Fish. Bull. 77(1): 213-227. 

Lewis, R. M. 1966. Effects of salinity and temperature on survival and development of

Atlantic menhaden, Breevoortia tyrranus. Trans. Am. Fish. Soc. 95:423-426.

MAFMC. August 1998. Amendment 8 to the Atlantic Mackerel, Squid, and Butterfish

Fishery Management Plan (Includes draft environmental assessment and draft

regulatory impact review). 

MAFMC. August 1998. Amendment 12 to the Summer Flounder, Scup, and Black Sea

Bass Fishery Management Plan (Includes draft environmental assessment and

draft regulatory impact review). 

MAFMC. July 1998. Amendment 1 to the Bluefish Fishery Management Plan (Includes

draft environmental assessment and draft regulatory impact review).

MacKenzie, C., L. S. Weiss-Glanz, and J. R. Moering. 1985. Species profiles: life

histories and environmental requirements of coastal fishes and invertebrates

(Mid-Atlantic) -American shad. U.S. Fish Wildl. Serv. Biol. Rep. 82 (11.37).

Mercer, L. P. 1989. Species profiles: life histories and environmental requirements of

coastal fishes and invertebrates (Mid-Atlantic) - weakfish. U.S. Fish Wildl. Serv.

Biol. Rep. 82 (11.109).

Morse, W. W. 1980. Spawning and fecundity of Atlantic mackerel, Scomber scombrus, in

the Middle Atlantic Bight. Fish. Bull. 78(1): 103-108. 

Morse, W. W. 1981. Reproduction in summer flounder, Paralichthys dentatus (L.) 

J.Fish. Biol. 19(2): 189-203.

Morton, T. 1989. Species profiles: life histories and environmental requirements of

coastal fishes and invertebrates (Mid-Atlantic) - bay anchovy. U.S. Fish Wildl.

Serv. Biol. Rep. 82 (11.97).

NEFSC [Northeast Fisheies Science Center]. 1997. Report of the 19th Northeast

Regional Stock Assessment Workshop, Woods Hole, MA:

NOAA/NMFS/MEFSC. NEFSC Ref. Doc. 97-14.

O'Reilly, R. and Austin, H. 1996. Status of stock assessment knowledge used to manage

important Virginia finfish species. Special report in Applied Marine Science and

Ocean Engineering No. 332.

Phillips, J. M., M. T. Huish and J. H. Kerby. 1989. Species profiles: life histories and

environmental requirements of coastal fishes and invertebrates (Mid-Atlantic) -

spot. U.S. Fish Wildl. Serv. Biol. Rep. 82 (11.98).

Pottern, G. B., M. T. Huish and J. H. Kerby. 1989. .Species profiles: life histories and

environmental requirements of coastal fishes and invertebrates (Mid-Atlantic) -

bluefish. U.S. Fish Wildl. Serv. Biol. Rep. 82 (11.94).

Rogers, S. G. and M. J. Van Den Avyle. 1989. Species profiles: Life histories and

environmental requirements of coastal fishes and invertebrates (Mid-Atlantic)

-Atlantic menhaden. U.S. Fish Wildl. Serv. Biol. Rep. 82 (11.108).

Smith, W. G. 1973. The distribution of summer flounder Paralichthys dentatus, eggs and

larvae on the continental shelf between Cape Cod and Cape Lookout, 1965-66.

Fish. Bull. Fish Wildl. Serv. U.S. 71:527-548. 
17

Wang, Johnson C .S., and Kernehan, R. J. 1979. Fishes of the Delaware Estuaries: a

Guide to the Early Life Histories. Ecological Analysts, Inc., Towson, MD.

Wenner, C. A. 1983. Biology of the Longfin Hake (Physis chesteri) in the Western

North Atlantic. Biol. Oceano., Vol. 3, No. 1: 41-75.
Table 1. Reported periods of spawning and egg and larval presence of fish species located within the Middle-Atlantic Bight categorized

by spawning mode (Pelagic or Benthic) and location of spawning. 

O = offshore (areas greater than 27 m in depth); I = inshore (areas less than 27 m in depth); 

T = Tributaries; E = Estuaries; B = Bays. Bays/Estuaries include Delaware Bay and Indian River Bay.

Fish Species Spawning Egg Larvae Reference FMP Presence Comments

PELAGIC SPAWNERS

OFFSHORE

Physis chesteri Feb-Nov, O Comyns and Grant, 1993 Frequent

late Sept-April late Sept -April Wenner, 1983 peak spawning in Dec and Jan off Virginia

Urophysis tenuis May-June, O Comyns and Grant, 1993 Frequent

Urophysis chuss Aug-Nov, O Comyns and Grant, 1993 Frequent

May-Nov May-Nov May-Nov Wang and Kernehan, 1979 infrequent in offshore Delaware waters (more northern species)

Urophysis regia Oct-May, O Comyns and Grant, 1993 Frequent

fall/winter fall/winter March/April, E/I Wang and Kernehan, 1979 peak spawning in Sept-Nov

Urophysis floridiana Feb-Mar, O Comyns and Grant, 1993 Infrequent

Urophysis cirrata Feb-Mar, O Comyns and Grant, 1993 Infrequent

Merluccius bilinearis May-Nov May-Nov Aug-Oct, O Grosslein and Azarovitz, eds. 1982 peak spawning in June-July

May-Nov May-Nov late summer/fall, O Wang and Kernehan, 1979; Fahay, 1974 Infrequent infrequent south of Hudson Canyon

June-Dec June-Dec Colton et al., 1979 Frequent

Gadus morhua Dec-May Dec-May O Grosslein and Azarovitz, eds. 1982 ? peak spawning in Dec and Jan

Lopholatilis chamaeleonticeps March-Sept March-Sept June-Oct, O Grosslein and Azarovitz, eds. 1982 Frequent

Pomatomus saltatrix June-Aug June-Aug July-Aug; O Hare and Cowen, 1993 Yes Frequent Larvae restricted to MAB in July-Aug

June-Aug Jun-Aug June-Sept; O/E Pottern et al., 1989 juveniles from the southern bight spring spawn episode migrate north 

June-Aug June-Aug June-Sept, O Grosslein and Azarovitz, eds. 1982 into the MAB bays and estuaries by early summer and fall

Aug, O Kendall and Walford, 1979

June-Aug,O/I/E June-Aug,O/I/E June-Sept, O/I/E Wang and Kernehan, 1979 Most spawning offshore, some in nearshore and some lower estuaries

Leiostomus xanthurus Oct-March Oct-March Dec-April, O/I/E Philips et al., 1989 Frequent some reports of spot spawning inshore

Dec-March Dec-March Dec-May, I/E Wang and Kernehan, 1979 young emigrate to estuaries during spring and migrate out in late fall

Micropogon undulatus Aug-Dec Aug-Dec Grosslein and Azarovitz, eds. 1982 peak spawning in Oct

Oct-Feb Oct-Feb Oct-July, E Wang and Kernehan, 1979 larvae and young move into low salinity nursery areas

Menticirrhus saxatilis May-Aug May-Aug Grosslein and Azarovitz, eds. 1982 Frequent

May-Aug May-Aug May-Oct, O/E Wang and Kernehan, 1979 young emigrate into estuaries throughout the summer

May-Sept May-Sept Fahay, 1974

Hippoglossina oblonga May-Oct May-Oct Colton et al., 1979 Frequent

May-Oct May-Oct May-Oct, O Grosslein and Azarovitz, eds., 1982 peak spawning in June-July; larvae become demersal ca. 8mm

Paralichthys dentatus Sept-Jan Sept-Jan Oct-May, I/E/O Able et al., 1990 Yes Frequent young and late larvae move inshore to estuaries

Sept-Feb Sept-Feb migrate I/E Grimes et al., 1989

Sept-Dec Sept-Dec Sept-Feb, O Grosslein and Azarovitz, eds. 1982

Sept-Feb Sept-Feb Morse, 1981

Sept-April Sept-April Colton et al., 1979

Aug-Feb Aug-Feb Oct-June, I/E Wang and Kernehan, 1979

Nov-Dec Nov-Dec Nov-Dec, I/O Kendall, 1976

Sept-Nov Sept-Nov Sept-Feb, O Smith, 1973

March-July March-July O Grosslein and Azarovitz, eds. 1982

 Limanda ferruginea April-Aug April-Aug Colton et al., 1979 Frequent peak spawning spawning in May-June

OFFSHORE AND INSHORE

Brevoortia tyrannus Spring/Fall, I Spring/Fall, I Oct-June, E Rogers and Van Den Avyle, 1989 Frequent possibility of estuarine spawning exists

April-Oct April-Oct April-Oct/E Grosslein and Azarovitz, eds. 1982

Spring/late Fall Spring/late Fall Dec-May/E; April-Oct/E Wang and Kernehan, 1979 Larvae emigrate to estuaries from offshore; young emigrate to offshore areas in autumn 

June - Dec, I/E Kendall, 1976

E Lewis, 1966 Spawning occurs during spring and fall migrations

Lophius americanus May-Nov May-Nov O/I Grosslein and Azarovitz, eds. 1982 Frequent eggs and larvae found both inshore and offshore

Prionotus carolinus May-Nov May-Nov Colton et al., 1979 Frequent

May-Oct May-Oct May-Oct, O/I/E Wang and Kernehan, 1979 larvae collected offshore (Alantic City), and in lower estuaries

May-Nov May-Nov June - Nov, O Kendall, 1976 larvae further offshore in late fall/winter

Scomber scombrus April-May, I April-June Grosslein and Azarovitz, eds. 1982 Yes Frequent

April-June April-June Colton et al., 1979; Morse, 1980 peak spawning in May
INSHORE AND BAYS/ESTUARIES

Anchoa species

 Anchoa mitchilli May-Oct, E Morton, 1989 Frequent

late April-Sept late April-Sept Grosslein and Azarovitz, eds. 1982 spawn throughout salinity gradient

May-Sept May-Sept June-Oct, E Wang and Kernehan, 1979

 Anchoa hespetus April-July April-July Grosslein and Azarovitz, eds. 1982 Frequent

May-Aug May-Aug fall/winter, E Wang and Kernehan, 1979 young migrate to deeper waters in fall/winter

 Engraulis eurystole July-Nov July-Nov May-Dec Grosslein and Azarovitz, eds. 1982 Infrequent spawn in polyhaline waters (offshore or lower estuary)

Centropristis striata April-Nov April-Nov June-Nov, I/E Able et al., 1995 Yes Frequent peak larval abundance in July-Sept or Oct 

June-Nov June-Nov I/E Grosslein and Azarovitz, eds. 1982 peak spawning in June and July

May-Oct May-Oct June-Nov, I/E Wang and Kernehan, 1979

Stenotomus chrysops May-Aug May-Aug Grosslein and Azarovitz, eds. 1982 Yes Infrequent peak spawning in June; nearshore only;Scup eggs and larvae not reported South of N. Jersey

Cynoscion regalis May-July May-July O/I Mercer, 1989 Frequent

May-Oct May-Oct Grosslein and Azarovitz, eds. 1982 peak spawning May and June

May-Aug May-Aug May-Aug, E Wang and Kernehan, 1979 young emigrate from estuaries by mid-November

Tautoga onitis May-July May-July I Eklund and Targett, 1990 Frequent

Peprilus triacanthus April-Dec, I/E April-Dec, I/E Grosslein and Azarovitz, eds. 1982 Yes Frequent

May-Oct May-Oct Colton et al., 1979 peak spawning in July-Aug

June-Aug, O June-Aug, O June-Sept, I/E Wang and Kernehan, 1979

Scopthalmus aquosus April-Dec April-Dec Grosslein and Azarovitz, eds. 1982 Frequent young move offshore in winter

April-Dec April-Dec Colton et al, 1979

April-Dec April-Dec May-Dec, I/E Wang and Kernehan, 1979

BAYS/ESTUARIES: OLIGOHALINE OR FRESHWATER PORTIONS

Alosa sapidissima Sept-Nov, O MacKenzie et al., 1985 Frequent spawning occurs in freshwater tributaries; young migrate into offshore regions in the fall

April-July April-July Fay et al, 1983

April-June April-June April-Aug, T Wang and Kernehan, 1979

April-July April-July April-Aug, T Chittenden, 1976 In Fall, young begin seaward migration

Morone saxatilis April-June April-June Fay et al., 1983 Frequent

March/April-June March/April-June fall, E Grosslein and Azarovitz, eds. 1982

April-July April-July summer-fall, E Wang and Kernehan, 1979

BENTHIC SPAWNERS

OFFSHORE

Clupea harengus harengus August-Oct August-Oct O Grosslein and Azarovitz, eds. 1982 Infrequent larvae pelagic; juveniles migrate inshore

Nov-Dec Nov-Dec Colton et al., 1979 Typically spawns north of Delaware

 Fall Fall Wang and Kernehan, 1979

INSHORE AND BAYS/ESTUARIES

Fundulus heteroclitus April-Sept April-Sept Abraham, 1985 Frequent eggs demersal on shells, leaves, Spartina, algal mats, pits in substrate

April-Sept April-Sept I/E/B Wang and Kernehan, 1979

Ammodytes species Dec-Feb Dec-Feb Colton et al., 1979 Frequent

Dec-April Dec-April Dec-May, E/B/I Wang and Kernehan, 1979 pelagic larvae for approximately 2-3 months; migrate to offshore areas

Pseudopleuronectes americanus Nov-June Nov-June Grimes et al, 1989 Frequent

Nov-April Nov-April Nov-June, E Wang and Kernehan, 1979 eggs demersal; larvae pelagic; late-larvae and young benthic-oriented

 

BAYS/ESTUARIES ONLY

Menidia menidia March-June March-June Fay et al., 1983 Frequent eggs attach to submerged objects; larvae are pelagic

April-Aug April-Aug April-Aug, E Wang and Kernehan, 1979

 
Table 2. Reported periods of spawning and egg and larval presence categorized by the peak spawning season for fish species 

that utilize the Middle-Atlantic Bight and surrounding areas. Spring = March through May; Summer = June through August; 

Fall = September through November; Winter = December through February. 

Fish Species Spawning Egg Larvae Reference FMP Presence Comments

Spring-Summer

Alosa sapidissima Sept-Nov, O MacKenzie et al., 1985 Frequent spawning occurs in freshwater tributaries; young migrate into offshore regions in the fall

April-July April-July Fay et al, 1983

April-June April-June April-Aug, T Wang and Kernehan, 1979

April-July April-July April-Aug, T Chittenden, 1976 In Fall, young begin seaward migration

Anchoa hespetus April-July April-July Grosslein and Azarovitz, eds. 1982 Frequent

May-Aug May-Aug fall/winter, E Wang and Kernehan, 1979 young migrate to deeper waters in fall/winter

Urophysis tenuis May-June, O Comyns and Grant, 1993 Frequent

Menidia menidia March-June March-June Fay et al., 1983 Frequent eggs attach to submerged objects; larvae are pelagic

April-Aug April-Aug April-Aug, E Wang and Kernehan, 1979

Morone saxatilis April-June April-June Fay et al., 1983 Frequent

March/April-June March/April-June fall, E Grosslein and Azarovitz, eds. 1982

April-July April-July summer-fall, E Wang and Kernehan, 1979

Stenotomus chrysops May-Aug May-Aug Grosslein and Azarovitz, eds. 1982 Yes Infrequent peak spawning in June; nearshore only; Scup eggs and larvae not reported South of N. Jersey

Menticirrhus saxatilis May-Aug May-Aug Grosslein and Azarovitz, eds. 1982 Frequent

May-Aug May-Aug May-Oct, O/E Wang and Kernehan, 1979 young emigrate into estuaries throughout the summer

May-Sept May-Sept Fahay, 1974

Cynoscion regalis May-July May-July O/I Mercer, 1989 Frequent

May-Oct May-Oct Grosslein and Azarovitz, eds. 1982 peak spawning May and June

May-Aug May-Aug May-Aug, E Wang and Kernehan, 1979 young emigrate from estuaries by mid-November

Scomber scombrus April-May, I April-June Grosslein and Azarovitz, eds. 1982 Yes Frequent

April-June April-June Colton et al., 1979; Morse, 1980 peak spawning in May

 Limanda ferruginea April-Aug April-Aug Colton et al., 1979 Frequent peak spawning spawning in May-June

Spring-Summer-Fall

Anchoa mitchilli May-Oct, E Morton, 1989 Frequent

late April-Sept late April-Sept Grosslein and Azarovitz, eds. 1982 spawn throughout salinity gradient

May-Sept May-Sept June-Oct, E Wang and Kernehan, 1979

Lophius americanus May-Nov May-Nov O/I Grosslein and Azarovitz, eds. 1982 Frequent eggs and larvae found both inshore and offshore

Fundulus heteroclitus April-Sept April-Sept Abraham, 1985 Frequent eggs demersal on shells, leaves, Spartina, algal mats, pits in substrate

April-Sept April-Sept I/E/B Wang and Kernehan, 1979

Lopholatilis chamaeleonticeps March-Sept March-Sept June-Oct, O Grosslein and Azarovitz, eds. 1982 Frequent

Peprilus triacanthus April-Dec, I/E April-Dec, I/E Grosslein and Azarovitz, eds. 1982 Yes Frequent

May-Oct May-Oct Colton et al., 1979 peak spawning in July-Aug

June-Aug, O June-Aug, O June-Sept, I/E Wang and Kernehan, 1979

Prionotus carolinus May-Nov May-Nov Colton et al., 1979 Frequent

May-Oct May-Oct May-Oct, O/I/E Wang and Kernehan, 1979 larvae collected offshore (Alantic City), and in lower estuaries

May-Nov May-Nov June - Nov, O Kendall, 1976 larvae further offshore in late fall/winter

Hippoglossina oblonga May-Oct May-Oct Colton et al., 1979 Frequent

May-Oct May-Oct May-Oct, O Grosslein and Azarovitz, eds., 1982 peak spawning in June-July; larvae become demersal ca. 8mm

Scopthalmus aquosus April-Dec April-Dec Grosslein and Azarovitz, eds. 1982 Frequent young move offshore in winter

April-Dec April-Dec Colton et al, 1979

April-Dec April-Dec May-Dec, I/E Wang and Kernehan, 1979

Summer

Tautoga onitis May-July May-July I Eklund and Targett, 1990 Frequent

Pomatomus saltatrix June-Aug June-Aug July-Aug; O Hare and Cowen, 1993 Yes Frequent Larvae restricted to MAB in July-Aug

June-Aug Jun-Aug June-Sept; O/E Pottern et al., 1989 juveniles from the southern bight spring spawn episode migrate north 

June-Aug June-Aug June-Sept, O Grosslein and Azarovitz, eds. 1982 into the MAB bays and estuaries by early summer and fall

Aug, O Kendall and Walford, 1979

June-Aug,O/I/E June-Aug,O/I/E June-Sept, O/I/E Wang and Kernehan, 1979 Most spawning offshore, some in nearshore and some lower estuaries

Summer-Fall

Engraulis eurystole July-Nov July-Nov May-Dec Grosslein and Azarovitz, eds. 1982 Infrequent spawn in polyhaline waters (offshore or lower estuary)

Urophysis chuss Aug-Nov, O Comyns and Grant, 1993 Frequent
May-Nov May-Nov May-Nov Wang and Kernehan, 1979 infrequent in offshore Delaware waters (more northern species)

Merluccius bilinearis May-Nov May-Nov Aug-Oct, O Grosslein and Azarovitz, eds. 1982 peak spawning in June-July

June-Dec June-Dec Colton et al., 1979 Frequent

May-Nov May-Nov late summer/fall, O Wang and Kernehan, 1979; Fahay, 1974 Infrequent infrequent south of Hudson Canyon

Centropristis striata April-Nov April-Nov June-Nov, I/E Able et al., 1995 Yes Frequent peak larval abundance in July-Sept or Oct 

June-Nov June-Nov I/E Grosslein and Azarovitz, eds. 1982 peak spawning in June and July

May-Oct May-Oct June-Nov, I/E Wang and Kernehan, 1979

Fall

Clupea harengus harengus August-Oct August-Oct O Grosslein and Azarovitz, eds. 1982 Infrequent larvae pelagic; juveniles migrate inshore

Nov-Dec Nov-Dec Colton et al., 1979 Typically spawns north of Delaware

 Fall Fall Wang and Kernehan, 1979

Fall-Winter

Urophysis regia Oct-May, O Comyns and Grant, 1993 Frequent

fall/winter fall/winter March/April, E/I Wang and Kernehan, 1979 peak spawning in Sept-Nov

Leiostomus xanthurus Oct-March Oct-March Dec-April, O/I/E Philips et al., 1989 Frequent some reports of spot spawning inshore

Dec-March Dec-March Dec-May, I/E Wang and Kernehan, 1979 young emigrate to estuaries during spring and migrate out in late fall

Micropogon undulatus Aug-Dec Aug-Dec Grosslein and Azarovitz, eds. 1982 peak spawning in Oct

Oct-Feb Oct-Feb Oct-July, E Wang and Kernehan, 1979 larvae and young move into low salinity nursery areas

Paralichthys dentatus Sept-Jan Sept-Jan Oct-May, I/E/O Able et al., 1990 Yes Frequent young and late larvae move inshore to estuaries

Sept-Feb Sept-Feb migrate I/E Grimes et al., 1989

Sept-Dec Sept-Dec Sept-Feb, O Grosslein and Azarovitz, eds. 1982

Sept-Feb Sept-Feb Morse, 1981

Sept-April Sept-April Colton et al., 1979

Aug-Feb Aug-Feb Oct-June, I/E Wang and Kernehan, 1979

Nov-Dec Nov-Dec Nov-Dec, I/O Kendall, 1976

Sept-Nov Sept-Nov Sept-Feb, O Smith, 1973

March-July March-July O Grosslein and Azarovitz, eds. 1982

Fall-Winter-Spring

Physis chesteri Feb-Nov, O Comyns and Grant, 1993 Frequent

late Sept-April late Sept -April Wenner, 1983 peak spawning in Dec and Jan off Virginia

Pseudopleuronectes americanus Nov-June Nov-June Grimes et al, 1989 Frequent

Nov-April Nov-April Nov-June, E Wang and Kernehan, 1979 eggs demersal; larvae pelagic; late-larvae and young benthic-oriented

Winter

Urophysis floridiana Feb-Mar, O Comyns and Grant, 1993 Infrequent

Urophysis cirrata Feb-Mar, O Comyns and Grant, 1993 Infrequent

Winter-Spring

Gadus morhua Dec-May Dec-May O Grosslein and Azarovitz, eds. 1982 ? peak spawning in Dec and Jan

Ammodytes spp. Dec-Feb Dec-Feb Colton et al., 1979 Frequent

Dec-April Dec-April Dec-May, E/B/I Wang and Kernehan, 1979 pelagic larvae for approximately 2-3 months; migrate to offshore areas

Spring and Fall

Brevoortia tyrannus Spring/Fall, I Spring/Fall, I Oct-June, E Rogers and Van Den Avyle, 1989 Frequent possibility of estuarine spawning exists

April-Oct April-Oct April-Oct/E Grosslein and Azarovitz, eds. 1982

Spring/late Fall Spring/late Fall Dec-May/E; April-Oct/EWang and Kernehan, 1979 Larvae emigrate to estuaries from offshore; young emigrate to offshore areas in autumn 

June - Dec, I/E Kendall, 1976

E Lewis, 1966 Spawning occurs during spring and fall migrations
Table 3. Species List of Mid-Atlantic Bight species examined in this study.

Scientific Name Common Name

Alosa sapidissima American Shad

Ammodytes spp. Sand Lances

Anchoa mitchilli Bay Anchovy

Anchoa hespetus Striped Anchovy

Brevoortia tyrannus Atlantic Menhaden

Centropristis striata Black Sea Bass

Clupea harengus harengus Atlantic herring

Cynoscion regalis Weakfish

Engraulis eurystole Silver Anchovy

Fundulus heteroclitus Mummichog

Gadus morhua Atlantic Cod

Hippoglossina oblonga Fourspot Flounder

Leiostomus xanthurus Spot

Limanda ferruginea Yellowtail Flounder

Lophius americanus Goosefish

Lopholatilis chamaeleonticeps Tilefish

Menidia menidia Atlantic Silverside

Menticirrhus saxatilis Northern Kingfish

Merluccius bilinearis Silver Hake

Micropogon undulatus Atlantic Croaker

Morone saxatilis Striped Bass

Paralichthys dentatus Summer Flounder

Peprilus triacanthus Butterfish

Physis chesteri Longfin Hake 

Pomatomus saltatrix Bluefish

Prionotus carolinus Northern Searobin

Pseudopleuronectes americanus Winter Flounder

Scomber scombrus Atlantic Mackerel

Scopthalmus aquosus Windowpane

Stenotomus chrysops Scup

Tautoga onitis Tautog

Urophysis cirrata

Urophysis regia Spotted Hake

Urophysis chuss Red Hake

Urophysis tenuis White Hake

Urophysis floridiana Southern Hake
The Department of the Interior

As the Nations’s principal conservation agency, the Department of the Interior has

responsibility for most of our nationally owned public lands and natural resources. 

This includes fostering sound use of our land and water resources, protecting our fish,

wildlife, and biological diversity, preserving the environmental and cultural values of

our national parks and historic places; and providing for the enjoyment of life

through outdoor recreation. The Department assesses our energy and mineral

resources and works to ensure that their development is in the best interests of all our

people by encouraging stewardship and citizen participation in their care. The

Department also has a major responsibility for American Indian reservation communities

and for people who live in island territories under U.S. Administration.

The Minerals Management Service Mission

As a bureau of the Department of the Interior, the Minerals Management Service’s

(MMS) primary responsibilities are to manage the mineral resources located on the

Nations’s Outer Continental Shelf (OCS), collect revenue from the Federal OCS and

onshore federal and Indian lands, and distribute those revenues.

Moreover, in working to meet its responsibilities, the Offshore Minerals

Management Program administers the OCS competitive leasing program and

oversees the safe and environmentally sound exploration and production of our Nation’s

offshore natural gas, oil and other mineral resources. The MMS Royalty Management

Program meets its responsibilities by entrusting the efficient, timely and accurate collection

and distribution of revenue from mineral leasing and production due to Indian tribes and

allottees, States and the U. S. Treasury

The MMS strives to fulfill its responsibilities through the general guiding principles of: (1)

being responsive to the public’s concerns and interests by maintaining a dialog with all

potentially affected parties and (2) carrying out its programs with an emphasis on working to

enhance the quality of life for all Americans by lending MMS assistance and expertise to

economic development and environmental protection.
Final Report OCS Study 2000-055

August 2000

Authors:

Jerome P.-Y. Maa and Sung. C. Kim

Virginia Institute of Marine Science

Project Manager:

Carl H. Hobbs, III

Virginia Institute of Marine Science

Prepared under MMS contract 1435-01-97-CT-30853 to

the

Virginia Institute of Marine Science

College of William & Mary

International Activities and Marine Minerals Division

Environmental Survey of Potential Sand Resource Sites Offshore

Delaware and Maryland 

Part 4: Potential Modifications to Waves Due to Dredging and

Other Oceanographic Considerations
DISCLAIMER

This report has been reviewed by the Minerals Management Service and approved for

publication. Approval does not signify that the contents necessarily reflect the views and

policies of the Service, nor does mention of trade names or commercial products constitute

endorsement or recommendation for use.
Final Report

Environmental Survey of Potential Sand Resource Sites

Offshore Delaware and Maryland

Part 4

Potential Modifications to Waves Due to Dredging and Other

Oceanographic Considerations

Jerome P.-Y. Maa and Sung. C. Kim

Principal Investigators

Virginia Institute of Marine Science

Carl H. Hobbs, III, Project Manger

Virginia Institute of Marine Science 

for 

U. S. Department of the Interior

Minerals Management Service

International Activities and Marine Minerals Division

Contract 1435-01-97-CT-30853

OCS Study 2000-055

August 2000
iii

TABLE OF CONTENTS

1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1. Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 

1.2. Scope of Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 

1.3. Methods and Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 

2. BATHYMETRY AND WAVE DATA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2. Bathymetric Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.3. Wave Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.4. Statistical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.5. Model Waves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.6. Wave Direction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3. SELECTION OF POSSIBLE WAVE TRANSFORMATION MODELS . . . . . . . . . . 25

3.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.2. SWAN Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.3. HISWA (Cycle 2). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.4. STWAVE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.5. RDE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.6. REF/DIF-S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

3.7. REF/DIF-1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

3.8. Calibration of REF/DIF-1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4. WAVE TRANSFORMATION FOR ORIGINAL BATHYMETRY . . . . . . . . . . . . . . . 39

4.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

4.2. Calculated Wave Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

4.3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

4.4. Criterion to Judge the Influence of Dredging . . . . . . . . . . . . . . . . . . . . . . . . . . 43

5. CHANGES OF WAVE ENVIRONMENTS AFTER ONE-TIME DREDGING . . . . . . 61

5.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

5.2. Wave Height Difference in Spatial Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

5.3. Changes in Breaking Wave Heights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

5.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

6. CHANGES OF WAVE ENVIRONMENTS AFTER THE ACCUMULATIVE

DREDGING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

6.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

6.2. Wave Height Difference in Spatial Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
iv

6.3. Changes in Breaking Wave Heights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

6.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

7. POSSIBLE INFLUENCE ON STORM SURGES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

7.2 SLOSH Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

7.3 Model Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

7.4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136

8. POSSIBLE IMPACT ON TIDAL CURRENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

8.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

8.2. The POM Hydrodynamic Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

8.3. Model Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

8.4. Lateral and Open Boundary Condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

8.5. Model Verification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

8.6. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144

8.7. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

9. BENTHIC STRESS ANALYSES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

9.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150

9.2. GMG Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151

9.3. GMG Model Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152

9.4. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152

10. SUMMARY AND CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 

11. REFERENCES CITED . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
v

LIST OF FIGURES

 

Fig. 1-1. Location Map of the Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Fig. 2-1. A Plot Depicts the High Density of Digital Bathymetric Data Points

(red)and the Relationship between the Model Grid Coordinates (XG, 

YG) and the Maryland State Plane Coordinates (MD-E, MD-N) . . . . . . . . . . . . 6

Fig. 2-2. Bathymetric Image of the Entire Computation Domain. The computing 

results will be displayed for a much smaller display domain marked by the 

white dashed lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Fig. 2-3. Bathymetric Contours (in meters) for the display Domain. The modeled 

extensive (24 x 106 m3) dredging sites are marked by red dashed polygons . . . . . 10

Fig. 2-4. Bathymetric Images after the One-time Dredging of 2 x 106 m3 Sand at each 

site. (a) Fenwick Shoal, and (b) Isle of Wight Shoal . . . . . . . . . . . . . . . . . . . . . . . 11

Fig. 2-5. Bathymetric Images at the Fenwick Shoal (a) Original; (b) after Dredging 

for 16 x 106 m3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Fig. 2-6. Bathymetric Images at the Isle of Wight Shoal (a) Original; (b) after 

Dredging for 8.4 x 106 m3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Fig. 2-7. Comparison of Measured Waves at Offshore ( 44009), and Near Shore 

(MD001 & MD002) from Nov. 1 to 30, 1997. (a) Hs; (b) Peak Energy 

Wave Period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Fig. 2-8. Significant Wave Height and Peak Energy Wave Period Joint 

Distribution at Station 44009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Fig. 2-9. Significant Wave Height Rose at Station 44009. The scale of 3.4% 

occurrence is plotted in the legend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Fig. 2-10. Peak Energy Wave Period Rose at Station 44009. The scale of 7.3% 

occurrence is plotted in the legend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Fig. 3-1. Comparison of REF/DIF-1 Calculated and Measured Hs at MD001 Using 

Idealized Wave Conditions at Offshore Boundary and Wave Friction 

Factor = 0.02 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Fig. 4-1. Bathymetric Image for the Display Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
vi

Fig. 4-2. Normalized Wave Height Distribution for the Original Bathymetry 

with Ho = 8 m, T = 16 s. (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . . . . . . . . . . . 44

Fig. 4-3. Normalized Wave Height Distribution for the Original Bathymetry 

with Ho = 8 m, T = 20 s. (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . . . . . . . . . . . . . 45

Fig. 4-4. Normalized Wave Height Distribution for the Original Bathymetry 

with Ho = 6 m, T = 14 s. (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . . . . . . . . . . . . . . 46

Fig. 4-5. Normalized Wave Height Distribution for the Original Bathymetry 

with Ho = 6 m, T = 16 s. (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . . . . . . . . . . . . . . 47

Fig. 4-6. Normalized Wave Height Distribution for the Original Bathymetry 

with Ho = 6 m, T = 20 s. (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . . . . . . . . . . . . . . 48

Fig. 4-7. Normalized Wave Height Distribution for the Original Bathymetry 

with Ho = 4 m, T = 10 s. (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . . . . . . . . . . . . . . 49

Fig. 4-8. Normalized Wave Height Distribution for the Original Bathymetry 

with Ho = 4 m, T = 12 s. (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . . . . . . . . . . . . . . 50

Fig. 4-9. Normalized Wave Height Distribution for the Original Bathymetry 

with Ho = 4 m, T = 14 s. (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . . . . . . . . . . . . . . 51

Fig. 4-10. Normalized Wave Height Distribution for the Original Bathymetry 

with Ho = 4 m, T = 16 s. (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . . . . . . . . . . . . . . 52

Fig. 4-11. Normalized Wave Height Distribution for the Original Bathymetry 

with Ho = 4 m, T = 20 s. (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . . . . . . . . . . . . . . 53

Fig. 4-12. Normalized Wave Height Distribution for the Original Bathymetry 

with Ho = 2 m, T = 10 s. (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . . . . . . . . . . . . . . 54

Fig. 4-13. Normalized Wave Height Distribution for the Original Bathymetry 

with Ho = 2 m, T = 12 s. (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . . . . . . . . . . . . . . 55

Fig. 4-14. Normalized Wave Height Distribution for the Original Bathymetry 

with Ho = 2 m, T = 14 s. (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . . . . . . . . . . . . . . 56

Fig. 4-15. Normalized Wave Height Distribution for the Original Bathymetry 

with Ho = 2 m, T = 16 s. (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . . . . . . . . . . . . . . 57

Fig. 4-16. Normalized Wave Height Distribution for the Original Bathymetry 
vii

with Ho = 2 m, T = 20 s. (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . . . . . . . . . . . . . . 58

Fig. 4-17. A Conceptual Diagram Showing the Criterion for Determining 

the Effect of Bathymetric Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Fig. 5-1. Calculated Changes of Wave Height (Normalized) for the One-time Dredging

with Ho = 8 m, T = 16 s coming from (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . 63

Fig. 5-2. Calculated Changes of Wave Height (Normalized) forthe One-time Dredging

with Ho = 8 m, T = 20 s coming from (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . 64

Fig. 5-3. Calculated Changes of Wave Height (Normalized) for the One-time Dredging

with Ho = 6 m, T = 14 s coming from (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . 65

Fig. 5-4. Calculated Changes of Wave Height (Normalized) for the One-time Dredging

with Ho = 6 m, T = 16 s coming from (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . 66

Fig. 5-5. Calculated Changes of Wave Height (Normalized) for the One-time Dredging

with Ho = 6 m, T = 20 s coming from (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . 67

Fig. 5-6. Calculated Changes of Wave Height (Normalized) for the One-time Dredging

with Ho = 4 m, T = 10 s coming from (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . 68

Fig. 5-7. Calculated Changes of Wave Height (Normalized) for the One-time Dredging

with Ho = 4 m, T = 12 s coming from (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . 69

Fig. 5-8. Calculated Changes of Wave Height (Normalized) for the One-time Dredging

with Ho = 4 m, T = 14 s coming from (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . 70

Fig. 5-9. Calculated Changes of Wave Height (Normalized) for the One-time Dredging

with Ho = 4 m, T = 16 s coming from (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . 71

Fig. 5-10. Calculated Changes of Wave Height (Normalized) for the One-time Dredging

with Ho = 4 m, T = 20 s coming from (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . 72

Fig. 5-11. Calculated Changes of Wave Height (Normalized) for the One-time Dredging

with Ho = 2 m, T = 10 s coming from (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . 73

Fig. 5-12. Calculated Changes of Wave Height (Normalized) for the One-time Dredging

with Ho = 2 m, T = 12 s coming from (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . 74

