OCS Study

BOEM 2020-002

NOAA NCCOS 270

US Department of the Interior

Bureau of Ocean Energy Management

Headquarters (Sterling, VA)

Regional Essential Fish Habitat 

Geospatial Assessment and Framework 

for Offshore Sand Features 

Volume 1: Fish Habitat Associations and the Potential 

Effects of Dredging on Fish of the Atlantic and Gulf of 

Mexico Outer Continental Shelf: Literature Synthesis 

and Gap Analysis
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OCS Study 

BOEM 2020-002

NOAA NCCOS 270

US Department of the Interior

Bureau of Ocean Energy Management

Headquarters (Sterling, VA)

Regional Essential Fish Habitat 

Geospatial Assessment and Framework 

for Offshore Sand Features 

Volume 1: Fish Habitat Associations and the 

Potential Effects of Dredging on Fish of the Atlantic 

and Gulf of Mexico Outer Continental Shelf,

Literature Synthesis and Gap Analysis

January 2020

Authors:

Bradley A. Pickens1,2

J. Christopher Taylor1

Deena Hansen3

Prepared under Interagency Agreement: BOEM NOAA IA M17PG00028

By

1

NOAA, National Ocean Service, National Centers for Coastal Ocean Science, Biogeography Branch, 

101 Pivers Island Road, Beaufort, North Carolina, USA 28516

2

CSS-Inc., 10301 Democracy Lane Suite 300, Fairfax, Virginia 22030, USA; Under NOAA/NCCOS 

Contract #GS-00F-217CA

3

BOEM, Marine Minerals Program, 45600 Woodland Rd, Sterling, Virginia 22209, USA
DISCLAIMER

This study was funded, in part, by the US Department of the Interior, Bureau of Ocean Energy 

Management (BOEM), Environmental Studies Program, Washington, DC, through Interagency 

Agreement Number IA M17PG00028 with the National Oceanic and Atmospheric Administration. This 

report has been technically reviewed by BOEM, and it has been approved for publication. The views and 

conclusions contained in this document are those of the authors and should not be interpreted as 

representing the opinions or policies of the US Government, nor does mention of trade names or 

commercial products constitute endorsement or recommendation for use.

REPORT AVAILABILITY

To download a PDF file of this report, go to the US Department of the Interior, Bureau of Ocean Energy 

Management Data and Information Systems webpage (http://www.boem.gov/Environmental-StudiesEnvData/), click on the link for the Environmental Studies Program Information System (ESPIS), and 

search on 2020-002. The report is also available at the National Technical Reports Library at 

https://ntrl.ntis.gov/NTRL/.

CITATION

Pickens BA, Taylor JC, Hansen D. 2020. Volume 1: Fish habitat associations and the potential effects of 

dredging on the Atlantic and Gulf of Mexico Outer Continental Shelf, literature synthesis and gap 

analysis. In: Pickens, BA, Taylor JC, editors. Regional Essential Fish Habitat geospatial assessment 

and framework for offshore sand features. Sterling (VA): US Department of the Interior, Bureau of 

Ocean Energy Management. OCS Study BOEM 2020-002 and NOAA NCCOS Technical Memorandum 

270. https://doi.org/10.25923/akzd-8556 122 pp.

ACKNOWLEDGMENTS

We acknowledge staff and scientists of the BOEM Marine Mineral Program for their insights through the 

scoping of this review. Numerous discussions with staff in the Habitat Conservation Divisions in the 

NMFS Regional Offices resulted in a focused review of issues relating dredging to impacts of EFH.

Specifically, we acknowledge David O’Brien and Mike Johnson with the Greater Atlantic Regional 

Office, Pace Wilber, David Dale and Rusty Swafford with the Southeast Regional Office. Rachel Carroll 

of the University of North Carolina Wilmington, Department of Mathematics and Statistics, provided 

useful insights into analyses of the international literature review of marine fish predictive models. CSSInc. work was provided under NOAA/NCCOS Contract #GS-00F-217CA.
i

Table of Contents

1 Introduction and Background ................................................................................................................ 1

1.1 Preceding Syntheses of Sand Shoals........................................................................................... 4

1.2 Scope of Literature Synthesis and Objectives .............................................................................. 5

1.3 Literature Search Methods............................................................................................................ 9

2 Effects of Sand Dredging on Fish .......................................................................................................10

2.1 Effects of Hydraulic Entrainment on Fish....................................................................................12

2.1.1 Entrainment Rates of Fish and Invertebrates .....................................................................12

2.1.2 Factors Affecting Vulnerability of Fish to Entrainment ........................................................14

2.2 Effects of Human-made Sound on Fish ......................................................................................15

2.2.1 Measures of Underwater Sound .........................................................................................15

2.2.2 Underwater Sounds Produced by Dredging-related Activity...............................................15

2.2.3 The Effects of Underwater Sounds on Fish with Implications for Dredging Impacts ..........18

2.3 Effects of Suspended Sediments on Fish and Their Habitats ....................................................20

2.3.1 Suspended Sediment Measures .........................................................................................20

2.3.2 Extent, Duration, and Concentration of Suspended Sediments Related to Dredging ........20

2.3.3 Consequences of Suspended Sediments and Sedimentation on Fish...............................25

2.3.4 Consequences of Suspended Sediments and Sedimentation on Nearby Corals, Coral 

Reefs, and Hard Bottom Habitats .......................................................................................................27

2.4 Effects of Physical Removal of Sediments with Implications to Fish..........................................29

2.4.1 Oceanographic and Physical Changes Associated with Shoals and Dredging..................29

2.4.2 The Effect of Dredging on Benthic Invertebrates, Sediment Grain Size, and Potential Food 

Web Changes .....................................................................................................................................31

2.4.3 Drawing Analogous Benthic Impacts from Bottom Trawl Fisheries ....................................35

2.4.4 Factors Promoting Physical Shoal Recovery......................................................................36

3 Review of Fish Habitat Associations in Relation to Geomorphology, Oceanographic Conditions, and 

Other Factors ..............................................................................................................................................37

3.1 GoM Fish Habitat Associations and Seasonality ........................................................................38

3.1.1 Introduction to the GoM Physical Setting and Fish.............................................................38

3.1.2 Red Drum in the GoM .........................................................................................................42

3.1.3 Reef Fish in the GoM ..........................................................................................................43

3.1.4 A Case Study of Adult Reef Fish on Artificial Reefs On and Adjacent to Ship Shoal, 

Offshore of Louisiana..........................................................................................................................44

3.1.5 Shoals as Habitat for Juvenile Reef Fish in the GoM .........................................................45

3.1.6 Red Snapper in the GoM ....................................................................................................45

3.1.7 Coastal Migratory Pelagics in the GoM: Spanish Mackerel, King Mackerel, Cobia ...........50

3.1.8 Shrimp in the GoM ..............................................................................................................52
ii

3.2 South Atlantic Fish Habitat Associations and Seasonality..........................................................58

3.2.1 Introduction to the South Atlantic Physical Setting and Fish ..............................................58

3.2.2 Snapper-Grouper Complex of the South Atlantic................................................................61

3.2.3 Coastal Migratory Pelagics of the South Atlantic: Spanish Mackerel and King Mackerel ..64

3.3 Greater Atlantic Fish Habitat Associations and Seasonality.......................................................66

3.3.1 Introduction to the Greater Atlantic Physical Setting and Fish............................................66

3.3.2 Review of Select Finfish Species in the Greater Atlantic....................................................68

3.4 Shark Habitat Associations .........................................................................................................73

3.4.1 Introduction to Sharks .........................................................................................................73

3.4.2 GoM Sharks ........................................................................................................................75

3.4.3 South Atlantic Sharks..........................................................................................................78

3.4.4 Greater Atlantic Sharks .......................................................................................................80

3.5 Tuna, Swordfish, and Billfish Habitat Associations .....................................................................82

4 Where are the Marine Fish? A Literature Review of Spatially Explicit Habitat Associations and 

Models of Fish Distribution..........................................................................................................................84

4.1 Introduction..................................................................................................................................84

4.2 Literature Search Methods..........................................................................................................85

4.3 Review Scope and Protocol........................................................................................................85

4.4 Statistical Analysis ......................................................................................................................88

4.5 Results and Discussion...............................................................................................................88

5 Literature Cited....................................................................................................................................94

Appendix A. Supplemental Tables............................................................................................................119
iii

List of Figures

Figure 1-1. Trend in quantity of sand leased in Federal waters.................................................................... 3

Figure 1-2. The GoM study area defined by the landward boundary of Federal waters and the seaward 

boundary of the 50-m depth contour line. ..................................................................................................... 6

Figure 1-3. The South Atlantic study area defined by the landward boundary of Federal waters and the 

seaward boundary of the 50-m depth contour line. ...................................................................................... 7

Figure 1-4. The northeast Atlantic study area defined by the landward boundary of Federal waters and the 

seaward boundary of the 50-m depth contour line. ...................................................................................... 8

Figure 2-1. Main components of a TSHD. Additionally, the draghead is attached to the bottom of the 

trailer arm and is where the hydraulic suction is generated........................................................................11

Figure 2-2. A) Cutterhead suction dredge and B) cutterhead.....................................................................11

Figure 2-3. An example of underwater sound emitted, as measured in sound pressure levels (SPL) (yaxis), at incremental distances (x-axis) from three trailing hopper suction dredges operating in offshore 

marine waters of the Atlantic Ocean near Wallops Island, Virginia. ...........................................................17

Figure 2-4. A conceptual model of suspended sediment movement from a dredging operation over time.

....................................................................................................................................................................23

Figure 2-5. The extent of a subsurface sediment plume and sedimentation resulting from aggregate 

dredging in the English Channel, UK. A) sediment plume extent and direction during ebb, flood, and slack 

tidal phases of a mean tide; B) sediment deposition two hours after dredging. .........................................24

Figure 2-6. Digital elevation models of post-dredging bathymetry for representative OCS borrow areas. 30

Figure 2-7. Conceptual diagram of ecological succession of benthic communities. ..................................33

Figure 3-1. GoM's Loop Current, spin-off eddies, major rivers, and select geological features.................39

Figure 3-2. Map of GoM hypoxic zone in July 2017. The black line shows the area where DO is < 2 mg L1, which is considered the threshold for hypoxia. .......................................................................................40

Figure 3-3. Seasonality of GoM spawning seasons. ..................................................................................41

Figure 3-4. GoM fish habitat associations with water temperature and depth............................................44

Figure 3-5. Summary of red snapper life stages and broad description of habitats used. Dates for larvae 

and juvenile stages depict an example with an egg hatching July 1st. ......................................................46

Figure 3-6. Predicted red snapper abundance and biomass for the GoM based on models of abundance 

accounting for artificial structures. ..............................................................................................................50

Figure 3-7. Life cycle of brown and white shrimp in the GoM.....................................................................53

Figure 3-8. Potential distribution of rock shrimp based on interviews, FMC documents, and scientific 

literature where available. ...........................................................................................................................57

Figure 3-9. The Gulf Stream current depicted in red moving from southern Florida toward the northeast.59

Figure 3-10. Timing of spawning (gray) and peak spawning (black) for selected species in the snappergroup EFH group of the southeastern Atlantic Ocean of the US................................................................60
iv

Figure 3-11. King mackerel seasonality with dates matching to approximate locations depicted by Trent et 

al. (1987). ....................................................................................................................................................65

Figure 4-1. Number of peer-reviewed marine fish spatially explicit predictive model studies 2007–2018. 89

Figure 4-2. Geography of studies that predicted the distribution of marine fish (2007–2018) (n=226). .....89

Figure 4-3. LDA results that show habitat variables tested in predictive modeling studies differ with fish 

functional groups.........................................................................................................................................92
v

List of Tables

Table 1-1. Dredging projects in Federal waters by state 1995–2019 ...........................................................3

Table 1-2. Classification of sand shoals from Rutecki et al. 2014. ...............................................................4

Table 2-1. Entrainment rates of Pacific Coast estuary studies...................................................................14

Table 2-2. Underwater sounds emitted by trailing suction hopper dredges. Distances of received levels 

differed by study as indicated. ....................................................................................................................16

Table 2-3. Summary of studies reporting underwater sounds produced by hydraulic dredges. ................18

Table 2-4. The effect of TSS concentrations on select marine and estuarine fish species and northern 

quahog. .......................................................................................................................................................26

Table 3-1. Species, or species groups, designated with EFH in the GoM. ................................................42

Table 3-2. Federally managed shrimp species and life history as depicted from the Gulf of Mexico Fishery 

Management Plan (1981)............................................................................................................................54

Table 3-3. For federally managed species within the South Atlantic, the proportion of area designated as 

EFH within each study area. .......................................................................................................................61

Table 3-4. Summary statistics of spawning females of reef-associated species in the South Atlantic ......63

Table 3-5. For federally managed species within the Greater Atlantic, the proportion of area designated 

as EFH within the study area. .....................................................................................................................68

Table 3-6. For federally managed shark species, the proportion of area designated as EFH within each 

study area....................................................................................................................................................74

Table 3-7. Reported CPUE (sharks hooks-100 h-1) across depth categories (range 2–366 m) for sharks on 

GoM surveys 1995–2008............................................................................................................................77

Table 3-8. For Highly Migratory Species (excluding sharks), the proportion of area designated as EFH 

within each study area. ...............................................................................................................................82

Table 3-9. Parameters used to develop habitat suitability models for bluefin tuna. ...................................83

Table 4-1. Fish functional group, examples of common species, and the number of predictive modeling 

papers obtained from the Web of Science database from 2007–2018. .....................................................87

Table 4-2. Methods used to predict the distribution of marine fish in studies spanning 2007–2018 (n = 

226). ............................................................................................................................................................91

Table 4-3. Discriminant coefficients from the three discriminants of the LDA and indicators of the driving 

variables for separation of the most separable groups...............................................................................93

Table A-1. Common and scientific names of species cited in the text......................................................119
vi

List of Acronyms and Abbreviations

BMAPA British Marine Aggregate Producer’s Association

BOEM Bureau of Ocean Energy Management

CEFAS Centre for Environment Fisheries and Aquaculture Science

cm centimeter

CPUE catch per unit effort

DO dissolved oxygen

DOI Department of the Interior

EEZ exclusive economic zone

EFH Essential Fish Habitat

ESPIS Environmental Studies Program Information System

FMC fishery management council

FMP fishery management plan

GIS Geographic Information Systems

GLM generalized linear model

GoM Gulf of Mexico

Ha hectare(s)

HAPC Habitat Areas of Particular Concern

km kilometer

km hr-1 kilometers per hour

LDA linear discriminant analysis

m meter(s)

m3 cubic meter(s)

mg L-1 milligram(s) per liter

Magnuson-Stevens Act Magnuson-Stevens Fishery Conservation and Management Act (US)

MARMAP Marine Resources Monitoring, Assessment, and Prediction

mm millimeter

MMP Marine Minerals Program

MPA Marine Protected Area

NCCOS National Centers for Coastal Ocean Science

NEPA National Environmental Policy Act

nGoM northern Gulf of Mexico

nm nautical mile(s)

NMFS National Marine Fisheries Service

NNBF Natural and nature-based features

NOAA National Oceanic and Atmospheric Administration

OCS Outer Continental Shelf

ODMDS Offshore Dredged Material Disposal Sites

PIANC World Association for Waterborne Transport Infrastructure

ppt parts per thousand

psu practical salinity unit(s)

SAV Submerged aquatic vegetation

se standard error

SST sea surface temperature

TL total length (specific fish measurement)

TSHD trailing suction hopper dredge

TSS Total suspended solids

USA United States of America

USACE United States Army Corps of Engineers

yd3 cubic yard(s) (reported for sand dredging volumes to coincide with industry 

standards)
vii

Abstract

Sand and sediment dredging in the Outer Continental Shelf of the Atlantic and US Gulf of Mexico has 

expanded in recent years as demand for sediments has increased. Marine sediment dredging occurs in 

shallow waters (≤ 50 m) and often utilizes sand shoals, where large volumes of sand can be efficiently 

extracted. Throughout this report, we use the term "sand" to broadly characterize sediment resources, and 

we recognize that sediment dredging may include a variety of grain sizes depending on the application.

With the goal of understanding the effects of dredging on fish, we first synthesize the known effects of 

sand dredging. This includes the potential dredging effects of hydraulic entrainment, underwater sounds, 

suspended sediments, and substrate removal. Secondly, fish habitat use and distributions are major 

determinants of dredging effects. A synthesis of international literature on spatially explicit marine fish 

distribution models and habitat associations provides context for the latest technologies for mapping fish 

distributions. Documented regional habitat associations were synthesized specifically for federally 

managed species in the Gulf of Mexico, South Atlantic, and Greater Atlantic. Overall, the literature 

synthesis summarizes how dredging may affect fish species and how the distribution of fish is influenced 

by physical, biological, and chemical habitat factors.
1

1 Introduction and Background

The demand for marine sand resources is increasing in the US (Drucker et al. 2004) and worldwide

(Charlier and Charlier 1992; de Jong et al. 2014; Kim et al. 2008; La Porta et al. 2009). For example, the 

Netherlands alone uses an estimated 24 million m3 (31 million yd3

) of dredged sand annually, and the

amount is expected to grow with sea-level rise effects (de Jong et al. 2014). In the US, coastal and 

offshore sands are commonly used for beach renourishment, barrier island restoration, and wetland 

restoration. As human populations and associated infrastructure continue to expand in coastal zones, 

erosion will continue to be problematic. Recently, there has been an emphasis placed on the benefits of

natural infrastructure to reduce erosion rather than sea walls or other hard structures (Ruckelshaus et al. 

2016; Sutton-Grier et al. 2015). For example, the US Army Corps of Engineers (USACE) produced a 

report after Hurricane Sandy that demonstrated the many benefits of natural infrastructure such as living 

shorelines and beaches (Bridges et al. 2015). Similarly, the GoM coast benefits greatly from barrier 

islands that reduce storm surge (Grzegorzewski et al. 2011). In many cases, the restoration and 

maintenance of wide beaches, wetlands, living shorelines, and barrier islands require substantial sediment 

resources. Furthermore, the dredging of OCS sand shoals is likely to increase in the near future because 

nearshore sand resources are being depleted while renourishment cycles for beaches, emergency repairs 

of beaches after storms, and projected effects of sea-level rise continue to increase demand (Nairn et al. 

2004). 

BOEM, part of the US Department of the Interior, is responsible for the management and development of 

energy and mineral resources on the OCS, including renewable energy, oil and gas, and marine minerals. 