Fig. 5-13. Calculated Changes of Wave Height (Normalized) for the One-time Dredging

with Ho = 2 m, T = 14 s coming from (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . 75
viii

Fig. 5-14. Calculated Changes of Wave Height (Normalized) for the One-time Dredging

with Ho = 2 m, T = 16 s coming from (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . 76

Fig. 5-15. Calculated Changes of Wave Height (Normalized) for the One-time Dredging

with Ho = 2 m, T = 20 s coming from (a) ENE, (b) E, (c) ESE, and (d) SE . . . . . 77

Fig. 5-16. Comparison of Breaking Wave Heights for Original Bathymetry and after 

One-time Dredging with Ho = 8 m, T = 20 s coming from (a) ENE, (b) E, 

(c) ESE, and (d) SE. Solid black line is for the original bathymetry, and 

Dashed red line is after the modeled dredging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

Fig. 5-17. Comparison of Breaking Wave Heights for Original Bathymetry and after 

One-time Dredging with Ho = 8 m, T = 16 s coming from (a) ENE, (b) E,

 (c) ESE, and (d) SE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

Fig. 5-18. Comparison of Breaking Wave Heights for Original Bathymetry and after 

One-time Dredging with Ho = 6 m, T = 20 s coming from (a) ENE, (b) E, 

(c) ESE, and (d) SE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

Fig. 5-19. Comparison of Breaking Wave Heights for Original Bathymetry and after 

One-time Dredging with Ho = 6 m, T = 16 s coming from (a) ENE, (b) E, 

(c) ESE, and (d) SE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

Fig. 5-20. Comparison of Breaking Wave Heights for Original bathymetry and after 

One-time Dredging with Ho = 6 m, T = 14 s coming from (a) ENE, (b) E, 

(c) ESE, and (d) SE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

Fig. 5-21. Comparison of Breaking Wave Heights for Original Bathymetry and after 

One-time Dredging with Ho = 4 m, T = 20 s coming from (a) ENE, (b) E, 

(c) ESE, and (d) SE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Fig. 5-22. Comparison of Breaking Wave Heights for Original Bathymetry and after 

One-time Dredging with Ho = 4 m, T = 16 s coming from (a) ENE, (b) E, 

(c) ESE, and (d) SE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

Fig. 5-23. Comparison of Breaking Wave Heights for Original bathymetry and after 

One-time Dredging with Ho = 4 m, T = 14 s coming from (a) ENE, (b) E, 

(c) ESE, and (d) SE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

Fig. 5-24. Comparison of Breaking Wave Heights for Original Bathymetry and after 

One-time Dredging with Ho = 4 m, T = 12 s coming from (a) ENE, (b) E, 

(c) ESE, and (d) SE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
ix

Fig. 5-25. Comparison of Breaking Wave Heights for Original Bathymetry and after 

One-time Dredging with Ho = 4 m, T = 10 s coming from (a) ENE, (b) E, 

(c) ESE, and (d) SE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

Fig. 5-26. Comparison of Breaking Wave Heights for Original Bathymetry and after 

One-time Dredging with Ho = 2 m, T = 20 s coming from (a) ENE, (b) E, 

(c) ESE, and (d) SE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

Fig. 5-27. Comparison of Breaking Wave Heights for Original Bathymetry and after 

One-time Dredging with Ho = 2 m, T = 16 s coming from (a) ENE, (b) E, 

(c) ESE, and (d) SE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

Fig. 5-28. Comparison of Breaking Wave Heights for Original Bathymetry and after 

One-time Dredging with Ho = 2 m, T = 14 s coming from (a) ENE, (b) E, 

(c) ESE, and (d) SE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

Fig. 5-29. Comparison of Breaking Wave Heights for Original Bathymetry and after 

One-time Dredging with Ho = 2 m, T = 12 s coming from (a) ENE, (b) E, 

(c) ESE, and (d) SE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

Fig. 5-30. Comparison of Breaking Wave Heights for Original Bathymetry and after 

One-time Dredging with Ho = 2 m, T = 10 s coming from (a) ENE, (b) E, 

(c) ESE, and (d) SE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

Fig. 6-1. Calculated Changes of Wave Height (Normalized) for the Accumulative

Dredging with Ho = 8 m, T = 16 s coming from (a) ENE, (b) E, c) ESE, 

and (d) SE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

Fig. 6-2. Calculated Changes of Wave Height (Normalized) for the Accumulative 

Dredging with Ho = 8 m, T = 20 s coming from (a) ENE, (b) E, (c) ESE, 

and (d) SE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

Fig. 6-3. Calculated Changes of Wave Height (Normalized) for the Accumulative 

Dredging with Ho = 6 m, T = 14 s coming from (a) ENE, (b) E, (c) ESE, 

and (d) SE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

Fig. 6-4. Calculated Changes of Wave Height (Normalized) for the Accumulative 

Dredging with Ho = 6 m, T = 16 s coming from (a) ENE, (b) E, (c) ESE, 

and (d) SE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

Fig. 6-5. Calculated Changes of Wave Height (Normalized) for the Accumulative 

Dredging with Ho = 6 m, T = 20 s coming from (a) ENE, (b) E, (c) ESE, 

and (d) SE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
x

Fig. 6-6. Calculated Changes of Wave Height (Normalized) for the Accumulative 

Dredging with Ho = 4 m, T = 10 s coming from (a) ENE, (b) E, (c) ESE, 

and (d) SE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

Fig. 6-7. Calculated Changes of Wave Height (Normalized) for the Accumulative 

Dredging with Ho = 4 m, T = 12 s coming from (a) ENE, (b) E, (c) ESE, 

and (d) SE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

Fig. 6-8. Calculated Changes of Wave Height (Normalized) for the Accumulative 

Dredging with Ho = 4 m, T = 14 s coming from (a) ENE, (b) E, (c) ESE, 

and (d) SE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

Fig. 6-9. Calculated Changes of Wave Height (Normalized) for the Accumulative 

Dredging with Ho = 4 m, T = 16 s coming from (a) ENE, (b) E, (c) ESE, 

and (d) SE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

Fig. 6-10. Calculated Changes of Wave Height (Normalized) for the Accumulative 

Dredging with Ho = 4 m, T = 20 s coming from (a) ENE, (b) E, (c) ESE, 

and (d) SE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

Fig. 6-11. Calculated Changes of Wave Height (Normalized) for the Accumulative 

Dredging with Ho = 2 m, T = 10 s coming from (a) ENE, (b) E, (c) ESE, 

and (d) SE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

Fig. 6-12. Calculated Changes of Wave Height (Normalized) for the Accumulative 

Dredging with Ho = 2 m, T = 12 s coming from (a) ENE, (b) E, (c) ESE, 

and (d) SE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

Fig. 6-13. Calculated Changes of Wave Height (Normalized) for the Accumulative 

Dredging with Ho = 2 m, T = 14 s coming from (a) ENE, (b) E, (c) ESE, 

and (d) SE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

Fig. 6-14. Calculated Changes of Wave Height (Normalized) for the Accumulative 

Dredging with Ho = 2 m, T = 16 s coming from (a) ENE, (b) E, (c) ESE, 

and (d) SE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

Fig. 6-15. Calculated Changes of Wave Height (Normalized) for the Accumulative 

Dredging with Ho = 2 m, T = 20 s coming from (a) ENE, (b) E, (c) ESE, 

and (d) SE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

Fig. 6-16. Comparison of Breaking Wave Heights for Original Bathymetry and after Accumulative

Dredging with Ho = 8 m, T = 20 s . . . . . . . . . . . . . . . . . . . . . . . . . . 115
xi

Fig. 6-17. Comparison of Breaking Wave Heights for Original Bathymetry and after Accumulative

Dredging with Ho = 8 m, T = 16 s . . . . . . . . . . . . . . . . . . . . . . . . . . 116

Fig. 6-18. Comparison of Breaking Wave Heights for Original Bathymetry and after Accumulative

Dredging with Ho = 6 m, T = 20 s . . . . . . . . . . . . . . . . . . . . . . . . . . 117

Fig. 6-19. Comparison of Breaking Wave Heights for Original Bathymetry and after Accumulative

Dredging with Ho = 6 m, T = 16 s . . . . . . . . . . . . . . . . . . . . . . . . . . 118

Fig. 6-20. Comparison of Breaking Wave Heights for Original Bathymetry and after Accumulative

Dredging with Ho = 6 m, T = 14 s . . . . . . . . . . . . . . . . . . . . . . . . . . 119

Fig. 6-21. Comparison of Breaking Wave Heights for Original Bathymetry and after Accumulative

Dredging with Ho = 4 m, T = 20 s . . . . . . . . . . . . . . . . . . . . . . . . . . 120

Fig. 6-22. Comparison of Breaking Wave Heights for Original Bathymetry and after Accumulative

Dredging with Ho = 4 m, T = 16 s . . . . . . . . . . . . . . . . . . . . . . . . . . 121

Fig. 6-23. Comparison of Breaking Wave Heights for Original Bathymetry and after Accumulative

Dredging with Ho = 4 m, T = 14 s . . . . . . . . . . . . . . . . . . . . . . . . . . 122

Fig. 6-24. Comparison of Breaking Wave Heights for Original Bathymetry and after Accumulative

Dredging with Ho = 4 m, T = 20 s . . . . . . . . . . . . . . . . . . . . . . . . . . 123

Fig. 6-25. Comparison of Breaking Wave Heights for Original Bathymetry and after Accumulative

Dredging with Ho = 4 m, T = 16 s . . . . . . . . . . . . . . . . . . . . . . . . . . 124

Fig. 6-26. Comparison of Breaking Wave Heights for Original Bathymetry and after Accumulative

Dredging with Ho = 2 m, T = 20 s . . . . . . . . . . . . . . . . . . . . . . . . . . 125

Fig. 6-27. Comparison of Breaking Wave Heights for Original Bathymetry and after Accumulative

Dredging with Ho = 2 m, T = 16 s . . . . . . . . . . . . . . . . . . . . . . . . . . 126

Fig. 6-28. Comparison of Breaking Wave Heights for Original Bathymetry and after Accumulative

Dredging with Ho = 2 m, T = 14 s . . . . . . . . . . . . . . . . . . . . . . . . . . 127

Fig. 6-29. Comparison of Breaking Wave Heights for Original Bathymetry and after Accumulative

Dredging with Ho = 2 m, T = 20 s . . . . . . . . . . . . . . . . . . . . . . . . . . 128

Fig. 6-30. Comparison of Breaking Wave Heights for Original Bathymetry and after Accumulative

Dredging with Ho = 2 m, T = 16 s . . . . . . . . . . . . . . . . . . . . . . . . . . 129
xii

Fig. 7-1. Bathymetry in the SLOSH Model Computation Domain . . . . . . . . . . . . . . . . . . . 135

Fig. 7-2. Bathymetry and 10 Coastal Monitoring Stations in the SLOSH Model. Also 

shown are the modeled dredging sites at two offshore shoals . . . . . . . . . . . . . . . . . 135

Fig. 7-3. Surge Envelop from Cross-shore Track . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136

Fig. 7-4. Time Series of Coastal Surges at 10 Monitoring Stations, Cross-shore Track . . 136

Fig. 7-5. Time Series of Coastal Surge Changes by Dredging at 10 Monitoring 

Stations, Cross-shore Track . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

Fig. 7-6. Surge Envelop from Shore-parallel Track . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

Fig. 7-7. Time Series of Coastal Surges at 10 Monitoring Stations, Shore-parallel Track . 138

Fig. 7-8. Time Series of Coastal Surge Changes by Dredging At 10 Monitoring 

Stations, Shore-parallel Track . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

Fig. 8-1. Comparison of Calculated and Analyzed Tidal Elevation at Ocean City Inlet 

from May 5-15, 1985. Solid line is the Calculated, Dashed line is the Analyzed . 143

Fig. 8-2. Contours of Near-bed Tidal Current Velocity (in cm/s), Zero Contour line 

is also the Shoreline. (a) Maximum Flood; (b) maximum Ebb . . . . . . . . . . . . . . . 146

Fig. 8-3. Near-bed Tidal Current Velocity Vectors at the maximum Flood (left) and 

Maximum Ebb (right). The Shoals are also Plotted . . . . . . . . . . . . . . . . . . . . . . . . 147

Fig. 8-4. Difference (in %) in Tidal Current Caused by Dredging at the Modeled Sites 

(a) for Maximum Flood, and (b) for Maximum Ebb . . . . . . . . . . . . . . . . . . . . . . . 148

Fig. 9-1. Definition Sketch of Two-layer Wave-current Bottom Boundary Layer . . . . . . 149

Fig. 9-2. Solution Procedure for the GMG Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151

Fig. 9-3. Effect of Wave Direction on Friction Velocity. Offshore wave condition 

is T = 14 s and H = 6 m . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

Fig. 9-4. Effect of Wave Direction on the Change in Bottom Friction Velocity. 

 waves condition is T = 14 s and H = 6 m . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154

Fig. 9-5. (1). Possible Maximum Change in Bottom Friction Velocity.

Offshore wave condition is T = 10 s and H = 2 m . . . . . . . . . . . . . . . . . . . . . . . . . . 155
xiii

Fig. 9-5. (2). Possible Maximum Change in Bottom Friction Velocity. 

Offshore wave condition is T = 10 s and H = 4 m . . . . . . . . . . . . . . . . . . . . . . . . . . 155

Fig. 9-5. (3). Possible Maximum Change in Bottom Friction Velocity. 

Offshore wave condition is T = 12 s and H = 2 m . . . . . . . . . . . . . . . . . . . . . . . . . . 156

Fig. 9-5. (4). Possible Maximum Change in Bottom Friction Velocity. 

Offshore wave condition is T = 12 s and H = 4 m . . . . . . . . . . . . . . . . . . . . . . . . . . 156

Fig. 9-5.(5). Possible Maximum Change in Bottom Friction Velocity. 

Offshore wave condition is T = 14 s and H = 2 m . . . . . . . . . . . . . . . . . . . . . . . . . . 157

Fig. 9-5. (6). Possible Maximum Change in Bottom Friction Velocity. 

Offshore wave condition is T = 14 s and H = 4 m . . . . . . . . . . . . . . . . . . . . . . . . . . 157

Fig. 9-5. (7). Possible Maximum Change in Bottom Friction Velocity. 

Offshore wave condition is T = 14 s and H = 6 m . . . . . . . . . . . . . . . . . . . . . . . . . . 158

Fig. 9-5. (8). Possible Maximum Change in Bottom Friction Velocity. 

Offshore wave condition is T = 16 s and H = 2 m . . . . . . . . . . . . . . . . . . . . . . . . . . 158

Fig. 9-5. (9). Possible Maximum Change in Bottom Friction Velocity. 

Offshore wave condition is T = 16 s and H = 4 m . . . . . . . . . . . . . . . . . . . . . . . . . . 159

Fig. 9-5. (10). Possible Maximum Change in Bottom Friction Velocity. 

Offshore wave condition is T = 16 s and H = 6 m . . . . . . . . . . . . . . . . . . . . . . . . . . 159

Fig. 9-5. (11). Possible Maximum Change in Bottom Friction Velocity. 

Offshore wave condition is T = 16 s and H = 8 m . . . . . . . . . . . . . . . . . . . . . . . . . . 160

Fig. 9-5. (12). Possible Maximum Change in Bottom Friction Velocity. 

Offshore wave condition is T = 20 s and H = 2 m . . . . . . . . . . . . . . . . . . . . . . . . . . 160

Fig. 9-5. (13). Possible Maximum Change in Bottom Friction Velocity. 

Offshore wave condition is T = 20 s and H = 4 m . . . . . . . . . . . . . . . . . . . . . . . . . . 161

Fig. 9-5. (14).18. Possible Maximum Change in Bottom Friction Velocity. 

Offshore wave condition is T = 20 s and H = 6 m . . . . . . . . . . . . . . . . . . . . . . . . . . 161

Fig. 9-5. (15). Possible Maximum Change in Bottom Friction Velocity. 

Offshore wave condition is T = 20 s and H = 8 m . . . . . . . . . . . . . . . . . . . . . . . . . . 162
xiv

LIST OF TABLES

Table 2-1. Corner Coordinates for the Modeled One-time Dredging (in Maryland 

State Plane Coordinates, NAD83, Meters) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Table 2-2. Corner Coordinates for the Modeled Accumulative Dredging 

(in Maryland State Plane Coordinates, NAD83, Meters) . . . . . . . . . . . . . . . . . . . . 9

Table 2-3. The Height and Period Distribution for Waves Coming from ENE (8.600%) . . 19

Table 2-4. The Height and Period Distribution for Waves Coming from E (13.086%) . . . 19

Table 2-5. The Height and Period Distribution for Waves Coming from ESE (13.709%) . .20

Table 2-6. The Height and Period Distribution for Waves Coming from SE (14.653%) . . . 20

Table 2-7. Observed Annual Maximum Significant Waves . . . . . . . . . . . . . . . . . . . . . . . . . 21

Table 2-8. Selected Model Waves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Table 3-1. Comparison of Spectrum Wave Prediction Models . . . . . . . . . . . . . . . . . . . . . . . 29

Table 3-2. Comparison of Wave Transformation Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Table 3-3. Wave Conditions Used for Calibrating REF/DIF-1 . . . . . . . . . . . . . . . . . . . . . . . 36

Table 4-1. Selected Wave Heights and Periods for Checking Wave Transformation . . . . . . 39

Table 4-2. Non-linear Dissipation of Wave Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .42

Table 5-1. Summary of the Changes on Breaking Wave Heigh Modulation for the 

One-time Dredging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

Table 6-1. Summary of the Changes on Breaking Wave Heigh Modulation for the 

Cumulative Dredging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .113

Table 8-1. Amplitude and Epoch of Major Constituents Specified at Offshore Boundary . .144
CHAPTER I. INTRODUCTION

1.1. Objective

A well maintained beach can serve several purposes, e.g., (1) providing a public

recreational area, (2) protecting valuable properties that are located near the coastline, and (3)

reducing the rate of land loss. Thus, a great deal of effort has been devoted to understanding the

processes that change the shoreline. Among the several erosion processes, waves are the most

important because their great energy can produce severe erosion and significantly alter the

shoreline.

One can use several approaches, either separately or in combination, to maintain a beach. 

In the coastal sector near the Maryland - Delaware border, especially around Ocean City,

Maryland, the beach has been nourished with sand from inland borrow pits throughout the past

two decades. It has become increasingly difficult to find terrestrial sources of good, beach-quality

sand. The shore’s continual loss of sand to shore normal and alongshore transport processes

requires a reliable source of good quality sand for future nourishment.

Two offshore shoals, Fenwick Shoal and Isle of Wight Shoal, have been identified as

potential sources for beach-quality sand (Figure 1-1). Fenwick Shoal is approximately 10 km west

of the Maryland - Delaware border. Isle of Wight Shoal is about 8 km south of Fenwick. There is

concern that utilization of sand from these shoals may cause unwanted alterations to the shoreline,

which may be near its equilibrium state. Dredging on the shoals definitely will alter the wave

transformation processes which, depending upon the dredging plan, may induce unfavorable

consequences.
-2-

Delaware

Maryland

Isle of

Wight

Fenwick

Ocean City

N

0 20 km

Fig. 1-1. Location Map of the Study Area
-3-

1.2. Scope of Study

In order to understand the possible changes in the shoreline resulting from dredging

requires a comprehensive understanding of the wave climate, the wave transformation processes,

the possible changes of wave transformation caused by changing the bathymetry, and the

associated shoreline responses.

 

In a study of a similar problem, Maa and Hobbs (1998) concluded that if the sand taken

from the shoal they studied were limited to the order of 106 m3, the impact on wave transformation

would be less than 5% for the maximum, severe sea that occurs once every 10 to 20 years. They

concluded that the 5% change is not significant because (1) the maximum 5% change was

determined using the wave transformation model (RCPWAVE) that is know to overestimate wave

height near shore (Maa et al, in press) and (2) the direction of wave approach is critical, the

calculated maximum change statistically has a very low likelihood of occurrence. The foci of the

present study are (1) check if the possible impact caused by an one-time dredging on the order of 2

x 106 m3 of sand at each of the two shoals acceptable or not, and (2) Is the impact on wave

alterations resulting from removal of significantly more sand, on the order of 107 m3, acceptable?

1.3. Methods and Approach

In order to estimate the possible impact, a computational bathymetric grid system must be

established. This is described in Chapter 2. What kind of waves have occurred in the area is the

next subject. We analyzed wave data measured at offshore station 44009 (from 1986 -1998) and

two near shore wave stations (MD001 and MD002) in the area. The results are summarized in

Chapter 2.

A brief description of available wave models is given in Chapter 3. After selecting

REF/DIF-1 as a feasible and practical model with which to explore the possible impact, we

calibrated the model by comparing 103 cases of calculated and measured wave heights at a near

shore wave station, MD001, using the given offshore wave conditions and the bathymetric grid

described in Chapter 2.
-4-

Wave transformations for 60 selected offshore wave conditions with the original

bathymetry were calculated first. The results, in terms of wave height distributions, are presented

in Chapter 4 as background information. 

In order to check if a small amount of dredging (2 x 106 m3) at each of the two shoals is

acceptable, the same 60 offshore wave conditions are re-run with an appropriately modified

bathymetric grid. The differences for each wave condition are given in Chapter 5.

To find the possible impact of accumulated dredging at the two sites, the same 60 offshore

wave conditions were re-run with bathymetry altered to provide a total of 24.4 x 107 m3 sand. The

differences of wave height distribution caused by dredging for each wave condition are given in

Chapter 6.

The possible influence on storm surge is considered and a study using a NOAA model has

been carried out. The results are presented in Chapter 7.

In this study, we further addressed the possible change of bed shear stress between the

targeted shoals and the coast. The objective is to examine the possible influence on benthic

organisms. For this reason, we studied the possible change of tidal currents (Chapter 8) because of

the accumulative dredging. After combining with the possible change of wave height (Chapter 6),

the possible change of bed shear stress distribution is given in Chapter 9.

The conclusions of possible physical impacts caused by dredging at the two offshore shoals

are summarized in Chapter 10.
-5-

CHAPTER 2, BATHYMETRIC AND WAVE DATA

2.1. Introduction

This chapter discusses the collection and processing of bathymetric and wave data. The

main objective is to put these data in a useful format for further access and use.

2.2. Bathymetric Data

 The bathymetric data were obtained from the NOAA Data Center. The data were sorted in one

degree latitude-longitude domains and distributed in CD-ROM. The software to retrieve

bathymetric data for selected areas was also in the CD-ROM. A previously developed

FORTRAN program was used to remove the bathymetric data for lakes and inland waterways. 

Then the remaining bathymetric data were merged with shoreline data (also from NOAA) to form

a completed data file (Fig. 2-1). The original coordinates of the data were latitude and longitude,

which were transferred to Maryland State Plane Coordinate System using the CORPSCON

software from the Waterways Experiment Station, U.S. Army Corps of Engineers. The relative

locations of wave station 44009, MD001, and MD002 are also indicated in Fig. 2-1.

In order to obtain a computation grid with evenly spaced grid size for wave and tidal

current calculation, a new grid system (marked by the white box with 44.970 km x 67.560 km in

XG and YG directions, respectively) was created (Fig. 2-1). The origin of this new grid is also

shown in Fig. 2-1. A FORTRAN computer program previously developed for other MMS

projects was used again to generate a computation grid for wave transformation computations. 

Figure 2-2 shows the bathymetry for this computing domain. Notice that the coordinates of this

grid are specified as XG and YG. The coordinates of the origin of this computation domain are

E561.000 km and N61.000 km in NAD83 Maryland State Plane Coordinate System, and YG is

rotated 4.2 degrees from the Maryland State Plane Coordinates’ North coordinate (Fig. 2-1).

 

This computation domain has a grid size of 30 m in the XG direction and 60 m in the YG

direction, respectively, i.e., 1500 and 1127 grid points in these two directions, respectively. The

small grid size is needed for having a better simulation of wave transformation, especially for
-6-

XG

Fig. 2-1. A plot depicts the high density of Digital Bathymetric Data Points (red) and the 

 Relationship between the Grid Coordinates (XG, YG) and the Maryland State Plane 

 Coordinates(MD_E, MD_N)
-7-

Fig. 2-2. Bathymetric Image of the Entrie Computing Domain. The Computing Results will be 

 displayed in a much smaller Display Domain marked by the white Dashed lines. 
-8-

 wave diffraction. The large computation domain is necessary to minimize the possible inaccurate

boundary effects specified at the lateral boundaries for wave transformation simulation. For

displaying the computation results, however, a much smaller domain is sufficient, hereafter called

the display domain (Fig. 2-3), and marked as the dashed lines in Fig. 2-2. Detailed water depth

contours at the vicinity of Fenwick Shoal and Isle of Wight Shoal are given in the display domain. 

Two possible scenarios for dredging were considered. The first plan is targeted for a total

removal of 4 x 106 m3 of sand from the two shoals, i.e., 2 x 106 m3 of sand from each shoal. The

purpose is to determine if a single harvest of the quantity of sand would result in an unacceptable

impact. In this scenario, the dredging would be the same on both Fenwick and Isle of Wight

Shoals. The NAD83 Maryland State Plane Coordinates for the four corners of the two modeled

dredging sites are given in Table 2-1. The resulted bathymetry is depicted in Fig. 2-4. 

The second scenario is to dredge at the two shoals with a possible maximum sand removal

(i.e., on the order of 20 x 106 m3). This scenario is for the possible accumulative sand removal

from the two shoals for about 10 years. The two possible dredging sites are drawn in Fig. 2-3. 

Site A is on Fenwick Shoal and site B is on Isle of Wight Shoal. The NAD83 Maryland State

Plane Coordinates for the four corners of the two possible dredging sites are given in Table 2-2. 

The selection of these two dredging areas was rather arbitrary and was based on the geometry. It

is suggested that it is better to flatten a shoal rather than to create a big hole on an otherwise flat

area. The two modeled dredging areas will give a total of 24.4 x 106 m3 sand if the dredging depth

is selected as a uniform 3 meters within the domain. The comparison of bathymetry before and

after dredging for the second plan is shown in Fig. 2-5 and 2-6. 
-9-

Table 2-1. Corner Coordinates for the Modeled One-time Dredging. 

(Maryland State Plane Coordinates, NAD83, meters)

-----------------------------------------------------------------

 Item Site A Site B

-----------------------------------------------------------------

Area 0.675 x 106 m2 0.675 x 106 m2

Volume 2 x 106 m3 2 x 106 m3

E (m) 579,600.250 580,301.938 

 N (m) 89,213.5391 81,892.4922 

 E (m) 580,469.813 581,458.563

 N (m) 90,322.8359 82,760.1094 

 E (m) 580,899.563 581,739.938

 N (m) 90,030.5781 82,358.4297

 E (m) 580,013.063 580,630.188

 N (m) 88,882.4141 81,447.2500

Table 2-2. Corner Coordinates for the Modeled Accumulative Dredging 

(Maryland State Plane Coordinates, NAD83, meters).

-----------------------------------------------------------------

 Item Site A Site B

-----------------------------------------------------------------

Area 5.36 x 106 m2 2.82 x 106 m2

Volume 16 x 106 m3 8.4 x 106 m3

E (m) 578,406.0000 580071.0630 

 N (m) 88,298.5469 81889.3906

 E (m) 581,607.3750 582308.5630 

 N (m) 92,705.9219 83820.7109

 E (m) 582,123.0000 582634.3130 

 N (m) 91,535.0156 83205.1953

 E (m) 579,939.2500 580428.6250 

 N (m) 88,286.2266 80750.1406 

-----------------------------------------------------------------
-10-

Fig. 2-3. Bathymetry Contours (in meter) for the Display Domain. The Modeled Extensive (24.4

x106 m3 ) Dredging Sites are Marked by Red Dash Polygons.
-11-

Fig. 2.4. Bathymetric Images after the One-time Dredging for 2x 106 m3 (a) Fenwick Shoal,

and (b) Isle of Wight Shoal.
-12-

Fig. 2.5. Bathymetric Images at Fenwick Shoal. (a) original, (b)after the accumulative

Dredging for 16 x 106 m3. 
-13-

XG (km)

a

b

Depth (m)

Fig. 2-6. Bathymetric Images at the Isle of Wight Shoal. (a) Original, (b) after Dredging for 8.4 

 x106 m3.
-14-

2.3. Wave Data

The National Data Buoy Center (NDBC) has a moored buoy station, 44009 (Latitude

38°27'49"N and Longitude 74°42'07"W), located about 40 km offshore at the Ocean City with a

water depth of 28 m. This station has collected wave height information since May 1986 and

wave directional wave spectrum information since 1993. Because of the sites proximity (Fig. 2-1),

these wave measurements are the most important wave information used in this study. 

At a near shore site, the U.S. Army Corps of Engineers had one wave station, MD001,

north of Ocean City, Maryland (Lat. 38°24'00"N, Long. 75°30'00"W) from Oct. 1993 to Jan.

1998. The Corps also has another station, MD002 (Lat. 38°20'24"N and Long. 75°04'12"W),

south of Ocean City. The water depth at both stations is 9 m. Their locations also are given in

Fig. 2-1.

 The recent wave measurement system at station 44009 used an accelerometer to record the

buoy's heave, pitch, and roll motions. A NDBC onboard Wave Data Analyzer computed the

wave spectral information from the time series of buoy motion and transmitted the results to the

Stennis Space Center in Mississippi for further analysis and quality assurance. The approach

proposed by Longguet-Higgins et al. (1963) was used to obtain the wave spectral data. The

overall accuracy of all systems for significant wave height, wave period, and wave direction is 0.2

m (or 5%), 1.0 s., and ±5o, respectively (Meindl and Hamilton 1992). All processed data were

achieved in National Oceanic Data Center (NODC) in Washington, D.C. using a special ASCII

format. These data were stored in CD-ROM and are easily retrieved.

Wave measurements at the two near shore stations MD001 and MD002 were carried out

with a pressure gauge to measure the surface displacements and a current meter to measure the two

horizontal velocity components. Based on the linear wave theory, the directional wave spectrum

can be calculated.

 

In this study, wave data at the two near shore stations are mainly used for checking the

accuracy of calculated wave heights using the wave information (significant wave height, peak
-15-

wave period, and peak wave direction) specified at the offshore boundary where station 44009 is

located. For example, wave records from Nov. 1 to Nov. 30, 1997 at both stations 44009,

MD001, and MD002 are plotted in Fig. 2-7 to show the differences in wave conditions among the

three stations. The comparison reveals that the significant wave heights (blue line and green line)

at both the near shore stations are very close, at least for the November 1997. Only a minor

difference (about 10 cm) observed in late November. The peak wave periods at the two near

shore stations are also very close, except a short period of time in late November, 1997. Notice

that in this selected period (November, 1997) the offshore wave height varies from less than 0.5 m

to 5 m, which is sufficient for the purpose of calibrating the wave transformation model selected.

2.4. Statistical Analysis

 The joint distribution of significant wave height and peak energy wave period for station 44009

(Fig. 2-8) reveals that the most frequently occurring wave has a period of 9 seconds and significant

wave height of 0.6 meters. Wave height greater than 6 m is rare, only occurring few times during

the entire 13 years of observation (1986-1998) with a total duration of 46 hours which is about

0.04 percent. The total duration of significant wave height greater than 5 meters increases to 94

hours (about 0.08 percent). 

The available directional wave data (1993-1997) indicates that waves mainly come from

the following 7 directions: SSE, SE, ESE, E, ENE, NE, and NNE (Figs. 2-9 and 2-10). A few

large waves came from NNW and NW, which are ignored in this study because these are

offshore-going waves.

Assuming the available wave directional information represents the true wave condition

distribution, we can regroup the waves into selected bins for future analyses. For the practical and

feasible computational purposes, wave height distribution is sorted from 0.25 to 8 m with an

interval of 0.5 m, wave period distribution is sorted from 3 to 20 s with an interval of 2 s, and wave

direction distribution is sorted for the 16 major directions. Using the above specified condition to

sort the waves, a table of wave occurrence for the major 4 directions is given in
-16-

Fig. 2-7. Comparison of Measured Waves at Offshore (44009) and Nearshore (MD001 and

MD002) from Nov. 1 to 30, 1997. (a) Hs; (b) peak energy wave period.
-17-

Table 2-3 to Table 2-6. Notice that wave conditions in these four directions counted for more than

50 percent of all the wave conditions. 

Table 2-7 shows the maximum significant wave heights that were observed at station

44009 from 1986 to 1998. The recorded maximum significant wave height (7.6 m with a peak

wave period of 16.7 seconds, occurred on 1/04/92) during the 13 observation years suggests that

the possible most severe sea probably can have a significant wave height of 8 m and wave period

of 20 s. Notice that among the observed maximum Hs, only one is possibly induced by a hurricane

(August 16, 1995). The famous “Halloween Storm,” also known as the “Perfect Storm,” in late

October 1991 did not produce the largest significant wave height in 1991. This may be because of

the different wind field location in the Atlantic Ocean. Along Virginia’s coast, the “Halloween

Storm” did produce the largest wave at station CHLV2 in 1991.

2.5. Model Waves

 Based on the measurements at station 44009 and Table 2-1, we have identified the

following four wave heights (2m, 4,m, 6m, and 8m) and five wave periods (10s, 12s, 14s, 16s and

20s) that require analysis for possible changes in wave transformation due to dredging at the

Fenwich Shoal and Isle of Wight Shoal. Short wave periods (less than 10 s) are excluded because

they were not affected by the shoals. This selection covers the majority of the wave conditions

that can be happened at the project site. We did not run a particular measured wave condition (for

example, the most frequently occurring waves, T = 9 s, H = 0.5 m) because (1) the period is too

short to be affected by the modeled dredging, and (2) we believe through checking the selected 60

wave conditions, all possible waves that might be affected by dredging were considered. Because

of the stochastic nature of waves, we should examine all possible wave conditions, and that is why

we selected 60 waves to represent the whole figure.

2.6. Wave Direction

 The available directional wave data indicate that large waves can come from the following

seven directions: NNE, NE, ENE, E, ESE, SE, and SSE (Fig. 2-9). The orientation of the coast

line at the Maryland and Delaware border is also given in the figure. Large waves coming from
-18-

NNE to ENE are mainly caused by northeasters. Long period waves coming from these two

directions, however, are relatively rare (Fig. 2-10). Most of the waves in NNE and NE are less

than 8 sec. Long period waves mainly come from ENE, E, ESE, and SE because of the long

fetch. Thus, waves coming from ENE, E, ESE, and SE are selected as the important wave

directions because of the possible large wave height and long period. Large and long waves

coming from SSE direction must be induced by hurricane and the chance of this is relatively small,

and thus, is not selected for study. 
-19-

Table 2-3. The Height and Period Distribution for Waves Coming from ENE (8.600%)

----------------------------------------------------------------------------------------------------------------------

 H(m) 4s 6s 8s 10s 12s 14s 16s >17s

----------------------------------------------------------------------------------------------------------------------

0.5 0.1638 0.3545 0.3009 0.3515 0.3277 0.0596 0.0060 0.0 

1.0 0.3128 0.9294 0.4737 0.5898 0.4975 0.0089 0.0 0.0 

1.5 0.0834 0.5898 0.4290 0.5362 0.3634 0.0119 0.0 0.0 

2.0 0.0 0.3426 0.3098 0.2800 0.1728 0.0149 0.0089 0.0 

2.5 0.0 0.0715 0.1430 0.0983 0.0626 0.0119 0.0 0.0 

3.0 0.0 0.0060 0.1370 0.1341 0.0626 0.0 0.0 0.0 

3.5 0.0 0.0030 0.0715 0.0745 0.0387 0.0 0.0 0.0 

4.0 0.0 0.0 0.0298 0.0298 0.0060 0.0 0.0 0.0 

4.5 0.0 0.0 0.0030 0.0268 0.0030 0.0 0.0 0.0 

5.0 0.0 0.0 0.0030 0.0060 0.0030 0.0 0.0 0.0 

5.5 0.0 0.0 0.0 0.0030 0.0030 0.0 0.0 0.0 

6.0 0.0 0.0 0.0 0.0119 0.0 0.0 0.0 0.0 

6.5 0.0 0.0 0.0 0.0 0.0089 0.0 0.0 0.0 

7.0 0.0 0.0 0.0 0.0 0.0030 0.0 0.0 0.0 

 -----------------------------------------------------

 E 2.1419 1.5522 0.1072 0.0149 0.0

---------------------------------------------------------------------------------------------------------------------------

Table 2-4. The Height and Period Distribution for Waves Coming from E (13.086%)

----------------------------------------------------------------------------------------------------------------------

 H(m) 4s 6s 8s 10s 12s 14s 16s >17s

---------------------------------------------------------------------------------------------------------------------- 

0.5 0.1013 0.4051 1.0962 0.9413 0.8073 0.2324 0.0447 0.0 

 1.0 0.2145 0.7328 1.1439 1.2333 0.9682 0.0923 0.0149 0.0 

 1.5 0.0238 0.3664 0.4647 0.6792 0.6643 0.1043 0.0 0.0 

 2.0 0.0030 0.1430 0.2353 0.4022 0.3485 0.2204 0.0119 0.0 

 2.5 0.0 0.0268 0.1698 0.2324 0.1549 0.0655 0.0209 0.0 

 3.0 0.0 0.0030 0.0923 0.1102 0.1341 0.0060 0.0 0.0 

 3.5 0.0 0.0 0.0238 0.1192 0.0715 0.0 0.0 0.0 

 4.0 0.0 0.0 0.0119 0.0298 0.0298 0.0 0.0 0.0 

 4.5 0.0 0.0 0.0 0.0149 0.0060 0.0030 0.0 0.0 

 5.0 0.0 0.0 0.0 0.0 0.0000 0.0060 0.0 0.0 

 5.5 0.0 0.0 0.0 0.0 0.0030 0.0 0.0 0.0 

 6.0 0.0 0.0 0.0 0.0 0.0030 0.0 0.0 0.0 

 6.5 0.0 0.0 0.0 0.0 0.0030 0.0 0.0 0.0 

 7.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

 -----------------------------------------------------

 E 3.7625 3.1936 0.7299 0.0924 0.0

----------------------------------------------------------------------------------------------------------------------
-20-

Table 2-5. The Height and Period Distribution for Waves Coming from ESE (13.709%)

----------------------------------------------------------------------------------------------------------------------

 H(m) 4s 6s 8s 10s 12s 14s 16s >17s

----------------------------------------------------------------------------------------------------------------------

 0.5 0.0894 0.6792 2.3236 1.8499 0.9145 0.3575 0.0745 0.0 

 1.0 0.2234 0.6286 1.3554 1.1797 0.8996 0.1370 0.0298 0.0 

 1.5 0.0179 0.1966 0.3366 0.4468 0.4468 0.0923 0.0089 0.0 

 2.0 0.0 0.1072 0.2026 0.1817 0.1311 0.0566 0.003 0.0 

 2.5 0.0 0.0357 0.1102 0.0834 0.1013 0.0268 0.0 0.0 

 3.0 0.0 0.0030 0.0298 0.0477 0.0566 0.0030 0.0 0.0 

 3.5 0.0 0.0030 0.0030 0.0209 0.0417 0.0089 0.003 0.0 

 4.0 0.0 0.0 0.0 0.0149 0.0357 0.0060 0.0 0.0 

 4.5 0.0 0.0 0.0 0.0089 0.0387 0.0089 0.0 0.0 

 5.0 0.0 0.0 0.0 0.0030 0.0030 0.0089 0.0 0.0 

 5.5 0.0 0.0 0.0 0.0 0.0060 0.0 0.0 0.0 

 6.0 0.0 0.0 0.0 0.0 0.0030 0.0 0.0 0.0 

 6.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

 7.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

 -----------------------------------------------------

 E 3.8369 2.678 0.7059 0.1192 0

----------------------------------------------------------------------------------------------------------------------

 Table 2-6. The Height and Period Distribution for Waves Coming from SE (14.653%)

----------------------------------------------------------------------------------------------------------------------

 H(m) 4s 6s 8s 10s 12s 14s 16s >17s

----------------------------------------------------------------------------------------------------------------------

 0.5 0.1221 1.2154 2.5172 1.6563 0.5392 0.2085 0.0328 0.0 

 1.0 0.2383 0.7596 1.8142 1.0456 0.7239 0.0685 0.0209 0.0 

 1.5 0.0179 0.2413 0.6703 0.4141 0.4319 0.1430 0.0209 0.0 

 2.0 0.0030 0.0923 0.3098 0.2949 0.1400 0.0626 0.0238 0.0 

 2.5 0.0 0.0209 0.0953 0.1400 0.0804 0.0328 0.0209 0.0030

 3.0 0.0 0.0030 0.0179 0.0417 0.0655 0.0149 0.0030 0.0030

 3.5 0.0 0.0030 0.0060 0.0268 0.0864 0.0149 0.0030 0.0 

 4.0 0.0 0.0 0.0030 0.0030 0.0417 0.0328 0.0 0.0 

 4.5 0.0 0.0 0.0060 0.0030 0.0179 0.0060 0.0 0.0 

 5.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

 5.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

 6.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

 -----------------------------------------------------

 E 3.6254 2.1269 0.584 0.1253 0.0060

----------------------------------------------------------------------------------------------------------------------
-21-

Table 2-7. Observed Annual Maximum Significant Waves

 

———————————————————————————————————————

Date Time H_significant T_Peak 

 (m) (sec)

 

———————————————————————————————————————

12/03/86 08:00 4.7 12.5

 01/02/87 08:00 5.9 11.1

 4/08/88 04:00 4.3 9.1

 2/24/89 17:00 5.4 11.1

10/26/90 18:00 4.6 10.0

11/10/91 03:00 4.9 9.1

 01/04/92 11:00 7.6 16.7

 10/27/93 12:50 4.6 11.1

 12/23/94 19:50 5.4 12.5

 8/16/95 10:50 4.2 14.3

 01/08/96 04:00 7.0 11.1

 11/08/97 06:00 5.2 11.1

 2/05/98 16:00 7.4 12.5

 

———————————————————————————————————————

Table 2-8. Selected Model Waves

———————————————————————————————————————

 Wave Height Wave Period Remark

 (m) (sec)

———————————————————————————————————————

 8.0 20 The most severe sea

 6.0 16 Severe sea

 4.0 14 Rough sea

 2.0 12 Northeaster

 10 

———————————————————————————————————————
-22-
-23-

500

4000

3000

1000

200

100

2 0

2000

5 0

1 0

1

1 0

1 0

1

Fig. 2-8. Significant Wave Height and Peak Energy Wave Period Joint Distribution at Sation 44009.
-24-

Fig. 2-9. Significant Wave Height Rose at Station 44009. The scale of 3.4% occurrence is

plotted in the legend.
-25-

Fig. 2-10. Peak Energy Wave Period Rose at Station 44009. The scale of 7.3% occurrence is

plotted in the legend.
-25-

MN

Mt

%

Mcx N

Mx

%

Mc

y

N

My

%

Mcù N

Mù

%

Mcè N

Mè

' S/ó (3-1)

CHAPTER 3, SELECTION OF WAVE MODELS

3.1. Introduction

There are many numerical models for simulating water waves. In general, they can be divided

into two categories: (1) Wave hindcast/prediction models (e.g., SWAN, HISWAP, NSW in Mike 21,

and STWAVE) and (2) Wave transformation models (e.g., RCPWAVE, REF/DIF-1, REF/DIF-S,

RDES, and EMS module in Mike 21). Models in the first category are designed for wave predictions. 

Although they are intended to provide accurate predictions at near shore areas by including some of the

wave transformation processes (e.g., shoaling, refraction, bottom friction), they cannot included all

(e.g., diffraction, reflection, resonance), and thus, the accuracy of these models is limited in the near

shore. Models in the second category are designed to simulate wave transformation processes, and

thus, they don’t have the capability to simulate wave growth, white capping, and wave-wave

interactions. Thus, the use of models in the second category is limited to the conditions in which wave

growth is not important, i.e., the wind is not strong or the study domain is not large enough to produce

significant wave growth.

 

Even in the second category, not all the models have the same capability. For example,

REF/DIF-1 and RCPWAVE deal with a much simple conditions (monochromatic waves, no wave

reflection, and weak diffraction) but with an excellent computing efficiency. Others deal with all the five

major wave transformation processes but at the cost of high computing time. A preprint of comparison

of six selected wave transformation models (Maa et al., in press) is given in Appendix I. The following

are brief descriptions of each available model. The key features are summarized in Tables 3-1 and 3-2.

3.2. SWAN

The SWAN (Simulating WAves Nearshore) model computes random, short-crested,

wind-generated waves in coastal regions and inland waters (Booij et al. 1996; Holthuijsen et al. 1997). 

This model solves the following equation:
-26-

N(t, x, y, è, ù ) ' E(t, x, y, è, ù )/(ù & k

.V) (3-2)

with

where N is the spectral action density (energy density, E, divided by absolute frequency, ù ), t is time, x

and y are two horizontal directions, è is spectral wave direction, cx, and cy are energy propagation

speed in x and y directions, respectively, cè and cù are energy propagation speed in è and ù domain,

respectively, ó = [gk tanh (kd)]1/2 is the intrinsic frequency, g is the gravitational acceleration, k is wave

number, d is water depth, and S is the summation of energy source and sink terms representing the

effects of wind energy input, Si, bottom friction, Sf, white capping, Sc, breaking dissipation, Sb, as well

as wave-wave interactions, Sw. Details of each source and sink terms are not presented here, but it

would be an important item that affect the performance of SWAN model. Although this equation is a

first order partial differential equation, there are five variables (t, x, y, è, ù ) to deal with, and thus, it is

very time consuming.

The current SWAN model is Cycle 2 with version 30.62. It accounts for the following physic

processes: (1) Wave propagation in time and space, (2) Shoaling, (3) Refraction due to current and

depth, (4) frequency shifting due to currents and non-stationary depth, (5) Wave generation by wind,

(6) Three- and four-wave interactions, (7) White capping, bottom friction, and depth-induced breaking. 

SWAN computations can be made on an evenly spaced Cartesian grid system or a curvilinear grid

system. Because it is an energy redistribution approach, the grid size can be large, on the order of half

wave length (L/2). Even with this relatively large grid size, however, the computing time is still

formidably long because of the complexity of Eq. 1. For this reason, we can not afford to use it at this

time.

A major drawback of this model is the lack of capability to resolve wave diffraction process

(Kaihatu et al., 1998). Under the first category of wave model, however, SWAN is the most

advanced and up-to-date model mainly because of the inclusion of wave evolution, and most accurate

source/sink terms. The question of “How significant is the limitation caused by not having the capability

to include wave diffraction,” needs more study. Also the relevance and significance of each source and

sink term should be examined and compared with other similar models (e.g., HISWA, STWAVE). 
-27-

mn(è) '

m

4

0

ù

n N(ù ,è) dù (3-3)

Mmo

Mt

%

Mc

(

x mo

Mx

%

Mc

(

y mo

My

%

Mc

(

è mo

Mè

' So

(3-4)

Mm1

Mt

%

Mcx m1

Mx

%

Mcy m1

My

%

Mcè m1

Mè

' c

(

ù mo

% S1

 (3-5)

3.3. HISWA (Cycle 2)

Unlike the SWAN model, HISWA uses a parametricization process on the frequency domain

to reduce the huge computing time required for SWAN model (Holthuijsen et al., 1997). The

parametricization is formulated in the zero and first order of moment of spectrum in each spectral

direction. Let the n-th order of moment of spectrum defined as 

Integrate Eq. 1 in frequency domain, one will get the evolution equation for the zero-order moment of

the action density spectrum.

where cx

*

 and cy

*

 are the propagation speed of zero-order moment in x and y directions, respectively,

cè

*

 is the propagation speed of zero-order moment in the direction domain, So is the sum of generation

and dissipation of mo. Notice that cx

*

, cy

*

, and cè

*

 can only be estimated at the mean frequency.

Multiple ù and Eq. 1, then integrate in the frequency domain and one will get the evolution

equation for the first-order moment of the action density spectrum.

where cx

+

 and cy

+

 are the propagation speeds of first-order moment in x and y directions, respectively. 

cè

+

 is the propagation speed of first-order moment in the direction domain; cù

*mo

 represents the effect

of time variations in currents and depth on the mean frequency; and S1 is the sum of generation and

dissipation of m1. Similarly, cx

+

, cy

+

, and cè

+

 can only be estimated at the mean frequency.
-28-

c

gx

ME(ù ,è)

Mx

% c

gy

ME(ù ,è)

My

' S (3-6)

Solving Eqs. 4 and 5 is the main body of the HISWA (Cycle 2) model: Inasmuch as the

equation use the central frequency propagation speed to represent all frequency bands, errors can result

if there is more than one energy peak in the wave spectrum. The positive side of this approach is that

the computer time is significantly reduced, compared with that of the SWAN model. The HISWA

model also suffers the same drawback that it cannot address wave diffraction accurately. 

3.4. STWAVE

The STWAVE model was developed on a series of studies carried out by Resio (1987, 1988)

and Resio and Perrie (1989). The first two characters, “ST,” stand for the steady state, and thus, there

is no local time derivative term in Eq. 6. For this reason, this model does not provide information on

wave evolution. It only provides wave condition under the fully developed condition. Two important

features of this model are (1) a depth independent equilibrium range of a wind wave spectrum in

shallow water and (2) a limitation on the growth of spectral peak frequency. In a coordinate system

moving with the group velocity of the spectral peak, the governing equation for the redistribution of the

spectrum energy is approximated as

where E(ù ,è) is the energy spectrum in frequency and direction space. Here, S is the sum of source

and sink terms including shoaling, refraction, wind energy input, wave-wave interactions, bottom friction

loss, and wave breaking. 

As the waves propagate toward the coast with all the effects from source and sink terms, Eq. 6

gives energy spectra at each grid point. Because of the assumption of steady state as well as the

moving with the spectral center, the computational speed of STWAVE is fast. The accuracy,

compared with other similar models, is not clearly documented at this time yet.
-29-

Table 3-1. Comparison of Wave Prediction Models

___________________________________________________________

 Model SWAN HISWA (cycle2) STWAVE 

------------------------------------------------------------------------------------------

 Wave direction spreading +180o--180o +180o--180o +60o--60o

 Multiple peak frequency Yes No No

Wave growth Yes Yes No

Shoaling Yes Yes Yes

Diffraction not having an accurate approximation yet 

 Reflection No No No

 Resonance No No No

 Grid size # L/2 # L/2 # L/2

 Bottom friction Yes Yes yes

 White Cap Yes Yes Yes

 Current Effect Yes No No

--------------------------------------------------------------------------------------------------------

Notice that the directional spread is limited in the STWAVE model, wave energy only

can transferred within ±60 degrees from the main direction. A full spectrum model, however, should

allow energy transfer for ±180 degrees.

Although all the aforementioned models have the source/sink terms specified in the energy

distribution equation, they do not necessarily use the same function for each possible source (e.g., wind

energy input) or sink (e.g., white capping). The different selection may affect their accuracy.

We foresee that the differences among the three above models should not be significant for a

narrow band, fully developed wave spectrum. For a spectrum with multiple peaks and developing sea,

the SWAN model should be a better model. The critical issue is that a comprehensive comparison
-30-

M

2ö

Mx

2

%

M

2ö

My

2

%

e0

h

Mh

Mx

Mö

Mx

%

Mh

My

Mö

My

% k

2(1 % ø %

ifd

nó

)ö ' 0 (3-7)

should be carried out first to understand the accuracy of the models, so that each model will be used in

its best application

3.5. RDE

With the previous support from MMS, we developed a wave transformation model by solving

the Elliptic governing equation directly (RDE, Maa and Hwung, 1997). This model provides accurate

information on wave height and direction distribution, but with a cost of long computing time (when

compared with REF/DIF-1 or RCPWAVE). The computing speed actually is better than most of the

models that solves the elliptic equation (Maa et al., in press).

This model solves the extended mild slope equation given by Massel (1995): 

where ö is the velocity potential function for a simple harmonic wave flow, eo is a function affected by

wave number (k = 2ð/L) and local water depth, h, L is local wave length, ø is a correction term for

steep bed slope and bed curvature, i =(-1)1/2, fd is a friction coefficient, ó = 2ð/T is wave frequency, n=

0.5[1+ 2kh/sinh(2kh)], Mh/Mx and Mh/My are bottom slopes in the x and y directions, respectively, and x

and y are the two horizontal coordinates. 

As mentioned before, in addition to simulate wave shoaling, refraction, and bottom energy loss,

the RDE is capable of simulating strong wave diffraction and wave reflection. The last two features

may not be needed for studies along an open coast, but the accurate wave direction information is a

baseline for comparison with other models. 

Inasmuch as the model lacks a method for incorporating wave-wave interaction in Eq. 6, wind

energy input and white capping energy loss cannot be included in this kind of model even though the

calculation of each frequency band can be performed separately and then combined together to study

spectrum transformation. This is because wind energy input and white capping energy loss mainly

occur in the high frequency domain, and only through wave-wave interactions, the energy can be

transferred across each frequency band.
-31-

3.6. REF/DIF-S

Using a parabolic approximation (Radder, 1979) to simplify Eq. 6, Kirby and Dalrymple

(1991, 1994) presented the REF/DIF-1 model for monochromatic waves. This model, however, is

capable of simulating only wave shoaling, refraction, and weak diffraction. Based on the same principle

as monochromatic waves, this model cuts the wave spectrum into many frequency bands and

determines a representative wave height for each band. Kirby and Ozkan (1994) then computed wave

refraction and diffraction for each component band and re-constructed the wave spectrum after all

frequency bands were calculated. For this reason, wave-wave interactions and wind energy input

cannot be incorporated in REF/DIF-S. Nevertheless, a model to simulate part of the spectrum wave

transformation was introduced as REF/DIF-S.

The above information clearly indicates that REF/DIF-S, and RDE are mainly wave

transformation models with no capability to simulate wave growth. On the other hand, SWAN,

HISWA (cycle 2), and STWAVE are mainly wave prediction models with limited capability to simulate

wave transformation. They all are capable of giving wave spectra in coastal seas.

Since there are other simple wave transformation models available (i.e., REF/DIF-1 and

RCPWAVE), it would be worthwhile to investigate wheather or not these simple models can do the

job reasonably well. In our previous study (Maa et al., in press), we found that overall performance of

RCPWAVE model is not accurate, and thus, discarded from this study. Although REF/DIF-1 is not

good in modeling wave direction, its accuracy in wave height calculation is excellent. Because a simple

model (i.e., REF/DIF-1) does not require tremendous computing time, it allows us to model many

wave conditions. For this reason, we carried out an experiment to check the applicability of REF/DIF1. Before the experiment, however, a briefing of REF/DIF-1 is given first.

3.7. REF/DIF-1

Radder (1979) developed a parabolic approximation of Eq. 3 which had several advantages

over the original elliptic equation. First, the down-wave end boundary conditions is not needed for a

parabolic equation. This implies the REF/DIF-1 model cannot simulate a process that has reflected
-32-

waves. Second, the computing efficiency is high for a parabolic equation, and the required grid size can

be relatively large (less than one-fifth-wave length). For these reasons, the parabolic approximation of

Eq. 3 has prevailed for simulating wave transformation on open coasts, where wave reflection is

negligibly small and only weak diffraction exists. 

A drawback of parabolic approximation is that the wave propagation direction can not deviate

too much from the assumed direction (usually the x-axis of the grid system). When they developed the

model REF/DIF-1, Dalrymple and Kirby (1991) had a special technique to insure that the model is

stable if the calculated wave angle is less than 60 degrees off from the x-axis.

Three important advantages of this model that deserve mention are (1) the fast computing speed

because of the full implicit scheme, (2) the high stability for waves propagating less than 60 degrees off

the x-axis, (3) the inclusion of various type of energy loss (e.g., bottom friction) as well as tidal current

effects on wave transformation, and (4) a choice of weak non-linear wave models.

 

Two important disadvantages of using this model are (1) the calculated wave direction is not

correct, and therefore, not usable, (2) it only deals with regular monochromatic waves.

In summary, we selected using the REF/DIF-1 model over others because of the following

reasons:

1. The spectrum models used for wave prediction do not have the capability to simulate combined

wave diffraction and refraction, which are the major effects that should be considered for our

study: dredging at shoals.

2. The spectrum model that can simulate combined wave diffraction and refraction, i.e., REF/DIF-S,

actually runs REF/DIF-1 many times by considering the major contribution from the major

direction, period, and wave heights with minor contributions from other frequencies, directions,

and heights. Instead of doing that for few selected wave conditions, we broke it down by doing

60 wave conditions, so we can see the differences between pre- and post dredging and

between different wave conditions more clearly.
-33-

3. When dealing with each component wave, the REF/DIF-1 is a very accurate wave transformation

model for wave energy distribution.

4. The concern of over/under prediction by using REF/DIF-1 is not necessary because we are dealing

with each component of a wave spectrum. The random sea is surely better represented using a

wave spectrum, but each component’s behavior still can be modeled accurately using a

monochromic wave model.

Table 3-2. Comparison of Wave Transformation Models

 _____________________________________________________

 Model REF/DIF-1 REF/DIF-S RDE 

 --------------------------------------------------------------------------------

 Refraction Yes Yes Yes 

 Diffraction weak Weak Yes

 Shoaling Yes Yes Yes 

 Reflection No No Yes

 Resonance No No Yes

 Grid size # L/5 # L/5 # L/10

 Bottom friction Yes yes Yes

 Current effect Yes Yes No

 Computing speed Excellent Fair Fair

 Accuracy Good* Good* Excellent

 ---------------------------------------------------------------------------

*Excellent in wave height, not good in wave direction

3.8. Calibration of REF/DIF-1

In this model, there are choices on using (1)linear wave, Hedged weak non-linear, or stoke

non-linear wave model; (2) selecting bottom friction type of laminar, percolation, and turbulent wave

boundary; (3) selecting a pass-through or reflection lateral boundary conditions.

To address the first possible choice, we tried the three possible options on two wave

conditions. The results from the second possible choice, i.e., Hedged weak non-linear wave model,

provide the closest match with observations at station MD001. Thus, for all other computations, we

used this option.
-34-

To allow oblique incident waves, or normal incident waves which changed their direction while

propagating toward the coast line, to pass-through the lateral boundary without causing reflection, the

passing-through boundary condition was selected for all the tests. The lateral dimension on the

computing grid was also selected to be large enough (67.56 km) to avoid any possible influence by the

imperfect boundary conditions implemented in the numerical scheme. These two processes take care

of the third option.

It is well documented that bottom friction caused by turbulent wave boundary is the major

source of energy loss among the three possible selections (e.g., Maa and Kim, 1992). It is also

documented that one should test the model by using different wave friction factors in order to match the

predictions and observations (Maa and Wang, 1995). In summary, the reasons to conduct the

calibration of a wave transformation are summarized as follows.

1. We want to make sure that the selected wave model can simulate the wave transformation processes

that are critical for the objective. Only through calibration, will we be able to know if the

selected model has been set up properly. Any bug in the application of the selected computer

model can be removed, assuring correct results.

2. After we know the model results are accurate, then we can interpret the shoreline responses based

on wave energy distribution. For example, the severe beach erosion on the south of Ocean

City has been noticed 100 years ago. This severe erosion correlates very well with the model’s

calculated relatively large waves in that area.

3. Application of any model requires acceptance of many assumptions, including select a proper value

for bottom friction. The calibration process provides the ability to apply rational selected as

opposed to arbitrary values.

4. Calibration is a form of quality control. Verification of model’s results against measurements

contributes to the confidence with which we view the work and to the credibility with which

others can attach to the results. 

In this study, we arbitrarily selected a month (Nov. 1 - 30, 1997, Fig. 2-10) to calibrate the

bottom friction coefficient.
-35-

To calibrate a wave transformation model’s performance, we need input wave conditions

specified at the offshore boundary of the computing domain as well as wave measurements at near

shore stations. Wave measurements at Station 44009 and MD001 were selected to serve this

purpose. The coordinates of MD001 were translated to grid locations and wave heights calculated at

all the nine neighboring grid points were averaged to compare with the measurements.

Even within one month, there are too many wave conditions to calculate if all the measured data

were used directly. For this reason, we have to idealize the wave conditions according to the regroup

process given in Chapter 2 to find a time series of representative wave conditions for model calibration. 

After regrouping the wave conditions that actually were observed at Station 44009, we have 113 wave

conditions (Table 3-3) based on different significant wave height (with 0.5 m interval), wave period at

the peak frequency of wave spectrum (with 2 s interval), and the wave direction at the peak frequency

of wave spectrum (with 22.5 degrees interval). Using these 113 wave conditions, a time series of

idealized wave conditions at the offshore boundary can be obtained (Fig. 3-1). Because waves that

travel away from shore are excluded in this comparison, some data are excluded, and thus, the time

series is not a continuous line. The calculated and observed wave height time series at Station MD001

also are plotted in Fig. 3-1. As a result of trial and error, we found that when the wave friction factor is

selected as 0.02, the calculated and measured wave heights matched the best

.

Considering that the significant wave height is a widely accepted parameter to represent wave

severity, a comparison given in Fig. 3-1 demonstrates that even the simple model (REF/DIF-1)

performs well in wave height. Since the sediment transport modeling is based only on the breaking

wave heights, not the breaking wave spectra, to estimate alongshore sediment transport, this study

demonstrates that the REF/DIF-1 is sufficient to do the job.

In the selected calibration period (Nov. 1-30, 1997), the maximum significant wave height at

the offshore boundary was five meters. This is not large enough to include the possible most severe sea

(Hs = 8 m), but is close to the severe sea condition (Hs = 6 m), thus, the calibration is considered

sufficient.
-36-

Table 3-3. Wave Conditions Used for Calibrating REF/DIF-1

Hs (m) T (s) Dir. Surge

 (Deg) (m) 

 5.00 10.00 45.00 2.0

 5.00 12.00 67.50 2.0 

 5.00 10.00 67.50 2.0 

 4.50 10.00 67.50 2.0

 4.50 10.00 45.00 2.0

 4.00 10.00 45.00 2.0

 4.00 8.00 45.00 2.0

 4.00 10.00 67.50 2.0

 4.00 8.00 67.50 2.0

 3.50 10.00 45.00 1.5

 3.50 8.00 45.00 1.5

 3.50 12.00 67.50 1.5

 3.50 10.00 67.50 1.5

 3.50 8.00 67.50 1.5

 3.50 10.00 90.00 1.5

 3.50 8.00 90.00 1.5

 3.00 10.00 45.00 1.5

 3.00 8.00 45.00 1.5

 3.00 12.00 67.50 1.5

 3.00 10.00 67.50 1.5

 3.00 8.00 67.50 1.5

 3.00 12.00 90.00 1.5

 3.00 10.00 90.00 1.5

 3.00 12.00 157.50 1.5

 3.00 10.00 22.50 1.5

 2.50 12.00 112.50 1.5

 2.50 10.00 112.50 1.5

 2.50 10.00 45.00 1.5

 2.50 6.00 45.00 1.5

 2.50 12.00 67.50 1.5

 2.50 10.00 67.50 1.5

 2.50 8.00 67.50 1.5

 2.50 6.00 67.50 1.5

 2.50 12.00 90.00 1.5

 2.50 10.00 90.00 1.5

 2.50 12.00 135.00 1.5

 2.50 10.00 135.00 1.5

 2.50 12.00 157.50 1.5

 2.50 10.00 157.50 1.5

 2.50 10.00 22.50 1.5

 2.00 6.00 45.00 1.0

 2.00 12.00 45.00 1.0

 2.00 10.00 45.00 1.0

 2.00 6.00 67.50 1.0

 2.00 10.00 135.00 1.0

 2.00 8.00 135.00 1.0

 2.00 10.00 157.50 1.0

 2.00 8.00 157.50 1.0

 2.00 10.00 22.50 1.0

 Hs (m) T (s) Dir. Surge

 (Deg) (m) 

 1.50 10.00 45.00 1.0

 1.50 8.00 45.00 1.0

 1.50 4.00 45.00 1.0

 1.50 12.00 67.50 1.0

 1.50 10.00 67.50 1.0

 1.50 8.00 67.50 1.0

 1.50 6.00 67.50 1.0

 1.50 4.00 67.50 1.0

 1.50 10.00 112.50 1.0

 1.50 10.00 135.00 1.0

 1.50 8.00 135.00 1.0

 1.50 12.00 157.50 1.0

 1.50 10.00 157.50 1.0

 1.50 8.00 157.50 1.0

 1.50 10.00 22.50 1.0

 1.00 8.00 22.50 1.0

 1.00 6.00 22.50 1.0

 1.00 4.00 22.50 1.0

 1.00 12.00 45.00 1.0

 1.00 10.00 45.00 1.0

 1.00 8.00 45.00 1.0

 1.00 6.00 45.00 1.0

 1.00 4.00 45.00 1.0

 1.00 12.00 67.50 1.0

 1.00 10.00 67.50 1.0

 1.00 8.00 67.50 1.0

 1.00 4.00 67.50 1.0

 1.00 12.00 90.00 1.0

 1.00 10.00 90.00 1.0

 1.00 8.00 90.00 1.0

 1.00 12.00 112.50 1.0

 1.00 10.00 112.50 1.0

 1.00 8.00 112.50 1.0

 1.00 6.00 112.50 1.0

 1.00 4.00 112.50 1.0

 1.00 10.00 135.00 1.0

 1.00 8.00 135.00 1.0

 1.00 12.00 157.50 1.0

 1.00 10.00 157.50 1.0

 1.00 8.00 157.50 1.0

 1.00 4.00 157.50 1.0

 

 0.50 10.00 112.50 1.0

 0.50 12.00 112.50 1.0

 0.50 14.00 112.50 1.0

 0.50 10.00 45.00 1.0

 0.50 14.00 67.50 1.0

 0.50 12.00 67.50 1.0

 0.50 10.00 67.50 1.0

 0.50 14.00 90.00 1.0

 0.50 12.00 90.00 1.0

Hs (m) T (s) Dir. Surge

 (Deg) (m) 

 0.50 10.00 90.00 1.0

 0.50 12.00 135.00 1.0

 0.50 10.00 135.00 1.0

 0.50 12.00 157.50 1.0

 0.50 10.00 157.50 1.0

 0.50 10.00 22.50 1.0

 0.50 8.00 112.50 1.0

 0.50 8.00 135.00 1.0

 0.50 6.00 22.50 1.0

 0.50 4.00 22.50 1.0

 0.50 6.00 45.00 1.0

 0.50 4.00 45.00 1.0

 0.50 4.00 135.00 1.0

 0.50 4.00 157.50 1.0
-37-

Nov. 1, 1997

0

2

4

6

SIGNIFICANT WAVE HEIGHT (m)

0 120 240 360 480 600 720

TIME (HR)

f = 0.02

Fig. 3-1. Comparison of REF/DIF-1 Calculated and Measurements Hs at MD001 Using Idealized Wave

Conditions at Offshore Boundary and Wave Friction Factor f = 0.02. 
-38-
39

CHAPTER 4. WAVE TRANSFORMATION FOR THE ORIGINAL BATHYMETRY

4.1. Introduction

In order to obtain a baseline for estimating the possible impact of dredging for sand resources at

the two offshore shoals, We calculated wave transformation for the original bathymetry first. The

details of calculated conditions and results are given in this chapter. A proposed criterion for deciding

the impact would be positive or negative is presented at the end of this chapter.

4.2. Calculated Wave Conditions

Based on available wave data, four model wave heights (2 m, 4 m, 6 m, and 8 m) were

selected to represent different sea severities: northeaster wave, rough sea, severe sea, and the most

severe sea. Although the REF/DIF-1 model is a linear wave transformation model with a weaknonlinear feature in wave transformation, each wave height has to be computed independently to

include the energy dissipation caused by bottom friction. 

Based on the recorded wave periods, a set of possible wave period also was selected for each

wave height. This combination of wave heights and periods (Table 4-1) was applied to all the four

major wave directions (ENE, E, ESE, and SE). Thus a total of 60 wave conditions, which cover the

majority of all possible waves that can be affected by dredging at the modeled sites, were calculated.

Table 4-1. A Selection of Wave Heights and Periods for Modeling

---------------------------------------------------

Wave Height (m) Wave Period (s) 

---------------------------------------------------

8 16, 20

6 14, 16, 20

4 10, 12, 14, 16, 20

2 10, 12, 14, 16, 20

---------------------------------------------------
40

4.3. Results

The calculated wave height distributions in the display domain (Fig. 4-1) are shown in Fig. 4-2

to 4-16. The relationship between the grid coordinates and Maryland State Plane Coordinates are

given in Figs. 2-1 and 2.2. Fenwick Shoal is located between XG = 16 - 18 km and YG = 29 - 35

km, and the Isle of Wight Shoal is located between XG = 17 - 20 km and YG = 23 - 27 km (Fig. 4-

1). The figures use a natural and intuitive color to show the variation of normalized wave height, H/Ho

(local wave height/incident wave height). In the figures, green indicates that the normalized wave height

is small than 1 (the smaller the number, the deeper the green), and red indicates that the normalized

wave height is large than 1 (the large the number, the deep the red). White means the normalized wave

height is one.

In general, large waves attenuated significantly (Figs. 4-2 to 4-6) because of the great energy

dissipation caused by large near-bed velocity. Large waves also may break because of the shoals, see

the dark green areas in Figs. 4-2 and 4-6 for the waves coming from E and ENE. Notice, however, at

the area between XG = 8 to 20 km and YG = 10 to 13 km, waves could be quite large if coming from

East.

For the Northeaster and rough sea conditions (Hs = 2 and 4 m, T = 10 - 20 s), wave height

distributions (Fig. 4-7 to 4-16) show a mixed results toward the coast. Near the location mentioned in

the past paragraph (i.e., XG = 4 - 15 km and YG = 10 -13 km) which is on the south of Ocean City,

however, waves coming from East have a tendency to converge. The high wave energy (for all wave

that coming from east) may be responsible for causing the shore line retreat at this area. 

The relatively severe beach erosion at the south side of Ocean City has been noticed at least

100 years ago. After the construction of jetties at the Ocean City Inlet (in 1930s), the severity

increases because the jetties block out the southward alongshore sediment transport, at least for many

years before the by-pass system was implemented (Smith, 1988).
41

Depth (m)

XG (km)

Fig. 4-1. Bathymetric Image for the Display Domain
42

Notice that near the Maryland-Delaware border (i.e., XG = 7-10 km, and YG = 24-28 km),

the extensive wave height attenuation is obvious (the dark green in Fig. 4-7 to 4-16). This may be

caused by wave shoaling and breaking, after wave passed the Fenwick Shoal which is approximately

10 km from the coast.

For a random sea that does not have the pure single wave frequency for the wave conditions

used as input for the REF/DIF-1 model, we may expect the wave height contours showing in Figs. 4-2

to 4-16 would be a little smoother. The general trend, however, should remain the same.

It is worthwhile to point out that energy loss caused by bottom friction is not a linear process. 

This is because the energy loss is proportional to ub

3

, where ub is the near bed 

velocity induced by wave. An example (Table 4-2) clearly shows the difference of local wave height

within the area between XG = 10-15 km and YG = 10-12 km for four wave heights with period = 16 s

and coming from E. 

Table 4-2. Wave Height within XG = 10-15 km and YG = 10-12 km 

to Show the Non-linear Dissipation of Wave Energy

_____________________________________________________

 Wave condition Ho(m) H (m) Fig. #

----------------------------------------------------------

 The most severe sea 8 ~8 4-2 

 Severe Sea 6 7-8 4-5 

 Rough Sea 4 4-6 4-10 

 Northeastern 2 3-4 4-15

 _____________________________________________________

If bottom friction is not considered, one may use an unit deepwater wave height, Ho = 1, to

estimate local wave heights for all levels of sea severity. The above table, however, shows that this idea

cannot be used because of the non-linear bottom friction energy dissipation. The ratio between H (the

3

rd column) and Ho

 (the 2nd column) in table 4-2 is not a constant. The ratio increases as the Ho

decreases. This is the reason why all four wave heights have to be included in the calculations. It also
43

can be interpreted that when simulating beach responses using all the reorganized wave heights and

wave periods given in Chapter 2, one has to calculate hundreds of wave conditions. It is not an

impossible but rather a time consuming and tedious job.