The Outer Continental Shelf Lands Act (1953) defines the OCS as submerged lands lying seaward of state 

coastal waters (3 nautical miles [nm] from most state shorelines; 9 nm from shorelines of Texas and the 

gulf coast of Florida) and within the US’ marine jurisdiction defined by the Exclusive Economic Zone

(EEZ). The EEZ is defined by a boundary 200 nm offshore of the US. 

The primary purpose of our study is to inform BOEM’s Marine Minerals Program (MMP), which

authorizes access to OCS sand, gravel, and shell resources and negotiates these resources on a 

noncompetitive basis (Public Law 103-426 [43 U.S.C. 1337(k)(2)], enacted 1994). Another potential

application of this review is BOEM’s Renewable Energy Program, which manages proposals and leases 

for OCS wind energy development. The program may have considerable interest in the potential for 

Key Points

• Demand for sand resources is increasing rapidly in the United States (US) and worldwide. 

Sand is critical for beach renourishment and barrier island restoration, which help reduce 

damage to infrastructure from erosion and storms, and support coastal economies. 

• The Bureau of Ocean Energy Management (BOEM) authorizes the use of Outer Continental 

Shelf (OCS) sand and gravel resources. BOEM consults with the National Oceanic and 

Atmospheric Administration’s (NOAA’s) National Marine Fisheries Service (NMFS) to 

assess dredging impacts to Essential Fish Habitat of federally managed species.

• A previous BOEM report by Rutecki et el. (2014) synthesized information on sand shoal 

geology, dredging impacts, shoal recovery time, benthic invertebrates, and provided basic 

information on fish related to sand shoals of the Atlantic and Gulf of Mexico (GoM).

• Volume 1 synthesizes two broad information needs: 1) What are the impacts of sand 

dredging on fish? 2) What are the habitat associations of federally managed fish species,

particularly as they relate to geomorphology? 
2

offshore infrastructure (e.g., submarine cables) in the shallow OCS zones that are preferred for sand and 

gravel extraction. The USACE also uses Federal submerged lands to dispose of dredged materials from 

channels and inlets within approved offshore dredged material disposal sites. Much of the content 

developed here will be applicable to understanding how these dredge materials impact marine organisms

in these areas. BOEM’s authorization for use of OCS sand and gravel resources requires an analysis of

impacts according to the National Environmental Policy Act (NEPA) using the best available science to 

understand environmental impacts. Additionally, measures may be applied to reduce potential impacts 

during sand dredging and conveyance to placement sites.

The Magnuson-Stevens Fishery Conservation and Management Act (Magnuson-Stevens Act), written in 

1976 and amended in 1996 and 2007, has the objectives of preventing overfishing, rebuilding overfished 

stocks, increasing long-term economic and social benefits, and ensuring a sustainable supply of seafood. 

Under the Magnuson-Stevens Act, NOAA NMFS is responsible for identification and protection of 

Essential Fish Habitat (EFH) of federally managed marine and anadromous fishes during each of their life 

stages. These habitats include “those waters and substrate necessary to fish for spawning, breeding, 

feeding or growth to maturity” (Magnuson-Stevens Act). Regional National Marine Fisheries Service 

programs, and their associated Fishery Management Councils (FMCs), work to define EFH. Over 1,000 

federally managed species have defined descriptions and associated maps of EFH, including finfish, 

mollusks, crustaceans, and other marine animals and plants; birds and mammals are under the authority of 

the US Fish and Wildlife Service. These maps depict the EFH of species in wetlands, coral reefs, 

seagrasses, rivers, estuaries, and marine environments. Assemblages of species may be mapped together 

(e.g., reef fish, coastal migratory pelagics); highly migratory species are mapped for the entire extent of 

the Atlantic Ocean and GoM. The recognition of EFH provides for a process of consultation for projects 

permitted, or conducted by, the Federal Government to ensure that fish and their habitats are not 

adversely affected without full consideration of the environmental effects and possible mitigation 

measures. Therefore, BOEM consults with the NOAA NMFS on impacts to EFH through a written 

assessment. In response, NMFS recommends mitigation measures. Additionally, NMFS and FMCs may 

designate Habitat Areas of Particular Concern (HAPC) as types of EFH that are high priority for habitat 

conservation, management, and research. HAPC designations are based on the importance of the 

ecological function provided by the habitat (e.g., seagrasses), sensitivity or vulnerability to humaninduced degradation, habitats that are slow to recover from disturbance (e.g., corals), and rarity of habitat 

types (Rosenberg et al. 2000). These areas do not have specific regulatory or management restrictions but 

are meant to bring special attention to specific areas of EFH.

As of November 2019, BOEM reports that > 125 million m3 (> 164 million yd3

) of OCS sands have been 

authorized for use within 58 leases since 1995. These projects include sands used to restore 577.1 km of 

shoreline (Table 1-1) (BOEM 2019). Throughout this report, we use the term "sand" to broadly 

characterize sediment resources, and we recognize that sediment dredging may include a variety of grain 

sizes depending on the application. The quantity of sand leased has risen from < 1.53 million m3 (2.0 

million yd3

) per year to nearly 19.1 million m3 (25.0 million yd3) during this time (Figure 1-1). Many of 

these sites are likely to have repeated dredging events. Nairn et al. (2004) further emphasizes that 

nearshore sand resources are being depleted, so coastal managers may look farther offshore, increasing 

the demand for OCS sand deposits. All 17 Atlantic and GoM coastal states now have cooperative 

agreements with BOEM to identify available sand resources. As of 2019, there are no BOEM sand and 

gravel leases in New England, but storms and erosion have led to an anticipation of offshore sand 

dredging in the region. For example, Maine, New Hampshire, Massachusetts, Rhode Island, and New 

York all signed cooperative agreements to evaluate sand resources in 2014 following Hurricane Sandy. In 

addition to leasing greater volumes, BOEM is also providing access to more diverse material, such as 

mixed sediments, that are used in coastal restoration (J. Mallindine, BOEM, pers. comm.). 
3

The overall strong upward trend of sand dredging necessitates a greater strategic vision for managing 

sand resources at a regional level rather than the site-by-site approach that has been undertaken to this 

point. As demand for OCS sands increases, BOEM faces complex multi-user interactions, including 

issues of resource allocation, cumulative impacts from repeated use, fisheries use and potential conflicts, 

protection of archaeological sites, oil and gas infrastructure, potential renewable energy infrastructure, 

and impacts on EFH (Michel et al. 2013). Marine sand dredging occurs in relatively shallow waters (≤ 50 

m), often with ridge and swale complexes where large volumes of sand can be extracted over relatively 

small areas. The role of sand shoals as fish habitat, and as habitat that supports common fish prey species, 

is the subject of our literature review. Although EFH refers to multiple taxa of marine life, we refer to 

"fish" as being finfish in our literature review. We do cover a few specific invertebrate species, but only 

those known to be of importance as prey to finfish. By understanding the role of sand shoals in regard to 

fish habitat, BOEM will be able to make effective decisions to efficiently use these resources and to 

mitigate any ecological effects of sand dredging.

Table 1-1. Dredging projects in Federal waters by state 1995–2019

State Number of 

Projects Shoreline Restored (km)

Louisiana 11 68.6

Mississippi 1 5.6

Florida 22 277.8

South Carolina 7 73.4

North Carolina 3 59.2

Virginia 10 58.7

Maryland 3 15.3

New Jersey 1 18.5

Source: BOEM (2019).

Figure 1-1. Trend in quantity of sand leased in Federal waters.

Source: BOEM Marine Minerals Information Systems sand lease data and https://www.boem.gov/MMP-in-YourState/, accessed 2019 March.
4

1.1 Preceding Syntheses of Sand Shoals

Our study follows an intensive literature review by Rutecki et al. (2014): Understanding the Habitat 

Value and Function of Shoals and Shoal Complexes to Fish and Fisheries on the Atlantic and Gulf of 

Mexico Outer Continental Shelf. Literature Synthesis and Gap Analysis. Rutecki et al. (2014) reviewed 

the geology, geography, and general biological values of sand shoals; sand shoals were explicitly defined 

as:

“A shoal is a natural, underwater ridge, bank, or bar consisting of, or covered by, sand or other 

unconsolidated material, resulting in shallower water depths than surrounding areas. The term 

shoal complex refers to two or more shoals (and includes adjacent morphologies, such as troughs 

separating shoals) that are interconnected by past and or present sedimentary and hydrodynamic 

processes. These complexes are also known as shoal fields.”

Furthermore, Rutecki et al. (2014) suggested a classification of sand shoal systems based on the geologic 

origins of shoals and previous research studies (Table 1-2). The process of sand shoal evolution was

documented as well as sedimentary processes that result in recovery of sand shoal systems to predredging conditions.

Table 1-2. Classification of sand shoals from Rutecki et al. 2014.

Shoals associated with Relict 

Holocene or Pleistocene 

Deposits

Cape-Associated 

Shoals

Bedform Shoals

Isolated Shelf

Shoals Shoal Fields Relict Shoals Sorted Ridges

Synonyms Banks Shelf retreat 

massifs

Shelf retreat massifs Rippled Scour 

depressions

Ridge and trough, 

Ridge and swale

Examples Sabine Bank, 

Heald Bank, 

St. Bernard

Shoal, Ship 

Shoal

Platt Shoal, 

Oregon Shoal, 

Albermarle 

Shoal

Cape Lookout Shoals, 

Diamond Shoals, 

Frying Pan Shoals,

Wimble Shoals-

(abandoned Cape)

Shoals along 

Wrightsville Beach 

shore face and 

inner shelf

Shoals along the 

inner shelf north of 

Cape Lookout,

along MD, DE, NJ, 

NY inner shelves

Rutecki et al. (2014) also reviewed habitat associations and distributions of benthic invertebrates utilizing 

sand shoals. In Mid-Atlantic studies (offshore of New Jersey, Delaware, and Maryland), shoal crests and 

troughs are known to differ in invertebrate species assemblages (Byrnes et al. 2000; Cutter Jr and Diaz 

2000; Slacum Jr et al. 2010b); physical differences in shoals include depth, sediment composition, and 

hydrodynamic regime (i.e., waves, currents). Although benthic invertebrates are well known to be 

distributed in relation to sediment texture (Rutecki et al. 2014), few fish studies have investigated the 

effect of substrate (with the exception of coral substrates). Benthic invertebrate species reported as having 

important economic value include blue crab, shrimp, Atlantic surfclam, ocean quahog, American lobster, 

sea scallop, hard clam, Florida stone crab, Gulf stone crab, spiny lobster, and slipper lobster (Rutecki et 

al. 2014). These species are likely to be important for higher trophic levels, and particularly for fish 

species that directly depend on them as prey. Additionally, Slacum et al. (2010a) reported abundant squid 

in Mid-Atlantic shoal systems, and squid compose a substantial part of the diet of predatory fish 

(Bowman et al. 2000; Watanabe et al. 2004). In regard to fish, Rutecki et al. (2014) summarized major 

studies of sand shoals and listed species specifically documented in close proximity to shoals for the 

GoM, South Atlantic, North- and Mid-Atlantic. They also recognized that shoals serve as: 1) refuges for 

juvenile fish and schooling planktivores, 2) habitat for species adapted to dynamic substrate and as a 

trophic base for demersal fish, and 3) spawning sites for some demersal fish and schooling planktivores 
5

(CSA International Inc et al. 2010; Gilmore 2008). Rutecki et al. (2014) also recognized substantial 

knowledge gaps and identified future research priorities regarding the distribution of fish in or near sand 

shoals, effects of keystone species utilizing shoals, temporal changes in fish communities, use by highly 

migratory fish species, habitat use of various life stages, and direct impacts of sand dredging.

1.2 Scope of Literature Synthesis and Objectives

In the Magnuson-Stevens Act, fish are defined as finfish, mollusks, crustaceans, and all other marine 

animal and plant life other than marine mammals and birds. To date, the effects of sand dredging on 

marine resources has primarily focused on benthic infauna and epifauna invertebrate communities, which

are directly impacted by substrate removal (Crowe et al. 2016; Newell et al. 1998; Palmer et al. 2008). In 

the literature synthesis presented here, we focus on finfish, particularly those that have EFH. However, 

we do address federally managed shrimp species in the GoM because of their importance as prey of fish

and their value in commercial fisheries. Likewise, invertebrate ecology and succession is briefly reviewed 

in the context of substrate removal and recovery because of their importance in the food web of fish. The 

scope of our review does not cover sea turtles and marine mammals. These topics are extensive and 

beyond the scope of this review.

Unconsolidated sediments of the OCS, including sand shoals, are the vast majority of the seafloor, yet 

they are poorly studied. Kritzer et al. (2016) used expert opinion to rank the importance of marine benthic 

habitats along the Atlantic Coast in terms of fisheries. They found unconsolidated sediment was ranked as 

the most important benthic habitat in the North Atlantic, Mid-Atlantic, and South Atlantic (ranked second 

in south Florida). These shallow waters are critical to juvenile fish (Diaz et al. 2003; Steves et al. 2000; 

Walsh et al. 2006) and many commercially valuable fish species. Therefore, a further investigation into 

the effects of sand dredging on OCS fishes, and particularly their shoal habitats, may lead to a better 

understanding of individual and population-level effects to fish. The focus of our literature synthesis is on 

shallow (≤ 50 m depth) federally managed waters of the OCS (Figure 1-2, Figure 1-3, and Figure 1-4). 

These respective maps define our study area in each region. We do not attempt to review fish habitats in 

deeper waters, state-managed waters, or estuaries. The 50-m depth limit covers the maximum possible 

extent for sand dredging.

The objectives of our literature synthesis were to:

1) Synthesize the main effects of marine sand dredging on fish, including the severity, duration, and 

extent of the effects.

2) Summarize international literature on spatially derived fish habitat associations that are typically 

mapped and modeled for EFH and other applications.

3) Summarize fish habitat associations in the GoM, South Atlantic, and Greater Atlantic for 

federally managed species, particularly for common species on shoals and soft sediment 

substrates.
6

Figure 1-2. The GoM study area defined by the landward boundary of Federal waters and the 

seaward boundary of the 50-m depth contour line.

Sand lease areas, as of the 2016 fiscal year, are shown as centroids for reference (not to scale).
7

Figure 1-3. The South Atlantic study area defined by the landward boundary of Federal waters and 

the seaward boundary of the 50-m depth contour line.

Sand lease areas, as of the 2016 fiscal year, are shown as centroids for reference (not to scale).
8

Figure 1-4. The northeast Atlantic study area defined by the landward boundary of Federal waters 

and the seaward boundary of the 50-m depth contour line.

Sand lease areas, as of the 2016 fiscal year, are shown as centroids for reference (not to scale).
9

1.3 Literature Search Methods

We used databases and various search engines to identify appropriate white papers, grey literature, 

government reports, and peer-reviewed publications. Whenever possible, peer-reviewed publications were 

cited. Preference was given to citing the most current research studies, summaries provided by review 

papers, and research that has been highly cited on the topic of relevance. Of particular relevance to the 

documentation of dredging impacts, the most current evidence presented by research studies helps to 

support, or refute, speculation or informal personal observations that were previously the only 

information available to interpret. Review papers contain dozens to hundreds of citations for papers of 

relevance, and we have attempted to derive summaries of the findings, concepts, and applications as 

concisely as possible. When necessary to provide case studies or derive specific metrics, we obtained 

original works cited by the most recent papers.

Search engines used included Google Scholar, Web of Science, and government agency databases

(USACE Dredging Operations and Environmental Research, USACE Research and Development Center, 

BOEM’s Environmental Studies Program Information System [ESPIS]). These searches found research 

from the British Marine Aggregate Producer’s Association (BMAPA), Marine Aggregate Levy 

Sustainability Research, and the Centre for Environment Fisheries and Aquaculture Science (CEFAS). All 

fields, including the title and abstract, were searched for each set of terms. For important or highly cited 

articles, we checked more recent papers that cited the original article to ensure that the synthesis included 

the most recent articles. References from Rutecki et al. (2014) were checked for more in-depth 

information on fish not already covered by their literature synthesis. In addition, details of fish habitat 

associations documented in FMC and EFH documents are presented in Volume 4: ShoalMATE (Shoal 

Map Assessment Tool for EFH) Manual and Data Tables.

Search terms and phrases that were queried in the databases included a combination of taxonomic groups 

or species, environmental characterizations, and geography. Geography-specific searches included Gulf of 

Mexico, South Atlantic, Mid-Atlantic, Atlantic, or Atlantic Ocean. Species-specific searches were also 

conducted. A separate search of international literature is further described below.

Examples of keywords for literature search:

Dredging effects:

‘dredging’ AND ‘entrainment’

‘dredging’ AND ‘fish’ AND ‘marine’

‘dredging’ AND ‘marine’

‘dredging’ AND ‘sound’

‘dredging’ AND ‘sound’ AND ‘fish’

‘fish’ AND ‘sound’ OR ‘noise’

‘marine’ AND ‘fish’ AND ‘sound’ OR ‘noise’

‘dredging’ AND ‘suspended sediments’

‘dredging’ AND ‘suspended sediments’ AND 

‘marine’

‘fish’ AND ‘marine’ AND ‘suspended 

sediments’

‘fish’ AND ‘suspended sediments’

‘corals’ AND ‘suspended sediments’

‘hard bottom’ AND ‘suspended sediments’

‘marine dredging’ AND ‘recovery’

Fish species:

<species name> AND ‘Gulf of Mexico’ AND 

‘habitat’

<species name> AND ‘Gulf of Mexico’

<species name> AND ‘Atlantic’ AND ‘habitat’

<species name> AND ‘Atlantic’

‘shark’ AND ‘Gulf of Mexico’ AND ‘habitat’

‘shark’ AND ‘Atlantic’ AND ‘habitat’
10

2 Effects of Sand Dredging on Fish

Two types of dredges are commonly used for sand dredging in marine environments, a trailing suction 

hopper dredge (TSHD) and a cutterhead dredge. The TSHD are more mobile and commonly used to 

dredge offshore sands, especially in the Atlantic; cutterhead dredges are better suited for calm seas and 

work closer to shore with some offshore work in the GoM. The TSHD vessel works by moving at 3–5 km

hr-1 (1.5–3 knots) as an onboard dredge pump creates a suction that is transmitted through 1–3 pipes

leading to each pipe’s draghead, which are 1.5–4 m in width and lie on the seafloor (Michel et al. 2013)

(Figure 2-1). Sand is suctioned through the trailer arm pipe and into the hopper located in the hull of the 

ship. The dredge then moves to a stationary in-water pump-out station to pump sand to shore via 

pipelines. A hydraulic cutterhead dredge agitates the sediments as the cutterhead rotates (Figure 2-2). To 

allow the cutterhead to swing back and forth, anchors, studs, or a stud pole, are used to moor the vessel. A 

cutterhead may pump sand directly to shore or use pipelines. For both dredges, additional boats are used 

to support operations, conduct monitoring, and to move anchors for cutterhead dredging. Further details 

of offshore dredging vessels and their operation are reviewed elsewhere (CSA International Inc et al. 

2010; Michel et al. 2013).

Key Points and Knowledge Gaps (gaps are in italics)

• Fish are most vulnerable to dredging effects during egg or larvae stages, spawning periods, 

or during migration, when compared to other life stages. Demersal species have been

suggested to be more vulnerable than pelagic, though evidence is lacking.

• Entrainment of benthic fish and invertebrates occurs locally during dredging. A few studies 

have examined entrainments rates of fish in estuaries, but rates in marine ecosystems are 

lacking.

• Turbidity occurs during and shortly after dredging activity, but resuspension of sediments at 

the borrow area has reoccurred 1.5 years post-dredging. Studies have regularly found 

turbidity to influence a 3-km radius around dredging, though concentrations are not high 

enough to cause direct fish mortality.

• Sedimentation may threaten hard bottom and coral reef fish habitats because of burial and 

coral mortality.

• Underwater sounds during dredging are not severe enough to cause fish mortality, but 

sounds may persist above ambient conditions for 400 m to 2.7 km.

• Avoidance responses (including response distance) of fish to underwater sounds and 

turbidity are unknown. Fish behavioral responses will determine habitat loss, disruptions to 

migration, and other impacts.

• Substrate removal by dredging may result in bathymetric depressions or more 

homogeneous, flattened topography within the footprint of dredging. 

• Recolonization by early successional benthic invertebrates and restoration of the density of 

individuals have been documented after one year post-dredging, while recovery of the full 

species assemblage ranges from 2.5 to > 7 years. Full recovery of invertebrate species tends 

to correspond with a return to the pre-dredging sediment grain size.

• More frequently, or intensively, dredged substrates may take double the time (~15 years) to 

recover compared to less intensively dredged sites. Frequent dredging tends to change 

sediment grain size more dramatically than less intensively dredged sites. 
11

Figure 2-1. Main components of a TSHD. Additionally, the draghead is attached to the bottom of 

the trailer arm and is where the hydraulic suction is generated.

Source: Adapted from https://oceanandairtechnology.wordpress.com/2013/06/11/trailing-suction-hopperdredger/

Figure 2-2. A) Cutterhead suction dredge and B) cutterhead.

Source: Adapted from Michel (2013) and www.dredgepoint.org.

The short-term effects of dredging include entrainment, human-made sounds, loss of prey/food web 

effects, suspended and resuspended sediment plumes, sedimentation of the seafloor, and release of 

contaminants (Kim et al. 2008; Suedel et al. 2008; Wenger et al. 2017). Wenger et al. (2017) provides a 

comprehensive review of dredging effects on fish in freshwater, estuarine, and marine environments. 

Some of these effects of dredging have been more commonly studied in environments like estuaries (e.g., 

entrainment of fish, release of contaminants), and information may be limited in offshore marine 

environments. Therefore, we note estuarine studies when necessary; no specifics of freshwater or 

restricted channel studies have been included because types of dredging, entrainment rates, and other 

factors differ dramatically in those systems.
12

Given the uncertainty surrounding the effects of dredging, seasonal restrictions on dredging operations are 

sometimes implemented in the US. Of the dredging operations implemented by the USACE from 1987 to 

1996, time window restrictions for dredging to address biological concerns were implemented in 85% of 

Atlantic operations, and for 18% of cases in the GoM (Dickerson et al. 1998). Seasonal restrictions on 

dredging are most likely to be implemented for protected species; however, a variety of species have been 

a basis for seasonal restrictions. Fish species used as a basis for seasonal dredging restrictions include 

American shad, Atlantic tomcod, blue crab, Gulf sturgeon, shortnose sturgeon, striped bass, winter 

flounder, brown shrimp, pink shrimp, and white shrimp (Dickerson et al. 1998) as well as Pacific herring 

(Suedel et al. 2008). Unfortunately, the lack of available data may continue to result in inefficient 

restrictions on dredging activity, which can drive up costs, increase transportation distances, and delay 

projects (Dickerson et al. 1998).

2.1 Effects of Hydraulic Entrainment on Fish

2.1.1 Entrainment Rates of Fish and Invertebrates

Hopper and cutterhead dredges use hydraulic suction fields to obtain and transport unconsolidated 

sediments from the seafloor of aqueous ecosystems. These actions, occurring at the seafloor, may result in 

the entrainment of benthic fish and invertebrates, as defined as the direct uptake of organisms due to the 

hydraulic suction field generated by a draghead or cutterhead dredge (Reine and Clarke 1998). The effect 

of entrainment of fish results in mortality of most individuals. However, entrainment is limited to the 

duration of the dredging activity, and the effects are localized to the direct area affected by the draghead

or cutterhead of the dredge vessel.

Entrainment rates are determined by species' population density at the time of dredging, footprint of area 

affected by the dredge, and vulnerability of the fish species present. The quantification and publication of 

entrainment rates of fish and invertebrates in dredging gear within marine ecosystems are limited. Most 

entrainment studies have been conducted at power plants or water-diversion structures in freshwater 

environments where anadromous species are particularly at-risk over continuous time periods (e.g., Kelso 

and Milburn 1979; Mussen et al. 2014). The relatively short duration of dredging activities and the 

relatively open nature of marine settings (rather than restricted waterways like channels or rivers) makes 

entrainment comparisons unrealistic in many situations. Therefore, we focus on information derived from 

entrainment studies of estuaries, which are more open and include similar species (or the same species) to 

the marine environment. We note that marine studies of entrainment rates are still needed, as estuaries or 

estuarine navigation channels are still very different than offshore marine borrow areas. Studies are also 

needed in the Atlantic and GoM to better understand entrainment rates and species affected in these 

regions. Additionally, marine borrow areas may provide opportunities for mitigation that may not be 

possible for navigation channels.

For the closest approximation of entrainment rates with applications to marine environments, Reine and 

Clarke (1998) provides an exhaustive review of documented entrainment rates, including several 

estuarine studies. The Dungeness crab has received considerable attention because they are commercially 

valuable, congregate in navigation channels, and migrate in and out of estuaries. As noted, navigation 

channels are far different than offshore marine environments. Dredging must be thorough in navigation 

channels and mitigation measures may be more limited. However, these are the most comparable 

entrainment rates currently available. The entrainment rate for combined studies of Dungeness crabs 

ranges 0.03–0.45 crabs/m3 (0.04–0.59 crabs/yd3

) of dredged material for adult crabs; comparatively, 

juvenile crabs were entrained at a higher rate (range 0.24–8.24 crabs/m3 (0.31–10.8 crabs/yd3

), mean = 

3.17 crabs/m3

) (4.15 crabs/yd3) compared to the adults (Reine et al. 1998). The authors had limited data 

that suggested entrainment mortality rate differed by crab size, as those >75 mm had observed mortality 

of 86% while crabs of 7–10 mm had mortality estimated at 5%. Entrainment rates were twice as much for 
13

male crabs compared to females, probably because of the timing of female emigration from the estuary. 

Plus, seasonal changes of crab densities are substantial (Wainwright et al. 1992). During entrainment 

studies focused on Dungeness crabs, benthic sand shrimp were the most commonly entrained species, and 

the rate of entrainment was reported as 2.58 shrimp/m3 (3.37 shrimp/yd3

) of dredged material for a TSHD

(Armstrong et al. 1981). There are no entrainment data documented for the commercially valuable shrimp 

species of the Atlantic Ocean or GoM (Reine and Clarke 1998). Overall, the impact to shrimp are a 

concern because they are the prey base for many fish species and support a large fishery. In regard to fish 

captured by the Dungeness crab studies in Pacific Coast estuaries, entrainment rates reported by Reine 

and Clarke (1998) are presented in Table 2-1 (Larson and Moehl 1990; McGraw and Armstrong 1990).

A specific concern for the Atlantic is the potential entrainment of economically and ecologically 

important horseshoe crabs, although the primary concern has been with dredging navigation channels or 

inlets where the species may congregate (Ray and Clarke 2010). Adults span much of the Mid- and South 

Atlantic, and the eggs and larvae provide a critical prey base for long-distance migratory shorebirds 

(Botton et al. 1994), particularly the federally threatened red knot (Karpanty et al. 2006). Adult horseshoe 

crabs are found in estuaries and shelf habitats at depths of < 30 m, although they have been found at a 

290-m depth offshore of Cape Hatteras, North Carolina (Botton and Ropes 1987). They have been 

documented as users of sandy shoals (Rutecki et al. 2014), although it remains unclear if they spawn at 

offshore shoals. In estuaries, observers documented entrainment of horseshoe crabs as ranging from 

0.000003–0.004 horseshoe crabs/m3 (0.000003–0.005 horseshoe crabs/yd3

) of dredge material (10–5,521 

horseshoe crab individuals entrained per project) (Ray and Clarke 2010). Study sites in their study 

included harbors, channels near harbors, an Atlantic Ocean channel, and a beach renourishment project 

near Virginia. In their study, differences in entrainment rates were ascribed to location, time of year, type 

of equipment, and specifics of the operation (Ray and Clarke 2010). Localized mortalities from 

entrainment may exacerbate stresses that are already incurred by horseshoe crab populations, such as 

harvesting for bait and mortality related to bleeding individuals for biomedical purposes.
14

Table 2-1. Entrainment rates of Pacific Coast estuary studies.

Source: Modified from Reine and Clarke (1998).

2.1.2 Factors Affecting Vulnerability of Fish to Entrainment

Broad concepts and characteristics that render a species vulnerable to entrainment in estuaries are likely 

to be comparable in marine ecosystems, although the rates may differ. In fact, Wenger et al. (2017)

suggests that general management guidelines could be combined for freshwater, estuarine, and marine 

ecosystems. Kim (2008) suggests species vulnerable to entrainment are benthic organisms, including 

shellfish (e.g., blue crab, shrimp) and demersal fish (e.g., flounder, flatfish) because of their position in 

the water column. However, documented evidence to support these assumptions is limited. Similarly, 

Reine and Clarke (1998) notes concerns over potential entrainment of anadromous fish, shrimp, crabs, 

shellfish, and threatened/endangered species; species of concern included Gulf and shortnose sturgeon, 

salmonids, American shad, blue crab, oyster larvae, and winter flounder. Again, supporting data are 

lacking. Drabble (2012) developed a sensitivity index and projected entrainment rates for marine fish 

based on qualitative rankings of potential for entrainment. Factors in the index were a) previous evidence 

of entrainment, b) sensitivity to sound (i.e., ability to avoid impact area), c) ability to move quickly, 

d) burial behavior, and e) fecundity. For example, sand lance bury themselves in response to disturbance, 

do not have a swim bladder, and may hibernate; thus, they are extremely vulnerable to entrainment. 

Entrainment rates were projected with the sensitivity index combined with the dredge production rate, 

footprint, and speed as well as distribution data for fish species (Drabble 2012).

In a meta-analysis incorporating dredging studies in all aquatic environments, eggs and larvae have 

shown more mortality effects compared to other life history stages (Wenger et al. 2017). This may be 

because of their size and inability to move quickly away from dredging disturbances. Although a variety 

of external factors have been proposed to affect entrainment rates (bottom depth, dredge speed, flow-field 

Common name Scientific name Fish entrained per m3 

of dredged material

Anchovy Engraulididae spp. 0.0008–0.0061

Northern anchovy Engraulis mordax 0.0138

Herring Clupeiformes 0.0061

Flounder, sole, sanddab, 

flatfish

Atheresthes stomias, Platichthys stellatus 

Pleuronectes vetulus, Psettichthys 

melanostictus, Lyopsetta exilis,

Citharichthys spp., Pleuronectiformes spp.

0.0008–0.0581

Pacific sand lance Ammodytes hexapterus 0.0275–0.454

Pacific sandfish Trichodon trichodon < 0.0008–0.0015

Surfperch Embiotocidae ≤0.0008

Pipefish Syngnathidae 0.0061

Big skate Raja binoculata < 0.0008

Longnose skate Raja rhina 0.0023

Spiny dogfish Squalus acanthias < 0.0008
15

velocities, volume of dredged materials, and direction of dredging with reference to tidal flow), these 

factors have been shown to have little influence (Reine and Clarke 1998). Reine and Clarke (1998)

reviewed the potential for biological impacts from hydraulic dredging and emphasized the following:

• Bottlenecks or congregations of fish in migration corridors (e.g., anadromous fish) are 

particularly vulnerable

• Because of the timing of migration, particularly for anadromous fish and crabs, the male to 

female ratio of entrained individuals may differ

• Life stages involving dormancy or limited movement (e.g., egg or larvae stages, blue crab

dormancy) are particularly vulnerable

• Juveniles may be more vulnerable to entrainment compared to adults

• Demersal fish are likely more vulnerable, though pelagics have been regularly entrained in the 

past (e.g., anchovy, herring)

• Small and large fish may be equally vulnerable (Armstrong et al. 1981)

2.2 Effects of Human-made Sound on Fish

2.2.1 Measures of Underwater Sound

Before detailing underwater sounds produced by dredging and dredge vessels, we first describe 

characteristics of sound and how underwater sounds are typically measured. A fundamental measure of 

sound is its loudness as quantified by the amplitude of sound waves at a range of frequencies and 

measured in units of decibels (dB) or micropascals (μPa). Each 10 dB increase represents a 10-fold 

increase in sound pressure. As an example, humans can hear a minimum of approximately 0 dB (or 20 

μPa), and sounds greater than 130 dB are generally considered painful to humans. Studies of underwater 

sounds often use decibel measurements as a ratio of root mean squared (rms) (average sound over an 

acoustic event) of pressure to background reference pressure. In water, the reference pressure is 1 μPa, 

and therefore, measurement units are dB re 1 μPa for studies conducted underwater. Sound frequency, or 

pitch, is the number of cycles per second with hertz (Hz) as the unit of measurement. For reference, 

humans hear in the range of 20 Hz to 20 kilohertz (kHz). Taken together, the frequency and amplitude 

measures describe what an organism can hear and how loud the sound is projecting.

Studies of underwater sounds typically use hydrophones placed at a few depths and over a range of 

distances from the source to measure the received level, and a mathematical interpolation of decibels by 

distance can show how sound varies by distance from the source. Conversely, when the source level of 

sound cannot be measured directly, this model can also be used to estimate the source level (at 1 m from a

sound-producing object). A common measurement standard of comparison for underwater sounds is that 

of ambient sound levels, which represents background sound. Natural ambient sounds include waves, 

wind, or living organisms and are dominant where human activities are scarce, such as the open ocean. In 

contrast, human-made sounds such as vessel propeller cavitation and generators are likely to be a 

dominant source of ambient sounds in harbors or in close proximity to human activities. Sound 

transmission may be highly variable in shallow waters due to environmental conditions such as seabed 

type, bathymetry, salinity, and stratification (Reine et al. 2014a). In a recent study, Halvorsen and Heaney 

(2018) found sound traveled further over sand substrates compared to mud at 10–30 m depths; however, 

differences were more pronounced among water depths of 10, 30, and 100 m compared to substrate type.

2.2.2 Underwater Sounds Produced by Dredging-related Activity

Dredging operations emit underwater sounds as vessels are in transit as well as during the dredging

activity itself. Studies of underwater sounds produced by dredging operations have recently received 

increased attention, though only a few studies have specifically examined sand dredging. More 
16

commonly, studies have examined underwater sounds produced by the extraction of aggregates (i.e., 

sand/gravel mixtures) or dredging that involves the breaking of rocks; these operations are conducted 

worldwide to obtain materials for construction. In regard to hydraulic dredges, sounds are produced from 

the following sources (Reine et al. 2014b; Reine and Dickerson 2014; Robinson et al. 2012) (see Figure 

2-1 for reference):

• Removal of material from the substrate when the draghead, or cutterhead, contacts the seafloor

• Pumps and impellers driving suction of materials into the pipes from the seafloor

• Movement of materials through the pipes

• Loading of materials into the hopper and overflow of undesirable materials

• Dredge machinery such as winches, generators, thrusters; for cutterhead dredges, propellerinduced cavitation movements of spuds and anchors

• Offloading of materials to placement site

• Water turbulence around ship’s hull

• Echosounding instruments

• Supporting vessels

Of research quantifying the underwater sounds emitted by dredging operations, Reine et al. (2014b) and 

Robinson et al. (2012) provide the primary studies that examine sand dredging in marine environments

(Table 2-2). In a comparison of underwater sounds produced by sand and gravel mining, Robinson et al. 

(2012) showed TSHD conducting sand dredging in the United Kingdom generally had a greater decibel

level than gravel mining, although gravel mining did result in greater decibels at the highest frequencies.

Table 2-2. Underwater sounds emitted by trailing suction hopper dredges. Distances of received 

levels differed by study as indicated.

The Reine et al. (2014b) study was conducted in the Mid-Atlantic, USA, and the Robinson et al. (2012) study was 

conducted in the United Kingdom.

Although dredging studies have included rivers, navigation channels, sounds, estuaries, and marine 

environments (Clarke et al. 2003; Reine et al. 2014a; Reine and Dickerson 2014), the results of sound 

production are relatively consistent (Table 2-2). Maximum sounds are commonly 140–150 dB re 1 μPa 

measured at 40–50 m from the source vessel, sound level at the source range from 161–183 dB re 1 μPa, 

and ambient sounds range from 112–119.5 dB re 1 μPa (e.g., Figure 2-3). Generally, strong declines in 

decibels are expected within the first 100–300 m of the source (Reine et al. 2014a; Reine et al. 2014b), 

and underwater sound levels remain elevated for a distance of 400–2,700 m from the dredging vessel

(Figure 2-3, Table 2-3).

Study Dredge activity

Estimated sound at 

source (dB re 1 μPa at 

1 m)

Observed sound (dB re 

1 μPa at distance measured 

from source)

Reine et al. (2014b) In transit to borrow site 168–174 133–137 at 50 m

Reine et al. (2014b) During dredging 171–174.5

141.2–146.8 at 50m; 

< 130 at 1,150 m; 

Approached ambient at 

850–2,700 m

Reine et al. (2014b) Return transit with full 

hopper NA 130-140 at 100 m

Robinson et al. (2012) During dredging 183 139 at 100 m
17

Figure 2-3. An example of underwater sound emitted, as measured in sound pressure levels (SPL) 

(y-axis), at incremental distances (x-axis) from three trailing hopper suction dredges operating in 

offshore marine waters of the Atlantic Ocean near Wallops Island, Virginia.