4.4. Criterion for Estimating the Influence of Dredging

When waves approach a coast, their trajectories may change because of wave transformation. 

Finally waves will break at a critical water depth, db, with a breaking wave height, Hb and a breaking

angle Ab. For a perfectly straight shoreline with parallel bathymetric contours and a uniform offshore

wave boundary condition, the line of Hb and Ab will be parallel to the shoreline (see the ideal condition

in Fig. 4-17). Under this condition, the alongshore sediment transport rate is the same anywhere along

the coast. Weather or not the beach will erode depends purely on the on-off shore sediment transport

which is a rather slow process when compared with the along shore sediment transport. In reality,

however, the breaking wave conditions will never be the same along a coast line, and a certain degrees

of modulation exists (see the dashed line in Fig. 4-17). If the bathymetric change from dredging amplify

the modulation, see the dotted-dashed lined in Fig. 4-17, the impact is not favorable. This is simply

because there is more severe erosion where the breaking wave height is large. On the other hands, it

would be a favorable change of bathymetry if the modulation decreased, see the dotted line in Fig. 4-

17.

In the evaluation of computing results given in the next section, we use the original breaking

wave height modulation as the basis (thus, a number of 1). For a favorable change of bathymetry, the

modulation should be reduced, i.e., less than 1.0 (e.g., 0.5 in Fig. 4-17). Any change of bathymetry

that increases the modulation (e.g., 1.35 in Fig. 4-17) can be classified as unfavorable.
44

Fig. 4-2. Normalized Wave Height Distribution for the Original Bathymetry with Ho = 8 m, T = 16 s.
45

Fig. 4-3. Normalized Wave Height Distribution for the Original Bathymetry with Ho = 8 m, T = 20 s.
46

Fig. 4-4. Normalized Wave Height Distribution for the Original Bathymetry with Ho = 6 m, T = 14 s.
47

Fig. 4-5. Normalized Wave Height Distribution for the Original Bathymetry with Ho = 6 m, T = 16 s.
48

Fig. 4-6. Normalized Wave Height Distribution for the Original Bathymetry with Ho = 6 m, T = 20 s.
49

Fig. 4-7. Normalized Wave Height Distribution for the Original Bathymetry with Ho = 4 m, T = 10 s.
50

Fig. 4-8. Normalized Wave Height Distribution for the Original Bathymetry with Ho = 4 m, T = 12 s.
51

Fig. 4-9. Normalized Wave Height Distribution for the Original Bathymetry with Ho = 4 m, T = 14 s.
52

Fig. 4-10. Normalized Wave Height Distribution for the Original Bathymetry with Ho = 4 m, T = 16 s.
53

Fig. 4-11. Normalized Wave Height Distribution for the Original Bathymetry with Ho = 4 m, T = 20 s.
54

Fig. 4-12. Normalized Wave Height Distribution for the Original Bathymetry with Ho = 2 m, T = 10 s.
55

Fig. 4-13. Normalized Wave Height Distribution for the Original Bathymetry with Ho = 2 m, T = 12 s.
56

Fig. 4-14. Normalized Wave Height Distribution for the Original Bathymetry with Ho = 2 m, T = 14 s.
57

Fig. 4-15. Normalized Wave Height Distribution for the Original Bathymetry with Ho = 2 m, T = 16 s.
58

Fig. 4-16. Normalized Wave Height Distribution for the Original Bathymetry with Ho = 2 m, T = 20 s.
59

Y (km)

 25 20 15 10 5 0

Shore line

Original 

Amplified

Reduced

Ideal condition

Hb

Hb

BHM=1

BHM=0.50

1.35

Dredging 

 site
60
61

CHAPTER 5. CHANGES OF WAVE ENVIRONMENTS AFTER A ONE-TIME DREDGING

5.1. Introduction

The dredging scenario considered in this chapter is for providing a one-time sand resource on

the order of 2 x 106 m3 for Maryland-Delaware coast. The objective of study for this scenario is to

determine if the impact of even a small amount of dredging at the selected shoals is acceptable. The

differences in computed wave heights between the offshore dredging sites and the shoreline for the preand post-dredging are presented first to obtain a general idea of the spatial differences. Then the

differences in breaking wave heights along the coast are presented. Conclusions on the possible impact

are presented at the end.

5.2. Wave Height Difference in Spatial Domain

The 60 wave conditions described in Chapter 4 were run with the bathymetry altered as shown

in Fig. 2-4. In order to clearly show the change in wave height distribution, only the normalized

difference (i.e., ÄH/H, in units of %, where H is the local wave height calculated using the original

bathymetry, and ÄH is the change of local wave height) in the display domain are plotted in an intuitive

manner (Figs. 5-1 to 5-15). Red represents increase (i.e., ÄH/H > 0%), green represents decrease

(i.e., ÄH/H < 0%), and white represents no change (i.e., ÄH/H = 0%). The modeled dredging areas

also are depicted as the dashed boxes in these figures.

Dredging always alters the wave transformation. Wave height is reduced at some places but

increased at others (Figs. 5-1 to 5-15). In general, the modeled dredging at Fenwick shoal has a

relatively severe influence on wave height (i.e., ÄH/H >> 0%) for waves that come from ENE and E. 

The area affected by dredging, however, is not as large as those waves come from SE and ESE. Only

occasionally, waves come from SE produce severe difference (Figs. 5-7 and 5-12).

 

Notice that the red-colored area is larger than the green-colored area. This indicates that the

difference is mainly in increasing wave height (red color). These figures also reveal that at some places,

the increase of wave height is quite large (the dark red areas in Figs. 5-1 to 5-15). The large increase
62

of wave height is a negative consequence of dredging and may have influence on sea-floor mobility. 

Because of the stochastic nature of waves, a quantitative conclusion on the sea floor mobility would

require more study.

Also notice that the changes of wave height at the dredging sites are not significant. Actually,

the major change of wave height occurs between the dredging sites and the shoreline.

The figures indicate that the change of local wave height can be as much as 100%. This is a

significant alteration on wave height itself. But when dealing with alongshore sediment transport, the

only parameter considered is breaking wave height. That is why we have to check the change of

breaking wave height profiles. For a nonlinear wave breaking process, a 100% increase of local wave

height at some place not right before the breaking point, the breaking wave height does not necessary

also increase 100%. Actually the increase of breaking wave height can be quite limited. 

It is a quite challenge question: “How much is the influence on the change of local wave height,

which is not necessarily close to the breaking line, to the shore line change?” It is understood that the

increase of local wave height on the shelf face might alter the on-off shore sediment transport rate. 

However, a predictive formulation and numerical model is still far from available. 
63
64
65

Fig. 5-3. Calculated Changes of Wave Height (Normalized) for the One-time Dredging with Ho=6 m, T=

14 s.
66

Fig. 5-4. Calculated Changes of Wave Height (Normalized) for the One-time Dredging with Ho=6 m, 

 T= 16 s.
67

Fig. 5-5. Calculated Changes of Wave Height (Normalized) for the One-time Dredging with Ho=6 m, 

 T= 20 s.
68

Fig. 5-6. Calculated Changes of Wave Height (Normalized) for the One-time Dredging with Ho=4 m, 

 T= 10 s.
69

Fig. 5-7. Calculated Changes of Wave Height (Normalized) for the One-time Dredging with Ho=4 m, 

 T= 12 s.
70

Fig. 5-8. Calculated Changes of Wave Height (Normalized) for the One-time Dredging with Ho=4 m, 

 T= 14 s.
71

Fig. 5-9. Calculated Changes of Wave Height (Normalized) for the One-time Dredging with Ho=4 m, 

 T= 16 s.
72

Fig. 5-10. Calculated Changes of Wave Height (Normalized) for the One-time Dredging with Ho=4 m, 

 T= 20 s.
73

Fig. 5-11. Calculated Changes of Wave Height (Normalized) for the One-time Dredging with Ho=2 m, 

 T= 10 s.
74

Fig. 5-12. Calculated Changes of Wave Height (Normalized) for the One-time Dredging with Ho=2 m, 

 T= 12 s.
75

Fig. 5-13. Calculated Changes of Wave Height (Normalized) for the One-time Dredging with Ho=2 m, 

 T= 14 s.
76

Fig. 5-14. Calculated Changes of Wave Height (Normalized) for the One-time Dredging with Ho=2 m, 

 T= 16 s.
77

Fig. 5-15. Calculated Changes of Wave Height (Normalized) for the One-time Dredging with Ho=2 m, 

 T= 20 s.
78

5.3. Changes in Breaking Wave Heights

As to possible shoreline variation, it is necessary to study the possible change of breaking wave

height along the coast (see the criterion in Chapter 4). For this reason, the changes of breaking wave

heights along the coast in the display domain are plotted in Figs. 5-16 to 5-30. The modeled dredge

areas are located between YG = 22 to 24 km and YG = 29 and 31 km. The black lines in these

figures represent the profiles of breaking wave height for the original bathymetry. The red dashed lines

in these figures are the breaking wave height profile after the modeled one-time dredging. Shore line

was also given in these plots for a better indication of location.

The possible changes for the most severe sea (Ho = 8 m, T = 20 s) that comes from ENE (Fig.

5-16) indicates that the Breaking wave Height Modulation (BHM, Chapter 4) increased a little (BHM

= 1.3) at YG = 18.5 km, but decreased a little at YG = 26 km (BHM = 0.85). For the most severe

sea coming from E, the possible impact is slightly unfavorable; At YG =22.5, BHM = 1.09, and

between YG = 17 - 18 km, BHM . 1. If the waves come from ESE, the results are all negative (BHM

= 1.6 at YG = 31 km; BHM =1.8 at YG = 22.5 km). If the waves come from SE, the only possible

impact can be seen is at YG = 31 km with a BHM . 1. The modulation has the same amplitudes but

shifted laterally along the shore. Thus, for the most severe waves come from SE, we can considered

that there is no significant change (marked as NG thereafter).

As an aid to assessing the overall effect, the breaking wave height modulation is summarized in

Table 5-1. It is clearly indicated in Table 5-1 that for a total of 60 wave conditions, only 10 wave

conditions show that the change of breaking wave height is noticeable, or measurable from Fig. 5-16 to

5-30. Among these 10 wave conditions, only for the most severe sea that comes from ESE may have a

significant impact on the breaking wave height modulation. All others either have a mixed impact (some

areas positive, some negative) or the impact is positive, i.e., the BHM decreases.
79

Table 5-1, Summary of Changes on Breaking Wave Height 

Modulation for the One-time Dredging

----------------------------------------------------------------------------------------------------

 Wave T = 10s 12s 14s 16s 20s

 Dir.

----------------------------------------------------------------------------------------------------

 ENE : M1 –-

5 

 -- -- 0.9/1.2 1.26/0.84

: S2-- -- 0.66 NG NG

: R3 NG6 0.9 0.9/1.25 0.83 NG

: N4 NG NG NG NG NG

 E : M -- -- -- NG 1.09/1

: S -- -- NG NG NG 

: R NG NG NG NG NG

: N NG NG NG NG NG

 ESE : M -- -- -- NG 1.6/1.8

: S -- -- NG NG NG

: R NG NG NG NG NG

: N NG NG NG NG NG

 SE : M -- -- -- NG NG

: S -- -- NG NG NG

: R NG NG 1.16 NG NG

: N NG 0.55/1.75 NG NG NG

 -----------------------------------------------------

1

: represents Northeastern wave condition.

2

: represents Rough Sea wave condition.

3

: represents Severe Sea wave condition.

4

: represents the Most Severe Sea wave condition.

5

: NG is a shorthand for negligible small.

6

: –- is a shorthand for not included in computation.

5.4. Conclusions

This study indicates that for the one-time dredging at Fenwick Shoal and Isle of Wight Shoal

for a total of 4 x 106 m3 of sand is acceptable in terms of potential modification to wave transformation. 

The major change to waves occurred not at the modeled dredging sites nor at the coast, but between

these two.
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95

CHAPTER 6. CHANGES OF WAVE ENVIRONMENTS AFTER THE ACCUMULATIVE

DREDGING

6.1. Introduction

The scenario of dredging considered in this chapter is for providing a long term (10 to 20 years)

sand resource on the order of 24 x 106 m3 for Maryland and Delaware coast. The objective is to

determine if the impact of this accumulated dredging at the two selected shoals is acceptable or not. 

The same wave conditions given in Chapter 5 are used here again. The difference in computed wave

height between the offshore dredging sites and the coast line are presented first to obtain a general idea

of spatial difference. Then the differences in breaking wave heights along the coast are studied. 

6.2. Results in Spatial Variation

The afore selected 60 wave conditions were run with the bathymetry altered as shown in Figs.

2-5 and 2-6. Similar to Chapter 5, the normalized difference in wave height (i.e., ÄH/H, in unit of %)

are given in Figs. 6-1 to 6-15. Again, red represents increase (i.e., ÄH/H > 0%), green represents

decrease (i.e., ÄH/H < 0%), and white represents no change (i.e., ÄH/H = 0%). The modeled

dredging areas are shown as the dashed boxes in these figures. 

Since the dredging areas are much larger that those given in Chapter 5, the affected areas also

are larger. It is important, however, to point out that because the distance between these two dredging

sites (Fenwick Shoal and Isle of Wight Shoal) is large, there is no interaction between the alterations to

wave transformation (Figs. 6-1 to 6-15). In other words, the possible impact caused by dredging at

the modeled sites can be treated independently. Notice that the severely affected (ÄH/H >> 1) areas

are limited on the north and south sides of the entire affected area, see the dark red areas on the south

and north for each affected area. In the middle of these severely affected area, wave heights are

actually reduced (ÄH/H < 1), see the light green area in Figs. 6-1 to 6-15. As pointed out in the

previous chapter, the most affected areas are between the coast line and the dredging sites. At the

dredging sites, there are only small differences in terms of wave height alteration.

 
96

Notice that large differences are mainly in increasing wave height (dark red color in Figs 6-1 to

6-15). Only occasionally in the affected area that wave height would be significantly reduced (see the

green area in Figs. 6-1 to 6-15). The large increase of wave height is negative and may have an

influence on sea-floor mobility. Again, because of the stochastic nature of waves, a quantitative

conclusion on sea floor mobility is not addressed at this time.
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112

6.3. Results in Changing Breaking Wave Height

As far as the possible shoreline variations are concerned, we need to consider the change of

breaking wave height along the shore (see the criterion in Chapter 4). For this reason, the changes of

breaking wave heights in the display domain are plotted in Figs. 6-16 to 6-30. The modeled dredge

areas are located between YG = 21 to 28 km and YG = 28 and 33 km. The black lines in these

figures represent the profiles of breaking wave height for the original bathymetry. The red dashed lines

in these figures are the breaking wave height profile after the proposed long-term dredging. 

Again, we will use one specific case to explain the possible changes in details, and summarize

the results in Table 6-1. For the most severe sea (Ho = 8 m, T = 20 s) that comes from ENE, Fig. 6-

16 indicates that the breaking wave height modulation increases a little (BHM = 1.33) between YG =

20 and 22 km, but the BHM has a significant decrease (BHM = 0.38) between YG = 25 to 27 km. If

the waves come from E, the possible impact is on positive side (i.e., BHM = 0.61 at YG = 30.5 km

and 0.75 at YG = 19 km). If the waves come from ESE, the results are all negative (BHM = 1.75 at

YG = 30.5 km and BHM = 2.67 at YG = 23 km). If the waves come from SE, then there is almost no

change.

Table 6-1 clearly shows that the results on breaking wave height modulation is a mix of positive

and negative impacts. Within the 60 studied wave conditions, 18 wave conditions do not show a

measurable/significant change. Although for some waves that come from the ESE there is a relatively

large negative impact (e.g., BHM > 2.), it is not a really big negative impact because of the original

small BHM. It is necessary to point out that the numbers displayed in Table 6-1 are indices to show

the relative significance, Figs. 6-16 to 6-30 are still the important information with which to make a

judgement. 
113

Table 6-1, Summary of Changes on Breaking Wave Height 

Modulation for the Cumulative Dredging

--------------------------------------------------------------------------------------------------------

Wave T = 10s 12s 14s 16s 20s

 Dir.

--------------------------------------------------------------------------------------------------------

 ENE : M1

--

5 

 

-- -- 0.5 0.38/1.33

: S2-- -- 0.6 0.64/1.22 0.69 

: R3 NG6 0.9 1.38 0.83 0.86/1.33

: N4 0.5 NG NG 0.83 NG

 E : M -- -- -- 0.5/1.54 0.61/0.75

: S -- -- 0.88 0.7/1.14 0.94/1.72 

: R NG NG NG 1.2 1.27/1.2

: N 0.9 0.33 0.87 0.5 0.71

 ESE : M -- -- -- 3.0 1.75/2.67

: S -- -- NG NG 1.22

: R 2.0 NG NG 2.25 1.6/0.75

: N NG NG 2.25 2.0 NG

 SE : M -- -- -- 1.12 NG

: S -- -- 1.33 1.28/1.16 1.55

: R NG 1.83 1.5 1.5 2.0

: N NG 0.46 2.0 1.55 NG

 --------------------------------------------------------

1

: represents Northeaster wave condition.

2

: represents Rough Sea wave condition.

3

: represents Severe Sea wave condition.

4

: represents the Most Severe Sea wave condition.

5

: NG is a shorthand for negligible small.

6

: –- is a shorthand for not included in computation.

The most important feature in Figs. 6-16 to 6-30 is the obvious reduction of BHM around YG

= 26 km, the border between Maryland and Delaware. The original low breaking wave height means

the sediment transport activities at this border location is minimal. The obvious reduction of BHM

(mainly for long period waves) actually increases the breaking wave height, and thus, indicates that the

along shore sediment transport will move more sediment away from this location. Shoreline recession

would be expected as a consequence. This is a negative impact at this particular location, however, the
114

increased amount of sediment transport, moving either north or south, will benefit the downstream

beaches. If sand resources have to be taken from the two modeled shoals, some sort of beach protect

project should be considered, or at least a monitoring project that closely checks the shoreline change

at the Maryland and Delaware border should be established. If it is necessary to maintain the original

shoreline, then part of the sand resources obtained from the offshore dredging should be placed at this

location.

6.4. Conclusions

The results suggest that the major change of wave height is between the dredging site and the

shore line. The local increase of wave height can be as much as two times. The change of breaking

wave height, on the other hand, is not so obvious except the clear reduction of BHM at the Maryland

and Delaware border. The reduction of BHM at this location, however, is not necessarily a positive

impact because it increases the breaking wave height at that location. As a consequence, more erosion

and shoreline recession at that location may be resulted. Otherwise, the possible impact is not

significant.
115

Fig. 6-16. Comparison of Breaking Wave Heights for Original Bathymetry and after Accumulative 

 Dredging with Ho = 8 m, T = 20 s.
116

Fig. 6-17. Comparison of Breaking Wave Heights for Original Bathymetry and after Accumulative 

 Dredging with Ho = 8 m, T = 16 s.
117
118

Fig. 6-19. Comparison of Breaking Wave Heights for Original Bathymetry and after Accumulative 

 Dredging with Ho = 6 m, T = 16 s.
119

Fig. 6-20. Comparison of Breaking Wave Heights for Original Bathymetry and after Accumulative 

 Dredging with Ho = 6 m, T = 14 s.
120

Fig. 6-21. Comparison of Breaking Wave Heights for Original Bathymetry and after Accumulative 

 Dredging with Ho = 4 m, T = 20 s.
121

Fig. 6-22. Comparison of Breaking Wave Heights for Original Bathymetry and after Accumulative 

 Dredging with Ho = 4 m, T = 16 s.
122

Fig. 6-23. Comparison of Breaking Wave Heights for Original Bathymetry and after Accumulative 

 Dredging with Ho = 4 m, T = 14 s.
123

Fig. 6-24. Comparison of Breaking Wave Heights for Original Bathymetry and after Accumulative 

 Dredging with Ho = 4 m, T = 12 s.
124

Fig. 6-25. Comparison of Breaking Wave Heights for Original Bathymetry and after Accumulative 

 Dredging with Ho = 4 m, T = 10 s.
125

Fig. 6-26. Comparison of Breaking Wave Heights for Original Bathymetry and after Accumulative 

 Dredging with Ho = 2 m, T = 20 s.
126

Fig. 6-27. Comparison of Breaking Wave Heights for Original Bathymetry and after Accumulative 

 Dredging with Ho = 2 m, T = 16 s.
127

Fig. 6-28. Comparison of Breaking Wave Heights for Original Bathymetry and after Accumulative 

 Dredging with Ho = 2 m, T = 14 s.
128

Fig. 6-29. Comparison of Breaking Wave Heights for Original Bathymetry and after Accumulative 

 Dredging with Ho = 2 m, T = 12 s.
129

Fig. 6-30. Comparison of Breaking Wave Heights for Original Bathymetry and after Accumulative 

 Dredging with Ho = 2 m, T = 10 s.
130
-131-

CHAPTER 7. POSSIBLE INFLUENCE OF STORM SURGES

7.1. Introduction

Coastal storm surge, defined as the anomaly of water level from astronomical tide, threats

coastal communities. The storm surge is also known as wind tide because it stems from storms of

tropical or extratropical origins. The storm surge has been expressed as barotropic response of

coastal water body to meteorological forcing and the controlling parameters are geometry of

coastlines and bathymetry (e.g. Murty, 1984). A concern would arise from the changing offshore

bathymetry by dredging operation.

SLOSH (Sea, Lake, and Overland Surges from Hurricanes) model was developed in

NOAA (Jelesnianski et al., 1992). This model is known as 2-1/2 dimensional model because it

assumes a vertical structure of velocity profile. The SLOSH model has been utilized to generate

evacuation maps for FEMA. It was also extended to incorporate extratropical storms (Kim et al.,

1996). In this study, the SLOSH model was used to study the impact of offshore shoal removal

by dredging. Two typical storm tracks—shore normal and shore parallel—were used. Only

category 4 storms were modeled because the objective is to see the possible maximum impact.

7.2. SLOSH model

We use horizontal coordinate, Z=x+iy where i denotes the complex number. When u and

v are velocities in the x- and y-directions of Cartesian coordinate, respectively, the complex

horizontal velocity, w, is expressed analogously

w = u + iv (7-1)

The horizontal pressure gradient, q, is defined with the water level, h, and the gravitational

acceleration, g,
-132-

















∂

∂

+

∂

∂

= −

y

i

x

q g

h h

(7-2)

We define transport, W, as depth integrated horizontal velocity

∫

=

1

0

W h w dz (7-3)

Here, the vertical coordinate, z, is normalized by water depth, h, and thus varies between 0 at the

bottom and 1 at the surface. The surface stress term, R, is defined as

2

*s

s R = = u

r

t

(7-4)

The bottom stress term, T, is defined as

2

*b

b T = = u

r

t

(7-5)

The gradient term, Q, is

Q = qh (7-6)

Here, ts is surface stress, tb is bottom stress, and r is water density.

Linearized transport equation then becomes

ifW Q R T

t

W

+ = + −

∂

∂

(7-7)
-133-

where f is Coriolis parameter. Solution of equation (7-7) depends on the bottom stress term T

which is not easy to obtain from depth integrated transport, W, because it is a function of flow

structure especially in the bottom boundary layer.

Assuming constant eddy viscosity (ν = 0.0225 m2/sec) and introducing a slip boundary

condition at the bed (

=0

= b zt s w ) with slip coefficient (s = 0.0009 m/sec), Jelesnianski et al.

(1992) derived

ifAW BQ CR

t

W

+ = +

∂

∂

(7-8)

Here, complex coefficients, A, B, and C, become functions of water depth, h, eddy viscosity, n,

Coriolis parameter, f, and slip coefficient, s. Equation (7-8) can be expressed as

CR

Z

ifAW gh B

t

W

+

∂

∂

+ = −

∂

∂

*

2

h

(7-9)

where the asterisk represents the complex conjugate. Now we consider conformal

transformation, z = c+iy. Then, we have

CR

dZ

d

ifAW gh B

t

W

+

∂

∂













+ = −

∂

∂

*

*

2

z

z h

(7-10)

A normalized transport term is introduced

W

d

dZ

*

















=

z

v (7-11)

Then, equation (7-10) becomes
-134-

CR

d

dZ ifA ghB i

t

*

















+ 













∂

∂

+

∂

∂

+ = −

∂

∂

z

h

c y

v

v

(7-12)

This gives a similar form as the original equation except that the complex magnification factor is

in the stress term. The continuity also gives

































∂

∂

+

∂

∂

= −

∂

∂

2 *

z

v

z

h z v

dZ

d

t

(14)

This becomes dependent on the Jacobian, |dz/dZ|

2

. The SLOSH model is a finite difference

model and utilizes Arakawa and Mesinger’s B-scheme which makes solution of the Coriolis term

easier compared to the C-scheme.

7.3 Model grid

A polar grid with 130 by 280 grid cells were constructed. The coastal grid cells are

approximately 150 m by 150 m in dimension. Figure 7-1 shows the bathymetry represented on

the computational grid. In order to investigate the impact from dredging of offshore sand shoals,

we selected 10 coastal stations to monitor surge behaviors. Figure 7-2 shows the location of

monitoring stations and two shoals to be removed by dredging.
-135-

Fig 7-1. Bathymetry in the SLOSH model computation domain

Fig 7-2. Bathymetry and 10 coastal monitoring stations in the SLOSH model. Also shown are the

intended dregding sites at two offshore shoals.
-136-

7.4 Results and discussion

Tropical storms with 86 mbar central pressure drop and 15-mile maximum wind radius

(comparable to category 4 storm) were used to simulated the coastal storm surges. Two

orthogonal tracks of across- and along-shore directions were simulated. Figure 7-3 shows the

ensemble of maximum surges from the cross-shore track simulation. It clearly shows the higher

coastal surges in the right hand side of the storm landfall points.

Fig 7-3. Surge envelop from cross-shore track

Figure 7-4 shows the time history of the coastal surges. Increased coastal surges from south (St.

1) to north (St. 10) was apparent. Figure 7-5 shows the change by dredging operation. The

changes are about 0.1 cm which is negligible compared to the maximum surges of 3.5 m. Figure

7-6 shows the surge envelop from the along-shore track. In general the coastal surges were lower

compared to the cross-shore track. South-north propagation of surges was evident (Fig 7-7). The
-137-

changes were also around 0.1 cm which is again negligible compared to the maximum surge of

2.5 m.

0

0.5

1

1.5

2

2.5

3

3.5

4

-12 -6 0 6

Hour

Surge height (m)

ST.1

ST.2

ST.3

ST.4

ST.5

ST.6

ST.7

ST.8

ST.9

ST.10

Fig 7-4. Time series of coastal surges at 10 monitoring stations. Cross-shore track

-0.1

-0.05

0

0.05

0.1

0.15

-12 -6 0 6

Hour

Surge Changes (cm)

ST.1

ST.2

ST.3

ST.4

ST.5

ST.6

ST.7

ST.8

ST.9

ST.10

Fig 7-5. Time series of the coastal surge changes by dredging at 10 monitoring stations. Crossshore track
-138-

Fig 7-6. Surge envelop from shore-parallel track

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

-12 -6 0 6

Hour

Surge height (m)

ST.1

ST.2

ST.3

ST.4

ST.5

ST.6

ST.7

ST.8

ST.9

ST.10

Fig 7-7. Time series of coastal surges at 10 monitoring stations. Shore-parallel track.
-139-

-0.1

-0.05

0

0.05

0.1

0.15

-12 -6 0 6

Hour

Surge Change (cm)

ST.1

ST.2

ST.3

ST.4

ST.5

ST.6

ST.7

ST.8

ST.9

ST.10

Fig 7-8. Time series of the coastal surge changes by dredging at 10 monitoring stations. Shoreparallel track
-140-
-141-

CHAPTER 8. POSSIBLE IMPACT ON TIDAL CURRENT

8.1. Introduction

This chapter describes the effort on the study of possible impact on tidal currents, especially the

near bottom current, potentially caused by dredging at the offshore shoals. A three-dimensional (3-D)

hydrodynamic model: the Princeton Ocean Model (POM) was used to simulate the possible influence

on the tidal current caused by dredging. The basics of the POM model are introduced first, followed

by a description of the open boundary conditions and model verification. The original tidal current

distributions at the modeled dredging sites are presented and followed by the differences in tidal current

after dredging.

8.2. The POM Hydrodynamic Model

The 3-D barotropic version of the POM (Blumberg and Mellor, 1987) was employed to study

the tidal currents in the Maryland-Delaware coastal waters. The POM model is a well established 3-D

numerical model for tidal simulation. For this reason, only the basics are presented as follows. The

governing equations of this bottom following sigma coordinate system are: 

(8-1)

¶h

¶

¶

¶

¶

¶

¶w

t ¶s

UD

x

VD

y

+ + + = 0

(8-2)

( )

¶

¶

¶

¶

¶

¶

¶ w

¶s

¶h

¶

r

t t

¶

¶s

¶

¶s

UD

t

U D

x

UVD

y

U

F

x

fVD gD

x

sx bx

K

D

M U

+ + + − − + =

− +













2

1

(8-3)

( )

¶

¶

¶

¶

¶

¶

¶ w

¶s

¶h

¶

r

t t

¶

¶s

¶

¶s

VD

t

UVD

x

V D

y

V

F fVD gD

y

K

D

V

y

sy by

M

+ + + − + + =

− +













2

1
-142-

where H, ôs and ôb are water depth, wind stress and bottom friction, KM is the vertical kinematic

viscosity calculated from the turbulence closure model (Mellor and Yamada, 1982) and 

F , (8-4)

x x

H AM

U

x y

HAM

U

y

V

x

=













+ +





























¶

¶

¶

¶

¶

¶

¶

¶

¶

¶

2

, F (8-5)

y y

H AM

V

y x

HAM

U

y

V

x

=











 + +





























¶

¶

¶

¶

¶

¶

¶

¶

¶

¶

2

A , (8-6) MC x y V V

T

= Δ Δ ∇ + ∇

1

2

D = H +h .

8.3. Model Grid

The orthogonal curvilinear model grid is the same as displayed in Fig. 2-2, except that the cell

size is increased to 300 m x 600 m in the east-west and north-south directions, respectively. The total

dimension is also the same: 44.97 km x 67.56 km. There are 6 sigma layers in the vertical direction,

i.e., the total water depth was divided into 6 layers with equal thickness, and the thickness of these

layers varies because of the different total water depth. The model computation internal and external

time steps were 90 and 6 seconds, respectively. The horizontal diffusion coefficient was set as a

constant of 50 m2/s.

8.4. Lateral and Open Boundary Conditions

Since the east open ocean boundary of the model domain is located only about 45 km offshore

from the Maryland and Delaware shorelines, the tidal level gradient is expected to be insignificant within

this short distance. Therefore, the east open ocean boundary can be specified using the tidal levels

measured from a tidal station on the west side of the study domain: Ocean City, Maryland. Hourly

water level records at Ocean City Inlet (NOS Station ID 8570283), Maryland for the entire year of

1985 were processed by using the least square harmonic analysis to obtain amplitudes and epoches of

29 tidal constituents (Table 8-1).
-143-

Fig. 8-1. Comparison of Calculated and AnalyzedTidal Elevation at Ocean City Inlet from May 5 -15, 1985.

 Solid line is the Calculated and Dashed line is the Analyzed Tidal Elevation.

Using the 29 constituents, a tidal time series can be reconstructed to remove the wind effects:

wind set-up or set-down. The reconstructed tide levels were specified as the east open ocean

boundary condition while a velocity radiation condition was specified at the north and south boundaries. 

 As mentioned before, the gradient of water surface elevation is negligible small in this small domain, the

reconstructed tidal elevations really serve two purposes: (1) to provide the boundary condition at the

offshore border, and (2) to serve as a verification of the model performance by comparing the

calculated and the reconstructed tidal elevation at the Ocean City Inlet.

8.5. Model Verification

Using the original bathymetry, the POM model was run with a cold start (assuming the tidal

elevation and tidal current are all zero is the computation domain) for 30 days. The model calculated

tide levels at Ocean City, Maryland are compared with the re-constructed tide levels and the results are

showing in Fig. 8-1. It reveals a very good agreement, less than 1 cm RMS

(root-mean-square) errors. This calibration indicates that the model is capable of reproducing the

water levels accurately for the existing bathymetry in the study area. Although there is no tidal current

data available to verify the simulated velocity, the model produces a reasonable (20 cm/s) maximum

surface current speed at proposed dredging sites. Therefore, the model will be used to evaluate the

tidal current changes before and after the proposed dredging.
-144-

Table 8-1. Amplitude and Epoch of Major Constituents Specified at offshore Boundary

 ____________________________________________________

Constituents Amplitude (M) Epoch (Degrees)

 ----------------------------------------------------------------------

M(2) 0.4873 191.73

N(2) 0.1185 164.85

O(1) 0.0843 234.62

K(1) 0.0848 114.43

K(2) 0.0243 235.72

MU(2) 0.0197 150.68

NU(2) 0.0212 158.97

Q(1) 0.0147 67.97

2N(2) 0.0152 142.26

L(2) 0.0148 174.59

M(6) 0.0124 159.93

M(1) 0.0054 129.06

J(1) 0.0068 131.36

LAMBDA(2) 0.0075 231.29

M(4) 0.0073 243.31

MN(4) 0.0056 192.86

OO(1) 0.0030 160.99

S(4) 0.0042 271.35

RHO(1) 0.0033 48.96

2Q(1) 0.0026 62.87

2SM(2) 0.0022 216.68

M(3) 0.0022 150.38

MS(4) 0.0017 32.22

2MK(3) 0.0015 94.34

M(8) 0.0014 357.20

MK(3) 0.0010 187.03

 -------------------------------------------------------------------------

8.6. Results

The contours of near-bed (bottom layer) tidal flow fields at the maximum flood and the

maximum ebb, within the display domain (Fig. 2-2), are given in Fig. 8-2. In general, these two figures

reveal that the near-bed tidal current is weak, less than 5 cm/s for the near coastal area (water depth <

10 m), except at the shoals, where the near-bed maximum flood velocity is on the order of 8 cm/s. For

the maximum ebb, the same conclusions also hold. Fig. 8-3 shows the details of velocity vectors within

a small domain that includes Fenwick Shoal and Isle of Wight Shoal for the maximum flood and ebb. It
-145-

reveals clearly the effect of bathymetry on tidal currents. When tidal current was forced to flow over

the shoal, the current velocity increased because of the decreasing water depth. If the tidal current can

find a way to avoid climb the shoal, it will take the easy route.

Since the influence of an one-time dredging on waves is limited, and also because tidal waves

have a much longer wave length, the small change in bathymetry may not be seen by the tidal waves,

we only investigated the scenario for long-term dredging. Using the same offshore boundary condition,

the same lateral boundary condition and the bathymetry with the long-term dredging, the POM model

was re-run to calculate the possible difference. For better visual presentation of the differences in such

a small tidal current environment, we normalized the differences by presenting the difference in

percentage (Fig. 8-4) for the maximum flood and ebb. It clearly shows that a maximum difference on

the order of 10% can result. In general, at the place that the water depth increases because of

dredging, the tidal current velocity decreases. Immediately after the dredging site, and on the leeward

side or tidal flow, tidal current velocity increases. The affected area is rather large, see Fig. 8-4, but the

amount of velocity increasing is rather small, even in percentage. If considering that the maximum tidal

current velocity is only around 8 cm/s at the shoal, and around 5 cm/s away from the shoal, the change

in tidal current caused by the proposed dredging is negligibly small to affect biological living conditions.

8.7. Conclusions

Tidal current is rather weak in this area. The maximum tidal current is only around 20 cm/s at

the surface layer. Near the bottom, the tidal current is even weaker, on the order of 5 cm/s except at

the shoals, where current velocity increases to around 8 - 10 cm/s. The postulated dredging at the

shoals will reduce the maximum near-bed tidal current velocity (around 10%) because of the increase

of water depth. This is a positive point for biological recovery in this area. Immediately on the leeward

side of tidal flow, the dredging increases the tidal velocity, up to 10%. Because of the weak currents in

this area, the 10% increase only contributes less than 1 cm/s increase of tidal current, from 5-8 cm/s to

6-9 cm/s. For this reason, the possible impact of tidal current is negligibly small.
-146-

(a)

Ocean

City

(b)

Fig. 8-2. Contours of Near-bed Tidal Current Velocity. Zero Contour Line is also the Shoreline. (a) Maximum

 Flood; (b) Maximum Ebb.
-147-
-148-
-149-

CHAPTER 9. BENTHIC STRESS ANALYSES

9.1. Introduction

Natural flow consists of steady and unsteady parts in velocity distribution. The combined

wave-current bottom boundary layer has been known to exert enhanced bottom stress on the bed.