Source: Image from Reine et al. (2014b).
18

Table 2-3. Summary of studies reporting underwater sounds produced by hydraulic dredges.

Study

Type of 

dredge 

(hopper 

capacity if 

applicable)

Location, 

ecosystem

Estimated 

sound at 

source (dB

re 1 μPa at

1 m)

Maximum 

sound 

observed 

(dB re 1 μPa)

Mean

ambient 

conditions 

(dB re 1 

μPa)

Distance for 

dredge noise to 

reach 

background 

ambient 

conditions

Reine et al. 

(2014b)

Hopper 

(2,754–5,003 

m3 or 3,603–

6,544 yd3)

Virginia,

marine 161–178

141.2–146.8 

(at 50 m) 112 850–2,700 m 

Robinson et 

al. (2012)

Hopper 

(1,418–4,832 

m3 or 1,855–

6,320 yd3)

United 

Kingdom, 

marine

156–183

139 (at 100 

m) 112 NA

Clarke et al. 

(2003)

Hopper

(8,517 m3 or 

11,140 yd3)

Mississippi, 

sound NA 142 (at 40 m) 74 NA

Unpublished 

data from 

Reine et al. 

(Reine and 

Dickerson 

2014)

Hopper (994 

m3 or 1,300 

yd3)

Maine, 

Kennebec 

River 

(removing 

sand shoals)

172–180 NA NA 700 m

Reine and 

Dickerson

(2014)

Pipeline 

cutterhead

California, 

shipping 

channel (river, 

estuary)

157.4

148.3 (at 

87 m) 119.5 400–480 m, ~122 dB

Reine et al. 

(2014a)

Cutterhead 

(fracturing 

rock; no 

material 

movement)

New York / 

New Jersey 

Harbor, 

estuary

181

151 (at 

100 m) 117.1 

~2 km; 

Remained 

≥132 dB re 1 

μPa at 740 m 

from vessel

Clarke et al. 

(2003) Cutterhead Mississippi, sound NA 112 74 Inaudible at~ 500 m 

Reine et al. 

(Reine et al. 

2014a)

Commercial 

vessels

New York / 

New Jersey 

Harbor, 

estuary

NA

Ferry 

approach= 

136 (at 

750 m)

Container 

ship = 141.7 

(at 321 m) 

117.1 NA

Notes: Sounds of other vessels are reported as reference points. Studies include both sand and aggregate dredging.

2.2.3 The Effects of Underwater Sounds on Fish with Implications for 

Dredging Impacts

Although fish respond most directly to the particle motion generated by sounds, most studies focus on the 

effect of sound waves (i.e., pressure) in underwater environments (Nedelec et al. 2016). Thus, our review 

focuses on the effects of sound waves. For fish, the otolith organs are the auditory portion of the ear. 

Sound pressure is also detected by the swim bladder, or other gas-filled structures, that re-radiate the 

energy to the otolith organs (Popper et al. 2014). Importantly, fish use hearing to establish an “auditory 

scene,” and this may include cues to navigation, detection of predators, detection of prey, conspecific 

attraction, and sensing of environmental characteristics (Bregman 1994). Most fish can detect sounds 

ranging from a frequency of approximately 50 to 1,500 Hz (Popper and Hastings 2009a), though some 
19

species with specialized hearing can detect frequencies beyond this range. For context, continuous sounds 

emitted from ships or smaller vessels are generally < 1,000 Hz (Popper et al. 2014), which overlaps well 

with fish hearing capability. There is broad recognition that fish with swim bladders (or a similar gasfilled cavity) are more vulnerable to the effects of human-made sounds and are more likely to detect 

underwater sounds over a broad range of frequencies (Popper et al. 2014). In contrast, fish without gasfilled cavities such as flat fishes, skates, rays, gobies, sharks, and other deep-sea species are likely to be 

comparatively less vulnerable to human-made sounds (Popper et al. 2014).

The effects of underwater sounds on fish has become an increasing concern as fish are exposed to humanmade sounds caused by shipping vessels, offshore wind farms, dredging operations, marine construction, 

and other human activities across all oceans (Hawkins et al. 2015; Popper and Hastings 2009a; 

Slabbekoorn et al. 2010). As outlined by Normandeau Associates (2012) and Popper and Hastings 

(2009a), the fundamental effects of man-made sound on fish include the following:

• Mortality or injury

• Tissue damage

• Temporary or permanent hearing loss

• Masking of sounds that fish react toward or depend on

• Behavioral changes such as avoidance

For fish, major concerns are that dredging sounds may block or delay anadromous fish migration, impair 

communication, or affect foraging (Reine et al. 2014b). The masking of communication among 

conspecifics could be detrimental to spawning and courtship of fish (Slabbekoorn et al. 2010). Much of 

the research conducted on human-made sounds have examined fish mortality rates and physiological 

changes, whereas the most likely effects are with fish behavior, including startle responses, temporary 

movements, avoidance, and changes to migratory behavior (Popper and Hastings 2009b). Furthermore, 

the direct effect of dredging sounds on fish have not been studied directly (McQueen et al. 2019);

therefore, we review studies that have examined the effects of human-made sounds on fish and then 

provide context by comparing these effects to the summary of sounds emitted from hydraulic dredges. 

Because of the tremendous number of fish species (~32,000 species), an enormous anatomical diversity 

exists and each anatomy may respond differently to sounds that vary in frequency, duration, and intensity 

(Popper et al. 2014). Therefore, previous results should be taken in the context of generalizations with 

knowledge that each individual species will vary in their response to sounds.

Research regarding the effects of underwater sounds on fish has primarily been conducted on pile driving 

for marine construction, military sonar, and high intensity seismic surveys (Popper and Hastings 2009b; 

Reine et al. 2014a). Only impulsive sounds such as pile driving have been documented to result in fish 

mortality (≥ 193 dB re 1 μPa), although results are inconsistent across studies with similar sound 

emissions (Popper and Hastings 2009b). Popper and Hastings (2009a) showed temporary hearing loss and 

tissue damage were more common than mortality, and damage decreased as distance from the source 

increased. Temporary or permanent hearing loss varied by species, duration, and intensity of exposure; 

damage to fish tissues were commonly noted when sounds were > 180 dB re 1 μPa. Because study 

methods have relied on the caging of fish, the effects of sound on behavior remain unknown (Popper and 

Hastings 2009a). A few studies have documented a decline in catch rate, movement away from vessels, 

and movements to greater depths, including studies of the federally managed Atlantic herring and Atlantic 

cod (Popper and Hastings 2009a; Popper et al. 2014; Slabbekoorn et al. 2010). In an experimental study, 

settlement-stage coral reef fish larvae moved toward a reef sound playback, but the response was reduced,

or avoidance behavior was observed, when boat sounds were added as a playback (Holles et al. 2013). 

Based on reviews and multiple studies, experts have detailed generated sound exposure guidelines 

(Popper et al. 2014).
20

To conclude, we have summarized the available evidence on the impact of underwater dredging sounds 

on fish. Assuming the maximum underwater sound directly at the source of dredging vessels of 183 dB re 

1 μPa (range = 157.4–183 dB re 1 μPa), fish mortality due to sound is extremely unlikely even 1 m from 

the vessel itself (minimum sound level at which mortality has been observed is ≥ 193 dB re 1 μPa). 

Damage to tissues, temporary, or permanent hearing loss is possible in a localized area near the dredge 

vessel, as such damage has been observed with sounds of > 180 dB re 1 μPa. Given the attenuation of 

sound over distance (Table 2-2 and Table 2-3, Figure 2-3), such damage would likely be limited to 

< 50 m from the vessel if fish do not avoid this particular area.

Behavioral responses and masking of natural sounds are expected because of dredging sounds, but there 

has been little research to confirm these expectations (Hawkins et al. 2015). Expected behavioral changes 

where sound is above ambient conditions may include avoidance, masking of conspecific communication, 

masking of predator or prey detection, or other behavioral changes (Hawkins et al. 2015; Slabbekoorn et 

al. 2010). Avoidance could have severe consequences if the particular area is critical for spawning, habitat 

is limited in the near vicinity, migratory corridors are blocked, or the area is important for other life 

history requirements. The distance for sounds associated with dredging to decrease to ambient levels 

ranges 400–2,700 m, although the maximum sound levels emitted by dredge activities are only present for 

approximately 0–300 m from the source of the vessel. Underwater sounds emitted from dredging 

operations could affect the behavior of fish at a considerable distance from the dredge operation. For 

example, Reine et al. (2014b) showed sounds near 130 dB re 1 μPa at approximately 1,200 m from a 

dredge operation (Figure 2-3) (Reine et al. 2014b). A 1,200 m radius around a dredge operation 

corresponds to a 452 ha area (1,117 acres) with a potential effect. But the range of response by fishes 

remains poorly understood for many species. In addition to these potential behavioral effects, chronic or 

cumulative impacts of non-lethal sound levels to fish are also poorly known.

2.3 Effects of Suspended Sediments on Fish and Their Habitats

2.3.1 Suspended Sediment Measures

Suspended sediment concentrations can be measured directly as total suspended solids (TSS) in units of 

mg L-1 or measured indirectly as nephelometric turbidity units (NTUs). NTUs measure scattered light 

from a water sample at a 90-degree angle from an incident light. Turbidity includes TSS but is also 

influenced by other factors such as plankton. We report studies that include TSS whenever possible. 

Ambient conditions of TSS in an offshore marine environment have been documented in the range of 5–

10 mg L-1 (Duclos et al. 2013; Hitchcock and Bell 2004). Sediment plumes in marine environments can 

also be detected and tracked via satellite imagery, though the imagery can represent relatively low TSS 

(Fisher et al. 2015).

2.3.2 Extent, Duration, and Concentration of Suspended Sediments Related 

to Dredging

For cutterhead dredges, the rotation of the cutterhead itself produces substantial sediment resuspension in 

the lower part of the water column; plume concentrations at the water surface may be half of the 

concentration at the bottom (Havis 1988). In addition to increased TSS at the seafloor, TSHDs are often 

allowed to overflow until the slurry is of an appropriate density, and the overflow can be extremely turbid 

in close proximity to the dredge, as fine-grained TSS may reach > 750 mg L-1 (Havis 1988). Additionally, 

undesirable fine sediments may be discarded in the sorting and screening process (Michel et al. 2013; 

Sutton et al. 2009). For example, a recent study conducted near Biloxi, Mississippi, USA found the 

screening process at the borrow site removed 61% of the fine sediments before placement on the beach 

(Smith SJ et al. 2019). Havis (1988) compared TSHD and cutterhead dredges, and showed TSS 

concentrations were much greater for TSHD (with overspill allowed), particularly at greater depths.
21

Suspended sediments resulting from dredging can result in light reduction, suspended and resuspended 

sediments in the water column, and deposition of sediments on the seafloor. In this context, resuspension 

of sediments is defined as sediments that enter the water column after initial settlement on the seafloor. 

Michel et al. (2013) reviewed offshore sand dredging and noted that the extent and duration of suspended 

sediment plumes are highly variable based on site-specific criteria. A comprehensive list of relevant 

environmental and technical variables that impact TSS levels were identified by Anchor Environmental 

(2003), with the following most relevant factors for the marine offshore environment:

• Water depth

• Grain size

• Density and specific gravity of 

sediments

• Organic/detritus content

• Debris content

• Dredge type, size, and production rate

• Dredge methods (cut depth, swing of 

cutterhead, overspill of hopper, design 

of hopper dredge overflow)

• Currents

• Tides

• Waves

• Ambient salinity, temperature 

(thermoclines), and water chemistry

Within the last decade, a plethora of research has been published on the effects of dredging regarding the 

extent, duration, and concentration of suspended sediment plumes in marine environments. 

Characteristics of TSS may include a near-field settlement of more coarse sediments and a far-field 

settlement of finer-grained sediments (Van Lancker and Baeye 2015). To a lesser extent, sedimentation 

has also been documented. Below, we highlight several case studies:

• Spearman (2015) reviewed TSHD studies of aggregate extraction (sand and gravel) in United 

Kingdom waters. He found sediment plumes often travel < 500 m, but may reach up to 3 km.

Sediment plume concentrations exceeded 70 mg L-1 within approximately 100 m of the dredge. 

However, projects were often monitored for only a few days, and the authors discarded two 

studies because they did not think the measurements were valid. In one of those studies, a nearbed extended down current 4.5 km from the dredge (Hitchcock and Bell 2004).

• Dredging offshore of Spain resulted in an increased TSS within 600 m of the dredging operation, 

and a second plume developed 3 hours post-dredging and moved 7.8 km (Van Lancker and Baeye 

2015). The sediments included medium and coarse sands with < 1% silt-clay.

• Duclos et al. (2013) reported a subsurface plume of silty sands that declined dramatically within 

10 minutes but remained at an elevated TSS concentration for two hours. During slack conditions, 

the plume expanded 600 m from the dredge; the range expanded up to 8.5 km (and a 100 m 

width) with tidal currents (Figure 2-4 and Figure 2-5). Sediment deposition was estimated up to 

800 m from the dredging. For this aggregate dredging, the overflow sediments were described as 

55.3% sand, 30% silt, and 14.7% clay.

• Fisher et al. (2015) documented TSS effects within 3 km of dredging, but one unusual instance 

resulted in a plume moving ~20 km because of a local oceanographic feature and the resulting 

flow direction. TSS fluxes continued over 11⁄2 years post-dredging, while no such turbidity fluxes 

were observed > 2 km from the dredge site. Dredge sites ranged from finer sediments (sand, silt, 

and clay at approximately 30% each) to a coarser, more offshore site (sand=70%, silt=10%, 

clay=10%). However, oceanographic factors and storms had a greater role in TSS movement 

compared to sediment grain sizes.

Overall, the pattern has emerged that extremely high TSS concentrations occur for a relatively short 

duration during and immediately following dredging. The area affected by high TSS, and potential 

sedimentation, is generally within 300–600 m of a dredging site, but moderate effects are expected to 

3 km. Under strong currents, sediments plumes may extend up to 20 km from the dredge site. Although 

the vast majority of studies measure TSS for only hours to a few days post-dredging, resuspension of 
22

sediments has now been documented for up to 11⁄2 years following dredging (compared to control sites). 

These resuspension events may span from concentrations similar to the dredge event to plumes with a 

very low TSS concentration. Given the limited number of studies that have monitored TSS beyond a few 

hours or days post-dredging, the frequency and mechanism of sediment resuspension events over a longer 

timeframe remains poorly understood and requires further study.
23

Figure 2-4. A conceptual model of suspended sediment movement from a dredging operation over time.

Source: Adapted from Duclos et al. (2013). Reproduced with permission from the Coastal Education and Research Foundation, Inc.
24

Figure 2-5. The extent of a subsurface sediment plume and sedimentation resulting from 

aggregate dredging in the English Channel, UK. A) sediment plume extent and direction during 

ebb, flood, and slack tidal phases of a mean tide; B) sediment deposition two hours after 

dredging.

Source: Adapted from Duclos et al. (2013). Reproduced with permission from the Coastal Education and Research 

Foundation, Inc.
25

2.3.3 Consequences of Suspended Sediments and Sedimentation on Fish

The vast majority of studies on the effects of suspended sediments and sedimentation on fish species have

been conducted in freshwater and estuarine environments because of heightened TSS concentrations 

associated with erosion, flood pulses, runoff pollution, dredging, and aquaculture (Wilber and Clarke 

2001). Relatively few studies have examined the effects of suspended sediments on marine fish species 

(Au et al. 2004; Partridge and Michael 2010). Generally, the combination of duration and concentration of 

TSS determine the consequences to fish. Environmental changes resulting from suspended sediments are

1) reduced light transmission, 2) reduced visibility, 3) decreased dissolved oxygen, 4) greater water 

temperature, and 5) potential release of contaminants (Kjelland et al. 2015). In offshore marine 

environments, contaminants are not as likely to be an important effect compared to rivers, estuaries, or 

harbors where pollution is commonly problematic (see Wenger et al. 2017 for a review). Potential 

responses of fish to suspended sediments are avoidance; changes in foraging and predation rates;

physiological stress; reduced growth; physical damage; and mortality of adults, juveniles, larvae, or eggs 

(Kjelland et al. 2015; Wilber and Clarke 2001). Additionally, sedimentation may change the grain size of 

the substrate and bury invertebrates that are prey for fish. Here, we focus on marine species whenever 

possible, but salmon, trout, and other anadromous species have been most intensively studied and provide 

further context to assess the effects of suspended sediments and sedimentation.

2.3.3.1 Fish Eggs and Larvae

Fish eggs and larvae are particularly susceptible to sedimentation and suspended sediments because of 

their lack of mobility, relatively high oxygen demand, and anatomy (Appleby and Scarratt 1989; Wilber 

and Clarke 2001). Each species’ natural history determines its exposure to suspended sediments. The 

timing and duration of the egg stage as well as its depth in the water column are particularly important. 

For example, herring eggs are most susceptible to mortality if exposed to ≥ 250 mg L-1 of TSS during the 

first 2 hours following egg release (Griffin et al. 2009). Eggs of bottom-spawning species and larvae near 

the substrate are particularly vulnerable to sedimentation (Wilber et al. 2005). In a review, Wilber and 

Clarke (2001) synthesized data on estuarine fish eggs and larvae. They found high variability among 

species in their tolerance to suspended sediments:

• Blueback herring and alewife eggs were not affected at 1,000 mg L-1

• American shad larvae mortality increased at 100 mg L-1

• Atlantic herring eggs were unaffected at 500 mg L-1

Striped bass larvae showed decreased feeding at 200 mg L-1 and increased mortality at 485 mg L-1

. 

Partridge and Michael (2010) examined pink snapper larvae with a specific limestone-based suspended 

sediment. When larvae began the open-mouth stage of their development, 12 hours of exposure resulted 

in the first observable effects at 4–14 mg L-1

, and LC50 (lethal concentration when mortality ≥ 50%) was

reported as 142–157 mg L-1

. By day 15, food intake of snapper larvae was reduced when TSS was as low 

as 15 mg L-1 (Partridge and Michael 2010). For a species that inhabit regularly turbid estuarine 

environments, Pacific herring larvae exposed to TSS of 200-400 mg L-1 for 16 hours did not differ from 

controls in regard to mortality, growth, heart rate, prey capture, or swimming velocity (Griffin et al. 

2012). In contrast, coral reef fish have been found to have delayed larvae development with TSS of 15–45 

mg L-1 (Wenger et al. 2014), and suspended sediments have been shown to interfere with larvae 

settlement into coral habitat at 45–180 mg L-1 (Wenger et al. 2011).

2.3.3.2 Juvenile and Adult Fish

For adult fish, the effects of TSS concentrations are thought to be primarily behavioral rather than 

physical because they are able to move away from such disturbances (Kjelland et al. 2015; Wilber et al. 

2005). In support of this notion, examples of TSS lethal concentrations and durations of exposure 
26

determined in lab settings are extremely high (Table 2-4). Additionally, bottom-feeding fish may be more 

tolerant of such events compared to species such as marine pelagics (Humborstad et al. 2006). Yet, 

evidence supporting this idea is sparse. Overall, sublethal physical effects of suspended sediments to fish 

include clogging or coating of the gills, lesions, swelling, mucus and tissue production, less oxygen 

uptake, respiratory problems, and general changes to the structure of the gill (Wenger et al. 2017; Wilber 

and Clarke 2001). Although the effects are rarely studied outside of lab settings, potential consequences 

are increased energy expenditure, reduced foraging, reduced growth, and high susceptibility to predation

(Wilber and Clarke 2001). Below, we highlight several lab studies on juvenile and adult fish exposed to 

suspended sediments.

• Adult Atlantic cod exposed to TSS of 550 mg L-1 of mud spanning 24 hours to 10 days had no 

mortality observed (Humborstad et al. 2006). However, after 24 hours, all cod were observed to 

have acute lesions to gill tissues at low to moderate TSS concentrations. Ten days following 

exposure, cod produced 1–2 layers of epithelial cells on their gill tissues, which may act as a 

protective measure.

• Juvenile orange-spotted grouper exposed to TSS of 128 mg L-1 for 10 days resulted in clogging of 

gills, increased mucous cells, and other signs of physiological stress (Wong et al. 2013). Exposure 

of the species to 6 weeks of TSS of 50–200 mg L-1 had mortality of rates 20–30%.

• For three coral reef fish species, TSS of ≥ 45 mg L-1 affected gill structure, reduced oxygen 

diffusion distances, and increased energy expenditures (Hess et al. 2017). They also showed 

oxygen uptake decreased in one species after exposure.

Table 2-4. The effect of TSS concentrations on select marine and estuarine fish species and 

northern quahog.

Species TSS (mg L-1) Duration of 

exposure (days) Percent mortality

Bluefish (juvenile) 800 1 day 100%

Atlantic menhaden (juvenile) 800 1 day 100%

Bay anchovy 2,310 1 day 10%

Striped bass 1,500 14 days 0%; Only physiological 

stress

Atlantic silverside 580 1 day 10%

Spot 13,090 1 day 10%

Northern quahog 1,000 10 days 10%

Source: Data summarized from Kjelland et al. (2015) and Ray et al. (2005).

Fish use both visual and chemical cues to detect prey, and turbidity may affect fish behavior, foraging 

success, and conversely, the survivorship of prey species. Collin and Hart (2015) and Utne-Palm (2002)

provide reviews of how fish vision and turbidity interact in the context of foraging. The vast majority of 

studies reviewed were on freshwater species (trout, bluegill, walleye, largemouth bass) or salmon. Only a 

few estuarine fish species have been studied in this context. Nonetheless, some useful conclusions can be 

drawn. The reaction distance of adult fish in response to planktonic prey are directly and negatively 

related to turbidity (Utne-Palm 2002; Wilber and Clarke 2001). Furthermore, increased turbidity is 

expected to decrease the ability of visual specialists to obtain food (Collin and Hart 2015). In this respect, 

turbidity is most likely to interfere with foraging of large piscivores that detect prey from a distance 

compared to other species such as planktivores (Utne-Palm 2002). Common prey species may also benefit 
27

from less predation. For example, shrimp are less predated in turbid environments (Macia et al. 2003).

The turbidity relationship with fish foraging and predation may even explain differences in fish 

community composition, including the evolutionary benefit for juvenile fish life stages to use more turbid

estuaries compared to clear marine waters used by adults (Utne-Palm 2002).

The effects of suspended sediments on the distribution, foraging, avoidance, and other behaviors of fish 

are often difficult to discern. For salmon, pulses of suspended sediments cause alarm reactions, disruption 

of schooling behavior, cessation of fish feeding, increased swimming, and relocation to undisturbed areas 

(Wilber and Clarke 2001). Such examples are rare, or nonexistent, for estuarine or marine fish species. A 

summary of relevant studies are provided below:

• Lowe et al. (2015) investigated juvenile pink snapper in New Zealand and showed estuaries with 

relatively high TSS (range 4–37 mg L-1

) had fewer fish captures and more gill structural changes. 

Snapper in low TSS environments fed primarily on pelagic prey, whereas snappers in estuaries 

with higher TSS fed on large benthic invertebrates (Lowe et al. 2015). A complementary lab 

study showed suspended sediments reduced foraging success, and physiological stress indicators 

were present after 1 month of TSS of 20–160 mg L-1 (Lowe et al. 2015).

• Leahy (2011) showed a coral reef fish foraged 40% less in turbid environments, and they had the 

strongest antipredator responses at the highest TSS concentration of 41 mg L-1

.

• A coral reef planktivore exposed to a TSS concentration of 45 mg L-1 had a slower reaction to 

food availability. Fish in this treatment consumed all of their food in only 52% of tests compared 

to 87% of control tests (although not statistically significant) (Wenger et al. 2012).

• For a coral reef fish, Wenger et al. (2012) found a TSS of 90 mg L-1 effected reaction time and 

food consumption; fish growth over the first two weeks of the experiment was less in the TSS 

treatments of 90 and 180 mg L-1 compared to the control (Wenger et al. 2012). Correspondingly, 

the fish showed less movement and settled in fewer live coral habitats when TSS was at 

30 mg L-1

, presumably because of the disruption of visual cues (Wenger and McCormick 2013).

• Given the paucity of data on the behavior of marine fish species exposed to relatively high TSS 

concentrations, further investigations are warranted for species that are likely to be affected by 

dredging.

2.3.4 Consequences of Suspended Sediments and Sedimentation on 

Nearby Corals, Coral Reefs, and Hard Bottom Habitats

Although sand dredging occurs in soft bottom substrates, impacts to nearby corals, coral reefs, and hard 

bottom habitats have occurred. Corals and coral reef habitats determine the distribution of a diverse array 

of fish species. The impacts of dredging on corals via increased TSS and sedimentation has a history of 

concern that has led to consistent research and reviews of such research (Dodge and Vaisnys 1977; 

Rogers 1990; Jones et al. 2016). In a comprehensive review of the impacts of dredging on corals, and 

coral reefs, Erftemeijer et al. (2012) documented 35 cases where dredging effects were studied. These 

studies ranged worldwide and included seven cases documented in Florida, USA. The dredging effects 

included no impacts, complete physical removal, burial, and tissue damage, as well as lethal and sublethal 

stress caused by relatively high TSS and/or sedimentation. Stress can result in reduced growth, reduced 

calcification rates, and bleaching (Erftemeijer et al. 2012; PIANC 2010). In some cases, high percentages 

of coral reefs were immediately destroyed, whereas other studies have shown that corals continue to die 

over the course of a few months as the cumulative stress added to the mortality rate (Erftemeijer et al. 

2012). Examples of measured effects include no detectable impacts 200 m from a dredging operation 

(Doorn-Groen and Foster 2007) to a loss of 80% of corals at a distance of 1 km from dredging activity 

(Stoddart and Stoddart 2005). Most recently, dredging at the Port of Miami, Florida, USA navigation 

channel resulted in a heightened rate of coral mortality up to 700 m from the dredged channel despite 

environmental monitoring initially being limited to within 50 m of the channel (Miller et al. 2016b). In 
28

this case, disease and complete colony loss was over twice as common for colonies near the dredging site 

compared to reference colonies; mortality was caused by burial of corals and stress (Miller et al. 2016b). 

Fisher et al. (2018) quantified sediment deposition, turbidity, and benthic light reduction at 26 sites 

ranging up to 33 km from a dredge operation, including control sites where coral mortality was monitored 

before, during, and after dredging. Water quality effects were detected as far as 19.6 km in the direction 

of a strong current and 2.1 km in the opposite direction (Fisher et al. 2015). In regard to corals, mortality 

was substantial and was disproportionately more common within 2 km of the dredging site compared to 

farther away sites (Fisher et al. 2018). Recommendations for best practices for dredging near corals and 

coral reefs are further provided by PIANC (2010).

Several mechanisms are responsible for stress and mortality of corals because of suspended sediments and 

sedimentation. Reef-building corals depend on photosynthetic activity of symbiotic, unicellular algae 

called zooxanthellae. For these corals, a heightened level of TSS causes stress or mortality as ambient 

light reaching zooxanthellae are reduced; non-photosynthetic corals may be vulnerable to clogging or 

smothering from sediments deposited on the coral’s surface (Erftemeijer et al. 2012). Both fine and coarse 

grain sediments can cause stress to corals. Fine sediments strongly reduce light levels, whereas coarse 

particles cause scouring and abrasion of coral tissues (PIANC 2010). Sedimentation on the surface of 

coral tissues may reduce feeding rates, reduce larvae survival and settlement, and increase energy 

expenditures to expel sediments (Erftemeijer et al. 2012). Corals can expel sediments through mucus 

secretion or new tissue production; however, the redirection of energy can lead to stress, suppressed 

growth and reproduction, and/or mortality (Erftemeijer et al. 2012). Morphology of corals generally 

corresponds with sensitivity of the species to sedimentation; branching corals tend to be more vulnerable 

to heightened TSS and less sensitive to sedimentation, and plate corals are less sensitive to suspended 

sediments, but are vulnerable to sedimentation (Erftemeijer et al. 2012; PIANC 2010).

The tolerance of coral species to suspended sediments and sedimentation varies widely by species and are 

dependent upon the duration and intensity (i.e., TSS) of the event, as well as the natural conditions in 

which the coral grows (e.g., inshore vs. offshore). Thus, a single threshold where TSS concentrations will 

cause mortality is difficult to predict (Erftemeijer et al. 2012). Stress effects have been commonly 

observed at concentrations spanning 50–200 mg L-1

, but some species do not show negative effects even 

at concentrations as high as 1,000 mg L-1 (Erftemeijer et al. 2012). Such differing responses of corals to 

TSS concentrations may lead to changes in coral reef community composition and a reduction of coral 

biodiversity, as only particularly tolerant species may remain. Fisher et al. (2018) studied a range of 

variables and found coral mortality was best predicted by mean daily sediment deposition over 60 days, a 

14-day running mean of turbidity, and a 14-day running mean of light reduction. An accumulation of 

multiple stressors may also interact to affect corals. For example, an experimental study showed bleached 

corals (generally associated with warming temperatures) could not clear sediments from their tissues at 

the same rate as unstressed corals (Bessell-Browne et al. 2017).

For a hard bottom ecosystem in the South Atlantic, Lindeman and Snyder (1999) conducted pre- and 

post-dredging fish surveys 0.8 km offshore of Florida, USA; the dredging buried 4.9–5.7 hectares (ha) of 

hard bottom with sediments. Both numbers of fish species (54 pre-dredging vs. 8 post-dredging) and their 

abundances declined for at least 15 months following dredging. For this study, the mechanism of fish loss 

could be either suspended sediment effects on fish behavior/physiology or the degradation of benthic 

habitats (Hess et al. 2017).
29

2.4 Effects of Physical Removal of Sediments with Implications to 

Fish

2.4.1 Oceanographic and Physical Changes Associated with Shoals and 

Dredging

Sand dredging may affect both the physical substrate and oceanographic conditions. Nairn et al. (2004)

categorizes the physical changes resulting from marine sand dredging as follows:

• Morphodynamics (e.g., topography, sediment transport and mobility, elevation/depth change)

• Oceanographic conditions (e.g., dissolved oxygen, water temperature, waves, currents)

• Seabed composition (e.g., sediment grain size, stratigraphy, compaction, mineralogy, dissolved 

oxygen, organic content)

We do not address mineralogy or stratigraphy in this review because they are not likely related to fish 

distribution. We also do not explicitly review dredging impacts on compaction and dissolved oxygen of

sediments. These properties are likely associated with invertebrates, which are much better studied. 

Changes in sediment grain sizes are closely related to benthic invertebrates, therefore, we focus on these 

topics together. The magnitude of the effects of physical removal will depend on the volume of sediment 

removed, surface area dredged, location of dredging in relation to shoals, accretion rates, the proportion 

of the shoal area that is dredged, and the availability of similar habitats nearby. Mitigation measures will 

also determine the effect of removal, though mitigation has likely been included in the reported studies. 

Morphodynamic changes in elevation/depth and topography are straightforward (Figure 2-6). Shallow 

areas may be partially or completely removed, and the terrain may be flattened by sand dredging. 

Therefore, fish species that prefer shallow waters, heterogeneity in water depths, or structural complexity 

(i.e., slope, hills, and valleys) may lose suitable habitat until the substrate is able to recover. Additionally, 

dredging may create depressions, furrows, or pits. As a result, changes in wave energy may affect 

sediment transport processes; particularly of concern is sediment transport to shorelines because of its 

impact on beach accretion or erosion (Nairn et al. 2004).

The removal of shoals has the potential to affect dissolved oxygen, water temperature, currents, and wave 

energy. In turn, these conditions may affect fish distribution. Dubois et al. (2009) presents evidence that 

Ship Shoal in the GoM may be a hypoxia refuge (i.e., when dissolved oxygen (DO) is > 2.0 mg L-1

) for 

benthic invertebrates where individuals may temporarily disperse to, and then later recolonize the

surrounding waters when DO returns to higher levels. Likewise, Craig et al. (2012) found a high 

abundance of demersal species (Atlantic bumper, Atlantic croaker, brown shrimp, spot, and sand seatrout) 

near large shoals as well as the edges of hypoxic zones of the GoM. Few species were abundant in low 

DO waters. They also found strong gradients of DO over 1 to 5 km distances, particularly over “shallow, 

wind-swept shoals” that were surrounded by low DO (Craig 2012).

Reeves et al. (2017a) also documented higher DO levels during summer on an artificial reef on Ship 

Shoal compared to a seaward artificial reef. Their study showed that hypoxia events resulted in reef fish 

avoiding waters > 12 m in depth because of low DO at the bottom.

Explicit documentation of shoals, or shoal removal, associated with water temperature changes are sparse. 

At Cape Canaveral, shoals were observed to be associated with a change in ocean currents and may result 

in water temperatures 2–3°C higher on the landward side of shoals (Reyier et al. 2014). In their study, 

Reyier et al. (2014) suggested this was enough to make Cape Canaveral a warm water refuge for sharks. 

To our knowledge, no simulations have been conducted to determine the threshold at which shoal 

dredging might disrupt oceanographic processes to the point where water temperature is changed.
30

Figure 2-6. Digital elevation models of post-dredging bathymetry for representative OCS borrow 

areas.

Source: Figure modified from Michel et al. (2013). The pink color represents hard bottom habitat for Dade County, 

Florida, USA.
31

Numerous studies have simulated or modeled how hydrodynamics and wave energy are expected to 

change as a result of dredging (Byrnes et al. 2004b; Kelley et al. 2001; Maa et al. 2004; Stone et al. 2009), 

although wave energy is not a common variable used to model the distribution of fish species. The 

emphasis on wave energy is because of the potential for the reduction of shoals to influence sediment 

transport to the shoreline and subsequent changes in erosion or accretion rates (Hayes and Nairn 2004; 

Maa et al. 2004). Currents are more often associated with fish, but modeling the effects of dredging on 

currents is rare.

Offshore of Alabama, USA Byrnes et al. (2004b) used wave transformation modeling to examine 

scenarios of sand extraction. Simulations of dredging showed increased wave heights of 0.20–0.50 m in 

the lee of the shoal and decreased wave heights (maximum -0.4 m) adjacent to the shoal. Changes became 

minimal when waves approached the shoreline, although it is noted that simulating the impact of storms is 

more difficult than projecting normal wave conditions (Byrnes et al. 2004b). Importantly, Byrnes et al. 

(2004b) also suggest that borrow site size, orientation, and proximity to shoreline played a role in the 

variability. Similarly, offshore of New Jersey, USA Byrnes et al. (2004a) showed that shoals affected the 

divergence and convergence of waves; sand extraction scenarios showed maximum wave height changes 

spanned 0.06–0.6 m with a variable amount of wave height dissipation when approaching the shoreline. 

Offshore of Delaware and Maryland, USA Maa et al. (2004) investigated the response of waves and 

bottom currents after the simulated removal of 24 million m3 (31.4 million yd3

) of sand shoals, as 

estimated for an accumulated impact over 10–20 years. In this study, modeled wave height increased by a 

factor of two after simulated removal, and these changes have the potential to cause increased shoreline 

erosion (Maa et al. 2004). Based on small- and large-scale sand removal simulations, Stone et al. (2009)

found that neither simulation would change abrupt changes in current direction. However, large-scale 

sand removal can change the velocity of currents and may affect sediment transport (Stone et al. 2009). A 

remaining concern with sand dredging is whether there is a threshold beyond which sand removal from a

shoal might result in deflation or the ultimate disappearance of the feature because the wave pattern is 

reduced in magnitude or if depth is increased to a point where sand deposition is minimal (Hayes and 

Nairn 2004).

2.4.2 The Effect of Dredging on Benthic Invertebrates, Sediment Grain Size, 

and Potential Food Web Changes

The effect of substrate removal as the result of dredging has the potential to disrupt food webs and 

negatively affect fish species that feed on benthic invertebrates (Kim et al. 2008; Nairn et al. 2004; 

Newell et al. 1998). The link between demersal fish and benthic invertebrates is important for fisheries

(Brooks et al. 2006; Newell et al. 1998; Smith et al. 2013); however, it has proven difficult to study. 

Pinnegear et al. (2000) reviewed documented marine trophic cascades, defined as the linkage of three or 

more trophic levels. They concluded that trophic cascades likely exist in soft-substrate ecosystems, but 

research simply has not been conducted in these less conspicuous ecosystems where polychaetes and 

crustaceans dominate (as opposed to the well-studied sea urchin populations or intertidal communities). A 

caging experiment of soft bottom substrates in the Chesapeake Bay has shown that the exclusion of blue 

crab and spot led to large increases in benthic infauna density and diversity (Virnstein 1977), which 

suggests a strong link exists between benthic infauna and these common prey species.