In general, a thin wave boundary layer can be viewed as nested beneath current boundary layer

with the thickness of water depth in coastal zone. In this study, we adopted the Grant-MadsenGlenn (GMG) model which is considered to be simple to solve but dynamically thorough. The

enhanced bottom stress is calculated by combining wave-model generated wave field with an

assumed steady current.

9.2. GMG model

The GMG model is based on the two-layer eddy viscosity model of Grant and Madsen (1979;

1986) and the modification by Glenn and Grant (1987) to account for suspended-sediment

stratification above the wave boundary layer of thickness, dcw. The eddy viscosities are

characterized by different friction velocities, u*cw and u* inside and above the wave boundary

layer, respectively (Fig 9-1):

U

τ

τcw

δc

δcw

z 0c

z0

τc

νt

κu*cwz

κu*cz

z

Fig 9-1. Definition sketch of two-layer wave-current bottom boundary layer.
-150-







>

<

=

* cw

* cw

 for z

 for z

k d

k d

n

u z

u z

c

cw

t

(9-1)

The bottom stress is represented by the wave–current stress, tcw = ru*cw

2 where r is water

density. This operates within the thin wave–current boundary layer just above the bed. The

wave–current boundary layer thickness, dcw , is

dcw= 2ku*cw w (9-2)

Taking account of the effects of sediment-induced stratification via the Monin-Obukov

length scale, Lc (= ru

3

*c /6g<r

’w’

>, where r

’ and w’ are turbulent fluctuations in fluid density and

vertical velocity, respectively, and < > is time average), the current structure becomes































+

































=

∫ cw

0

*

cw

0

*

*

*

ln for z >

ln for z <

b d

k

d

k

d

z

c c

c

c

cw

c

c

cw L

dz

z

u z

z

u z

u

u

u (9-3)

Here, k is von Karman’s constant (~0.4), roughness height z0 is related to the height, kr , of the

effective roughness elements by z0=kr/30, and z0c is the intercept expressing apparent roughness.

By matching velocity at z =dcw, apparent roughness, z0c, is related to “true” (i.e. without wave

effects) bottom roughness, z0, by

















−

















=

cw

c

u

u

c cw

z z

z *

* 1

0 0

0 d

(9-4)

The roughness height, kr, is considered to be related to grain size, ripple geometry, and

bed load transport:

r s bm k = D + + k

l

h

8h (9-5)
-151-

Here, Ds is sediment grain diameter and h and l are ripple height and length, respectively.

Ripple geometry was calculated according to Wiberg and Harris (1994). The moveable bed

roughness, kbm, is given by

( ) bm Ds m crk = 5 y′ −y (9-6)

where ym

′ is the maximum Shields parameter and ycr is the critical Shields parameter. Solution

scheme is shown in Fig 9-2.

Sediment: n,Dn,wfn, Ds, wfs

Wave-Current: T, ub, uc, z r, φc

Skin Friction

Roughness

Main Model

End

τ

’

cw

kb (z0)

τ cw (u*cw), u*c,

δcw, z0c,U(z),

C0,C(z)

Fig 9-2. Schematics of the GMG model solution procedure
-152-

9.3. GMG Model Simulation

The same grid as was used in the wave model (1500 by 1127, Δx = 30 m, Δy = 60 m) was

used. Current was assumed to be constant with 10 cm/s at 50 cm above bed. Also assumed was

that the wave propagation aligned with the current direction. Grain size was assumed to be 0.1

mm (very fine sand). Only the cumulative dredging (2.4 x 107 m3) was considered for checking

the possible impact caused by sand removal. The offshore wave conditions tested were given in

Table 4-1. For each of the wave conditions tested, four different directions of wave propagation

were calculated as described in Chapter 2 (i.e., ENE, E, ESE, and SE), respectively.

9.4. Results

Fig 9-3 shows the effect of propagation angle. For a given offshore wave height-period

condition (6 m and 14 s in this case), ENE waves result in the most variability; higher bottom

friction over shoals and lower bottom friction near the coastline along the trough between two

shoals. Figure 9-4 shows the changes in bottom friction by removal of shoal materials according

to different directions of wave propagation. The most visible changes were induced by ENE

wave setting near the coastline along the trough between two shoals. Because the lower bottom

stresses were seen in the troughs compared with the shoal crests, the visible changes in the

trough were translated into the slight change. In Fig 9-5, the distribution of the estimated bottom

frictions and their change induced by the dredging were compiled for the ENE offshore waves

with varying period and significant wave conditions which are supposed to have the most impact

if there are any. Consistently, the most visible changes were observed in the troughs where the

bottom frictions are small. This indicates that the impact from the dredging is not substantial.
-153-

Fig 9-3. Bottom friction velocity from different directions of wave propagation for offshore

waves of T = 14 s and hmo = 6 m.
-154-

Fig 9-4. Change resulting from removal of sands from the shoals in bottom friction velocity from

different directions of wave propagation for offshore waves of T = 14 s and hmo = 6 m.
-155-

Fig 9-5. (1) NE simulation result for T=10 s and Hmo=2 m

Fig 9-5. (2) NE simulation result for T=10 s and Hmo=4 m
-156-

Fig 9-5. (3) NE simulation result for T=12 s and Hmo=2 m

Fig 9-5. (4) NE simulation result for T=12 s and Hmo=4 m
-157-

Fig 9-5. (5) NE simulation result for T=14 s and Hmo=2 m

Fig 9-5. (6) NE simulation result for T=14 s and Hmo=4 m
-158-

Fig 9-5. (7) NE simulation result for T=14 s and Hmo=6 m

Fig 9-5. (8) NE simulation result for T=16 s and Hmo=2 m
-159-

Fig 9-5. (9) NE simulation result for T=16 s and Hmo=4 m

Fig 9-5. (10) NE simulation result for T=16 s and Hmo=6 m
-160-

Fig 9-5. (11) NE simulation result for T=16 s and Hmo=8 m

Fig 9-5. (12) NE simulation result for T=20 s and Hmo=2 m
-161-

Fig 9-5. (13) NE simulation result for T=20 s and Hmo=4 m

Fig 9-5. (14) NE simulation result for T=20 s and Hmo=6 m
-162-

Fig 9-5. (15) NE simulation result for T=20 s and Hmo=8 m
163

CHAPTER 10. SUMMARY AND CONCLUSIONS

The following conclusions provide a review of the impacts to waves and other oceanographic

processes that might be caused by the modeled dredging at Fenwick Shoal and Isle of Wight shoal.

1. Using high quality raw bathymetric data, a grid of 44.970 km x 67.560 km was created for studying

the possible changes on wave transformation and tidal currents. This grid is large enough to directly use

wave data measured at an NOAA offshore wave station, 44009, and to exclude the possible

inaccuracy caused by side boundaries. The grid cell size, however, is small enough (30 m x 60 m) to

show the effect of wave diffraction.

2. Fenwick Shoal and Isle of Wight Shoal are identified as the potential sources of beach quality sand. 

It is recommended to borrow sand from a shoal as opposed to a flat.

3. Two possible scenarios for dredging were considered. The first scenario is 2 x 106 m3 of sand from

each shoal. The purpose is to determine if a single harvest of sand would result in an unacceptable

impact. The second scenario is a total of 2.4 x 107 m3 of sand from the two shoals. The objective is to

determine if a cumulative sand borrow is acceptable or not.

4. A total of 13 years wave measurements from NDBC station 44009, about 45 km offshore at the

Ocean City, were used to analyze the possible choices of wave heights, periods, and directions that

should be analyze for alteration because of the modeled dredging at the two shoals. 

5. Two near shore wave stations, MD001 and MD002, provide about 4 years measurements. The

measured waves at these two near shore stations are almost identical. Data from both the offshore and

near shore stations provide a complete set for checking the selected wave transformation model.

6. Sixty wave conditions are selected as model wave conditions. These wave conditions include four

possible wave heights (2 m, 4 m, 6 m, and 8 m), five wave periods (10 s, 12 s, 14 s, 16 s, and 20 s),
164

and four wave directions (ENE, E, ESE, SE). Wave energy loss caused by bottom friction is not a

linear process, and thus, all four wave heights have to be included in the calculations.

7. Among the available numerical models for simulating water waves, wave hindcast/prediction models

(e.g., SWAN, HISWAP, NSW in Mike 21, and STWAVE) are not recommended because the

objective is not to look for wave growth nor to predict what kind of waves may be developed for a

given wind field. Among the wave transformation models (e.g., RCPWAVE, REF/DIF-1, REF/DIFS, RDE, and EMS module in Mike 21), REF/DIF-1 was selected because of the excellent accuracy in

wave height and computing efficiency. 

8. REF/DIF-1 was calibrated using one-month wave measurements (Nov. 1 to 30, 1997) from station

44009 and MD001. A total of 113 wave conditions were calculated and compared.

9.The calculated wave height distributions for the original bathymetry indicate that large waves

attenuated significantly because of the great energy dissipation caused by large near-bed velocity. 

Large waves also may break because of the shoals.

10. Near a location on the south of Ocean City, waves coming from the East have a tendency to

converge. The high wave energy (for all waves that come from the east) may be responsible for

causing the shore line retreat at the south of Ocean City. 

11. Near the Maryland-Delaware border, there is an area of extensive wave height attenuation because

of wave shoaling and breaking, after waves pass Fenwick Shoal which is approximately 10 km from

the coast. The relatively small breaking wave heights at this area may explain the relatively stable

shoreline near the border.

12. This study indicates that the one-time dredging at Fenwick Shoal and Isle of Wight Shoal for a total

of 4 x 106 m3 of sand is acceptable in terms of potential modification to wave transformation. The

major change to waves occurred not at the modeled dredging sites nor at the coast but between these
165

two.

13. For the cumulative dredging at Fenwick Shoal and Isle of Wight Shoal for a total of 2.4 x 107 m3 of

sand, this study suggests that the major change of wave height is also between the dredging site and the

shore line. The increase of local wave height can be as much as two times. The change of breaking

wave height, on the other hand, is not so obvious except the clear reduction of BHM at the Maryland

and Delaware border. 

14. The reduction of BHM at this location, however, is not necessarily a positive impact because it

increases the breaking wave height at that location. As a consequence, more erosion and shoreline

recession at that location might occur. Otherwise, the possible impact is not significant.

15. The SLOSH model developed in NOAA (the standard model used by FEMA) was used to check

the possible change of storm surge caused by the modeled dredging. A polar grid with 130 by 280 grid

cells was constructed for this project. 

16. Tropical storms with an 86 mbar central pressure drop and 15-mile maximum wind radius

(comparable to a category 4 storm) were used to simulate the coastal storm surges. Two orthogonal

tracks, one across- and one along-shore were simulated. The maximum change on storm surges are

about 0.1 cm which is negligible compared to the maximum surge (around 3 m). 

17. The maximum near-bed tidal current is weak, on the order of 5 cm/s except at the shoals, where

current velocity increases to around 8 cm/s. The postulated dredging at the shoals will reduce the

maximum near-bed tidal current velocity (around 10%). Immediately on the leeward side of tidal flow,

the dredging increases the tidal velocity, up to 10%. Because of the weak currents in this area, the

10% change only contributes less than 1 cm/s increase/decrease of tidal current. For this reason, the

possible impact on tidal currents is negligible.

18. The results from Chapters 7 and 8 provide evidence that dredging at the two selected offshore
166

shoals has little influence on tidal currents and storm surges. For future studies, these two processes

can be excluded.

19. The Grant-Madsen-Glenn (GMG) model was used to study the possible change of bed shear stress

caused by dredging. The results indicate that the change of bottom stress distribution is not substantial.

20. After dredging and beach nourishment sites have been decided, further studies on the shoreline

responses would be necessary. For studying the possible shoreline change, however, deterministic

wave forces are needed. An idealized time series (up to many years) should be established first to

change the original stochastic nature of wave occurrence to a deterministic process. In Chapter 2, a

method to idealize the wave time series has been suggested. All wave conditions (even small waves) in

the idealized time series should be calculated with a properly selected wave transformation model to

provide accurate breaking wave height and direction information for studying beach responses. How to

incorporate the extreme wave condition (e.g., the most severe sea reported in Chapter 2) in the

idealized time series for checking the possible beach responses may require more studies.
167

CHAPTER 11. BIBLIOGRAPHY

Blumberg, A.F. and Mellor, G.F., 1987. A Description of a Three-dimensional Coastal Ocean

Circulation Model, In: Three-dimensional Coastal Ocean Models, Coast and Estuarine

Science, Vol. 4 (Heaps, N.S. ed.), American Geophysical Union, Washington, D.C., 1-16.

Booij, N., Holthuijsen, L.H. and R.C. Ris, 1996. The SWAN Wave Model for Shallow Water, Proc.

25th Int. Conf. Coastal Engng., Orlando, Fla., USA, 1: 668-676. 

CERC Shore Protection Manual, 1984. 4th Ed., U.S. Army Engineer Waterways Experiment Station,

Coastal Engineering Research Center, Vicksburg, Miss.

Ebersole, B. A., 1985. Refraction-Diffraction Model for Linear Water Waves, J. of Waterway, Port,

Coastal, and Ocean Engrg., ASCE, 111(6): 939-953.

Glenn S. M. and W. D. Grant, 1987. A Suspended Sediment Stratification Correction for Combined

Wave and Current Flows, Journal of Geophysical Research, 92: 8244-8264.

Grant W. D. and O. S. Madsen, 1979. Combined Wave and Current Interaction with a Rough

Bottom, Journal of Geophysical Research, 84: 1797-1808.

Grant W. D. and O. S. Madsen, 1986. The Continental Shelf Bottom Boundary Bayer, Annual

Review of Fluid Mechanics, 18: 265-305.

Holthuijsen, L.H., N. Booij, R. Padilla-Hernandez, 1997. A Non-stational, Parametric Coastal Wave

Model, Ocean Wave Measurement and Analysis, ASCE, Ed.: B.L.Edge and J.M. Hemsley,

1: 630-638.

Jelesnianski, C.P., J. Chen, and W.A. Shaffer, 1992. SLOSH: Sea Lake, and Overland Surges from

Hurricanes, NOAA Technical Report NWS 48, Department of Commerce, USA, 71.

Kaihatu, J.M., W.E. Rogers, Y.L. Hsu, and C.O’Reilly. 1998. Use of phase-resolving numerical wave

models in coastal areas, Proceedings, 5th International Workshop on Wave Hindcasting

and Forecasting, January 26-30, Melborne, Fla.

Kim, S.-C., J. Chen and W.A. Shaffer, 1996. An Operational Forecast Model for Extratropical Storm

Surges along the US East Coast, Proceedings, Conference on Oceanic and Atmospheric

Prediction, American Meteorological Society, 281-286.

Kirby, J.T., and Dalrymple, R.A., 1991. User's Manual, Combined Refraction/Diffraction Model,

REF/DIR 1, Version 2.3, Center for applied Coastal Research, Dept. of Civil Engineering,

Univ. of Delaware, Newark, DE 19716.

Kirby, J. T and R.A. Dalrymple. 1994. Combined refraction/ diffraction model REF/DIF-1, version

2.5, documentation and user’s manual. CACR report 94-22, Center for Applied Coastal

Research, University of Delaware, Newark, Delaware, 171 p.

Kirby, J. T. and H. T. Ozkan, 1994. User Manual of REF-DIF S, Version 1.1, CACR-94-04, Center

for applied Coastal Research, Dept. of Civil Engineering, Univ. of Delaware, Newark, DE

19716.

Longuet-Higgins, M.S., Cartwright, D.E., and Smith, N.D., 1963. Observations of the Directional

Spectrum of Sea Waves Using the Motions of a Floating Buoy, in Ocean Wave Spectra,

Prentice-Hall, 111-136.

Maa, J. P.-Y., C. H. Hobbs, III, and C. S. Hardaway Jr., in press. A Criterion for Determining the

Impact on Shorelines Caused by Altering Wave Transformation, J. Coastal Research.
168

Maa, J.P.-Y., T.-W. Hsu, C.-H. Tsai and W.J. Juang, in press. Comparison of wave refraction and

diffraction models, Journal of Coastal Research.

Maa, J. P.-Y. and C.H. Hobbs, III, 1998. Physical Impact of Waves on Adjacent Coasts Resulting

from Dredging at Sandbridge Shoal, Virginia, J. Coastal Research, 14(2):525-536.

Maa, J. P.-Y, and H.-H. Hwung, 1997. A Wave Transformation Model for Harbor Planning, in 

B.L.Edge and J.M. Hemsley (eds.), Ocean Wave Measurement and Analysis, ASCE, 1:256-

270.

Maa, J. P.-Y. and D. W-C. Wang, 1995. Wave Transformation near Virginia Coast: The 1991

Halloween Northeaster, J. Coastal Research, 11(4), 1258-1271.

Maa, J. P.-Y. and C.-S. Kim, 1992. The Effect of Bottom Fiction on Breaking Waves using

RCPWAVE Model, J. Waterway, Port, Coastal and Ocean Engrg., 118(4), 387-400.

Massel, S. R., 1995. Ocean Surface Waves: Their Physics and Prediction, World Scientific Publ.,

Singapore.

Meindl. E.A., and Hamilton, G.D., 1992. Programs of the National Data Buoy Center. Bulletin of the

American Meteorological Society, 73(7), 985 - 993.

Mellor, G.L. and Yamada, T., 1982. Development of a Turbulence Closure Model for Geophysical

Fluid Problems, Rev. Geophys. Space Phus., 20(4): 851-875.

Murty, T.S., 1984, Storm surges-meteorological ocean tides, Canadian Bulletin of Fisheries and

Aquatic Sciences, 212: 897.

Radder, A.C., 1979. On the Parabolic Equation Method for Water Wave Propagation, J. Fluid

Mechanics, 95: 159-176.

Resio, D.T., 1987. Shallow water waves. I: Theory, J. of Waterway, Port, Coastal, and Ocean

Engineering, 113: 264-281.

Resio, D.T., 1988. Shallow water waves. II: Data comparison, J. of Waterway, Port, Coastal, and

Ocean Engineering, 114: 50-65.

Resio, D.T. and Perrie, W., 1989. Implication of f-4 Equilibrium Range for Wind-generated Waves, J.

of Physical Oceanography, 19: 193-204.

Smith, E.R., 1988. Case Histories of Corps Breakwater and Jetty Structures, Technical Report

REMR-CO-3. Coastal Engineering Research Center, Dept. of the Army, Waterways

Experiment Station, Corps of Engineers, Vicksburg, Mississippi, 133 pp.

Watts, G.M., 1953. A Study of Sand Movement at South Lake Worth Inlet, Florida, Tech. Memo.

No. 42, Beach Erosion Board.
The Department of the Interior

As the Nations’s principal conservation agency, the Department of the Interior

has responsibility for most of our nationally owned public lands and natural

resources. This includes fostering sound use of our land and water resources,

protecting our fish, wildlife, and biological diversity, preserving the

environmental and cultural values of our national parks and historic places; and

providing for the enjoyment of life through outdoor recreation. The

Department assesses our energy and mineral resources and works to ensure that

their development is in the best interests of all our people by encouraging

stewardship and citizen participation in their care. The Department also has a

major responsibility for American Indian reservation communities and for

people who live in island territories under U.S. Administration.

The Minerals Management Service Mission

As a bureau of the Department of the Interior, the Minerals Management

Service’s (MMS) primary responsibilities are to manage the mineral resources

located on the Nations’s Outer Continental Shelf (OCS), collect revenue from the

Federal OCS and onshore federal and Indian lands, and distribute those revenues.

Moreover, in working to meet its responsibilities, the Offshore Minerals

Management Program administers the OCS competitive leasing program and

oversees the safe and environmentally sound exploration and production of our

Nation’s offshore natural gas, oil and other mineral resources. The MMS Royalty

Management Program meets its responsibilities by entrusting the efficient, timely

and accurate collection and distribution of revenue from mineral leasing and

production due to Indian tribes and allottees, States and the U. S. Treasury

The MMS strives to fulfill its responsibilities through the general guiding principles of: (1)

being responsive to the public’s concerns and interests by maintaining a dialog with all

potentially affected parties and (2) carrying out its programs with an emphasis on working to

enhance the quality of life for all Americans by lending MMS assistance and expertise to

economic development and environmental protection.
Environmental Survey of Potential Sand Resource Sites Offshore Delaware and Maryland

Part 5: Maryland/Delaware Shoreline: Long-Term Trends and Short-Term Variability

This Adobe Acrobat file includes bookmarks and hot links. It has been set up to print two-sided. This

means that blank sheets may be inserted into the report to keep the page numbers correct. Also,

Figures 14 and 15 were created on tabloid size (11"x17" ) paper.
Final Report

July 2000

Authors:

C. S. Hardaway, Jr., D. A. Milligan, R.C.H. Brindley, 

and C. H. Hobbs, III

Virginia Institute of Marine Science

Project Manager:

Carl H. Hobbs, III

Virginia Institute of Marine Science

Prepared under MMS contract 1435-01-97-CT-30853 to the

Virginia Institute of Marine Science

College of William & Mary

International Affairs and Marine Minerals Division

Environmental Survey of Potential Sand Resource Sites

Offshore Delaware and Maryland 

Part 5: Maryland/Delaware Shoreline: Long-Term Trends and Short-Term

Variability
DISCLAIMER

This report has been reviewed by the Minerals Management Service and approved for publication.

Approval does not signify that the contents necessarily reflect the views and policies of the Service, nor

does mention of trade names or commercial products constitute endorsement or recommendation for use.
Final Report

Environmental Survey of Potential Sand Resource Sites

Offshore Delaware and Maryland

Part 5

Maryland/Delaware Shoreline: Long-Term Trends and Short-Term Variability

C. S. Hardaway, Jr., D. A. Milligan, R.C.H. Brindley, and C. H. Hobbs, III

Virginia Institute of Marine Science

Carl H. Hobbs, III, Project Manager

Virginia Institute of Marine Science

for

U. S. Department of the Interior

Mineral Management Service

Contract 1435-01-97-CT-30853

July 2000
ii
iii

TABLE OF CONTENTS

TABLE OF CONTENTS ....................................................... iii

LIST OF FIGURES ........................................................... iv

LIST OF TABLES ............................................................. v

1. INTRODUCTION ........................................................... 1

1.1. Study Scope and Purpose .............................................. 1

1.2. Environmental Impacts of Sand Mining and Emplacement ....................... 3

2. COASTAL SETTING ........................................................ 7

2.1. Geomorphic Setting ................................................... 7

2.1.1. Geologic History and Sea-Level Change ............................ 8

2.1.2. Coastal Barrier Migration ....................................... 9

2.1.3. Linear Shoals ............................................... 14

2.1.4. Tidal Inlets ................................................. 16

2.2. Hydrodynamic Processes .............................................. 18

2.2.1. Wave Climate ............................................... 20

2.2.2. Tide Levels ................................................. 21

2.2.3. Storms .................................................... 21

2.2.4. Sediment Characteristics and Transport ............................ 24

2.3. Cultural History and Shore Management Strategies ........................... 26

2.3.1. Rehoboth Beach ............................................. 26

2.3.2. Dewey Beach ............................................... 27

2.3.3. Bethany and South Bethany Beaches .............................. 28

2.3.4. Fenwick Island .............................................. 31

2.3.5. Ocean City ................................................. 31

2.4. Offshore Sand Resources .............................................. 32

3. METHODS ............................................................... 33

3.1. Background: Shore Change Analysis ..................................... 33

3.2. Data Analysis ....................................................... 36

3.2.1. Rates of Change ............................................. 36

3.2.2. Regression Analysis ........................................... 36

4. RESULTS AND DISCUSSION ............................................... 39

4.1. Delaware .......................................................... 39

4.1.1. Historic Shore Change ........................................ 39

4.1.2. Recent Shore Change ......................................... 45

4.1.3. Shoreface Retreat ............................................ 49

4.2. Maryland .......................................................... 54

4.2.1. Historic Shoreline Change ...................................... 54

4.2.2. Recent Shore Change ......................................... 54

4.2.3. Shoreface Retreat ............................................ 65

4.3. Wave Analysis ...................................................... 67

5. CONCLUSION ............................................................ 69

6. REFERENCES ............................................................ 71

Appendix 1. Dewey Beach regression analysis results
iv

LIST OF FIGURES

Figure 1. Delaware and Maryland study site location. ............................... 2

Figure 2. Coastal geomorphic compartment of the East Coast (after Kraft, 1985). .......... 7

Figure 3. Paleochannels and the position of the Delaware coast approximately 7,000 

years ago (Kraft, 1971). .............................................. 8

Figure 4. Present day coastal geomorphology of the Delaware coast from the Rehoboth 

headland south (Belknap and Kraft, 1985). ............................... 10

Figure 5. Present day coastal geomorphology of the Delaware coast from the Bethany 

headland north (Belknap and Kraft, 1985). ............................... 11

Figure 6. Evolution of transgressive coastal systems during net shoreface erosion and 

sea-level rise at Rehoboth Bay and Rehoboth Beach. ....................... 13

Figure 7. Location of linear ridges on the Mid-Atlantic shelf. ......................... 15

Figure 8. The genesis of linear ridges based on a model developed by 

McBride and Moslow (1991). ........................................ 16

Figure 9. Location of historical tidal inlets along the Delaware and Maryland coasts 

(McBride 1999). .................................................. 17

Figure 10. Aerial photos of A.) Indian River Inlet and B.) Ocean City Inlet.. ............... 19

Figure 11. Aerial photos of A.) Bethany Beach and B.) South Bethany Beach. ............ 30

Figure 12. Location of Delaware and Maryland ocean coast beach profiles as well as

Delaware coastal segments from U.S. Army Corps of Engineers (1996) analysis. . . 35

Figure 13. Historic shorelines at A.) Cape Henlopen and B.) Indian River Inlet. ............ 41

Figure 14. Historic shore positions and historic and recent rates of change along the 

Delaware and Maryland shoreline. ..................................... 43

Figure 15. Erosion of the shoreface represented by beach profiles taken in 1964, 1982, 

and 1999. ....................................................... 51

Figure 16. Alongshore variability in seaward distance of active profile envelope relative 

to the two shoreface-attached shoals (Stauble et al., 1993). .................. 58

Figure 17. Three-dimensional plot of pre- and post- state fill beach profiles ............... 59

Figure 18. Three-dimensional plot of state fill pre- and post- storm beach profiles 

(Stauble et al., 1993). .............................................. 60

Figure 19. Three-dimensional plot of pre- and post- federal fill beach profiles 

(Stauble et al., 1993). .............................................. 61

Figure 20. Three-dimensional plot of federal fill pre- and post- storm beach profiles 

(Stauble et al., 1993). .............................................. 62

Figure 21. Percent of fill remaining above NGVD, below NGVD, and over the total profile 

length after the 28 month state and federal fill monitoring as measured from 

September 1988 to January 1992 (Stauble et al., 1993). .................... 63

Figure 22. Normalized wave height distribution offshore of Delaware and Maryland 

in the vicinity of the proposed dredging sites (Maa and Kim, 2000). ............ 68
v

LIST OF TABLES

Table 1. Ocean tide ranges in centimeters for the study area (NOAA, 1989). ............ 21

Table 2. Vertical Datums for Breakwater Harbor, Lewes, and Indian River Inlet, 

Delaware and Ocean City, Maryland. ................................... 22

Table 3. High tides of record for Breakwater Harbor, Lewes, Delaware. Elevations 

referenced to MLLW (modified from Ramsey et al., 1998). ................. 23

Table 4. The adopted stage frequency data at Rehoboth and Dewey Beaches 

(modified from House Document No. 105-144, 1997). ..................... 24

Table 5. Information on beach nourishment projects that have taken place along the 

Delaware Coast (Valverde et al., 1999). ................................ 29

Table 6. Information on beach nourishment projects that have taken place along the 

Maryland Coast (Valverde et al., 1999). ................................ 32

Table 7. Rates of change along the northernmost section of Delaware between 1845 

and 1943 ........................................................ 39

Table 8. Delaware historic regression analysis results by profile. ...................... 47

Table 9. Recent regression of Delaware’s ocean coast profile data. .................... 48

Table 10. Comparison of rates of change obtained by various methods. ................. 49

Table 11. Regression analysis of profile data taken at Dewey Beach. ................... 50

Table 12. Shoreface rate of change by offshore contour. ............................. 53

Table 13. Maryland historic regression analysis results. .............................. 55

Table 14. Ocean City Beach condition on October 1995. ............................ 57

Table 15. Ocean City Beach Condition Assessment Table for May 1996. ................ 64

Table 16. Ocean City Beach Condition Assessment in June 1997. ..................... 65

Table 17. Ocean City Beach Condition Assessment Table for October 1997. ............. 66

Table 18. Maryland recent regression analysis results. ............................... 66
vi
1. INTRODUCTION

1.1. Study Scope and Purpose

The Maryland-Delaware ocean coast is a continuous shoreline with varying geomorphology,

shoreline erosion rate, and degree of human development. The Delaware coast stretches from Cape

Henlopen in the north to the border with Maryland at Fenwick Island (Figure 1). The shoreline has an

approximate north-south orientation and consists of six distinct locales, Henlopen Acres, Rehoboth

Beach, Dewey Beach, Bethany Beach, South Bethany, and Fenwick Island, that together have 39 km

of sandy shoreline. Several unincorporated private developments exist in the study area as do three

state parks. Cape Henlopen State Park is located at the northern end of the study area extending from

Cape Henlopen to the private community of North Shores. Delaware State Park occupies the central

portion of the state’s coast both north and south of Indian River Inlet. Fenwick Island State Park is

located in the southern section of Delaware between South Bethany and Fenwick Island. Most of the

residential communities are characterized by a barrier beach system, except for Rehoboth Beach and

Bethany Beach, which are formed against headlands. The coast of Delaware is broken midway by

Indian River Inlet.

The Maryland coast stretches from the Maryland-Delaware border on Fenwick Island to about

three-fourths the way down Assateague Island (Figure 1). The Ocean City portion of Fenwick Island,

which extends from the Maryland-Delaware state line to Ocean City Inlet, is 14 km long. The shoreline

from Ocean City Inlet to Fishing Point, Virginia (Assateague Island) is about 60 km long. Only the

Ocean City portion of the Maryland shore will be discussed in this report.

The Maryland-Delaware Atlantic coast lies in the Atlantic Coastal Plain province which is

comprised fundamentally of unconsolidated sediments with emerged (coastal plain) and submerged

(continental shelf) sections divided by the Atlantic shoreline. This coast is characterized by the Atlantic

barrier system which consists of coastal marshes, beach-dune complexes, and tidal lagoons; all of

which are constantly changing. Historically an erosive shoreline, the Maryland/Delaware coast erodes

across the entire shoreface in response to sea- level rise and storm events. Shoreline erosion and

shoreface retreat provide sediments to the littoral system.
Pennsylvania

Maryland

Cape

Henlopen

Baltimore

Virginia

Washington

D.C.

Atlantic

City

CapeMay

Ocean

City

MD

VA

Fishing

Point

Cape

Charles

CapeHenry

AlbemarleSound KittyHawk

Virginia

NorthCarolina

Norfolk

N

Kilometers

25 0 25

Miles

25 0 25

390

400

780

770

760

750740

380

370

360

Atlantic

Ocean

USA

Figure1. DelawareandMarylandstudysitelocation.

Indian

RiverInlet

OceanCity

Inlet

IsleofWight

Bay

Assawoman

Bay

Indian

River

Bay

Rehoboth

Beach

Dewey

Beach

Bethany

Beach

Fenwick

Island

Delaware

Maryland

Ocean

City

Cape

Henlopen

Rehoboth

Bay

NauticalMiles

0 1 2 3 4

0 5

Kilometers

2
3

Beaches are constantly in transition. Their shape is related to both short-term and long-term

factors including relative sea level, beach migration, and wave climates. Relative sea level is a long-term

factor associated with beach transition since geological processes such as emergence or subsidence of

the land and level of the sea effect changes. Relative sea level change causes the coast to re-adjust to

the new sea level which causes beach migration by either accretion or erosion of the shoreline and

shoreface (Bosma and Dalrymple, 1997).

The Maryland-Delaware coast is one of the most intensely studied coasts in the world.

Professor John Kraft of the University of Delaware and his students have performed the bulk of the

geological research within the Delaware coastal system. The Maryland Geological Survey and the U.S.

Army Corps of Engineers have performed several studies of the Ocean City area.

Over the past 20 years, several beach nourishment projects have injected a significant volume

of sand into the region’s littoral system. These beach nourishment projects have used sand from both

submarine sand bodies and upland borrow areas. Sand mining beyond the 3-mile limit in Federal

waters is under the jurisdictional authority of the U. S. Department of the Interior Minerals Management

Service (MMS). 

The purpose of this study is to evaluate the impacts of beach nourishment on shoreline change

along the Maryland-Delaware coast. Modifying the offshore topography through mining and the shore

through nourishment potentially can cause significant change to the littoral transport system and can alter

the patterns of shoreline change along the entire coast. 

1.2. Environmental Impacts of Sand Mining and Emplacement

Both Maryland and Delaware have beach nourishment needs. Delaware has coastal resort

communities at Rehoboth, their largest resort, Dewey Beach, just south of Rehoboth, and Bethany

Beach. Maryland has one the largest resorts on the east coast at Ocean City. Most of the Delaware

Atlantic coastline is in a state of erosion (Bosma and Dalrymple, 1997). The state’s Department of

Natural Resources and Environmental Control (DNREC) has been responsible for preservation and
4

enhancement of the beaches since 1972. DNREC has used several methods including sand bypassing, beach nourishments, groins, and dune stabilization to stabilize the shoreline. Delaware projects

a sand need for18 million m3 of sand over the next 50 years. Ocean City, Maryland will require 9.2

million m3 within the next 50 years (Maryland Geological Survey Website, 2000). The only cost

effective means of supplying sand for these projects is the use of offshore sources of beach quality sand. 

Sand resources dot the offshore region of Maryland and Delaware, but known borrow sites within the

5 km (3 nautical miles) limit may not be sufficient to meet demands.

Existing geologic conditions place constraints on offshore sand mining since geomorphic

features and geologic features with obtainable and suitable sand reserves determine the mining site. 

However, dredging may impact present geologic, physical, and biological processes and conditions. 

Indeed, the act of mining offshore sand reserves has potential environmental impacts to the mining area

as well as the adjacent coast. 

The sedimentary condition at the offshore borrow site also is impacted by offshore dredging. 

Dredging increases water depth by changing the bathymetry at the borrow site by removing 

topographic features (i.e. a shoal) or creating a depression or pit. If large alterations in the bathymetry

occur, they can impact local wave and current patterns which, in turn, can alter depositional and

erosional trends. These changes may affect both local biological resources and water chemistry. 

After completion of a beach nourishment project, the longshore transport system may be

affected by an increase in sediment transport away from the nourished beach. At fill sites without sand

retention structures, there may be a temporary positive impact on beaches downdrift of the nourished

beach as the newly placed material spreads. Changes in nearshore bathymetry may occur as finer

material winnows from the newly-placed sand and deposits offshore. Several months of tidal and wave

action may be needed to redistribute this material so that the substrate elevations and slopes can return

to pre-placement conditions (Louis Berger Group, 1999). Other possible impacts onshore from

dredging include nourishment impact from sands that are too fine or too course and altered offshore

current patterns from the dredging site which can affect onshore sediment transport and wave energy.
5

Burial due to sand placement and subsequent transport can have long-term biological and

cultural impacts. Even though grain size distribution is of concern when choosing a beach fill material,

shell content, sediment type, and color may be as important for recreational beaches since aesthetic

appearances of the beach are important in affecting the user’s perception of the beach.
6
7

Figure 2. Coastal geomorphic compartment of the East Coast (after

Kraft, 1985).