Generally, benthic invertebrates are classified as epifauna or infauna. Epifauna live on the surface of the 

substrate, whereas infauna live within unconsolidated sediments. Both types of species are vulnerable to 

removal from dredging. Benthic invertebrates are related to sediment grain size, topography, and 

oceanographic variables (Rutecki et al. 2014). For a further review on this topic, Rutecki et al. (2014)

details factors that affect the distribution of benthic invertebrates and documents common species found 

on soft bottom substrates of the US Atlantic and GoM.
32

Marine benthic communities, their successional states, and the effects of dredging are reviewed by Newell 

et al. (1998) (Figure 2-7). From a summary of worldwide studies, they showed that dredging consistently 

results in reduced benthic invertebrate species richness, abundance, and biomass as well as a change in 

species composition. Each of these changes have variable recovery times. In addition, measures of species 

evenness may increase because of the increased abundance of a few pioneer species (Crowe et al. 2016). 

In their review, Newell et al. (1998) state that benthic recovery to a pre-dredging state may come in 2–3 

years except for complex biological associations that depend on relatively slow growing fauna; in these 

cases, 5–10 years may be needed to fully recover. Since the 1998 review, further studies have primarily 

supported these conclusions (Crowe et al. 2016; Newell et al. 2004; Simonini et al. 2007; Waye-Barker et 

al. 2015).
33

Figure 2-7. Conceptual diagram of ecological succession of benthic communities.

Source: Adapted from Newell et al. (1998) and based on Pearson & Rosenberg (1978) and Rhoads et al. (1978). 

Reproduced with permission from the Taylor and Francis.

We have provided further details on the effect of dredging on benthic invertebrates. Below, we highlight 

major conclusions reached from international studies post-1998. We do note that recovery rates may 

differ based on project-specific attributes such as sediment grain size, depth of dredging, proximity to 
34

sediment sources and the sizes of those sediments, and mitigation measures that may assist with recovery. 

Nonetheless, these conclusions are robust in that they represent similar findings from multiple studies.

• Conclusion #1: Recolonization of a few early successional benthic invertebrates occurs 

quickly, while full recovery of the benthic community may take from 2.5 to > 7 years to

recover. Specific evidence includes the following:

o Species composition shifts quickly (i.e., < 100 days) toward early successional 

species, sometimes with only a few species that are short-lived and colonize rapidly

(Boyd and Rees 2003; Newell et al. 2004).

o Crowe et al. (2016) found more abundant amphipods and polychaetes following 

dredging, but fewer mollusks and other species. They found density, species richness, 

and species composition still differed with reference sites 6–8 years post-dredging.

o In the GoM, a dredge pit was dominated by one species 3 years post-dredging, 

leading to lower benthic community metrics compared to reference sites (Palmer et 

al. 2008). In this study, three species were found inside the pit and 9 to 27 species 

were found at reference sites.

o One year after dredging, density of individuals were similar to reference sites because 

of a single pioneer species (Cooper et al. 2007). In their study, Cooper et al. (2007) 

found full recovery of benthos occurred after 7 years post-dredging.

o Advanced stages of recovery may be observed as soon as 12 months following 

extraction, and the original community can be restored in 2.5 years (Simonini et al. 

2007; Simonini et al. 2005).

• Conclusion #2: For aggregate dredging, more frequently, or intensively, dredged 

substrates take longer for benthic communities to recover compared to less frequently 

dredged substrates. We note that such studies are lacking where sediments are 

primarily sand. Specific evidence includes the following:

o At a site with high frequency dredging, recovery of the benthic community was 

similar to reference sites after 15 years, nearly double the time found for nearby low 

frequency dredging sites (Waye-Barker et al. 2015). The reference sites in this study 

had approximately 50% gravel, 30% coarse to fine sands, and 20% silt/clay.

o Compared to a site with a low frequency of dredging, a high frequency dredging site 

(deeper pits, anchor dredging technique) showed a greater post-dredging reduction in 

benthic species richness, density, and biomass up to ~1.5 years post-dredging; one 

pioneer species was particularly dominant at this site (Newell et al. 2004). Sediments 

in this study primarily included silty sands, silty gravel, and sandy gravel.

o Community composition of benthos at low and high frequency dredging sites differ 

and differences persisted for ≥ 6 years (Boyd et al. 2005; Boyd and Rees 2003). In 

these two studies, sediments at reference sites ranged from 41–66% gravel and 24–

40% sand.

o The recolonization process for areas that sustained multiple dredging events may 

create new habitat for pioneer species not observed at lower frequency dredging sites 

(La Porta et al. 2009).

o A low frequency dredge site had benthic fauna recovery 7 years post-dredging, but a 

high frequency site (i.e., with 25 years of dredging) had not recovered 11 years after 

dredging ceased (Hussin et al. 2012). The reference sites in their study were 

approximately 45% gravel, 35% coarse to fine sands, and 20% silty/clay. 
35

• Conclusion #3: Change in sediment grain size corresponds to changes in benthic 

communities. Specific evidence includes the following:

o Simonini et al. (2007) found little change in sand sediment grain size post-dredging 

and found recovery of microbenthic communities to be complete by 2.5 years.

o Offshore of South Carolina, Crowe et al. (2016) showed surficial sand sediment 

became more fine-grained after dredging and infauna species were still different than 

reference substrates 8 years later.

o Sediments inside a dredge pit offshore of Louisiana had more silt and clay compared 

to reference substrates and recovery had not occurred after 38 months (Palmer et al. 

2008).

o Desprez (2000) showed benthic communities changed corresponding to the 

sediments, which changed from coarse to fine sands. Species richness was restored

by 16 months, but density and biomass were still reduced by 40% and 25%, 

respectively, 28 months post-dredging.

o Recovery of benthic fauna of a high impact dredging site occurred after 15 years, 

concurrent with the recovery of pre-dredging sediment grain sizes (Waye-Barker et 

al. 2015).

2.4.3 Drawing Analogous Benthic Impacts from Bottom Trawl Fisheries

Because of the parallels of marine dredging and the disturbance of substrate from bottom trawling 

conducted by commercial fisheries, we provide a brief overview of bottom trawling impacts to benthic 

communities and the potential for cumulative impacts. Bottom trawling is similar to dredging in that 

substrate is disturbed, but trawling does not result in removal of substrate like dredging does. Hiddink et 

al. (2017) conducted a meta-analysis and found that the depletion of fauna was highly correlated with the 

trawl type and its penetration into the seabed. For example, otter trawls with a penetration depth of 2.4 cm 

removed an average of 6% of organisms, while hydraulic dredges used for fishing had a penetration depth 

of 16.1 cm and removed 41% of organisms per pass (Hiddink et al. 2017). They estimated median 

recovery times to be 1.9–6.4 years. In a meta-analysis of bottom trawling studies, Hiddink et al. (2018)

showed that the shortest-lived organisms (< 1-year lifespan) increased in abundance immediately after 

trawling and were similar to reference conditions. In their study, organisms with a > 1 year lifespan 

decreased and were less abundant than reference locations. The abundance of organisms with lifespans of 

> 10 years declined the most (-37%) (Hiddink et al. 2018). Additionally, sessile (e.g., bivalves) and low 

mobility fauna are expected to take the longest to recover (Sciberras et al. 2018). Therefore, substrates 

with a relatively high proportion of long-lived organisms are expected to be the most sensitive to bottom 

trawling disturbances (Hiddink et al. 2018).

Despite the link of benthic invertebrates to fish, few studies have examined the effect of substrate 

disturbance on fish. At Georges Bank offshore of New England, Smith et al. (2013) found total 

abundance and biomass of benthic epifauna was greater at sites undisturbed by fisheries bottom trawling, 

although results were inconsistent by site. Smith et al. (2013) showed that several fish species had a 

greater length at undisturbed sites, and these species included haddock, longhorn sculpin, Atlantic cod, 

and winter flounder. In contrast, little skate and longhorn sculpin were found to have greater lengths at a 

disturbed site; notably, the difference was marginal and only occurred in one year (Smith et al. 2013). 

Additionally, fish diet dissimilarities among sites corresponded to epibenthic fauna that were sensitive to 

bottom disturbance (Smith et al. 2013).The authors further acknowledge that the fish sampled were not 

restricted to undisturbed waters and less mobile species might show greater differences than mobile 

species.
36

2.4.4 Factors Promoting Physical Shoal Recovery

The extent, duration, and severity of the impact of substrate removal via dredging will depend on the 

recovery time needed to restore the substrate and its fauna. Rutecki et al. (2014) reviews the geological 

underpinnings of sand shoal evolution and growth, including the role of storms, cold fronts, waves, and 

other hydrodynamics. These fundamental dynamics ultimately influence sediment transport, and thus, the 

potential for shoal growth and recovery following dredging operations.

Borrow design, size, location on a shoal, depth of excavation, water depth, and orientation of the 

excavation site will all influence recovery rates (Xu et al. 2014). For example, Xu et al. (2014) reported 

that a borrow site designed as a wide pit with shallow sloping walls did not accumulate mud sediments in 

the borrow area, presumably because water regularly flushed out these fine sediments before they could 

settle. Dibajnia and Nairn (2011) proposed using a relative shoal height ratio (shoal relief: depth of the 

base) to determine potential for shoal growth and concluded that shoals in water depths of > 30 m would 

not grow or recover following dredging because of the importance of wave-induced currents (Dibajnia 

and Nairn 2011). Although recommendations are difficult to generalize across regions, Rutecki et al. 

(2014) summarized recommendations from CSA International Inc. et al. (2010) and Dibajnia and Nairn 

(2011) as follows.

Promoting physical recovery of sand shoals may occur by the following:

• Extracting sand from depositional areas, the leading edge, or downdrift margin of a shoal

• Avoiding dredging in upstream erosional areas that feed the depositional areas

• Shallow dredging spread out over a larger area rather than deep dredging in a smaller area

• Alternating dredged versus undredged areas down the longitudinal axis of the shoal crest

• Excavation in the higher portions of the shoal that are exposed to wave-generated turbulence

Further recommendations summarized by Rutecki et al. (2014) include the following:

• Shoals with a base depth deeper than 30 m of water should not be dredged because deeper 

shoals have limited potential to grow after dredging.

• Shoals with a relative shoal height (defined as height/base depth, or height divided by base 

depth) of less than 0.5 should not be dredged because shoals with a smaller H/BD ratio are 

not likely to recover after dredging.

• If shoal recovery to its pre-dredge height is desired, then only shoals that have reached their 

maximum relative shoal height, where (height/base depth)max = (base depth-5)/base depth, are 

recommended for dredging.

• For shoals with a base depth of 21 m (as determined from the modeling of Isle of Wight 

Shoal), dredging from the shoal crest is not recommended. When dredging from the top of 

the shoal, relative shoal height should not be reduced to less than 0.65 (i.e., removal of more 

than 1.3 m) after dredging, or the shoal will not re-grow to the same pre-dredge height.

• Sand should not be removed from the entire length of the shoal, i.e., dredging along the axis 

of the shoal, because it affects wave-focusing processes and the shoal does not recover to the 

same pre-dredge height.

• For the Mid-Atlantic, it is recommended that sand be dredged from the SW side of the shoal, 

because a) wave-focusing is concentrated on the NE side of the shoal and b) overall shoal 

migration is toward the southwest. 
37

3 Review of Fish Habitat Associations in Relation to 

Geomorphology, Oceanographic Conditions, and Other Factors

In this section, we review the literature on habitat associations of federally managed fish. The review is 

organized following US Fishery Management Council regions of the GoM, South Atlantic, Greater 

Atlantic (collectively Mid-Atlantic and New England), and then highly migratory species are addressed 

together for all regions. Because of the large number of federally managed species encompassing EFH 

designations of “reef fish,” sharks, and other species groups, we limited the scope of our review in several

ways. 

First, our literature review is restricted to research within our three study areas: Greater Atlantic, South 

Atlantic, and GoM. Although some species have a worldwide distribution (e.g., shark species), habitat 

associations have the most relevance when applied to the area where observations have been made. For 

example, water temperatures or depths used by blacktip shark are likely to differ between North America 

and Australia. 

Second, we limited our review to species that have designated EFH that commonly overlap with sand 

shoals. To provide this context, we provide tables that quantify how much of the EFH of each species, or 

species group, overlaps with the three study areas (Federal waters, ≤ 50 m depth). Specific methods 

included using the ArcMap (ESRI, Redlands, CA) clip tool to reduce EFH polygons to the respective 

study area. Then, this remaining EFH area (in km2

) was divided by the total study area. We also focused 

on species documented to use sand shoals, particularly those listed by Rutecki et al. (2014).

Thirdly, we do not attempt to characterize the natural history of species unless there is direct relevance to 

the fish’s habitat use or seasonality. Such descriptions are deliberately kept brief; EFH documents and 

other sources should be consulted for such information. Likewise, we do not determine what constitutes a 

particular life stage (e.g., juvenile vs. adult) because measures may differ by study. Each study is reported 

upon as it is presented by the author. The focus here is on habitat associations. A summary of basic 

habitat associations (sand affinity, temperature, depth, salinity) is provided in Volume 4: ShoalMATE 

(Shoal Map Assessment Tool for EFH). We report the units given by each study, with the exception of 

salinity, which had a simple conversion from parts per thousand (ppt) to practical salinity units (psu). 

Fourth, the literature review relies primarily on peer-reviewed science. In this manner, we are limiting 

speculation or data that has not been thoroughly evaluated. Overall, the following sections provide a 

foundation of understanding specific habitat associations of species. Although we attempted to focus on 

fish-geomorphology relationships, such relationships are often poorly studied or results are nonexistent 

for species. Therefore, there is no section on geomorphology relationships. On the occasions when fishgeomorphology relationships were observed, we do clearly identify such associations with the word 

"geomorphology" or "substrate" to allow for searches of such information throughout these sections. The

habitat use of each species, or species group, is organized by life stage in the marine environment in the 

order of juvenile (if relevant), adult, spawning adult, larvae, eggs, and then any related spatial mapping 

studies. We focus on life stages within the marine environment and provide only brief overviews of other 

life stages (e.g., juveniles that inhabit estuaries).

To better document the knowledge gap of marine fish and relationships with geomorphology, we 

conducted an international literature review in later sections. The focus of this search was on spatially

explicit habitat associations, as spatial mapping is an important aspect of defining EFH. Here, our goal 

was to quantify how species, categorized into functional groups (e.g., sharks, demersal fish, hard bottom 

fish, medium pelagics), relate to geomorphology, physical oceanography, fish-based oceanography, 

geographic, and biological predictors. The goal of this international review was to highlight our state-ofknowledge and quantify knowledge gaps.
38

Overall, the habitat associations addressed within this literature synthesis have the goal of describing how 

each species is distributed and what particular habitats are important or are not important. The reader may 

use this knowledge combined with knowledge of the effects of dredging in previous sections of this report

to assess environmental impacts. Such an assessment is beyond the scope of our review here and may be 

highly site-specific.

3.1 GoM Fish Habitat Associations and Seasonality

3.1.1 Introduction to the GoM Physical Setting and Fish

The GoM ecosystem includes 1.6 million km2 of coastal and marine waters and is considered the ninth 

largest body of water in the world (Karnauskas et al. 2013). In the northern Gulf of Mexico (nGoM), 

shallow waters are strongly influenced by riverine inputs and deeper waters are strongly influenced by the 

Loop Current. The Loop Current enters the GoM through the Yucatan Channel, goes northward in the 

basin, and then exits via the Straits of Florida. Importantly, the Loop Current produces spin-off eddies 

with typical diameters of 300–400 km and with a current velocity of 1–2 m s-1

; these eddies usually take a 

westward path originating generally near the Mississippi Delta (Johnson et al. 2017) (Figure 3-1). To a 

lesser extent, wind stress curl, or the influence of wind on water vertical structure, also contributes to the 

vorticity of eddies (Ohlmann et al. 2001). The end result is an exchange of shelf waters and deeper waters 

(Johnson et al. 2017; Ohlmann et al. 2001). The interaction of eddies with the shelf can produce 

upwelling (in cold-core eddies) and downwelling (in ware-core eddies), which are important for 

biological productivity (Spies et al. 2016).

Key Points and Knowledge Gaps (gaps are in italics)

• In shallow waters of the Gulf of Mexico, federally managed species both of direct socio-economic 

importance and likely to be common on sand shoals include red drum; red snapper; Spanish 

mackerel; king mackerel; several shark species; and brown, pink, and white shrimp. 

• Seasonal hypoxia, defined as waters with ≤ 2 mg L-1 of dissolved oxygen, may cause fish and shrimp 

to aggregate at the edge of hypoxic waters or move vertically in the water column. Windswept sand 

shoals are suspected to act as temporary refugia for fish and invertebrates where oxygen is available, 

even when surrounding waters are hypoxic. 

• Red drum are poorly studied in ocean habitats. Spawning aggregations are known to occur near 

inlets and estuaries where larvae disperse to juvenile habitats.

• Juvenile reef-associated fish use sand shoal habitats; red snapper and lane snapper have been the two 

most common species observed. Adult reef-associated fish that use artificial structures may forage in 

surrounding soft substrates, but the use of defined shoals remains uncertain.

• The shallow water habitats of coastal migratory pelagic species are unstudied. Of relevance to 

shoals, Spanish and king mackerel are piscivores whereas cobia primarily feed on demersal 

crustaceans and fish. 

• Brown shrimp are positively related to mud substrate, whereas pink shrimp are positively associated 

with sand and rock. Other habitat associations of these species include depth, salinity, water 

temperature, proximity to shoreline, and dissolved oxygen. Detailed white shrimp habitat 

relationships remain largely undocumented.
39

Figure 3-1. GoM's Loop Current, spin-off eddies, major rivers, and select geological features.

Source: Adapted from Spies et al. (2016). Reproduced with permission from the University of Southern Mississippi, 

Gulf and Caribbean Research.

The majority of sediments on the continental shelf west of the W 88o longitude (near Mobile Bay, 

Alabama) are mud with some sand interspersed; east of this longitude are predominately sands. Offshore 

of the Florida peninsula, designated EFH for corals depict substrate with an interspersion of sand and 

corals. Ship Shoal, Sabine Shoal, St. Bernard Shoal, and Heald Bank Shoal are examples of isolated shelf 

shoals, and bedform shoals are common on the west Florida shelf (Rutecki et al. 2014). Estuaries, 

subaquatic vegetation (SAV), oyster reefs, and coastal wetlands in the nGoM also contribute toward

offshore marine productivity by providing habitat for estuarine-dependent life stages and producing 

common prey species of the marine environment (e.g., menhaden, crabs) (Spies et al. 2016). GoM reef 

fish habitats have changed dramatically with the introduction of > 3,900 oil platforms and > 20,000 

artificial reefs established since the 1940s (Shipp and Bortone 2009).

Over 150 rivers contribute freshwater to the nGoM with the Mississippi River contributing over half of 

the annual total volume (Spies et al. 2016). The westward direction of Mississippi River plume lowers 

salinity in the nearshore of central and western Louisiana. Of particular importance, the nGoM hypoxic 

zone is a prominent feature in the summer season, and it generally ranges from 15,100–18,000 km3

(Obenour et al. 2013) (Figure 3-2). Hypoxia is generally defined as waters with a DO < 2 mg L-1

. This 

“dead zone” is caused by excessive nutrients and the subsequent proliferation of phytoplankton near the 

mouth of the Mississippi River. DiMarco et al. (2010) found the distribution of hypoxic waters were

associated with winds, freshwater flow, bathymetry, and they suggest the area between shoals undergoes 
40

frequent hypoxic events because of the change in water flow due to topography. Shoals themselves 

contribute to mixing and tend to have fewer hypoxic events (DiMarco et al. 2010). Additionally, studies 

have examined the effects of the hypoxia on shrimp (Craig et al. 2005), shrimp trawlers (Purcell et al. 

2017), reef fish (Reeves et al. 2017a), and forage fish (Craig and Crowder 2005). Together, these studies 

have found species tend to either aggregate at the edges of hypoxic zones or move their depth in the water 

column to avoid hypoxic waters.

Figure 3-2. Map of GoM hypoxic zone in July 2017. The black line shows the area where DO is 

< 2 mg L-1, which is considered the threshold for hypoxia.

Source: NOAA, https://www.noaa.gov/media-release/gulf-of-mexico-dead-zone-is-largest-ever-measured (accessed 

2019 Aug 8) and is courtesy of N. Rabalais, LSU/LUMCON.

Tarnecki et al. (2016) described the current state-of-knowledge of food webs in the GoM. They showed 

species, or species groups, that are animal prey for many other functional groups include bivalves, brown 

shrimp, pink shrimp, other shrimp, carnivorous macrobenthos, crabs and lobsters, infaunal mesobenthos, 

zooplankton, small demersal fish, other demersal fish, Sciaenidae, seatrout, sessile filter feeders, 

sheepshead, small pelagic fish, small reef fish, and squid. In particular, Gulf menhaden has been a focus 

of food web studies (Geers et al. 2016; Robinson et al. 2015), and the Gulf menhaden fishery is one of the 

largest by volume in US with a mean of 447,000 metric tons harvested annually from 2005–2009 

(Karnauskas et al. 2013).

For commercial fisheries of the nGoM, key federally managed species include red snapper, grouper 

species, shrimp, spiny lobster, and tuna (National Marine Fisheries Service 2017). Other key species 

listed include blue crab, menhaden, and mullets. For recreational fisheries, federally managed species of 

economic importance include red drum, porgies (particularly sheepshead), red snapper, and Spanish 

mackerel (National Marine Fisheries Service 2017). In individual states, federally managed species of 

economic importance include sharks, vermilion snapper, gag, red grouper, gray snapper, and king 

mackerel (National Marine Fisheries Service 2017). As of 2018, overfished stocks in the nGoM include 

greater amberjack, gray snapper, and lane snapper (NOAA Fisheries 2019). Spawning seasons for GoM

fish span much of the year, but are particularly concentrated June 1 through mid-October (Ainsworth et 

al. 2015) (Figure 3-3).
41

Figure 3-3. Seasonality of GoM spawning seasons.

Source: Adapted from Ainsworth et al. (2015). Reproduced with permission from the Creative Commons Attribution 

License.

The EFH designations for GoM federally managed fish show a strong overlap with Federal waters that are 

≤ 50 m in depth (Table 3-1). Although red drum EFH appears to have a relatively low overlap with these 

waters, designated EFH overlaps with shoals commonly used for sand dredging offshore of Louisiana 

(e.g., Ship Shoal, St. Bernard Shoals), Mississippi, Alabama, and the peninsula of Florida.
42

Table 3-1. Species, or species groups, designated with EFH in the GoM.

Species or EFH species group

Proportion of GoM

study area

designated as EFH

Fishbase description of 

habitat

Red drum 0.22 Demersal

≥ 10 m

Reef fish (49 species) 0.98 Reef-associated

Coastal migratory pelagics

- Spanish mackerel

- King mackerel

- Cobia

0.98 Pelagic

Shrimp

- Brown shrimp

- White shrimp

- Pink shrimp

- Royal red

- Rock shrimp

- Seabob shrimp

0.80 NA

Corals 0.18 NA

Spiny lobster 0.06 NA

Notes: Those overlapping with > 50% of the study area are bolded. Study area is defined in 

Figure 1-2. All life stages were included.

3.1.2 Red Drum in the GoM

Red drum are a demersal fish that use estuarine waters as juveniles, and then leave those areas at maturity 

(~3.5 years old) for offshore marine waters (Matlock 1987). Post-settlement and juvenile red drum in the 

nGoM use a variety of estuarine habitats, including seagrass, bare substrate, oyster reef, rivers, habitat 

edges, and, in particular, waters in close proximity to salt marsh (Spartina alterniflora) (Dance and 

Rooker 2016; Matlock 1987; Moulton et al. 2017; Williams et al. 2016). In contrast to the somewhat 

well-studied estuarine habitats of juvenile red drum, adult red drum habitat use of marine environments is

mostly unknown.

Adult red drum were present, but rarely caught in a study of Sabine and Trinity Shoals offshore of Texas 

(Brooks et al. 2005). Powers et al. (2012) analyzed adult red drum surveys in state and Federal waters east 

of Louisiana and south of the Alabama-Florida border. They reported red drum are year-round residents 

of the sampling area (water temperatures ranging 12.3–31.7°C), but catch per unit effort (CPUE) peaked 

in March, April, and November. The majority of red drum collected by longline were in waters < 20 m in 

depth, and the maximum depth was 63 m (Powers et al. 2012). We note that sampling was concentrated in 

inshore waters with more shallow depths compared to offshore environments. Matlock (1987) speculates 

that most red drum are within 16 km of Texas shoreline, but also describes red drum catches near oil 

platforms in Louisiana and reefs in Texas as far as 113 km offshore.

Spawning has long been speculated to be in nearshore waters and in close proximity to inlets or passes 

(Matlock 1987), and recent evidence has confirmed this notion. Powers et al. (2012) reported aerial 

surveys that found red drum spawning aggregations near inlets from Ship Island (Mississippi) to Dauphin 

Island (Alabama) and surrounding Louisiana’s Chandeleur Islands. Lowerre-Barbieri et al. (2016)

quantified spawning aggregations in the nearshore marine environment just offshore of Tampa Bay, 

Florida, and southward to Charlotte Harbor, Florida. Passive acoustic monitoring provided evidence that 

most adult red drum moved elsewhere before and after the spawning season (Lowerre-Barbieri et al. 
43

2016). The protection of spawning habitats are key to the current management strategy of red drum 

(Powers et al. 2012); therefore, these habitats should be further examined. Across the nGoM, red drum 

physiology indicates a spawning season ranging from mid-August to early October (Wilson and Nieland 

1994); this was further evidenced by acoustic arrays in nearshore waters of Florida with first and last 

detection of adults being August 26 and October 15, respectively (Lowerre-Barbieri et al. 2016).

3.1.3 Reef Fish in the GoM

Numerous reef fish species use artificial reefs, including oil platforms, which are sometimes located on 

shoals in the nGoM. However, reefs and hard bottom are particularly vulnerable to dredge impacts, and if 

these habitats are present, a range of buffer distances are implemented to designate where dredging cannot

occur. To address general reef fish habitat associations on shoals, we first address species that are found 

within or in close proximity to artificial reefs on shoals. Secondly, we provide a case study of fish 

associated with artificial reefs on and near Ship Shoal. Third, we address reef fish that use unconsolidated 

substrates as habitat in their juvenile life stage. In particular, we use red snapper as a case study to 

represent reef fish that have a juvenile life stage over soft sediment substrates. Red snapper adults also use 

soft sediment substrates for foraging as they venture away from artificial reefs and oil platforms. These 

associations are also reviewed.

Murawski et al. (2018) assessed bottom longline surveys of adult reef fish in the nGoM and reported the 

median, interquartile range, 5th and 95th percentiles of depth and temperature for common fish species 

(Figure 3-4). The following results were observed:

• Red grouper, sharpnose shark, red snapper, and scalloped hammerhead were primarily in waters 

with relatively higher temperatures and shallower depths. Each of these species had their median 

depth intersect with waters ≤ 50 m in depth and temperatures ≥ 19°C.

• Golden tilefish and yellowedge grouper were primarily found at greater depths and cooler 

temperatures (≥ 100 m depth and < 19°C).

• Scamp, greater amberjack, and red porgy were found at intermediate depths and temperatures.

In order of the most commonly sampled species via fishery-independent longline, the snapper/grouper 

complex included red snapper, red grouper, yellowedge grouper, wenchmen, red hind, scamp, snowy 

grouper, silk snapper, gag, speckled hind, vermilion snapper, blackfin snapper, coney, yellowtail snapper, 

warsaw grouper, and yellowmouth grouper. All other snapper/grouper complex species had a sample size 

< 10 individuals (Murawski et al. 2018). For reef fish, hypoxia may cause fish to avoid bottoms and 

vertical changes in depth of fish may be observed (Reeves et al. 2017a). Switzer et al. (2015) noted that 

little is known about fine-scale dynamics of hypoxia and refugia may exist where reef fish congregate.
44

Figure 3-4. GoM fish habitat associations with water temperature and depth.

Source: Adapted from Murawski et al. (2018) with open access permissions.

Gruss et al. (2018; 2017) used a coarse interpolation modeling method to estimate the spatial distribution 

of red grouper, gag, juvenile red grouper, juvenile gag, and juvenile red snapper in the nGoM. They based 

their model on locations of known samples and assumed the grid cells closer together are more similar 

than those cells farther apart. We caution that this type of modeling omits the effects of fine-scale 

features, like geomorphology, or substantial changes in environmental conditions. The distribution of 

several other species and functional groups of interest were estimated for the peninsula of Florida, 

including amberjack, reef carnivores, and reef omnivores (Gruss et al. 2018).

In regard to fish shifts in habitat use, Gruss et al. (2017) showed gag and red grouper males, nonspawning 

females, spawning females, and juveniles had differing spatial distributions. Adults were generally more 

widespread, while juveniles were found in more focused areas. Habitat associations were not quantified in 

their mapping, but geostatistical modeling of fish sampling locations showed a trend of red grouper and 

gag being commonly found at depths < 60 m (Gruss et al. 2017). Red grouper and gag were most 

common in the northeast section of the nGoM, with a relatively high probability of encounter offshore of 

west Florida shelf waters (Gruss et al. 2017). Juvenile red snapper were most abundant west of 

Tallahassee, Florida (Gruss et al. 2018); a finer scale of distribution was not discernible.

3.1.4 A Case Study of Adult Reef Fish on Artificial Reefs On and Adjacent 

to Ship Shoal, Offshore of Louisiana

Reeves et al. (2017a) studied reef fish inhabiting oil platforms at Ship Shoal (mean = 8 m depth) and an 

oil platform 7.5–15 km seaward of the shoal (mean = 15 m depth). The most common species 

documented include Atlantic spadefish (100% of surveys), sheepshead (93%), gray snapper (90%), blue 

runner (85%), Atlantic bumper (73%), red snapper (65%), Bermuda chub (63%), and horse-eye jack 

(43%) (Reeves et al. 2017a). Ship Shoal only had one hypoxic (DO of < 2 mg L-1

) event over a 2-week 

timeframe in the summer of 2016, while seaward platforms were consistently hypoxic from June 27 to 
45

August 22 (Reeves et al. 2017a). They found a total of 29 fish species, and fish were generally not 

observed > 12 m in depth when hypoxia was present at seaward platforms. Prior to hypoxic conditions, 

shoal platforms had more red drum, black drum, and yellow jack; during and after hypoxic conditions 

more gray triggerfish were observed at Ship Shoal. Some fish, such as Atlantic bumper, were more 

abundant in seaward waters, presumably because of the deeper depths. Importantly, juvenile gray snapper 

abundance increased by a factor of 10 on Ship Shoal as the season progressed, while the seaward area 

remained at a stable level. Aggregation of gray snapper may occur because they are benthic invertebrate 

feeders and their prey may be more abundant on the shoals compared to the surrounding hypoxic waters 

(Gelpi et al. 2009; Reeves et al. 2017b). Overall, shoal and non-shoal oil platforms differed in their fish 

species assemblages, but hypoxia did not appear to affect the abundance of most species.

3.1.5 Shoals as Habitat for Juvenile Reef Fish in the GoM

Juvenile reef fish have been documented in surveys specifically focused on sand shoals. In trawl surveys 

conducted at, or near, a sand borrow site offshore of Florida, Zarillo et al. (2008) commonly found 

juvenile grunt species and sand seabass. Other reef-associated species found included lane snapper, white 

grunt, red grouper, dwarf sand perch, tomtate, and gray snapper (Zarillo et al. 2008).

Brooks et al. (2005) examined fish species at Heald Bank, Sabine Bank, Trinity Shoal, and Tiger Shoal

offshore of Louisiana. Reef-associated fish species observed included lane snapper, rock seabass, dwarf 

sand perch, sand perch, and red snapper. Offshore of Texas and Galveston Bay, Heald Bank, Sabine 

Bank, and Freeport Banks have been documented as important nursery habitat for lane snapper (Mikulas 

and Rooker 2008). Peak densities of juvenile lane snapper occurred August 4 to September 1. In regard to 

geomorphology, a higher abundance of year-0 lane snapper occurred at offshore mud and shell ridges 

compared to inshore mud sediments (Mikulas and Rooker 2008). In an investigation of shoals offshore of 

Texas, Wells et al. (2009) found juvenile red snapper and lane snapper occurred in > 50% of trawl 

surveys; dwarf sand perch were also common. As evidenced by the temporal variability of lane snapper, 

Wells et al. (2009) speculates that shoals in their study area may provide an important link between 

estuarine and offshore habitats.

3.1.6 Red Snapper in the GoM

Red snapper in the nGoM are relatively well studied and provide a case study for both juvenile reef fish 

that utilize unconsolidated substrates and for adult reef fish that use these substrates for foraging near 

reefs. Gallaway et al. (2009) summarizes the life history of red snapper in the nGoM, and we provide a 

synopsis here (Figure 3-5). The red snapper is a demersal species that matures by two years of age. Major 

life stages include the following:

• Pre-recruit (< 50 mm total length [TL]) stages of egg, larvae, and post-settlement juveniles. At 

50 mm, they become susceptible to the shrimp fishery gear as bycatch.

• Post-recruit (> 50 mm TL) stages are juvenile (0 and 1 year), young adults (2–7 years), and 

mature adults ≥ 8 years).
46

Figure 3-5. Summary of red snapper life stages and broad description of habitats used. Dates for 

larvae and juvenile stages depict an example with an egg hatching July 1st.

Newly settled red snapper move to habitats with some structure, and juvenile red snappers have been 

documented to have relationships with geomorphology and selection for shell, low-relief shell, and 

substrate complexity:

• An experimental study of age-0 red snapper showed a preference for shell substrate compared to 

sand (Szedlmayer and Howe 1997).

• Rooker et al. (2004) found abundance to be greater at low-relief, relic-shell habitats compared to 

mud bottoms.

• An experimental study spanning August–November showed that 1-m2 artificial reefs attracted 

more juvenile red snapper, and a greater diversity of juvenile reef fish, when the substrate was 

more complex (Lingo and Szedlmayer 2006).

• Szedlmayer et al. (2014) found a greater abundance of juvenile red snapper at a site with a silt 

substrate compared to sand and coarse sand at other sites.

• Juvenile red snapper have been documented using muddy substrates offshore of Texas (Rooker et 

al. 2004) and Alabama (Szedlmayer and Lee 2004).

• Powers et al. (2018) suggested that juvenile abundance was greater in a region with more muddy 

sediments compared to waters with sandy sediments. However, they did not formally examine 

relationships with sediment grain size.

Gallaway et al. (1999) developed a habitat suitability index for age-0 and age-1 red snapper based on 

trawl surveys. They found that red snapper selected for depths of 28–37 m (range: 18–64 m), bottom 

temperatures of 22–29°C (range: 20–29°C), salinities of 34–35% (range: 30–37%), dissolved oxygen of ≥

5 mg L-1

; and that the number of oil platforms were negatively related to both age classes of red snapper. 

The relationship with higher platform density may be because of predators at these sites or because these 

fish are already recruiting to platforms where trawls cannot logistically survey (Gallaway et al. 1999). In a 

study of the artificial reef zone offshore of Alabama, Powers et al. (2018) found juvenile red snapper had 

the highest abundance in the 20–40 m depth class, compared to deeper waters. Juvenile red snapper are 

also affected by hypoxia. Relative abundance of age-0 and age-1 red snapper offshore of Louisiana were 

reduced during years with severe hypoxia (i.e., hypoxic conditions over > 20,000 km2

), and juveniles 
47

moved to deeper, colder, and higher salinity waters during these years (Switzer et al. 2015). Juvenile red 

snapper still maintained similar peaks in abundance by depth (15–25 m for age-0; 35–55 m for age-1), but 

more juvenile red snapper were found in depths between 30–45 m (age-0) and 45–70 m (age-1) during 

years with severe hypoxia. Additionally, Szedlmayer et al. (2014) found hypoxic waters caused nearly a 

complete loss of juvenile red snapper, and Gallaway et al. (1999) found few red snapper in hypoxic 

waters.

Gallaway et al. (2009) suggested that age-0 red snapper begin moving to reefs of intermediate relief 

(1 m2

) when near 100 mm TL and are entirely at these reefs by December of their age-0 year. This is 

supported by Wells et al. (2008), who showed red snapper juveniles gradually changed their habitat use 

from sand and low-relief shell (0–0.5 years) to low- and high-relief shell (1 year). After reaching 

18 months of age, red snapper recruit to natural outcroppings, oil platforms, and artificial reefs (Gallaway 

et al. 2009). This conclusion is generally supported by video surveys reported by Powers et al. (2018), 

who found juvenile red snapper began to move to natural and artificial reefs at 200 mm TL and were fully 

recruited to reefs by 280 mm.