2. COASTAL SETTING

2.1. Geomorphic Setting

The Maryland-Delaware coast comprises the northern half of the major geomorphic coastal

compartment that extends from Cape Henlopen, Delaware on the northern boundary, southward to

Cape Charles, Virginia (Figure 2). Maryland and Delaware occupy the northern three-fourths of the

subreach from Cape Henlopen to Fishing Point, Virginia. The compartment is subdivided into four

geomorphic regions: a spit complex on the north, a series of eroding headlands and baymouth barriers, 

linear barriers, and, finally, a long reach of island barriers. 
8

Figure 3. Paleochannels and the position of the Delaware coast

approximately 7,000 years ago (Kraft, 1971).

2.1.1. Geologic History and Sea-Level Change

About 14,000 years ago, the ocean coast of Maryland-Delaware was about 100 km to the

east, and sea level was about 90 m lower. The lower coastal plain was dissected with a complex

dendritic drainage pattern that, in large part, was controlled by previous low sea levels as channels were

incised into older Pliocene strata to set the drainage “template” for subsequent transgressive/regressive

sedimentary sequences. Figure 3 schematically portrays the Delaware coast 7,000 years B.P.

Since that time,

sea level has been rising

across the low gradient

coastal plain (Kraft and

John, 1976). Eroded

sands are transported

alongshore and crossshore (on-offshore) by

the impinging waves and

currents. However, the

background driving force

behind shore change is

the slow, continuous rise

in sea level. As a result,

the shoreline and

shoreface region has

transgressed across the upland system eroding interfluves and flooding and filling the adjacent stream

and river beds. 

Today, these eroding interfluves are headlands, and the cross drainage sedimentary sequences

are bay-mouth barriers. Tidal records at Baltimore, Maryland from 1903 to1986, indicate that relative

sea level continues to rise at about 0.3 m per century (Lyles, Hickman, & Debaugh, 1988) which
9

would force continued shoreline regression. Erosion of the underlying material along the shoreface

provides the sediment, particularly the sands and gravels, necessary to create and maintain the sandy

barrier beaches and dunes. The degree of down-cutting and the type underlying strata dictate the type

and quantity of sediment that is available. 

Belknap and Kraft (1985) show the three dimensional nature of the present marine

transgression. Figure 4 and Figure 5 indicate the current coastal morphology from the Rehoboth Beach

headland to the Bethany Beach headland and describe the stratigraphic relationships of the Pleistocene

substrate, ancestral creeks and rivers, and Holocene barrier, backbarrier, and nearshore sedimentary

environments. Shoreface erosion supplies material to the littoral system, and these materials may be

predominantly sand, silts, or clays depending on the nature of the eroding strata. Historical shoreline

recession rates are a measure of shoreface retreat and averaged about 1 m/yr from 1843 to 1961

(Belknap and Kraft, 1985). Cape Henlopen was the only area of significant deposition.

The Maryland coast has a similar but distinctive transgressive history. Although, the study area

is confined to Fenwick Island, a transgressive barrier, from Ocean City Inlet to the MD-DE state line, it

is noteworthy that Assateague Island is a major, transgressive barrier that is morphologically part of the

MD-DE coastal compartment. Fishing Point at the distal end of Assateague Island has been is a major

sediment sink. Nearshore linear sand ridges are common to this coastal compartment and have a

significant impact on the impinging wave climate and, therefore, the nature and patterns of shoreline

recession and shoreface retreat. 

2.1.2. Coastal Barrier Migration

A coastal barrier beach must retreat and adjust to a new equilibrium as sea level rises. Erosion

occurs from the beach face as sea level rises. The eroded sediment generally is deposited landward

across the barrier. The U.S. Army Corps of Engineers (1996) reports that this principal edge of marine

transgression has advanced across nearly two-thirds of the Coastal Plain province of Maryland and

Delaware in the past 15,000 years.
Figure4. Presentdaycoastalgeomorphologyof theDelawarecoastfromtheRehobothheadlandsouth(BelknapandKraft,1985).
11

Figure 5. Present day coastal geomorphology of the Delaware coast from the

Bethany headland north (Belknap and Kraft, 1985).

Beach erosion and reconstruction are phases that occur cyclically. Some studies suggest that

the phases are controlled by the seasons. Winter storms erode beaches and summer swells rebuild

beaches (Dubois, 1988). Other studies suggest that beach erosion and reconstruction does not

coincide with the seasons. They suggest that because storms occur throughout the year, the erosional

phase is not strictly in the winter (Hayes and Boothroyd, 1969; Owens, 1977; Carr et al., 1982). 

They state that a typical summer or winter profile can occur in any season. However, one variable that

may control the extent of progradation is the amount of transportable sediments in the nearshore. The

processes responsible for aggradation were more important in the total construction of the beach than

those responsible for progradation (Dubois, 1988).

Barrier beaches normally migrate landward with overwash of storm waves which transports

sand from the ocean side of the barrier to the bay side. Inlets also contribute to beach migration as the

ebb and flood tidal deltas serve as traps and reservoirs of sediment.
12

Fenwick Island is a sandy barrier spit that extends between Indian River Inlet, Delaware to the

north and Ocean City Inlet, Maryland to the south. The distance between these two points is

approximately 32 km. Isle of Wight and Assawoman Bays back Fenwick Island in Maryland

(excluding back-bay wetlands and marinas). The average widths of the island between the two bays

and ocean are about 610 m and 460 m, respectively. Assateague Island, which lies south of Ocean

City Inlet, is a 60 km long sandy barrier island backed by Sinepuxent Bay to the north and by

Chincoteague Bay to the south. It terminates at Chincoteague Inlet, Virginia.

Assateague and Fenwick Islands once were joined. A powerful hurricane separated the islands

on 23 August 1933 by opening what is now called Ocean City Inlet. The inlet was made permanent by

the construction of jetties in 1934 by the U.S. Army Corps of Engineers which maintains the inlet for

navigation. The jetties have allowed the development of large ebb and flood tidal shoals which have

diminished the supply of sediment which normally would have reached Assateague Island through

littoral drift which is from the north (Stauble et al., 1993).

Fenwick Island is interrupted by the Bethany Beach headland and Cottonpatch Hill headland

before reaching Indian River Inlet. This part of the barrier is backed by Little Assawoman Bay south of

Bethany Beach and Indian River Bay north of Bethany and landward of Cottonpatch Hill. North of the

inlet, the barrier continues toward Dewey Beach and the Rehoboth Beach headland. This unnamed

barrier is backed by Rehoboth Bay.

Rehoboth and Bethany Beach are found against headlands and are characterized by a lack of

lagoons and higher elevations. Headland erosion occurs by sediment transport along and offshore, not

by overwash (Bosma and Dalrymple, 1997). The difference between lagoon-baymouth barrier

transgression and headland beach transgression is illustrated in Figure 6 for past, present, and future

projections of the present sea level rise for Rehoboth Bay and Rehoboth Beach, respectively. Barrier

beach transgression is characterized by shoreface erosion of Pleistocene and Holocene substrates. 

Shoreface erosion of headland beaches works primarily on the underlying pre-Holocene material.
Figure6. Evolutionoftransgressivecoastalsystemsduringnetshoreface erosion and se-level riseatRehobothBay andRehobothBeach.
14

2.1.3. Linear Shoals

Besides Cape Henlopen, other Holocene accretionary features along the MD-DE coastal

compartment include various linear ridge shoal systems. Linear ridges are a dominant topographic

feature of the Mid-Atlantic shelf, and they are particularly well-defined on the Maryland shelf 

(Figure 7). Here sand ridges can be seen in all stages of formation. A systematic morphologic change

occurs from shoreface ridges, through nearshore ridges to offshore ridges, reflecting changes in the

hydraulic regime (Swift and Field, 1981). As one examines more seaward ridges, maximum side

slopes decrease, the ratio of maximum seaward slope to maximum landward slope decreases, and the

cross-sectional area increases. These changes in ridge morphology with depth and distance from shore

appear to be equivalent to the morphologic changes experienced by a single ridge during the course of

the Holocene transgression (Swift and Field, 1981). 

The genesis of the shoal ridges is depicted with a model that couples inlet migration with shore

retreat (Figure 8). A genetic relationship exists between the ebb-tidal delta sand body and the location

and orientation of shoreface-attached sand ridges (McBride and Moslow, 1991). The changes in

bottom characteristics that accompany large-scale morphologic features include megaripples, sand

waves, and mud lenses in the troughs between nearshore and offshore ridges. These changes indicate

that storm flows, which maintain ridges, are less frequently experienced in the deeper ridge field sectors

and that the role of high-frequency wave surge becomes less important relative to the role of the mean

flow component shaping the sea-floor.

Sand ridges are considered a consequence of constructive feedback between initial topography

and the resulting distribution of bottom shear stress. The relationship between grain size and

topography supports this model but does not account directly for the oblique angle of the ridge with

respect to the coastline. A more rapid alongshore migration of the inshore edge of the ridge than the

offshore edge as well as the relationship between this migration and the rate of shoreface retreat may

create the angle (Swift and Field, 1981). 
Figure7. LocationoflinearridgesontheMid-Atlantic shelf.

15
16

Figure 8. The genesis of linear ridges based on a model developed by McBride and

Moslow (1991).

Shore-attached and nearshore sand ridges have the most economically feasible sources of

beach-quality sand. Those shoals have been exploited for beach nourishment in the past and will be

looked to in the future. Removal of a small volume of sand has a relatively insignificant impact on the

impinging waves. Removal of very large volumes, however, can have the effect of focusing or defocusing storm waves which, in turn, may impact the littoral processes on adjacent shorelines.

2.1.4. Tidal Inlets

 The historical existence of tidal inlets has controlled, in part, sand movement through

sedimentation processes associated with ebb and flood shoals. The inlets have, through time, partially

determined the rate and patterns of shoreline change. As discussed in the previous section, inlets have

been an influential parameter in the development and morphology of shelf sand ridges. McBride (1999)

identified 22 historical inlets from as early as 1649 along the MD-DE coast (Figure 9) . As the number

of inlets increase from north to south so too does the density of offshore sand ridges.
Figure9. Locationof historicaltidal inletsalongtheDelawareandMarylandcoasts(McBride1999).
18

Two major tidal inlets interrupt the MD-DE coastal reach between Indian River and Ocean

City (Figure 10). They have been “hardened” with jetties and dramatically indicate the net alongshore

movement of beach sands in their respective shore subreaches. In 1937, Congress authorized a 5 m

deep channel from the ocean to 2130 m into Indian River Bay and two jetties to stabilize the channel

(House Document No. 105-144, 1997). Before its construction in 1939 (Smith, 1988), Indian River

Inlet was an intermittent tidal channel that breached during storms. It was an area of extensive sand

washover and accretion. Since installation in 1937, the jetties have had to be extended landward and

the channel bulkheaded in order to maintain steady tidal flow. In 1990, a sand bypassing system was

installed to reduce downdrift erosion and provide for more continuous alongshore sediment transport. 

The plant pumps sand from the south side of the inlet to various points along a 460 m section of the

shore north of the inlet. The plant was designed to pump on the average 76,000 m3 of sand per year

(House Document No. 105-144, 1997). 

In 1934, Ocean City inlet was hardened with jetties and dredged (Smith, 1988). This provided

for a permanent separation between Fenwick and Assateague Islands. Since the net alongshore

sediment drift along this reach is southward, the inlet and jetties accumulate sand to the north. Several

major beach nourishment projects at Ocean City have supplied a relatively large amount of sand to the

littoral system. Some of this material is directed offshore by the north side inlet jetty where it becomes

part of the ebb-shoal. This ebb shoal currently is reported to be bypassing sand effectively to the north

end of Assateague Island (D. Stauble, personal communication).

2.2. Hydrodynamic Processes

The shoreline along the MD-DE coastal reach generally is recessive in response to present day

hydrodynamic forces. The coast is eroding along most of its length with alongshore transport patterns

showing a dominant southerly drift from about the state line southward and a dominant northerly drift

from about Bethany Beach north. There is a sediment transport node in between. The resultant

sedimentary features include accretionary spits at the distal end of the MD-DE coastal reach -- Cape

Henlopen and Fishing Point. 
Figure 10. AerialphotosofA.)IndianRiverInletandB.)OceanCityInlet

(Beach-NetWebsite,2000).

A

B

19
20

2.2.1. Wave Climate

With the exception of gravity, waves are the dominant factor affecting the form of a beach

profile. “The energy they carry to the shoreline and the variety of currents and turbulence they generate

play the leading role in the orientation of the beach” (Bosma and Dalrymple, 1997). 

Bosma and Dalrymple (1997) used the 20-year hindcast of general wave climatology,

presented in the Corps of Engineers’ WIS Report 30, to develop wave data for shoreline research

along the Delaware coast. Waves approach the shore from the northeast to the southeast directions,

with the greatest incident levels coming from the east and southeast. A March 1962 northeaster (from

the hindcast data) generated the largest wave height at 7.7 m. The highest wave height off Dewey

Beach between 1992 and 1993 recorded by a wave gauge was 4.1 m during a December 1992

northeaster. 

As of 1980, there were no recorded wave data for the Ocean City, Maryland area. Data from

the Atlantic City wave gage, located 70 miles north, was taken as representative of the wave climate at

Ocean City. Average waves had a height of 0.8 m and a period of 8.2 seconds. Average storm

conditions had a wave height of 2.7 m and a period of 11 seconds (Louis Berger Group, 1999).

The annual average wind speed along the Delaware Coast is 6.5 m/s. In the 5-degree

quadrangle nearest the Delaware Coast, the winds over the offshore areas are distributed with respect

to direction as follows: onshore (northeast, east, and southeast) 27%; (south) 11%, offshore

(southwest, west and northwest) 44 %; and (north) 15%. Winds from the northeast have the greatest

average velocity of approximately 9m/s. The wind data also show that winds in excess of 13 m/s occur

from the northeast more than twice as frequently as from any other direction (House Document No.

105-144, 1997).

Maa and Kim (2000) investigated the impacts of linear shoals on waves along the MD-DE

coast with goal of evaluating the effect of shoal removal from mining for beach nourishment. They also

report on the statistical distribution of wave characteristics in the present study area.
21

2.2.2. Tide Levels

The tide at Ocean City is semidiurnal (i.e., there are two low and two high waters each day). 

The mean and spring tide ranges for the study are shown in Table 1. Generally, the tide range is

greatest at Cape Henlopen and decreases toward the south. The National Ocean Service records for

tidal datums at the Ocean City Fishing Pier for the tidal epoch 1960-1978 show mean

high water (MHW) at 55 centimeters (cm) above the National Geodetic Vertical Datum (NGVD),

mean low water (MLW) is 49 cm below NGVD, and mean sea level is 4 cm above NGVD. At

Rehoboth and Dewey Beaches, MHW is 70 cm above NGVD. The location of MHW at Bethany,

South Bethany, and Fenwick Island is 64 cm NGVD (A. Kansak, personal communication). Table 2

indicates the relationships amongst the various vertical datums for Lewes and Indian River Inlet,

Delaware and Ocean City, Maryland.

Table 1. Ocean tide ranges in centimeters for the study area (NOAA, 1989).

Location Mean (cm) Spring (cm)

Cape Henlopen 125 149

Rehoboth Beach 119 143

Fenwick Island Light 113 137

Ocean City, Maryland 107 128

2.2.3. Storms

Northeasters are the most frequent significant major storm systems that strike the Atlantic

coasts of Maryland and Delaware (Grosskopk and Resio, 1988). Northeasters are extratropical

cyclones that mainly occur from late fall through late spring. They usually form along the mid-Atlantic

shore, often off the capes of North and South Carolina. They tend to be relatively large in size and can

work on any specific section of shore for more than one tidal cycle.

Hurricanes are tropical cyclones that form in lower latitudes and often travel northward along

the U. S. Atlantic coast. Although hurricanes are more intense storms with higher winds and larger

waves than northeasters, usually they move more rapidly and may not even last through a full tidal cycle
22

at any particular location. The hurricane season extends from the first of June through the end of

November, but September and October are the most active months. During the late fall, the area is

susceptible to both hurricanes and northeasters. As hurricanes move up the mid-Atlantic coast, their

tracks tend to swing clockwise through north toward northeast rarely crossing the Maryland-Delaware

shore. No hurricane center has made landfall along the Delaware coast since 1871 when formal

records began being kept (House Document No. 105-144, 1997). 

Table 2. Vertical Datums for Breakwater Harbor, Lewes, and Indian River Inlet, Delaware and

Ocean City, Maryland.

Breakwater Harbor, 

Lewes, DE

Indian River Inlet,

DE

Ocean City, MD

Datum Elevation (m) Elevation (m) Elevation (m)

Highest observed water level 2.89

6 Mar 1962

2.06

5 Feb 1998

1.95

5 Feb 1998

Mean Higher High Water (MHHW) 1.44 0.92 0.79

Mean High Water (MHW) 1.31 0.83 0.72

NAVD 88 0.89

Mean Sea Level (MSL) 0.4

Mean Tide Level (MTL) 0.68 0.45 0.38

NGVD 29 0.52

NGVD 29 (1972 Adjustment) 0.41

NGVD 29 (1967 Adjustment) 0.27

Mean Low Water (MLW) 0.05 0.08 0.04

Mean Lower Low Water (MLLW) 0 0 0

Lowest Observed Water level -1.2 -1.19 -0.59

In addition to producing high waves, storms also affect water level. Elevated water-levels

cause coastal flooding. Storm tides, or storm surges as they also are known, raise the level of wave

attack. The high water levels and high waves frequently cause overwash of low barriers. Table 3 lists

the high tides of record for Breakwater Harbor, Delaware.
23

Table 3. High tides of record for Breakwater Harbor, Lewes, Delaware. Elevations referenced

to MLLW (modified from Ramsey et al., 1998). 

Rank Date Tide Height (m) Storm Type Name

 1 6 March 1962 2.89 Northeaster

 2 4 January 1992 2.75 Northeaster

 3 28 January 1998 2.73 Northeaster

 4 5 February 1998 2.67 Northeaster

 5 27 September 1985 2.53 Hurricane Gloria

 6 3 March 1994 2.52 Northeaster

 7 25 October 1980 2.50 Northeaster

 8 29 March 1984 2.49 Northeaster

 9 8 March 1996 2.47 Northeaster

10 12 December 1992 2.44 Northeaster

11 22 October 1961 2.43 Hurricane Esther

12 14 October 1977 2.41 Hurricane Evelyn

13 31 October 1991 2.38 Northeaster “Perfect”

14 18 September 1936 2.35 Hurricane No. 13

22 October 1972 2.35 Northeaster

14 November 1997 2.35 Northeaster

17 2 January 1987 2.32 Northeaster

18 8 October 1996 2.32 Northeaster

19 14 October 1953 2.31 Northeaster

9 December 1973 2.31 Northeaster

The most extreme storm on record in regards to the highest offshore waves, greatest surge, and

longest duration, was the northeaster of March 6-8, 1962. The storm is referred to as the “Ash

Wednesday” or the “Five High” storm. The storm moved directly offshore from the Ocean City area,

intensified, and became stationary. This created persistent onshore winds in excess of 27 m/s (60 mph,

52 kn) for five high tides (i.e., “Five High”). These conditions continued for over 60 hours and

produced the area’s record high storm surge (Table 3). The storm caused $16.7 million (1962 dollars)

in damage.
24

As well as the water levels reported in Table 3, it should be noted that the third and fourth

highest tides occurred within eight days of one another, January 28 and February 5, 1998. Also only

one of the top ten storms was a hurricane, Gloria in September 1985. The 13th highest tide level

reported for Breakwater Harbor, DE, October 31, 1991, is the so called “Perfect Storm” which had its

greatest impacts north of Delaware Bay.

Table 4 presents a tide-stage frequency analysis for Delaware’s ocean coast. Although storms

commonly are referred to as “the ten year storm” or the “hundred year storm,” it is better to consider

the probability that a particular storm strength, or water level, will occur in any one year as the sense of

frequency can give a false sense of security – “We had the 50 year storm last year so we don’t have

anything to worry about for decades.”

Table 4. The adopted stage frequency data at Rehoboth and Dewey Beaches (modified from

House Document No. 105-144, 1997).

Probability of Exceedance 

(%) in Any Year

Return Interval (years) Elevation 

(m NGVD)

99 1 1.7

50 2 2

10 10 2.2

5 20 2.4

2 50 2.7

1 100 3

0.5 200 3.2

0.2 500 3.5

2.2.4. Sediment Characteristics and Transport

The U.S. Army Corps of Engineers Automated Coastal Engineering System (ACES) was used

to develop composite beach grain size curves for Dewey Beach, Rehoboth Beach, Bethany Beach, and

South Bethany. Predicated on winter and summer beach composites, Dewey and Rehoboth beaches

had a mean grain size of 0.28 mm with a standard deviation of 0.23 mm, and Bethany Beach and South

Bethany had a mean grain size of 0.29 mm with a standard deviation of 0.48 mm. This relates to a
25

poorly graded, or well sorted, fine to medium sands for all four beaches. Ocean City sediments have a

mean grain size of 0.25 mm with a standard deviation of 0.51 mm. Assateague Island has a mean grain

size of 0.23 mm and a standard deviation of 0.69 mm (Louis Berger Group, 1999).

Typically, waves strike beaches at an oblique angle which creates both longshore and crossshore sediment transport. Cross-shore sediment transport is the movement of sand on and offshore

while longshore sediment transport is the movement of sand along the beach (i.e. littoral drift). Crossshore sediment transport is normally temporary or seasonal. Dick and Dalrymple (1983) found that

cross-shore sediment movement (on- and offshore movement of sand) was directed seaward during

winter seasons and landward in the summer and fall. Longshore transport has the most central influence

on the amount of sediment leaving or entering a given location. Longshore transport direction changes

with the course of wave approach.

The change in orientation of the coast which occurs along Fenwick Island may have

consequences for longshore sand transport direction. Evidence of impoundment at the south jetty and

erosion at the north jetty at Indian River Inlet indicates net longshore sand transport on the order of

76,000 m3/yr to the north (Clausner et al., 1991). Impoundment at the north jetty of Ocean City Inlet

and shoreline recession on Assateague Island adjacent to the south jetty, indicate transport toward the

south end of Fenwick Island. A divergent nodal point in transport along Fenwick Island has been

placed in the vicinity of Bethany Beach. However, long-term trends in the wave climate imply great

spatial and temporal variability in the location of the nodal point (Dean and Perlin, 1977).

Cape Henlopen, at the northern end of the study area, has an historic northward growth spit

which is evidence of a predominant northward longshore transport whose rate ranges between 115,000

m3/yr to 191,000 m3/yr. At Indian River Inlet, 21 km south of Cape Henlopen, sediment transport also

is toward the north as evidenced by long-term erosion and deposition patterns on either side of the

inlet. Indian River Inlet had a northward net transport ranging between 57,000 m3/yr to 115,000 m3/yr. 

A southward net transport ranging between 96,000 m3/yr to 153,000 m3/yr was calculated along the

southern border of Delaware with Maryland (Louis Berger Group, 1999).
26

Average net longshore transport rates for Ocean City were computed over a twenty year

period from 1956 through 1975. Ocean City Inlet’s littoral drift is to the south, deposition on the updrift inlet jetty, and erosion on the down-drift beach (Assateague Island) is evidence of this southward

predominance. Ocean City had a net longshore transport to the south ranging between 115,000 m3/yr

to 230,000 m3/yr. Assateague Island also indicated a net southerly transport of 122,000 m3/yr with the

exception of Fishermans Point which had a net sediment transport rate of 18,000 m3/yr to the north

(Louis Berger Group, 1999). 

Due to the northward littoral drift, sand is bypassed across Indian River Inlet from the south

side of the inlet to the north side of the inlet. Between 1990 and 1995, approximately 350,000 m3 of

sand was pumped across the inlet. Bypassing helps stabilize areas just north of the inlet. While south of

the inlet, bypassing created short-term mining effects on the shoreline that quickly dissipated. During

the summer season the inlet effect is more pronounced, while during the winter months maximum

changes occur further from the inlet due to partial reversal of littoral drift.

2.3. Cultural History and Shore Management Strategies

Development of the beaches began by settling on the headlands then moving onto the barriers

as population increased. Since shoreline recession has been the rule along the MD-DE coast, early

developments had to retreat with the shoreline. Historically, beach erosion has been recorded in

Delaware since 1843; it was not considered a significant problem until the 1950s when tourism, and

hence development, increased and the economic impact of beach erosion became apparent. Local

interests constructed nine groins in Rehoboth Beach between 1922 and 1964 indicating that beach

erosion was a problem prior to 1950 (House Document No. 105-144, 1997). 

2.3.1. Rehoboth Beach

Rehoboth Beach is highly developed, has generally suburban characteristics, and is a heavily

populated resort area on the Delaware ocean coast. The beach is lined with high rise hotel and

condominium complexes as well as typical summer cottages. A boardwalk runs for 1,600 m along the

beach. The northernmost 1,070 m of the boardwalk has commercial development whereas the
27

remaining part of the boardwalk is fronted by residences. There are a total of 3,105 housing units

within the town, of which only 21% are occupied year round. While there are only 1,234 year-round

residents, Rehoboth Beach attracts thousands of summer residents with its beaches and boardwalk. 

The tourist population can increase to over 100,000 on a typical holiday weekend (House Document

No. 105-144, 1997).

Rehoboth Beach is located on a headland and extends for about 1,500 m along the shore. The

general condition at Rehoboth is a beach with substantial width, little or no dune, bulkheads, and high

backshore elevations. The elevation of the beach generally is +2 m NGVD, and the elevation of the

upland area generally ranges between +7 m NGVD at the north part of town to +5 m NGVD on the

southern edge of town. The shoreline has groin fields which have acted to stabilize the shoreline in

recent history. All nine groins are in poor condition, but they are functioning and are not in imminent

danger of failing. The three northernmost groins continue to function; however, they are exposed to the

predominant wave action from the northeast and have been damaged in recent storm events (House

Document No. 105-144, 1997).

In 1958, congress authorized the Corps of Engineers to participate in the cost of restoration

and subsequent periodic renourishment for up to ten years in a beach erosion control project that

extended from Rehoboth Beach to Indian River Inlet. About 795,000 m3 of sand was placed on the

beach in 1957, 1961, and 1963. Much of the material was lost during the Five-High Storm in March

of 1962 (House Document No. 105-144, 1997). Other beach fill projects are listed in Table 5.

2.3.2. Dewey Beach

Dewey Beach lies just south of Rehoboth Beach and is about 1.6 km long. The northern

portion is a headland whereas the southernmost 900 m is a barrier with the Atlantic Ocean to the east

and Rehoboth Bay to the west. The barrier is about 460 m wide. The elevation of the beach is about

+2 m NGVD whereas the elevation of the upland is about +5 m NGVD in the north decreasing to +3

m NGVD in the southern part of the town. The town of Dewey Beach is highly developed with

generally suburban characteristics (House Document No. 105-144, 1997). 
28

Dewey Beach has become a developed overflow area for Rehoboth Beach, but it is a changing

community where older residences still exist. Over 41% of the population is retired,

 and although more than half of the current residents have lived in their present homes for the past 15

years, the situation is starting to change. Many of the older properties are being sold, the cottages then

razed, and new modern townhouses built in their place. The population at Dewey can rise to 35,000

on a summer holiday weekend (House Document No. 105-144, 1997).

According to Bosma and Dalrymple (1997), the area has experienced major flooding, erosion,

and wave attack causing damage to many shoreline structures. A system of bulkheads and groins has

been installed to stabilize the northern shore. The southern shore which is a barrier between the

Atlantic Ocean and Rehoboth Bay, is highly vulnerable to storm damage. Along the entire beach, there

are seven timber bulkheads totaling 530 m in length and five bulkheads totaling 350 m in length (House

Document No. 105-144, 1997).

Several times between 1992 and 1996, the President of the United States declared the region a

National Disaster Area because of storm damage (U.S. Army Corps of Engineers, 1996). In the

summer of 1994, 453,300 m3 of a beach-fill was placed on Dewey Beach. The majority of the beachfill was taken from the Hen and Chickens Shoal which is approximately 3 to 5 km offshore of the

Rehoboth Beach area. The fill extended roughly 2,000 m alongshore from Collins Street in the south to

the south end of Silver Lake in the north (Bosma and Dalrymple, 1997). Several other beach fills that

have taken place at Dewey are shown in Table 5.

2.3.3. Bethany and South Bethany Beaches

Bethany Beach is the second largest beach municipality in Delaware (Figure 11A). It is less

developed than Rehoboth Beach but more developed than Dewey Beach and accommodates many

thousands of summer visitors in its hotels, motels, condos, and beach cottages. The town also maintains

a boardwalk along its approximately 800 m long oceanfront. The general conditions at Bethany are

beaches with substantial width, little or no dune, bulkheads, and high backshore elevations. Bethany

has groin fields which have acted to stabilize the shoreline. South Bethany is different (Figure 11B). 

Beach front cottages and houses dominate the landscape along with a much smaller business area

(House Document No. 105-144, 1997).
29

Table 5. Information on beach nourishment projects that have taken place along the Delaware Coast

(Valverde et al., 1999).

Beach Location Date Funding Type Volume 

(m3)

Length

(m)

Documented

Cost $

Fort Miles 1962 Federal: Emergency 72,943 2560.32 $46,300

North Shores 1962 Federal: Emergency 53,063 1493.52 $36,100

Rehoboth Beach 1962 Federal: Emergency 165,307 1524 $318,900

Dewey Beach 1962 Federal: Emergency 62,697 1082.04 $132,500

Dewey Beach 1993 State/Local 4,400 579.12 $30,210

Dewey Beach 1994 State/Local 442,607 1828.8 $2,342,230

Dewey Beach 1994 Federal: Storm and Erosion 10,707 422.148 $60,000

North Indian Beach 1994 Private 16,057 122.834 $61,400

Indian Beach 1962 Federal: Emergency 113,696 5638.8 $94,700

Indian Beach Private 3,670 46.9392 $20,435

N. Indian River Inlet 1957 Federal: Storm and Erosion 391,781 $316,500

N. Indian River Inlet 1961 Federal: Navigation 36,701 

N. Indian River Inlet 1962 Federal: Emergency 221,734 2560.32 $182,600

N. Indian River Inlet 1963 Federal: Storm and Erosion 451,343 $374,900

N. Indian River Inlet 1965 Federal: Navigation 68,814 

N. Indian River Inlet 1972 Federal: Storm and Erosion 592,030 1524 $637,200

N. Indian River Inlet 1974 Federal: Emergency

N. Indian River Inlet 1975 Federal: Storm and Erosion 108,956 2560.32 $276,600

N. Indian River Inlet 1978 Federal: Emergency 409,367 1524 $714,600

N. Indian River Inlet 1982 State 171,194 

N. Indian River Inlet 1984 Federal: Emergency 412,884 

N. Indian River Inlet 1990 State 133,805 

N. Indian River Inlet 1991 State 52,681 

N. Indian River Inlet 1992 State 30,584 

Beach Cove to Bethany Beach 1962 Federal: Emergency 28,367 2897 $32,900

Bethany Beach 1961 Federal: Storm and Erosion 76,460 

Bethany Beach 1962 Federal: Emergency 53,293 $138,400

Bethany Beach 1989 State 217,529 1566.06 $1,630,241

Bethany Beach 1992 State 168,009 1566.06 $1,037,303

Bethany Beach 1994 State/Local 141,032 1264.92 $838,953

South Bethany to York Beach 1962 Federal: Emergency 49,699 7563.92 $119,000

South Bethany 1989 State 177,081 1267.36 $1,307,849

South Bethany 1992 State 147,376 1478.28 $905,786

South Bethany 1994 State/Local 75,251 777.24 $452,165

York Beach to Fenwick Island 1962 Federal: Emergency 227,621 4632.96 $454,900

Fenwick Island 1962 Federal: Emergency 51,687 $121,900

Fenwick Island 1988 State 254,994 1828.8 $1,572,993

Fenwick Island 1991 Federal: Storm and Erosion 96,951 487.68 $443,603

Fenwick Island 1992 Federal: Storm and Erosion 28,290 515.112 $269,234

Fenwick Island 1992 State 110,791 1264.92 $716,916

Fenwick Island 1994 Federal: Emergency 52,173 701 $369,809
Figure11. Aerial photosof A.)BethanyBeach andB.)SouthBethanyBeach

(Beach-NetWebsite,2000).

A

B

30
31

Between 1937 and 1943, Delaware constructed nine groins which have been effective at

accumulating sand during normal wave conditions (Bosma and Dalrymple, 1997). From 1938 to 1977, the

overall rate of accretion was 0.1 to 0.2 m/yr (Dick and Dalrymple, 1983). More groins were constructed in

the 1970s. In 1989, 545,000 m3 of beach fill was placed at Bethany and South Bethany (Table 5). In

1992, 168,000 m3 of material was placed at Bethany and 147,000 m3 was place at South Bethany. In

1994, 141,000 m3 of material was placed at Bethany and 75,000 m3 was placed at South Bethany.

2.3.4. Fenwick Island

Fenwick Island is somewhat different in character from Rehoboth, Dewey, and Bethany Beaches. 

Beach front cottages and houses dominate the landscape along with much smaller associated business

communities (House Document No. 105-144, 1997).

2.3.5. Ocean City

Maryland’s Atlantic coast is a popular year-round recreational destination. Ocean City is

Maryland’s only coastal resort. It hosted over eight million visitors in 1996. In 1988, the U.S. Army Corps

of Engineers started the Ocean City Beach replenishment project which was designed to restore the

recreational beach to a uniform 67 m width by pumping approximately 1.7 million m3 of sand from a shoal in

state waters along 13.4 km of beach over a 5 month period. In 1990, a 13.8 km long “hurricane protection

dune” was constructed with 2.7 million m3 of dredged sand (Maryland Geological Survey website, 2000).

The original plan called for the maintenance of the beach with between 535,000 and 764,000 m3 to

be placed on the beach every four years for the next 50 years. Since 1988, 7.3 million m3 of offshore sand

have been placed on Ocean City beaches (Maryland Geological Survey website, 2000). Table 6 shows the

beach nourishment projects that have taken place at Ocean City.
32

Table 6. Information on beach nourishment projects that have taken place along the Maryland Coast

(Valverde et al., 1999).

Beach Location Date Funding Type Volume

(m3)

Length

(m)

Documented

Cost $

Ocean City 1963 Federal: Emergency 802,830 12,875 $1,517,600

Ocean City 1988 State/Local 2,064,420 11,265 $14,200,000

Ocean City 1991 Federal: Storm&Erosion 2,905,480 11,265 $15,003,269

Ocean City 1992 Federal: Emergency 933,873 $10,800,000

Ocean City 1993 Federal: Emergency 170,900 $960,000

Ocean City 1994 Federal: Emergency 993,980 $8,800,000

2.4. Offshore Sand Resources

The Delaware Geological Survey (DGS) is examining its database of core samples taken from state

and federal waters to identify borrow sites for beach nourishment. Two areas beyond the 5 km (3-mile)

limit have been identified. One area lies off the Indian River Inlet; the other is the Fenwick 

Shoal Field which borders Delaware and Maryland. The southern portion of the Fenwick Shoal Field 

lies in Maryland. The Indian River Inlet site is associated with ebb/flood tidal delta shoals and is a relatively

flat area. The Fenwick Shoal Field is near the Maryland border in a ridge and swale field. 

The DGS (1988) recommended that sand used to nourish the beaches have a median grain size of

0.35 to 0.71 mm; a sorting of 0.71 mm or less, and negative skewness. Both sites contain sand that meet

the criteria for Delaware beach renourishment.

The Indian River Inlet offshore borrow area is larger than 5 km by 6 km. The upper 3 m is

estimated to have approximately 69 million m3 of suitable and available sand. The Fenwick Shoal borrow

site extends over a 5 km by 4 km area. The upper 3 m is estimated to have approximately 46 million m3 of

suitable and available sand (Louis Berger Group, 1999).
33

3. METHODS

3.1. Background: Shore Change Analysis

The coastal geology and management literature is rife with studies describing the pros and cons of

many methods of interpreting the temporal and spatial variations in shoreline change (McBeth 1956,

Shalowitz 1964, Stafford 1971, Ellis 1978, Dolan et al. 1978, Morton 1978, Dolan et al. 1980, 

Leatherman 1983, Elliot and Clarke 1989, Foster and Savage 1989, Smith and Zarillo 1990, Crowell et al.

1991, Dolan et al. 1991, Anders and Byrnes 1991, Larson and Kraus 1994 Crowell et al. 1997, Lacey

and Peck 1998, Morton and Speed 1998). In general, calculation of long-term rates of shoreline change

have relied on positioning a tidal datum, usually mean high water (MHW), on a map utilizing cartographic

data or aerial photos (Everts et al. 1983). According to Shalowitz (1964), prior to 1927 the topographic

supplement to hydrographic charts was made with plane-table surveys which since have been replaced by

(aerial) photogrammetric methods. Both methods embody error in the determination of the horizontal

location of the “shoreline,” the lateral position of mean high water on the date of the survey. Foster and

Savage (1989) determined that the error associated with analysis of shoreline change is dependent upon the

method of study. They calculated that the error can be +/- 9.1 m for map data, +/- 6.1 m for aerial

photographs, and +/- 3.1 m for surveyed points. In all cases, more closely spaced data points yielded tighter

error limits.

Fenster et al. (1993) described a simple method to determine rates of shoreline change from profile

data. The End Point Rate (EPR) method utilizes the distance from the profile benchmark to the intersection

of the profile with Mean High Water (MHW) on the earliest and latest surveys at each profile; dividing this

differential distance by the number of years between the profiles gives a rate of shoreline change comparable

to rates obtained from aerial photos or maps. 

Since the 1980s, and in some cases even earlier, many localities have monitored their beaches

through surveying. How the short-term changes reflected in profile data relate to the long-term historical

trends has been the subject of debate. Eliot and Clarke (1989) indicated that approximately 10 years of

monthly profiles are necessary to minimize the effects of seasonal and other short-term changes. Lacey and
34

Peck (1998), utilizing 33 years of profile data, attributed annual variations in beach morphology and volume

to the onshore-offshore transport of sediment associated with seasonal variations in storm frequency and

intensity. Shorelines can exhibit trends toward erosion, accretion, or relative stability depending on the

individual profile. These trends relate to average wave height, sediment transport, depth to 10 m contour or

closure, and regional management issues such as how a beach nourishment project will perform.

Delaware has intensely monitored its entire ocean coast for the last 18 years. One survey was

performed in 1964, but it was not until after 1982 that most of the thirty six profiles (Figure 12) were

surveyed regularly. These profiles are spaced an average of 1,200 m apart along the Delaware ocean coast. 

In addition, the state has established more closely-spaced profile lines at Rehoboth, Dewey, Bethany, South

Bethany and along Fenwick Island. Most of those profiles were established in the late 1980s or early 1990s

to monitor the beach nourishment projects that have taken place along the shoreline. At Dewey Beach, 16

profiles were established 150 m apart. Data were collected from May 1991 to January 1996. Nine to

eleven surveys were performed at each profile over the five year period. Four surveys were performed after

the beach was nourished; the others were pre-nourishment. All the Delaware profile data were made

available in digital format. These data have been analyzed in great detail by Dick and Dalrymple (1983),

Dubois (1988), Clausner et al. (1991), U.S. Army Corps of Engineers (1996), and Bosma and Dalrymple

(1997). 

Maryland has a long-term beach monitoring program that began in earnest in 1988 just before the

large beach fill project (Figure 12). These data have been analyzed by Grosskopf and Resio (1988),

Anders and Hanson (1990), and Stauble et al. (1993). The Maryland profile data were not available in

digital format at the writing of this report which greatly reduced the amount of analysis that could be

performed. As a result of availability, we used data from published (Stauble et al.,1993) and unpublished

reports (provided by Jordan Loran, Maryland Department of Natural Resources (MD-DNR)).

We have not attempted to duplicate the efforts of earlier researchers. Instead our focus is tying

together the many studies that have been performed on this section of coast in order to assess the impact of

beach nourishment on shoreline/shoreface change.
Figure12. Locationof DelawareandMarylandoceancoastbeachprofiles aswellasDelaware

coastal segmentsfromU.S.ArmyCorpsofEngineers(1996)analysis.

Indian

RiverInlet

OceanCity

Inlet

IsleofWight

Bay

Assawoman

Bay

Indian

River

Bay

Rehoboth

Beach

Dewey

Beach

Bethany

Beach

Fenwick

Island

Delaware

Maryland

Ocean

City

Cape

Henlopen

Rehoboth

Bay

NauticalMiles

0 1 2 3 4

0 5

Kilometers

1

4

8

10

13

17

20

24

27

30

34

38

40

44

DelawareProfileLocations

MarylandProfile Locations

(approximate)

CH

1

2

3

4

5

7

6

35
36

3.2. Data Analysis

3.2.1. Rates of Change

In order to determine the historical rates of change along the shoreline, the location of the profiles

was plotted on topographic scale NOAA/NOS-CERC Cooperative maps prepared for the Shoreline

Movement Study in the 1980s. The position of the shoreline was determined for all available shoreline

survey dates between 1849 and 1980 at the profile locations. Once the shoreline locations were determined

from the position of the profile benchmark, rates of shoreline change were calculated. This analysis was

performed for both the Delaware and Maryland coasts. For recent rates of change (1982-1999), the

Delaware survey benchmark and profile data were used to determine the horizontal position of NAVD. 

For Maryland, profile data had to be manually extracted from published sources (Stauble et al.,

1993). The position of zero elevation (NGVD) either was taken from the profile data published in ISRP

format or determined from profile plots provided by Jordan Loran (MD-DNR). The analysis is limited to 13

profiles selected along the Ocean City shore. Shoreline position dates used in the analysis are June 1998,

June 1990, February 1994, May 1997, and October 1997. These generally correspond with pre-fill

conditions of the beach and the last date of profile-data availability.

3.2.2. Regression Analysis

Linear regression analysis was performed on both historical (1845-1980) and recent (1982/1988-

present) shoreline position data. In order to determine a long-term rate of change, regression analysis was

performed on the positions of the historic shorelines. The analysis was customized to the particular

geographic subreach. For instance, some of the Maryland historical data show a much higher regression

coefficient for the period 1929 and 1980 than for 1880 to 1980. This likely is due to construction of the

inlet jetty in the 1930s. The geologically-sudden, anthropogenic change in coastal processes caused by the

jetties was significant enough to displace the long-term trend since analyses of the time periods before and

after construction of the jetties show consistent trends.
37

The Corps of Engineers has divided Delaware’s shoreline into eight segments, based on shore

morphology, for a linear regression analysis of beach profile data (Figure 12). These recent data tend to

have low regression coefficients since the adjustment of beach fill is non-linear. However, analysis of

shoreline movement between beach nourishment projects yields similar rates of change for individual

projects. 
38
39

4. RESULTS AND DISCUSSION

4.1. Delaware

4.1.1. Historic Shore Change

The net change between the first date shown on the shoreline change maps (1845/50/82) and 1929

probably represents the long-term historical rate of change relatively unaffected by anthropogenic activities

along the coast. Average rates of change show that between 1845 and 1929, the Delaware shoreline was

retreating at a rate of about -1.7 m/yr. 

Two events occurred between 1929 and 1943 that impacted shoreline change rate. First, the 1933

hurricane greatly impacted the region. Between 1929 and 1933 from Cape Henlopen to Dewey Beach or

profiles 39 to 51 (the only location shore change data were available), the rate of shoreline change was -12

m/yr (Table 7). This is significantly greater than the rates of change on either side of these dates. This can

be attributed partially to the short time span; but the shoreline retreated an average of about 46 m in this

four-year period. The second event was the installation of the jetties at Indian River Inlet in the late 1930s. 

At the time of construction, these jetties extended 183 m from the shoreline. Gebert et al. (1992) found that

the jetties created significant problems including accelerated scour along the jetties and massive downdrift

erosion due to the predominant northward littoral transport system. 

Table 7. Rates of change along the northernmost section of Delaware between 1845 and 1943.

Rate of Change (m/yr) Shore Change (m)

Profile 1845-1929 1929-1933 1933-1943 1929-1933

39 -3 -15 0 -61 

40 -4 -10 -2 -41

41 -4 -13 2 -52

42 -3 -13 2 -50

43 -3 -10 -0 -61

44B -3 -10 0 -24

44 -2 -12 2 -61

44A -2 -13 2 -40

45 -2 -4 3 -38

45A -2 -8 5 -49

46 -2 -11 4 -52

47 -2 -10 4 -15

48 -1 -19 5 -32

49 -1 -15 6 -44

50 -2 -6 5 -41

51 -2 -15 10 -76

Average -2 -12 3 -46
40

Cape Henlopen has been eroding along its ocean coast. The northerly drift of sand has elongated

the Cape through time (Figure 13A). Further south at the present Indian River Inlet, significant change has

occurred (Figure 13B). The Inlet was an ephemeral coastal feature until it was stabilized with jetties in

1939.

Between 1929 to 1943, shoreline modifications began along the Rehoboth and Bethany Beaches. 

Bulkheads and groins were built. These likely affected the local rates of shoreline change but probably had

little effect either up or down drift. Overall, the rate of shoreline change along the Delaware coast decreased

during this period to only -0.5 m/yr.

Between 1943 and 1962, the average rate of change for the Delaware coast was -1.9 m/yr

(Figure14). This is in-line with the long-term rate of change. The impact of Indian River Inlet is 

evident with continued erosion on the north side of the inlet. In an attempt to mitigate downdrift impacts,

approximately 38,000 m3 was dredged from the Inlet every five years between 1957 to 1990 and placed on

the north side of the inlet. Also, a total of about 428,000 m3 of sand was placed on the beach north of

Indian River Inlet in 1957 and 1961 (Valverde et al., 1999). However, much of the material was lost during

the Ash Wednesday/Five High storm in March 1962. The month and day of the 1962 shoreline used in the

historical analysis is unavailable. Most likely the field survey was performed after the storm but before the

emergency placement of fill. 

Shore positions were plotted in 1961 and 1962 for the stretch of shore between just north of Indian

River Inlet and South Bethany Beach. While the shore positions varied, in general, there was severe erosion

in South Bethany and Bethany. These two localities lost about 8 m of beach width in that one year, likely as

a result of the Five-High Storm. The beach north of Bethany, however, gained an average of about 6 m in

beach width during that same year. Severe scour occurred on either side of the Indian River Inlet jetties,

particularly on the northern side which lost 52 m. Sand appeared to be transported to the north as the

section of beach just north of the Inlet showed an increased beach width between those two dates.
N

March1980

1962

1845

1943

1969/70

1933

1929

1903

1882

7505' 7506' 0

0

3848' 0

3847' 0

3846' 0

CapeHenlopen

A

1845

1929

1943

1961

1962

1969

March1980

7504' 0

3837'30" 0

3837' 0

3836' 0

IndianRiver

Inlet

B

41
42
Figure14. Historicshorepositionsandhistoricandrecentratesofchange alongtheDelawareandMarylandshoreline.

MD DE

Historic Rates of Change

at MHW (m/yr)

-8

-4

0

4

8

Maryland Shore Change

at NGVD (m/yr)

Jun1988-Jun1990

Jun1990-Feb1994

Feb1994-May1997

May1997-Oct1997

Recent Rates of Change

at MHW (m/yr)

Henlopento

IndianRiverInlet

Oct1982-Aug1993

Aug1993-Apr1994

Apr1994-Mar1998

Mar1998-Sep1998

Sep1998-Apr1999

Oct1982-Jan1993

Jan 1993-Apr1998

Apr1998-Sep1998

Sep1998-May1999

Indian RiverInlet

toSouthBethany Fenwick

Oct 1982-Jan1993

Jan1993-Apr1994

Apr1994-Sep1994

Sep1994-May1999

1845/50/82to1929

1929to1943

1943to1962

1962to1980

1845/50/82

1929

1943

1962

1980

1850

1929

1942

1962

1980 Historic Shoreline Positions (m)

Delaware Profile Locations

MarylandProfileLocations

(approximate)

NauticalMiles

0 1 2 3 4

0 5

Kilometers

43
44
45

The alteration of shoreline change rates due to the influence of post-storm beach fill is shown in the

1962 to1980 data. Following the 1962 storm, 689,000 m3 of material was placed along 15,000 m of shore

north of Indian River Inlet. 411,000 m3 of sand was placed along the shore from Indian River Inlet to

Fenwick Island (Valverde et al., 1999). Both Rehoboth and Bethany Beaches show positive rates of

change which likely is the result of the addition of the emergency beach fill sand following the 1962 Ash

Wednesday/Five High storm since no other fill projects took place at those beaches during that period

(Figure 14). Between 1963 and 1980, approximately 1.6 million m3 of sand was placed north of Indian

River Inlet (Valverde et al., 1999). That is a lot of sand to be placed along the shore, yet it is not seen in the

rate of change along most of the shoreline. There is accretion at the southern jetty and on the northernmost

section of the reach at Cape Henlopen. The positive rates do influence the overall shoreline rate of change

to +0.4 m/yr. The overall net rate of change between the mid 1800s and 1980 was -1.5 m/yr. 

4.1.2. Recent Shore Change

In order to determine the more recent trends along the shoreline, the beach profile data were used to

calculate the horizontal position of MHW. In an attempt to show the influence of beach fill along the shore,

the data graphed on Figure 14 are from profile dates that most closely correspond to beach nourishment

projects along each section of the Delaware coast. In 1994, 453,000 m3 of sand was placed at Dewey

Beach. 

The most obvious difference between the long-term and short-term change rates is their order of

magnitude. Short-term rates can be an order of magnitude greater than the long-term rates. The high rates

generally are associated with beach fill and its subsequent dispersal at a higher rate than the usual

background. Also, shorter time frames tend to exaggerate trends. Such is the case for Rehoboth and

Dewey Beaches. A large beach nourishment project in the summer of 1998 resulted in an average rate of

change of +35.7 m/yr. The dispersal of this material between September 1998 and April 1999 resulted in a

rate of -13.4 m/yr. However, the net change between 1982 and 1999 was -0.5 m/yr. 

Bosma and Dalrymple (1997) analyzed profile data from Dewey Beach. They measured the

cumulative shoreline change relative to the position of the shoreline in May 1991. The shoreline retreated
46

approximately 23 m (75 ft) after July of 1993. During the winter of 1994, the beach fill spread quickly while

high waves acted on the shoreline. The next survey showed that the coast had recovered substantially, but

was roughly 15 m (50 ft) short of the original post-nourishment stance. However, the stations further north

retreated only 9 m (25 ft) over the first two surveys. 

Analysis of the long-term data for Delaware yielded rates of change ranging from -3 m/yr to +6 m/yr

with an average rate of -1 m/yr (Table 8). The areas near Indian River Inlet and just south of Cape

Henlopen had relatively high rates of erosion. The only area of accretion was Cape Henlopen. In general,

the negative rates of change decrease south of Indian River Inlet, with the exception of a slight increase in

erosion rates at Bethany Beach. The minimum occurs near the Maryland-Delaware line which has been

reported as the nodal zone of sediment transport. These trends in the historic data are not shown in the

analysis of recent profile data which showed that the average rate of change had decreased to -0.3 m/yr

(Table 9). Many areas showed accretion due to the influence of beach fill. However, a small region just

south of Bethany Beach (Profiles 63, 64, and 65) showed erosion while accretion in recent years occurred

on either side. Likely this indicates that the nodal zone of sediment transport is not fixed but it can move

alongshore in response to changes in wave climate, nearshore bathymetric features, and possibly the influx of

sand from beach fill projects.

The U.S. Army Corps of Engineers performed a regression analysis on the Delaware profile data. 

They determined the rates of change for the short-term profile data (Table 10). Table 10 compares their

data with calculated rates of change with an analysis done by the authors of this report and Belknap and

Kraft (1985). General agreement and orderliness occurs among the rates of change.

A more detailed analysis of shore change using linear regression was performed on the profile data

taken at Dewey Beach after renourishment took place in 1991 (Appendix 1). Table 11 displays the results

of this effort. Regressions were performed on profile data taken between projects in order to determine the

rates of change after beach fills. While regression coefficients are low for this analysis, the beach displayed

similar rates and patterns in response to the fill during the four years between the first two projects. An initial

large adjustment period takes place immediately after the project as demonstrated by the data regressed
47

between 1998 and 1999. The regression coefficients are high for this analysis indicating a linear loss of fill

material. However, after this initial adjustment, the rate of change tapers off creating a non-linear fill

adjustment period. 

Table 8. Delaware historic regression analysis results by profile.

Profile

Rate of

Change

 (m/yr) R

2

No.

points Dates Spanned Comment

37 6.2 91% 3 1962 1980 Cape Henlopen

38 0.5 47% 3 1943 1980 

39 -3.2 94% 4 1882 1980 

40 -2.4 84% 8 1882 1980 

41 -3.1 89% 9 1845 1980 

42 -2.7 97% 8 1845 1980 

43 -2.3 93% 8 1845 1980 

44 -1.4 78% 8 1845 1980 

44A -1.1 59% 8 1845 1980 

44B -2.1 90% 8 1845 1980 

45 -1.0 48% 8 1845 1980 

45A -0.8 42% 8 1845 1980 

46 -0.8 41% 8 1845 1980 

47 -1.2 72% 7 1845 1980 

48 -1.3 86% 7 1845 1980 

49 -1.0 79% 7 1845 1980 

50 -1.1 82% 7 1845 1980 

51 -1.4 78% 7 1845 1980 

52 -2.7 94% 7 1845 1980 

53 -2.5 98% 7 1845 1980 

54 -2.2 90% 7 1845 1980 North of Indian River Inlet

55 -1.2 50% 7 1845 1980 South of Indian River Inlet

56 -0.9 77% 7 1845 1980 

57 -0.8 81% 6 1845 1980 

58 -0.4 73% 6 1845 1980 

59 -1.1 68% 6 1845 1980 

60 -0.8 72% 6 1850 1980 

60A -0.9 65% 6 1850 1980 

60B -1.1 88% 6 1850 1980 

61 -1.3 90% 6 1850 1980 

62 -1.0 95% 6 1850 1980 

62A -0.8 97% 6 1850 1980 

63 -1.0 95% 6 1850 1980 

64 -0.5 63% 4 1850 1980 

65 -0.2 33% 4 1850 1980 

66 -0.1 45% 4 1850 1980 

67 -0.3 97% 4 1850 1980 North Ocean City, MD

Average -1.1
48

Table 9. Recent regression of Delaware’s ocean coast profile data.

1964-1999 1982-1999

Rate of

Change

Rate of

Change

Profile (m/yr) R

2

(m/yr) R

2 Comment

38 -0.8 19% -0.8 19% Cape Henlopen

39 0.4 6% 0.9 79%

40 -0.3 3% -0.3 3%

41 -1.2 45% -1.2 45%

42 -1.2 55% -1.2 55%

43 -3.0 84% -3.0 84%

44 -0.3 4% -0.3 4%

44A 0.2 3% 0.2 3%

44B -1.6 35% -1.6 35%

45 -0.3 4% -0.3 4%

45A -0.5 8% -0.5 8%

46 0.3 2% 0.3 2%

47 -1.2 23% -1.2 23%

48 -1.4 48% -1.4 48%

49 -0.9 31% -0.9 31%

50 -1.9 58% -1.9 58%

51 -0.8 28% -0.8 28%

52 -1.1 27% -1.1 27%

53 -1.3 37% -1.3 37%

54 -1.3 48% -1.3 48% North of Indian River Inlet

55 0.3 1% 0.9 3% South of Indian River Inlet

56 0.9 74% 0.9 74%

57 0.0 0% 0.0 0%

58 -0.1 4% -0.1 4%

59 1.2 54% 1.2 54%

60 1.5 24% 1.5 24%

60A 0.8 15% 0.8 15%

60B 0.7 26% 0.7 26%

61 0.1 0% 0.1 0%

62 -0.3 4% -0.3 4%

62A 2.4 35% 2.4 35%

63 -0.5 12% -0.4 8%

64 -0.4 6% -0.4 6%

65 -0.3 3% -0.3 3%

66 0.5 8% 0.7 12%

67 1.3 22% 1.3 22% North Ocean City, MD

Average -0.3 -0.2
49

Table 10. Comparison of rates of change obtained by various methods.

Historical Recent

Method EPR* EPR Linear

Regression

Linear

Regression

Linear

Regression

Linear

Regression

Dates

Spanned

1843-1961 1850-1980 1850-1980 1962-1994 1982-1994 1964-1999

Units (m/yr) (m/yr) (m/yr) (m/yr) (m/yr) (m/yr)

Shore

Section

Reference Belknap and

Kraft (1985)

VIMS (2000) VIMS (2000) Corps (1996) Corps (1996) VIMS (2000)

CH -2.2 +0.3 +0.9 +0.3 -0.3

1 -2.9 -2.7 -2.0 -1.6 -1.8

2 -1 -1.3 -1.2 -0.5 -0.6 -0.5

3 -1.2 -1.2 -1.2 -2.0 -1.1 -1.2

4 -0.9 -2.0 -1.9 -1.7 -0.8 -0.5

5 -1.1 -1.0 -0.6 -0.4 -0.8 -0.1

6 -0.9 -1.1 -1.0 +0.1 -1.1 +0.7

7 -0.9 -0.3 -0.4 -2.1 +0.3

*EPR=End Point Rate Method

4.1.3. Shoreface Retreat

Profile data collected between 1964 and 1982 show a net retreat of the shoreface along Delaware’s

coast from the intertidal zone to approximately -9 meters (Figure 15). However, between 1982 and 1999,

the data show that, in general, the shoreface has experienced a net accretion, particularly in the nearshore. 

The beach nourishment placed along the shore in the last 28 years has eroded from the shoreline, but it has

been deposited farther offshore. After the 1962 storm, eight beach nourishment projects were done with the

largest (1.1 million m3) just north of Indian River Inlet bringing the total to about 2.0 million m3 (Valverde et

al., 1999). Between 1963 and 1982, about 1.2 million m3 was placed north of Indian River Inlet. It was

not until after 1982 that further beach nourishment projects were performed particularly at Dewey Beach,

Bethany Beach and Fenwick Island. Between 1982 and 1994, another 630,800 m3 was placed north of

Indian River Inlet for a total sand placement of about 2.5 million m3along the Delaware Coast between

1963 and 1994.
50

Table 11. Regression analysis of profile data taken at Dewey Beach.

Profile

1991-1994 1994-1998 1998-1999

Rate of

Change

(m/yr)

R

2 Rate of

Change

(m/yr)

R

2 Rate of

Change

(m/yr)

R

2

145 -0.3 0.1% -4.3 20.4% -2.2 1.7%

140 -8.2 25.1% -6.2 35.2% -81.8 100.0%

135 -4.2 12.0% -6.7 43.7% -29.1 86.2%

130 -11.2 38.6% -7.1 41.2% -37.8 93.7%

125 -8.3 24.1% -8.3 51.5% -59.0 95.1%

120 -3.9 26.4% -8.6 51.8% -35.5 78.3%

115 -7.4 25.4% -9.6 53.2% -52.4 94.2%

110 -4.7 18.0% -10.9 64.3% -38.0 81.4%

105 -6.8 37.4% -8.4 36.7% -31.4 89.5%

100 -4.3 9.7% -9.5 54.8% -43.9 91.6%

225 -3.6 10.7% -4.5 22.3% -17.2 63.5%

230 -5.9 15.0% -4.1 22.1% -3.5 6.8%

Average -5.7 -7.4 -36.0

The profiles depicted in Figure 15 are representative of portions of the Delaware coast (i.e

headlands and barriers). Analysis of data from the three dates shows that the entire shoreface profile had a

net recession between 1964 and 1982 eroding the sand placed in the large beach nourishment project of

1962. Not only did the shoreline recede but the shoreface at the -3, -4.6, -6.1, and -7.6 m (-10, -15, -20,

and -25 ft) contours eroded as well (Table 12). In contrast, between 1982 and 1999, 5 of the 10 profiles

receded at the beach, particularly those on the north end, but all profiles showed a net advance at the

subaqueous contours. It would appear that the nourishment, although lost from the shoreline, has moved

offshore and remained, abating the shoreface retreat for now. The consequence is that shoreface processes

are being influenced by beach nourishment projects and that shoreface erosion/accretion is occurring on

beach fill rather than the underlying geology. This generally agrees with Bosma and Dalrymple (1997) who,

utilizing the same data, found that since 1982, the shorelines south of Indian River Inlet have shown accretion

rather than erosion indicating recent preventative measures (i.e. beach nourishment) have been beneficial.
Figure15. Erosionoftheshorefacerepresentedbybeachprofilestakenin1964,1982,and1999.

Shoal

Erosion

1964-1982

1982-1999

1964-1999

Salt

Pond Indian

River

Inlet

P67

P66

P63

P60

P58

P50

P49

P47

P45

P42

23Sep1964

1 Oct1982

12May1999

Date

Distance(ft)

10

5

0

-5

-10

-15

Distance (m)

0 500 1,000 1,500

51
52
53

Table 12. Shoreface rate of change by offshore contour.

Profile

0 contour -3.0 m contour

1964-1982 1982-1999 1964-1999 1964-1982 1982-1999 1964-1999

(m/yr) (m/yr) (m/yr) (m/yr) (m/yr) (m/yr)

42 -1.62 -1.77 -1.69 -2.04 -0.94 -1.51

45 -0.48 -0.17 -0.33 0.11 1.44 0.75

47 -1.15 -0.81 -0.99 -3.09 1.13 -1.07

49 -2.00 -2.10 -2.05 -2.88 0.60 -1.22

50 -3.08 0.09 -1.56 -2.12 0.74 -0.75

58 -0.86 0.51 -0.20 -1.44 0.92 -0.31

60 0.27 0.97 0.60 -0.51 1.33 0.37

63 -1.23 -1.15 -1.19 -1.19 0.58 -0.34

66 -0.83 0.73 -0.08 -1.71 1.63 -0.11

67 -0.41 1.90 0.70 -1.61 3.41 0.80

Avg. -1.14 -0.18 -0.68 -1.65 1.08 -0.34

Profile

-4.6 m contour -6 m contour

1964-1982 1982-1999 1964-1999 1964-1982 1982-1999 1964-1999

(m/yr) (m/yr) (m/yr) (m/yr) (m/yr) (m/yr)

42 -3.54 0.26 -1.72 -5.54 1.93 -1.96

45 0.47 1.07 0.75 0.22 1.14 0.66

47 -3.73 1.40 -1.27 -3.62 0.68 -1.56

49 -3.32 0.63 -1.43 -4.49 0.63 -2.04

50 -2.25 0.68 -0.84 -3.01 1.85 -0.68

58 -1.54 0.84 -0.40 -3.83 1.74 -1.16

60 -1.42 1.07 -0.23 -2.56 1.46 -0.63

63 -2.65 0.05 -1.36 -3.18 0.19 -1.57

66 -1.81 1.06 -0.44 -2.46 1.62 -0.50

67 -3.47 3.10 -0.32 -3.06 0.71 -1.25

Avg. -2.33 1.02 -0.73 -3.15 1.20 -1.07

Profile

-7.6 m contour

1964-1982 1982-1999 1964-1999

(m/yr) (m/yr) (m/yr)

42 -3.57 1.94 -0.93

45 -0.94 0.61 -0.20

47 -3.56 -0.85 -2.26

49 -8.19 1.96 -3.33

50 -9.60 N/A N/A

58 -3.87 0.93 -1.57

60 -4.27 1.30 -1.60

63 -2.89 -0.69 -1.84

66 -3.33 2.03 -0.76

67 -4.40 -0.04 -2.31

Avg. -4.46 0.80 -1.64
54

4.2. Maryland

4.2.1. Historic Shoreline Change

In Maryland, less historical shoreline coverage exists for Fenwick Island. Coverage for 1850 to

1929 showed erosion at the MD-DE state line, then a shoreline advance along the northern half of

Maryland’s Fenwick Island, followed by shoreline recession on the lower half down to Ocean City Inlet

(Figure 14). The 1942 shoreline is only given for the southern third of Fenwick Island. These data show

significant accretion north of the inlet, most likely in response to construction of the jetties. Between 1962

and 1980, there is a general balance of erosion and advance along the Fenwick Island shoreline. The lack

of significant accretion at the channel jetties is somewhat unexpected.

Linear regression of the shoreline position data at the Fenwick Island profile locations shows overall

erosion of the shoreline except at profiles 1, 2 and 4 (adjacent to and north of the channel jetties) for the

period 1850 to 1980 (Table 13). This accretionary trend also occurs in the analysis from 1929-1980 up to

profile 9. The 1929-1980 occurred mostly after the construction of the Ocean City Inlet jetties. The

remainder of the shoreline northward shows an increase in erosion rates.

4.2.2. Recent Shore Change

Until the major beach fill project in 1988, the main influences on the Fenwick Island shoreline were

the extensive groin system and the emergency beach fill of over 800,000 m3 in 1963. In 1988, about 2.1

million m3 of sand was placed alongshore as part of a state/local project (Valverde et al., 1999). In 1990-

1991 about 2.9 million m3 of sand was placed alongshore as part of a Federal project. Both nourishment

projects utilized sand mined by dredge from borrow sites about 3.7 km off the south end of Ocean City and

about 4 km offshore just south of the MD-DE state line.

The 1988 Maryland state nourishment project was designed to widen 13.4 km (8.35 miles) of 

beach to a uniform width of 67 m from its average width of 40 m. The 1991 project was one of the longest

continuous beach fill projects constructed in one season in the United States. At its northern end, the project

tied into a previously nourished beach in Delaware (Maryland Geological Survey, unpublished). About twothirds of the sand used in the project was mined from “Area 2;” the remainder from “Area 3.”
55

Table 13. Maryland historic regression analysis results.

1850-1980 1929-1980 Comments

Profile Rate of

Change

(m/yr)

R

2 No.

points

Rate of

Change 

(m/yr)

R

2 No.

points

1 0.9 22% 5 2.6 87% 4 North of Ocean City Inlet Jetty

2 0.4 7% 5 3.7 89% 4 

4 0.1 0% 5 2.6 87% 4 

6 -0.5 43% 5 0.6 25% 4 

8 -0.6 41% 5 0.8 35% 4 

9 -1.0 79% 5 0.2 12% 4 

10 -1.2 87% 5 -0.1 12% 4 

12 -0.9 86% 5 -0.5 24% 4 

13 -0.7 99% 5 -0.6 92% 4 

15 -0.7 86% 5 -0.1 7% 4 

17 -1.0 94% 4 -1.0 68% 3 

18 -0.8 99% 4 -1.0 100% 3 

20 -0.3 59% 4 -1.0 97% 3 

22 -0.5 32% 4 -2.2 88% 3 

24 -0.6 31% 4 -2.6 96% 3 

26 -0.5 29% 4 -2.2 89% 3 

27 -0.4 36% 4 -1.8 99% 3 

29 -0.3 32% 4 -1.5 97% 3 

30 0.1 1% 4 -1.2 71% 3 

32 -0.1 3% 4 -1.7 93% 3 

34 -0.1 1% 4 -1.7 93% 3 

36 -0.2 16% 4 -1.4 90% 3 

38 -0.4 54% 4 -1.2 94% 3 

40 -0.4 58% 4 -1.2 99% 3 

42 -0.8 96% 4 -1.0 93% 3 

44 -0.8 91% 4 -1.5 98% 3 Near MD-De Line

45 -0.7 95% 4 -1.0 97% 3 240 m North of Line

46 -0.8 89% 4 -1.4 99% 3 600 m North of Line

Average -0.5 -0.6

The federally funded second phase of the project was to provide hurricane (100 year storm)

protection and extended 520 m into Delaware. About 40 percent of the sand was mined from “Area 2 ”

and 60 percent from “Area 3.” The construction included a vegetated dune along most of its length

(Maryland Geological Survey, unpublished).
56

“Area 2” was a shoal located about 3.7 km (1 mile) offshore of southern Ocean City. It was an

elongated linear shoal with three crests identifiable on the 10 m isobath. The most suitable sand for beach

nourishment was located on the western flank of the southern crest. The two phases of the project

essentially exhausted the resource (Wells, 1994)

“Area 3” is 3 to 3.5 km (2 to 2.25 miles) offshore on a seaward extension of the MarylandDelaware boundary. The large shoal contains medium grained sand that was very well suited for the project

(Wells, 1994; Maryland Geological Survey, unpublished)

Initial monitoring Ocean City beach fills are summarized in Stauble et al. (1993) for selected

profiles. Table 14 relates the profile number of the 1993 profiles to the street location. Analysis of the data

yields several trends and identifies “hot spots” or areas of chronic beach loss. The hot spots generally are

attributed to the position of shore attached linear shoals between 37th Street (profile 13) and 103rd Street

(profile 32) (Figure 16). Areas where the deeper water between the shoals comes close to the shore,

particularly 81st Street, are reportedly the chronic “hot spots” between “headland” shore attached shoals.

Two significant northeasters occurred in 1989 and 1991 and had a significant impact on the patterns

and rates of beach change. In particular, sand was driven offshore, accentuating the “hot spot”. (Stauble et

al., 1993). The fate of the state-funded fill is clearly seen and primarily is due to the impacts of the March

1989 storms (Figure 17 and Figure18). Erosion of the subaerial beach is apparent along with a parallel gain

in the nearshore. The same pattern holds for the federal fill as impacted by the Halloween (“Perfect”) Storm

of 1991 and a storm in January 1992 (Figure 19 and Figure 20).

Stauble et al. (1993) summarized volume change from the pre-State fill condition of the beach

through time for Ocean City for the first 28-month period (Figure 21). The combined gains and losses

included both above and below NGVD volumes. The general trend was one of accretion along the profile

length at all locations. Stauble et al. (1993) found that, in general, the losses from the subaerial beach were

balanced by gains in the nearshore so the net volume change on the 900-ft long profiles was accretionary

after the beach fills. The pattern of adjustment varied alongshore as the material redistributed across the
57

profile. The storms in 1989 had a significant impact on the shoreline as some profiles eroded and others

accreted. As of January 1992, the total project performance of both the State and Federal fills indicated

that the shoreface at Ocean City has 260 m3/m of sand more than before the first fill was placed. Stauble et

al. (1993) found that most of this material is located in the nearshore, below NGVD.

Table 14. Ocean City Beach condition on October 1995.

Profile Street Condition

1 S. 1st 100 Year +

2 Dorchester 100 Year +

4 3rd 100 Year +

6 10th 100 Year +

8 15th 100 Year +

9 20th 100 Year +

10 25th 100 Year +

12 32nd 100 Year +

13 37th 100 Year +

15 45th 100 Year +

17 52nd 100 Year +

18 56th 100 Year +

20 63rd 100 Year +

22 69th 100 Year +

24 74th 100 Year

26 78th 100 Year

27 81st 100 Year

29 86th 100 Year

30 92nd 100 Year +

32 100th 100 Year ?

34 112th 100 Year

36 120th 100 Year

38 124th 100 Year

40 132nd 100 Year +

42 138th 100 Year

44 146th 100 Year -

The general trend over the total profile supports the premise that areas of erosion and loss of fill are

located where profiles were steepest and located near the point of connection of the shoreface-attached

shoal with the shoreline. The survey lines that retained the most volume of fill were profiles that had a

bar/trough configuration or were located in the lee of the shoreface-attached shoal. 
Figure16. Alongshorevariabilityinseawarddistance ofactiveprofileenveloperelative

tothetwoshoreface-attachedshoals(Stauble et al.,1993).

58
State Fill

Note: Distancealongshorehasbeencompressed5times.

Figure17. Three-dimensionalplot ofpre-andpost-statefillbeachprofiles(Stauble etal.,1993).
Pre- and Post- March 1989 Storms

Note: Distancealongshorehasbeencompressed5times.

Figure18. Three-dimensionalplot ofstate fillpre-andpost-stormbeachprofiles(Stauble et al.,1993).
Note: Distancealongshorehasbeencompressed5times.

Federal Fill

Figure 19. Three-dimensionalplotof pre-andpost-federalfillbeachprofiles(Stauble et al.,1993).
Note: Distancealongshorehasbeencompressed5times.

Pre- and Post- Storms

Figure20. Three-dimensional plot offederal fillpre-andpost-stormbeachprofiles(Stauble et al.,1993).
63

Figure 21. Percent of fill remaining above NGVD, below NGVD, and over the total

profile length after the 28 month state and federal fill monitoring as measured

from September 1988 to January 1992 (Stauble et al., 1993).

Monitoring continues on the Ocean City beach. Summary data from project data reports are

presented in Tables 14, 15, 16, and 17 for surveys between 1995 to 1997. These data show that “hot

spots” continue, particularly at 81st Street and 146th Street. One must remember that the data are for major

beach fills along Ocean City. To measure how the overall shoreline condition has changed, a linear

regression analysis was performed (Table 18). This data is most significant for the data set extending from

June 1988 to Oct 1997 between 32nd and 81st Streets (profiles 13 through 27). Although known “hot

spots” occur through the reach, the net trend from June 1988 (initial pre-fill) is accretionary. This would

indicate that there has been a reversal in historic rates due to anthropogenic impacts (i.e. beach nourishment)

which initially were all erosional along this reach.
64

Table 15. Ocean City Beach Condition Assessment Table for May 1996.

Profile Street Volume Change

(Oct95-May96)

Shoreline Change

(Oct95-May96)

Satisfies Design

Protection Level

1 S. 1st -3 yd3/ft -36 ft yes

2 Dorchester -45 -81 yes

4 3rd -11 -35 yes

6 10th +26 0 yes

8 15th 0 -12 yes

9 20th -4 -20 yes

10 25th -11 -26 yes

12 32nd +8 -23 no

13 37th -14 -36 yes

15 45th +20 -16 yes

17 52nd -2 -22 yes

18 56th -15 -62 yes

20 63rd +4 -38 yes

22 69th -24 -50 yes

24 74th -17 -14 yes

26 78th -13 -32 marginal

27 81st -6 -52 no

29 86th -5 -33 yes

30 92nd -16 -39 yes

32 100th +27 -4 yes

34 112th -5 -39 yes

36 120th -9 -46 yes

38 124th -6 -44 yes

40 132nd -7 -28 yes

42 138th -4 -33 yes

44 146th -9 -83 no

45 240 m North of State Line +37 -45 transition

46 600 m North of State Line +21 -46 transition
65

Table 16. Ocean City Beach Condition Assessment in June 1997.

Profile Street Project Condition

4 3rd 100 yr

6 10th >100 yr

8 15th marginal

9 20th 100 yr

10 25th >100 yr

12 32nd <100 yr

13 37th >100 yr

15 45th >100 yr

17 52nd >100 yr

18 56th >100 yr

20 63rd >100 yr

24 74th Deficient

26 78th Deficient

27 81st Deficient

29 86th <100 yr

30 92nd >100 yr

32 104th <100 yr

34 112th Deficient

36 120th <100 yr

38 124th <100 yr

40 132nd >100 yr

42 138th 100 yr

44 146th Deficient

4.2.3. Shoreface Retreat

Before the major beach fills along Ocean City, the historical trend from 1929 to 1980 is a higher

erosion rate between the shoals (on either side of 81st Street) relative to the shore attached shoals at 37th

and 103rd Streets. The longer term trend between 1850 and 1980 was still erosional but less severe. 

Although the “hot spot” in the area of 81st Street seems to have persisted through recent data, the actual

shoreline has advanced with ongoing beach fills. Therefore, the implication is that shoreface retreat has also

been arrested for the time being.
66

Table 17. Ocean City Beach Condition Assessment Table for October 1997.

Volume Change

Profile Street May-Oct 1997 Condition

1 S. 1st -13.6 cy/ft Beyond Project Limits

2 Dorchester +41.6 Beyond Project Limits

4 3rd +2.3 100 yr +

6 10th -18.6 100 yr

8 15th -7.4 Deficient

9 20th +6.0 100 yr

10 25th +6.0 100 yr

12 32nd -8.2 100 yr

13 37th -6.8 100 yr +

15 45th -5.2 100 yr +

17 52nd +22.1 100 yr +

18 56th +11.8 100 yr +

20 63rd -10.8 100 yr +

22 69th +8.9 100 yr +

24 74th -1.0 Deficient

26 78th +6.3 Deficient

27 81st -3.4 Deficient

29 86th -7.3 Deficient

30 92nd -0.7 100 yr +

32 100th +0.9 100 yr -

34 112th +1.8 Deficient

36 120th +3.1 100 yr

38 124th -3.4 100 yr

40 132nd +0.0 100 yr +

42 138th -11.9 100 yr

44 146th -9.2 Deficient

Table 18. Maryland recent regression analysis results.

Profile R

2

Rate of

Change

(m/yr)

No.

points Dates Spanned

1 100% -6.2 2 Feb 1994 - Oct 1997

4 53% 2.1 3 Feb 1994 - Oct 1997

8 79% -3.1 3 Feb 1994 - Oct 1997

10 76% -0.3 3 Feb 1994 - Oct 1997

13 87% 4.2 5 June 1988 - Oct 1997

17 75% 4.2 5 June 1988 - Oct 1997

20 75% 4.2 5 June 1988 - Oct 1997

24 87% 3.5 5 June 1988 - Oct 1997

27 83% 2.5 5 June 1988 - Oct 1997

30 90% 4.5 5 June 1988 - Oct 1997

34 71% 5.2 3 Feb 1994 - Oct 1997

38 15% 1.1 3 Feb 1994 - Oct 1997

44 95% 4.9 3 Feb 1994 - Oct 1997
67

4.3. Wave Analysis

Maa and Kim (2000) have evaluated the regional wave environment for the purpose of evaluating

the potential impacts of mining Fenwick Shoal and Isle of Wight Shoal which are located on the north and

south sides of the MD-DE boundary respectively (Figure 7). Their analysis of present conditions shows the

impacts of the shore attached-shoals on waves that reach the shoreline between Ocean City Inlet on the

south to Bethany Beach to the north. This region is exposed primarily to waves from the east northeast

through the southeast. A significant high frequency deep water wave condition for each of these directions

is wave period of 10 s and wave height of 2 m. The results of Maa and Kim’s analysis of the transformation

that would be experienced by these wave conditions are shown in Figure 22. In Maryland, the highest rates

of erosion occurred midway between Ocean City Inlet and the Maryland-Delaware state line. This same

area was shown to be an erosional “hot spot” by a study performed on profile data taken between 1988 and

1992.
Figure22. Normalizedwave height distributionoffshoreofDelawareandMarylandinthe

vicinityof theproposeddredgingsites(MaaandKim,2000).

68
69

5. CONCLUSION

The ocean shore of Delaware and Maryland is a highly utilized resource. Much of it has been

modified with bulkheads, groins, jetties around two inlets, and several substantial episodes of beach

nourishment in a continuing effort to maintain the viability of the beach as a recreational, residential, and

commercial attraction. The long term history of the shore is one of retreat. Storm waves are the most

obvious agent of shoreline erosion, but continuing sea-level rise is a major contributing factor. The jetties at

Ocean City, Maryland, and Indian River Inlet, Delaware had predictable major impacts on longshore

sediment transport.

It appears that the cumulative magnitude of beach nourishment in some locations appears to have

modified the local processes. While the actual shoreline has continued to retreat, sand removed from the

(formerly) subaerial beach has remained in the subaqueous nearshore. There it has modified the natural

beach profile. The new longer, thus “flatter,” profile has the potential to dissipate more wave energy and

provide some protection to the high beach. This beneficial consequence of the long program of nourishment

would not be visible without an organized program of monitoring the shore and profiling the beach. The

progressively positive influence of successive beach nourishment projects suggests that future nourishment

projects have a relatively high likelihood of success, assuming the rate of sea-level rise does not accelerate

and other factors remain relatively constant.

Sites of chronically greater rates of shoreline erosion (“hot spots”) are related to patterns of wave

refraction governed by the regional wave climate and the location of offshore shoals. Modification of those

shoals by sand mining will alter the specific patterns of wave refraction but might not have significant impact

on the shoreline situation. This is especially true if residual sand from beach nourishment winds up in the

nearshore where it has the potential to cause larger waves to break farther offshore. Knowing this,

nourishment projects could be designed to modify both the active recreational beach and the generally

invisible but important nearshore.
70

This study focused on shoreline change which is only part of the story. Perhaps the most intriguing

aspect of this study was the nature of shoreface retreat in light of the mobile linear ridges and ongoing beach

nourishment projects. In particular, the Delaware profile data set should be analyzed further to better detail

the offshore profile changes. We suggest future efforts toward this understanding. If Maryland profile data

became available, the offshore area could be assessed as well.
71

6. REFERENCES

Anders, F. J. and M. R. Byrnes, 1991. Accuracy of shoreline change rates as determined from maps and

aerial photos. Shore and Beach, 59: 17-26.

Beach-Net Website, 2000. http://www.beach-net.com/aerialphotos/

Belknap, D.F. and J.C. Kraft, 1985. Influence of antecedent geology on stratigraphic preservation potential

and evolution of Delaware’s barrier systems. Mar. Geo. 63:235-262

Bosma, K.F. and Dalrymple, R.A., 1997. Beach Profile Analysis Along the Delaware Atlantic Coastline.

University of Delaware, Newark, Delaware, 165pp.

Carr, A.P., Blackley, M.W.L., and King, H.L., 1982. Spatial and seasonal aspects of beach stability. Earth

Surface Processes Landforms 7:267-282.

Clausner, J.E., Gerbert, J.A., Rambo, A.T., and Watson, K.D., 1991. Sand bypassing at Indian River Inlet,

Delaware. Proceedings Coastal Sediments ‘ 91, American Society of Civil Engineers, NY, pp

1177-1191.

Crowell, M., Leatherman, S. P., and Buckley, M. K., 1991. Historical shoreline change: Error analysis and

mapping accuracy. Journal of Coastal Research, 7(3): 839-852.

Crowell, M., B. C. Douglas, and S. P. Leatherman, 1997. On forecasting U.S. shoreline positions: A test of

algorithms. Journal of Coastal Research, 13(4): 1245-1255.

Dean, R.G. and Perlin, M., 1977. Coastal engineering study of Ocean City Inlet, Maryland. Proceedings

Coastal Sediments ‘77, American Society of Civil Engineers, NY, pp 520-542.

Delaware Geological Survey, 1988. Texture of beach sands - Atlantic coast of Delaware. An Initial Report

of SEASIP. July 12, 1988.

Dick, J.E. and Dalrymple, R.A., 1983. Coastal Engineering Study of Bethany Beach, Delaware. Ocean

Engineering Research Report No. CE 83-38, University of Delaware, Newark, Delaware 19716.

Dolan, R., M. S. Fenster, and S. J. Holme, 1991. Temporal analysis of shoreline recession and accretion. 

Journal of Coastal Research, 7(3): 723-744.

Dolan R., B. P. Hayden, and J. Heywood, 1978. A new photogrammetric method for determining shoreline

erosion. Coastal Engineering, 2: 21-39.

Dolan, R., P. May, and S. May, 1980. The reliability of shoreline change measured form aerial

photographs. Shore and Beach, 48: 22-29.

Dubois, R.N., 1988. Seasonal Changes in Beach Topography and Beach Volume in Delaware. Marine

Geology 81:79-96.

Elliott, I. and D. Clarke, 1989. Temporal and spatial bias in the estimation of shoreline rate-of-change

statistics from beach survey information. Coastal Management, 

17: 129-156.

Ellis, M. Y. (ed.), 1978. Coastal Mapping Handbook. U. S. Department of the Interior, Geological

Survey; U. s. Department of Commerce, National Ocean Service; U. S. Government Printing

Office, Washington, D. C., 199p.

Everts, C. H., J. P. Battley, Jr., and P. N. Gibson, 1983. Shoreline Movements: Report 1: Cape

Henry, Virginia to Cape Hatteras, North Carolina, 1849-1989. Technical Report CERC-83-,

Report 1. U. S. Army Corps of Engineers.

Fenster, M.S., Dolan, R., and Elder, J.F., 1993. A new method for predicting shoreline positions from

historical data. Journal of Coastal Research, 9(1): 147-171.
72

Foster, E. R. and R. J. Savage, 1989. Methods of historical shoreline analysis, In: Magoon, O. T., H.

Converse, D. Miner, L. T. Tobin, and D. Clark (eds.), Coastal Zone ‘89, American Society of

Civil Engineers, 5: 4434-4448.

Gebert, J., Watson, K., and Rambo, A. Q992. 57 years of coast engineering practice at a problem inlet:

Indian River Inlet, Delaware. Coastal Eng. Practice, ASCE:1-17

.Grosskopf, W.G. and Resio, D.T., 1988. Storm hindcast for Ocean City, Maryland. Draft report to the

USAED, Baltimore, Offshore Coastal Technologies, Inc.

Hayes, M.O. and Boothroyd, J.C., 1969. Storms as modifying agents in the coastal environment. Coastal

Research Group, Coastal Environments NE Massachusetts and New Hampshire. University of

Massachusetts Department of Geology, Amherst, Massachusetts, pp.245-265.

House Document No. 105-144, 1997. Beach Nourishment Project. Communication from The Acting

Assistant Secretary (Civil Works), the Department of the Army. U.S. Government Printing Office,

Washington, 381 pp.

Kraft, J.C. and John, C.J., 1976. The Geological Structure of the Shorelines of Delaware. University of

Delaware, Newark College of Marine Studies, Sea Grant, Report No. DEL-SG-14-76.

Kraft, J.C., 1971. Sedimentary facies patterns and geologic history of a Holocene marine transgression. 

Geological Soc. of Am. Bull. 82:2131-2158.

Kraft, J.C., 1977. Late Quaternary paleogeographic changes in the coastal environments of Delaware,

Middle Atlantic Bight, related to the archaeologic settings. In: (eds. W.S. Newman and B. Salwen)

Amerinds and Their Paleoenvironments in Easter North America. Annals of the New York

Academy of Sciences 288: 35-69.

Lacey, E.M. and Peck, J.A., 1998. Long-term beach profile variations along the south shore of Rhode

Island, USA. Journal of Coastal Research, 14(4): 1255-1264.

Larson, M. and N. C. Kraus, 1994. temporal and spatial scales of beach profile change, Duck, North

Carolina. Marine Geology, 115: 75-94.

Leatherman, S. P., 1983. Shoreline mapping: a comparison of techniques. Shore and Beach, 51: 28-33.

Louis Berger Group, 1999. Environmental Report: Use of Federal Offshore Sand Resources for Beach and

Coastal Restoration in New Jersey, Maryland, Delaware, and Virginia. OCS Study 99-0036, U. S.

Department of the Interior, Minerals Management Service, Office of International Activities and

Marine Minerals.

Maa J. P.-Y and S. C. Kim, 2000. Potential Modifications to Waves Due to Dredging and Other

Oceanographic Considerations. Part 4 of Environmental Survey of Potential Sand Resource Sites

Offshore Delaware and Maryland. Final Contract Report. Virginia Institute of Marine Science,

Gloucester Point, VA.

Maryland Geological Survey Website, 2000. http://mgs.dnr.md.gov/goals.html

McBeth, F. H., 1956. A method of shoreline delineation. Photogrammetric Engineering, 22:400-405.

McBride, R.A., 1999. Spatial and temporal distribution of historical and active tidal inlets: Delmarva

Peninsula and New Jersey, USA. In: Proceedings Coastal Sediments 1999: 1505-1521.

McBride, R.A. and Moslow, T.F, 1991. Origin, evolution, and distribution of shoreface sand ridges,

Atlantic inner shelf, USA. Marine Geology 97: 57-85.

Morton, R., 1978. Analysis of sequential shoreline changes:, In: Tanner W. F. (ed.) Standards for 

Measuring Shoreline Changes, Report of Workshop, published jointly by Coastal Research and the

Department of Geology, Florida State University, 43-48.
73

Morton, R.A. and Speed, F.M., 1998. Evaluation of shorelines and legal boundaries controlled by water

levels on sand beaches. Journal of Coastal Research, 14(4): 1373-1384.

NOAA, 1989. Tide Tables. U.S. Department of Commerce, National Oceanic and Atmospheric

Administration.

Ramsey, K.W., Leathers, D.J., Wells, D.V., and Talley, J.H., 1998. Summary Report: The Coastal Storms

of January 27-29 and February 4-6, 1998, Delaware and Maryland. Open File Report No. 40,

Delaware Geological Survey, University of Delaware, Newark, DE, 43p.

Shalowitz, A. L., 1964. Shore and Sea Boundaries, v. 2. U. S. Department of Commerce, Publication

10-1, U. S. Government Printing Office, Washington, D. C., 749p.

Smith, G. L., and G. A. Zarillo, 1990. Calculating long-term shoreline recession rates using aerial

photographic and beach profiling techniques. Journal of Coastal Research, 6(1): 111-120.

Smith, E.R., 1988. Case Histories of Corps Breakwater and Jetty Structures. Technical Report

REMR-CO-3. Coastal Engineering Research Center, Dept. of the Army, Waterways Experiment

Station, Corps of Engineers, Vicksburg, Mississippi.

Stafford, D. B., 1971. An aerial photographic technique for beach erosion surveys in North Carolina. 

CERC Tech Memorandum No 36, 115p.

Stauble, D.K., Garcia, A.W., Kraus, N.C., Grosskopf, W.G., and Bass, G.P., 1993. Beach Nourishment

Project Response and Design Evaluation: Ocean City, Maryland; Report 1 - 1988-1992. Technical

Report CERC-93-13.

Swift D.J.P. and M. Field, 1981. Evolution of a classic sand ridge field, Maryland sector, North America

inner shelf. Sedimentology 28: 462-482.

U. S. Army Corps of Engineers, Philadelphia District, 1996. Rehoboth Beach/Dewey Beach Interim

Feasibility Study: Final Feasibility Report and Final Environmental Impact Statement. U.S. Army

Corps of Engineers, Philadelphia, Pennsylvania, 146pp. and appendices A-G.

Valverde, H.R., A.C. Trembanis, and O.H. Pilkey, 1999. Summary of beach nourishment episodes on the

U.S. East Coast barrier islands. J. Coast. Res. 15(4): 1100-1118.

Wells, D. V., 1994. Non-Energy Resources and Shallow Geological Framework of the Inner Continental

Margin off Ocean City, Maryland. Coastal and Estuarine Geology Open File Report No. 16,

Maryland Geological Survey, Baltimore.

APPENDIX 1

Dewey Beach regression analysis results
229000 229400 229800 230200

Easting (m)

75500

76000

76500

77000

77500

78000

78500

145

140

135

130

125

120

115

110

105

100

205

210

215

220

225

230

235

46

47

Dewey Profile

Ocean Coast Profiles
33239 33604 33969 34334 34699 35064 35429 35794 36159 36524

Julian Day

-50

0

50

100

150

200

250

FitResults

Fit1: Linear 1991-1994

EquationY= -0.03832196554*X+ 1340.97898

Number of datapoints used= 7

Average X= 33978.3

Average Y= 38.8643

Residual sum of squares = 11568.7

Regressionsum of squares = 1241

Coef of determination,R-squared= 0.0968792

Residual meansquare, sigma-hat-sq'd= 2313.75

Fit2: Linear 1994-1998

EquationY= -0.08514258571*X+ 3090.696809

Number of datapoints used= 14

Average X= 35289.7

Average Y= 86.0393

Residual sum of squares = 16582.4

Regressionsum of squares = 20140.8

Coef of determination,R-squared= 0.548449

Residual meansquare, sigma-hat-sq'd= 1381.87

Fit3: Linear1998-1999

EquationY= -0.3949489617* X+ 14419.89925

Number of datapoints used= 5

Average X= 36144.4

Average Y= 144.706

Residual sum of squares = 1310.83

Regressionsum of squares = 14217.6

Coef of determination,R-squared= 0.915585

Residual meansquare, sigma-hat-sq'd= 436.942

Dewey

Profile 100

1 Jan 1991 30 Dec 1999

1 2

3
33239 33604 33969 34334 34699 35064 35429 35794 36159 36524

Julian Day

-50

0

50

100

150

200

250

Dewey

Profile 105

1 Jan 1991 30 Dec 1999

FitResults

Fit1:Linear 1991-1994

EquationY= -0.06136329187* X+ 2133.745718

Number of datapoints used= 6

AverageX= 33982

AverageY= 48.4983

Residual sum of squares = 5332.81

Regressionsum of squares = 3179.76

Coef of determination, R-squared= 0.373537

Residual meansquare, sigma-hat-sq'd= 1333.2

Fit2:Linear 1994-1998

EquationY= -0.07573638731* X+ 2760.812286

Number of datapoints used= 15

AverageX= 35291.6

AverageY= 87.954

Residual sum of squares = 27538.9

Regressionsum of squares = 15940.8

Coef of determination, R-squared= 0.366626

Residual meansquare, sigma-hat-sq'd= 2118.38

Fit3:Linear1998-1999

EquationY= -0.2820025969*X+ 10357.17293

Number of datapoints used= 3

AverageX= 36041.3

AverageY= 193.423

Residual sum of squares = 62.3253

Regressionsum of squares = 530.806

Coef of determination, R-squared= 0.894922

Residual meansquare, sigma-hat-sq'd= 62.3253

1 2

3
33239 33604 33969 34334 34699 35064 35429 35794 36159 36524

Julian Day

-50

0

50

100

150

200

250

Dewey

Profile 110

1 Jan 1991 30 Dec 1999

FitResults

Fit1:Linear 1991-1994

EquationY= -0.04248044283* X+ 1503.944547

Number of datapoints used= 5

AverageX= 33898.2

AverageY= 63.934

Residual sum of squares = 5197.04

Regressionsum of squares = 1143.72

Coef of determination, R-squared= 0.180376

Residual meansquare, sigma-hat-sq'd= 1732.35

Fit2:Linear 1994-1998

EquationY= -0.09839249662* X+ 3573.942274

Number of datapoints used= 14

AverageX= 35334.2

AverageY= 97.3207

Residual sum of squares = 12879.4

Regressionsum of squares = 23212.5

Coef of determination, R-squared= 0.64315

Residual meansquare, sigma-hat-sq'd= 1073.28

Fit3:Linear 1998-1999

EquationY= -0.3419366914*X+ 12508.47255

Number of datapoints used= 5

AverageX= 36144.4

AverageY= 149.376

Residual sum of squares = 2428.55

Regressionsum of squares = 10657

Coef of determination, R-squared= 0.81441

Residual meansquare, sigma-hat-sq'd= 809.517

1

2

3
33239 33604 33969 34334 34699 35064 35429 35794 36159 36524

Julian Day

-50

0

50

100

150

200

250

Dewey

Profile 115

1 Jan 1991 30 Dec 1999

FitResults

Fit1:Linear 1991-1994

EquationY= -0.06617705462* X+ 2302.655727

Number of datapoints used= 7

AverageX= 33978.3

AverageY= 54.0729

Residual sum of squares = 10884.2

Regressionsum of squares = 3700.75

Coef of determination, R-squared= 0.253738

Residual meansquare, sigma-hat-sq'd= 2176.83

Fit2:Linear 1994-1998

EquationY= -0.08625917913* X+ 3150.124446

Number of datapoints used= 15

AverageX= 35291.6

AverageY= 105.9

Residual sum of squares = 18224.8

Regressionsum of squares = 20678.1

Coef of determination, R-squared= 0.531532

Residual meansquare, sigma-hat-sq'd= 1401.9

Fit3:Linear 1998-1999

EquationY= -0.4707844957*X+ 17159.6477

Number of datapoints used= 4

AverageX= 36093.5

AverageY= 167.388

Residual sum of squares = 537.088

Regressionsum of squares = 8717.25

Coef of determination, R-squared= 0.941964

Residual meansquare, sigma-hat-sq'd= 268.544

1 2

3
33239 33604 33969 34334 34699 35064 35429 35794 36159 36524

Julian Day

-50

0

50

100

150

200

250

Dewey

Profile 120

1 Jan 1991 30 Dec 1999

Fit Results

Fit 1:Linear 1991-1994

EquationY= -0.0353747593* X+ 1282.794666

Number of datapoints used= 5

AverageX= 33898.2

AverageY= 83.654

Residual sum of squares = 2211.64

Regressionsum of squares = 793.099

Coef of determination,R-squared= 0.26395

Residual meansquare, sigma-hat-sq'd= 737.212

Fit 2:Linear 1994-1998

EquationY= -0.07727811391* X+ 2834.353004

Number of datapoints used= 14

AverageX= 35251.5

AverageY= 110.184

Residual sum of squares = 13558.1

Regressionsum of squares = 14579.8

Coef of determination,R-squared= 0.518155

Residual meansquare, sigma-hat-sq'd= 1129.84

Fit 3:Linear 1998-1999

EquationY= -0.3192940211* X+ 11696.76882

Number of datapoints used= 5

AverageX= 36144.4

AverageY= 156.078

Residual sum of squares = 2574.15

Regressionsum of squares = 9292.34

Coef of determination,R-squared= 0.783074

1 Residual meansquare, sigma-hat-sq'd= 858.051

2

3
33239 33604 33969 34334 34699 35064 35429 35794 36159 36524

Julian Day

-50

0

50

100

150

200

250

Dewey

Profile 130

1 Jan 1991 30 Dec 1999

FitResults

Fit1:Linear 1991-1994

EquationY= -0.1003954077*X+ 3498.455665

Number of datapoints used= 5

AverageX= 33903.4

AverageY= 94.71

Residual sum of squares = 10582.2

Regressionsum of squares = 6643.4

Coef of determination, R-squared= 0.38567

Residual meansquare, sigma-hat-sq'd= 3527.41

Fit2:Linear 1994-1998

EquationY= -0.06424379819* X+ 2389.237762

Number of datapoints used= 15

AverageX= 35291.6

AverageY= 121.971

Residual sum of squares = 16365.6

Regressionsum of squares = 11470

Coef of determination, R-squared= 0.412062

Residual meansquare, sigma-hat-sq'd= 1258.89

Fit3:Linear 1998-1999

EquationY= -0.3394543771*X+ 12436.69279

Number of datapoints used= 5

AverageX= 36144.4

AverageY= 167.318

Residual sum of squares = 711.042

Regressionsum of squares = 10502.8

Coef of determination, R-squared= 0.936593

Residual meansquare, sigma-hat-sq'd= 237.014

1

2

3
33239 33604 33969 34334 34699 35064 35429 35794 36159 36524

Julian Day

-50

0

50

100

150

200

250

Dewey

Profile 135

1 Jan 1991 30 Dec 1999

FitResults

Fit1:Linear 1991-1994

EquationY= -0.0378067786*X+ 1418.197742

Number of datapoints used= 5

AverageX= 33898.2

AverageY= 136.616

Residual sum of squares = 6657.27

Regressionsum of squares = 905.899

Coef of determination, R-squared= 0.119778

Residual meansquare, sigma-hat-sq'd= 2219.09

Fit2:Linear 1994-1998

EquationY= -0.06030999876* X+ 2281.457019

Number of datapoints used= 15

AverageX= 35291.6

AverageY= 153.021

Residual sum of squares = 13034.8

Regressionsum of squares = 10108.3

Coef of determination, R-squared= 0.436774

Residual meansquare, sigma-hat-sq'd= 1002.68

Fit3:Linear 1998-1999

EquationY= -0.2616474282*X+ 9638.503951

Number of datapoints used= 4

AverageX= 36093.5

AverageY= 194.732

Residual sum of squares = 429.354

Regressionsum of squares = 2692.58

Coef of determination, R-squared= 0.862472

Residual meansquare, sigma-hat-sq'd= 214.677

1

2

3
33239 33604 33969 34334 34699 35064 35429 35794 36159 36524

Julian Day

-50

0

50

100

150

200

250

Dewey

Profile 140

1 Jan 1991 30 Dec 1999

FitResults

Fit1:Linear 1991-1994

EquationY= -0.07353263802* X+ 2641.870127

Number of datapoints used= 7

AverageX= 33978.3

AverageY= 143.357

Residual sum of squares = 13664.1

Regressionsum of squares = 4569.15

Coef of determination, R-squared= 0.250594

Residual meansquare, sigma-hat-sq'd= 2732.82

Fit2:Linear 1994-1998

EquationY= -0.05554241722* X+ 2134.856581

Number of datapoints used= 16

AverageX= 35325.1

AverageY= 172.814

Residual sum of squares = 17276.9

Regressionsum of squares = 9405.48

Coef of determination, R-squared= 0.352498

Residual meansquare, sigma-hat-sq'd= 1234.06

Fit3:Linear 1998-1999

EquationY= -0.735* X+ 26678.94

Number of datapoints used= 2

AverageX= 36008

AverageY= 213.06

Residual sum of squares = 1.32349E-023

Regressionsum of squares = 4.3218

Coef of determination, R-squared= 1

Residual meansquare, sigma-hat-sq'd= 0

1

2

3
33239 33604 33969 34334 34699 35064 35429 35794 36159 36524

Julian Day

0

50

100

150

200

250

300

Dewey

Profile 145

1 Jan 1991 30 Dec 1999

FitResults

Fit1:Linear 1991-1994

EquationY= -0.002786080657* X+ 274.5231193

Number of datapoints used= 5

AverageX= 33898.2

AverageY= 180.08

Residual sum of squares = 6140.51

Regressionsum of squares = 4.91958

Coef of determination, R-squared= 0.000800527

Residual meansquare, sigma-hat-sq'd= 2046.84

Fit2:Linear 1994-1998

EquationY= -0.03875425808* X+ 1563.799058

Number of datapoints used= 13

AverageX= 35254.4

AverageY= 197.542

Residual sum of squares = 13212.8

Regressionsum of squares = 3380.24

Coef of determination, R-squared= 0.203715

Residual meansquare, sigma-hat-sq'd= 1201.16

Fit3:Linear 1998-1999

EquationY= -0.01953812273* X+ 913.8097423

Number of datapoints used= 4

AverageX= 36179

AverageY= 206.94

Residual sum of squares = 1525.31

Regressionsum of squares = 25.6543

Coef of determination, R-squared= 0.0165409

Residual meansquare, sigma-hat-sq'd= 762.653

1

2

3
33239 33604 33969 34334 34699 35064 35429 35794 36159 36524

Julian Day

-50

0

50

100

150

200

250

Dewey

Profile 205

1 Jan 1991 30 Dec 1999

FitResults

Fit1:Linear 1991-1994

EquationY= -0.01642978008* X+ 603.6672569

Number of datapoints used= 5

AverageX= 33898.4

AverageY= 46.724

Residual sum of squares = 3591.74

Regressionsum of squares = 170.95

Coef of determination, R-squared= 0.0454328

Residual meansquare, sigma-hat-sq'd= 1197.25

Fit2:Linear 1994-1998

EquationY= -0.09190342717* X+ 3318.940324

Number of datapoints used= 15

AverageX= 35291.6

AverageY= 75.5213

Residual sum of squares = 17401.9

Regressionsum of squares = 23472.7

Coef of determination, R-squared= 0.574262

Residual meansquare, sigma-hat-sq'd= 1338.61

Fit3:Linear 1998-1999

EquationY= -0.3677220145*X+ 13420.00758

Number of datapoints used= 5

AverageX= 36144.4

AverageY= 128.916

Residual sum of squares = 1507.05

Regressionsum of squares = 12324.9

Coef of determination, R-squared= 0.891045

Residual meansquare, sigma-hat-sq'd= 502.35

1

2

3
33239 33604 33969 34334 34699 35064 35429 35794 36159 36524

Days

-50

0

50

100

150

200

250

Dewey

Profile 210

1 Jan 1991 30 Dec 1999

FitResults

Fit1: Linear 1991-1994

EquationY= -0.04748294111*X+ 1634.091437

Number of datapoints used= 7

Average X= 33978.4

Average Y= 20.6957

Residual sum of squares = 10953.1

Regressionsum of squares = 1903.78

Coef of determination,R-squared= 0.148075

Residual meansquare, sigma-hat-sq'd= 2190.62

Fit2: Linear 1994-1998

EquationY= -0.0885140609* X+ 3188.110266

Number of datapoints used= 16

Average X= 35325.1

Average Y= 61.34

Residual sum of squares = 21511

Regressionsum of squares = 23886.7

Coef of determination,R-squared= 0.526166

Residual meansquare, sigma-hat-sq'd= 1536.5

Fit3: Linear 1998-1999

EquationY= -0.4312834185* X+ 15711.48839

Number of datapoints used= 5

Average X= 36144.4

Average Y= 123.008

Residual sum of squares = 1183.43

Regressionsum of squares = 16953.9

Coef of determination,R-squared= 0.934751

Residual meansquare, sigma-hat-sq'd= 394.478

1 2

3
33239 33604 33969 34334 34699 35064 35429 35794 36159 36524

Julian Days

0

50

100

150

200

250

300

Dewey

Profile 225

1 Jan 1991 30 Dec 1999

Fit Results

Fit 1:Linear 1991-1994

EquationY= -0.03247997294* X+ 1172.647115

Number of datapoints used= 5

AverageX= 33898.4

AverageY= 71.628

Residual sum of squares = 5571.82

Regressionsum of squares = 668.092

Coef of determination,R-squared= 0.107067

Residual meansquare, sigma-hat-sq'd= 1857.27

Fit 2:Linear 1994-1998

EquationY= -0.0407371152* X+ 1512.394316

Number of datapoints used= 14

AverageX= 35242.6

AverageY= 76.7107

Residual sum of squares = 13128.3

Regressionsum of squares = 3776.63

Coef of determination,R-squared= 0.223404

Residual meansquare, sigma-hat-sq'd= 1094.02

Fit 3:Linear 1998-1999

EquationY= -0.1545231664* X+ 5671.292165

Number of datapoints used= 3

AverageX= 36122.7

AverageY= 89.5033

Residual sum of squares = 399.562

Regressionsum of squares = 695.374

Coef of determination,R-squared= 0.635082

Residual meansquare, sigma-hat-sq'd= 399.562

1

2

3
33239 33604 33969 34334 34699 35064 35429 35794 36159 36524

Julian Day

0

50

100

150

200

250

300

Dewey

Profile 230

1 Jan 1991 30 Dec 1999

FitResults

Fit1: Linear 1991-1994

EquationY= -0.05343838141*X+ 1880.485303

Number of datapoints used= 6

AverageX= 33908

AverageY= 68.4967

Residual sum of squares = 10317.6

Regressionsum of squares = 1816.37

Coef of determination,R-squared= 0.149692

Residual meansquare, sigma-hat-sq'd= 2579.41

Fit2: Linear 1994-1998

EquationY= -0.03728635724*X+ 1395.761425

Number of datapoints used= 13

AverageX= 35254.4

AverageY= 81.2538

Residual sum of squares = 11037.2

Regressionsum of squares = 3129.02

Coef of determination,R-squared= 0.220879

Residual meansquare, sigma-hat-sq'd= 1003.38

Fit3: Linear 1998-1999

EquationY= -0.03154752693*X+ 1219.622977

Number of datapoints used= 4

AverageX= 36179

AverageY= 78.265

Residual sum of squares = 914.712

Regressionsum of squares = 66.8845

Coef of determination,R-squared= 0.0681386

Residual meansquare, sigma-hat-sq'd= 457.356

1

2

3
33239 33604 33969 34334 34699 35064 35429 35794 36159 36524

Days

-50

0

50

100

150

200

250

300

Dewey

Profile 240

1 Jan 1991 30 Dec 1999

FitResults

Fit1:Linear 1991-1994

EquationY= -0.1119870987*X+ 3862.785208

Number of datapoints used= 6

AverageX= 33908

AverageY= 65.5267

Residual sum of squares = 13695.8

Regressionsum of squares = 7976.87

Coef of determination, R-squared= 0.368061

Residual meansquare, sigma-hat-sq'd= 3423.96

Fit2:Linear 1994-19998

EquationY= -0.0140890509*X+ 570.4250604

Number of datapoints used= 12

AverageX= 35212.8

AverageY= 74.3108

Residual sum of squares = 9734.82

Regressionsum of squares = 393.08

Coef of determination, R-squared= 0.0388116

Residual meansquare, sigma-hat-sq'd= 973.482

Fit3:Linear 1998-1999

EquationY= -0.093646873* X+ 3457.098111

Number of datapoints used= 3

AverageX= 36122.7

AverageY= 74.3233

Residual sum of squares = 243.998

Regressionsum of squares = 255.398

Coef of determination, R-squared= 0.511414

Residual meansquare, sigma-hat-sq'd= 243.998

1

2

3