Young red snapper adults of 2–8 years of age primarily occur on high-relief reefs, particularly oil 

platforms and various types of artificial reefs (Gallaway et al. 1999). At ≥ 8 years of age, fully mature 

adult red snapper move to open bottom habitat away from reefs or artificial structures, presumably

because predation is no longer a threat (Gallaway et al. 1999). Red snapper may not reach their full 

reproductive potential as adults until age14–15 (Cowan 2011). Powers et al. (2018) used vertical longline 

to sample 2- to 8-year-old red snapper at artificial and natural reefs. The highest CPUE was on artificial 

reefs (0.27 fish/hook/5 min.) compared to natural reefs (0.07 fish/hook/5 min) or unstructured bottoms 

(0.01 fish/hook/5 min.). Similarly, Karnauskas (2017) showed artificial reefs and oil platforms had 16–20 

times the abundance of red snapper at depths ≤ 50 m. The mean age of red snapper collected by habitat 

were as follows: artificial reefs = 4.9 years, natural hard bottom = 6 years, and unstructured bottoms = 8.6 

years. Bottom longline used away from reefs sampled larger and older fish with a mean age of 9.25 years 

(range 5–42 years) (Powers et al. 2018). They found no difference regarding depth of habitat use for these 

older fish. Froehlich et al. (2019) tracked adult red snapper for ~ 92 days at an artificial reef offshore of 

Texas. They found depth used by red snapper increased when temperatures decreased, particularly during 

cold fronts when temperatures were < 20°C (Froehlich et al. 2019). Emigration from reefs has also been 

reported following cold fronts elsewhere and fish returned when waters were > 20°C (Topping and 

Szedlmayer 2011).

Given that red snapper adults use unconsolidated substrates for foraging near artificial reefs, we briefly 

review movements and foods of adult red snapper. We found the following in regard to movements:

• Red snapper move away from oil platforms and artificial reefs at night and move between 

platforms (Peabody and Wilson 2006; Topping and Szedlmayer 2011; Williams-Grove and 

Szedlmayer 2016).

• Monitoring by acoustic receivers showed 72% of adult red snapper stayed at least one year at 

particular artificial and natural reefs (Topping and Szedlmayer 2011). Movements away from 

reefs tended to occur at night, and regular movements spanned 2–8 km (Topping and Szedlmayer 

2011).

• Froehlich et al. (2019) reported maximum distances moved from capture locations ranged 1–

1,038 m. Kernel density home ranges (95%) averaged 77.9 km2, which is likely to be an 

underestimate because the spawning season was not included.

• Summarizing long-term movements of tagged subadult and adult red snapper, Patterson et al. 

(2007) showed mean movements among studies ranged from 0.3–30.9 km with maximum 

distances of 5–558 km.
48

In regard to food consumption, Schwartzkopf et al. (2017) examined stomach contents and stable isotopes 

of red snapper at oil platforms and natural reefs offshore of Louisiana. They found individuals using oil 

platforms fed primarily in the surrounding soft bottom habitats and in the water column, while red 

snapper at natural reefs fed at the reefs. Although the reason is unknown, these differences may be due to 

the underlying substrates, as artificial reefs were surrounded by mud, whereas natural reefs were a 

patchwork of corals and sand (Schwartzkopf et al. 2017). Soft bottom prey species of red snapper were 

dominated by fish, including lizardfish and searobin (Schwartzkopf et al. 2017), which have been 

documented as common species on sand shoals (Rutecki et al. 2014). At artificial reefs offshore of Texas, 

a comparative analysis of red snapper and gray triggerfish provides additional evidence that red snapper 

may depend more on non-reef prey associated with mud or sand bottoms (Streich et al. 2018). The results 

showed red snapper eat diverse foods from site to site, including fish, crabs, gastropods, and stomatopods 

(Streich et al. 2018). In addition, Szedlmayer and Lee (2004) showed red snapper diet shifted with age 

toward more reef-associated prey, although some similarities between age groups still existed.

Spawning of red snapper in the nGoM spans from April–Sept 30, with a peak June–August. Highest 

larvae abundances have been observed at 50–100 m depths, but red snapper larvae have consistently been 

observed at 100–200 m depths as well (Lyczkowski-Shultz and Hanisko 2007). Larvae are also much 

more common in waters west of the Mississippi River (Lyczkowski-Shultz and Hanisko 2007).

The spatial distribution of red snapper has been modeled by three primary studies.

1) Most recently, Dance and Rooker (2019) modeled the distribution of red snapper separately for

the western and eastern nGoM, delineated by the Mississippi River Delta. They found the 

following relationships for red snapper in the eastern nGoM:

• The CPUE of age-0 red snapper was highest at the longitude near Mobile Bay, Alabama 

and declined eastward, declined with a higher DO level, had an uneven relationship with 

temperature, peaked at depths of 15–30 m, and declined in close proximity to artificial 

reefs.

• The CPUE of age-1 red snapper was highest at the longitude near Mobile Bay, Alabama,

and declined eastward, had both positive and negative relationships with DO and bottom 

temperature, peaked at depths of 15–50 m, and increased in close proximity to artificial 

reefs.

• The CPUE of adult red snapper was related to longitude, peaked at depths of 15–60 m, 

increased in a close proximity to artificial reefs, and increased in close proximity to 

natural reefs.

And in the western nGoM, Dance and Rooker (2019) found the following relationships:

• The CPUE of age-0 red snapper was related to latitude, longitude, increased with higher 

DO, increased with higher bottom temperature, declined with depths > 50 m, and was 

lower in close proximity to artificial reefs.

• The CPUE of age-1 red snapper was related to latitude and longitude, increased with 

higher DO, increased with higher bottom temperature, peaked at depths of 30–65 m, 

declined in close proximity to artificial reefs, and was higher in close proximity to natural 

reefs.

• The CPUE of adult red snapper was related to latitude, increased with higher DO, 

increased with greater depth, and was higher within 40 km of artificial reefs.

2) Gruss et al. (2018) modeled the presence/absence of red snapper in the nGoM with four predictor 

variables: depth, terrain ruggedness index (i.e., geomorphology), percent sand (for juveniles), and 

percent hard bottom (for adults). Adult red snapper were modeled only for the Florida continental 

shelf, while juveniles were modeled for the nGoM. The following summarize the findings:
49

• In contrast to previous studies (see above), Gruss et al. (2018) showed both juveniles and 

adult red snapper had peak abundance near 100 m in depth and declined substantially at 

150–300 m depths.

• Adults and juveniles were both negatively related to terrain ruggedness. This is the 

opposite of expectations given their life history. This may be a function of including 

deeper waters over the continental shelf or from missing variables in the analysis (e.g., 

artificial reefs).

• Juveniles selected for a greater percent of sand substrate, and adults selected for less hard 

bottom. Given red snapper life history, the relationship with hard bottom is the opposite 

of expectations.

3) Karnauskas et al. (2017) examined habitat relationships of age-1 and older red snapper. Bottom 

longline was used to assess non-reef habitats and vertical line surveys estimated relative 

abundance at artificial reefs and oil platforms (Karnauskas et al. 2017). Depth, longitude, and 

type of reef (i.e., oil platform, artificial reef, no reef) were used to model the distribution of red 

snapper. The authors note that the variance explained by the model is quite low. Other habitat 

factors may have been important, or red snapper may be responding to habitat at a finer spatial 

scale than the 10 km2 cell size that they used (Karnauskas et al. 2017). For the nGoM, they

estimated that 13.3% of the number of red snapper and 7.8% of the biomass were on artificial 

structures (Figure 3-6).
50

Figure 3-6. Predicted red snapper abundance and biomass for the GoM based on models of 

abundance accounting for artificial structures.

Source: Adapted from Karnauskas et al. (2017). Reproduced with permission from the Creative Commons Attribution 

License.

3.1.7 Coastal Migratory Pelagics in the GoM: Spanish Mackerel, King 

Mackerel, Cobia

3.1.7.1 Spanish Mackerel

The Spanish mackerel is a piscivorous pelagic species that generally uses depths of 10–35 m (Froese and 

Pauly 2018), although a paucity of published papers exist for them in the GoM. Spanish mackerel in the 

nGoM likely winter in south Florida or the Campeche-Yucatan region near Mexico, migrate in early 

spring to the nGoM, and then migrate back to their wintering grounds in the fall (Sutherland and Fable 

1980).

Confirming the neritic (i.e., shallow) habitat use of Spanish mackerel, Gruss et al. (2018) analyzed data 

from offshore of western Florida and found that both Spanish mackerel juveniles and adults had the 

greatest probability of presence near the shoreline, particularly < 50 km of the shoreline, and that the 

probability of presence declined steadily moving farther from the shoreline. Schrandt et al. (2016) showed
51

the seasonality of subadult and adult Spanish mackerel, as they were present April–November 30 along 

shorelines and estuaries spanning from Horn Island, Mississippi to Pensacola, Florida. Juveniles were 

caught April–August with most caught in May (Schrandt et al. 2016). They also found that 1- to 3-yearold Spanish mackerel used estuarine waters with a wide range of environmental conditions, including sea 

surface temperatures (SSTs) of 15–34°C, salinities of 0–31%, DO of 2.8–10.8 mg L-1

, and depths of 1.8–9 

m.

Larvae of Spanish mackerel have been collected offshore of Mississippi and Alabama May–August 31 

(Ransom et al. 2016) and offshore of Texas May–September 30 (McEachran et al. 1980). McEachran et 

al. (1980) also found Spanish mackerel larvae were more abundant in waters < 50 m in depth compared to 

deeper waters; there was no difference in larvae abundance between depths above or below 35 m. Larvae 

were captured within a water temperature range of 19.6–29.8°C and salinity range of 28.3–37.4 psu 

(McEachran et al. 1980). However, we note that no analyses were conducted to determine environmental 

preferences for temperature or salinity.

3.1.7.2 King Mackerel

The king mackerel is a piscivorous species generally known to use a depth range of 5–140 m (Froese and 

Pauly 2018), although a paucity of data exists for the GoM. King mackerel in the nGoM likely winter in 

south Florida from Cape Canaveral to the Florida Keys, migrate in early spring along shallow waters into 

the nGoM, and then migrate back to their wintering grounds in the fall (Sutherland and Fable 1980; Sutter 

et al. 1991).

Juvenile and adult king mackerel offshore of western Florida have the highest probability of presence 

with SSTs of 14–23°C and a sharp decline in presence at SST > 23°C (Grüss et al. 2018). Wall et al. 

(2009) found the CPUE of king mackerel caught in recreational fishing tournaments was positively 

related to temperature fronts within 10 km, as well as chlorophyll concentration. In a study of fish 

otoliths, Dzaugis et al. (2017) showed king mackerel growth rates were highest in years when the spring 

season warmed rapidly, as evidenced by relatively warm SST in March and winds from the south and east 

during this time.

Serial spawning of king mackerel occurs from May to early October, with a peak in September in the 

nGoM (Grimes et al. 1990; McEachran et al. 1980). Offshore of Texas, McEachran et al. (1980) analyzed 

ichthyoplankton surveys ranging depths of 12–139 m. They found king mackerel larvae were more 

abundant at ≥ 35 m depths compared to depths of < 35 m; larvae abundance did not differ above or below 

50 m depths, indicating that larvae were relatively common at 50–183 m depths. Furthermore, larvae were 

captured within a water temperature range of 19.6–29.8°C and salinity range of 28.3–37.4 psu 

(McEachran et al. 1980). However, we note that no analyses were conducted to determine environmental 

preferences for temperature or salinity.

3.1.7.3 Cobia

Cobia migrate from their wintering grounds off south Florida into the northeastern GoM during early 

spring (Dippold et al. 2017; Franks and Brown-Peterson 2002). More specifically, they occur offshore of 

northwest Florida, Alabama, Mississippi, and southeast Louisiana from late March through October 

(Biesiot et al. 1994; Franks et al. 1999). In stomach content analyses, cobia have been found to have 

demersal feeding habits. Meyer and Franks (1996) found diets of cobia captured between Louisiana and 

Florida were dominated by crustaceans (79% of stomachs). Portunid crabs were the most frequently 

found prey (73% of stomachs) followed by hardhead catfish Arius felis (24% of stomachs), eels (18% of 

stomachs), Sicyoniidae (prawns) and Penaeidae (shrimp) (9.6% of stomachs), and stomatopods/Squilla 

spp. (mantis shrimp) (6.9% of stomachs) (Meyer and Franks 1996).
52

Most recently, Gruss et al. (2018) showed cobia offshore of western Florida had the greatest probability 

of presence in shallow waters (0–50 m being the highest) and probability declined steadily at greater 

depths; peak presence was found when chlorophyll-a concentrations were ~3–10 mg m-3

.

As summarized by Franks and Brown-Peterson (2002), cobia spawning season in the nGoM has been 

reported as April–September 30 with populations near Texas possibly starting to spawn by May. Cobia 

spawn on multiple occasions per year (Biesiot et al. 1994), yet spawning locations are unknown for the 

GoM (Franks and Brown-Peterson 2002). Cobia larvae collected by Ditty and Shaw (1992) throughout 

the GoM were found in both estuary and offshore marine environments with SSTs ranging 24–32°C and 

depths ranging of 3–300 m. We note that the sampling they used was limited and opportunistic. 

Observations from the Atlantic population may provide further clues regarding spawning locations. 

Observations have suggested that cobia spawn at the mouth of Chesapeake Bay and immediately offshore 

of Virginia Capes (Joseph et al. 1964), generally near inlets (Smith 1995), inshore (Lefebvre and Denson 

2012), and offshore (Shaffer and Nakamura 1989).

3.1.8 Shrimp in the GoM

In order of economic importance (greatest to least), shrimp species in the nGoM Shrimp Fishery 

Management Plan (1981) include brown, white, pink, royal red, rock, and seabob shrimp. Of these, the 

Penaeid species of brown, pink, and white shrimp represent the bulk of the commercial shrimp landings.

These species all have estuary-dependent life stages and complex life cycles. For these species, growth 

and survival of shrimp within estuaries is primarily dependent on salinity and temperature; as shrimp 

grow, they shift to deeper waters and become more predatory (Gulf of Mexico Fishery Management 

Council 1981). The timing of emigration to the nGoM is dependent on size, tide, and temperature (Gulf of 

Mexico Fishery Management Council 1981). Growth continues rapidly under ideal temperatures and 

spawning probably occurs before shrimp are 1 year old (Gulf of Mexico Fishery Management Council 

1981). Because of the complexity involved in the various life stages of Penaeid shrimp, we have detailed 

their general habitat use and notable dates of each life stage (Figure 3-7, Table 3-2).

The importance of estuarine habitats to shrimp production is exemplified by Turner (1977), who found 

strong relationships between commercial yields of brown shrimp harvested in inshore waters and hectares 

of intertidal wetlands. Zimmerman et al. (2002) has since reviewed studies of brown and white shrimp 

estuarine relationships and found that marsh-edge habitat does support a greater abundance of shrimp 

compared to nonvegetated waters. However, densities of shrimp in submerged aquatic vegetation (SAV) 

have been documented as being lower, higher, or similar to marsh-edge habitats (Clark et al. 2004; 

Glancy et al. 2003; Zimmerman et al. 2002). Therefore, both SAV and marsh edges are likely important 

to shrimp in estuaries; the importance of estuarine habitat may be geography specific.
53

Figure 3-7. Life cycle of brown and white shrimp in the GoM.

Source: Adapted from Spies et al. (2016) and diagram by J.R. Allen. Reproduced with permission from the University 

of Southern Mississippi journal, Gulf and Caribbean Research.

Gruss et al. (2018) modeled the distribution of shrimp as a single group, which included pink, brown,

white, and rock shrimp, plus five other species. Shrimp had a higher probability of presence with less 

sandy sediments and peaked when mud was 20–40% of the sediments. Shrimp presence increased as 

depth increased (highest increase was 0–50 m, but shrimp still increased up to 300 m); abundance peaked 

when bottom temperatures were 10–15°C, although abundances were still high at 15–30°C (Grüss et al. 

2018).

3.1.8.1 Brown Shrimp

Montero et al. (2016) developed a species distribution model for brown shrimp using depth, percent mud, 

salinity, SST, DO, season, latitude, and longitude. In order of importance (highest to lowest), the specieshabitat relationships showed a greater CPUE of brown shrimp with a greater proportion of mud 

sediments, depths of 20–100 m, bottom salinity of 10–20 psu, SST of 23–30°C, and DO of 2–7 ppm 

(Montero et al. 2016). Seasonality did not affect CPUE, but latitude and longitude were substantial

factors. The predictive model showed a much higher brown shrimp abundance offshore of Mississippi, 

Louisiana, and Texas compared to the shelf of Alabama and Florida. During summer, a higher density 

was observed nearshore, while fall populations were more often further offshore (Montero et al. 2016). 

The authors note that this pattern might be because of emigration of late juveniles from the estuaries 

during the summer months. The offshore habitat use in the fall might characterize the species’ spawning 

habitat because peak spawning is thought to be September–November 1 in water depths of 27–100 

m(Renfro and Brusher 1982). The association with a relatively low salinity is related to the proximity to 

estuaries and major river outflows (Montero et al. 2016).
54

Table 3-2. Federally managed shrimp species and life history as depicted from the Gulf of Mexico 

Fishery Management Plan (1981).

Note: See Figure 3-7 for a visual of the life stages.

Species Life stage Ecosystem Depth and geomorphology 

relationships Notable dates

Brown 

shrimp Spawning GoM

Depths: 18–110 m

Substrate: mud, silt, or 

mud/sand/shell (TX–AL)

Spawning peaks:

Sept–Nov 1 (major) & 

April–June 1 (minor)

Brown 

shrimp

Free-swimming 

larvae GoM - Peak: Aug–Nov 1 (TX)

Brown 

shrimp Postlarvae Recruits to estuaries -

Peaks: March–April 15 & 

June–Sept 30 (TX);

Feb–March 1 (LA)

Brown 

shrimp Adult Moves to GoM May stopover in open bays on way to GoM

Peaks: May–Aug 31 (TX); 

June–July 30, range of 

May 15–Nov 1 (LA)

White 

shrimp Spawning GoM

Depths: 7–31 m, possibly near 

inlets

Substrate: mud, silt, clay/shell, 

sand/shell (TX–AL)

Spring through fall 

(multiple spawning events)

White 

shrimp

Free-swimming 

larvae GoM -

White 

shrimp Postlarvae Recruits to estuaries -

Peaks:

May–Oct 31 (MS)

June–Aug 1 (LA)

May–Oct 31 (TX)

White 

shrimp Adult Moves to GoM -

Sept–Nov 30 OR

Oct–Dec (fall spawning, 

with cold fronts)

White 

shrimp

Adult (for small, 

late spawned 

individuals)

Moves back to 

estuary, then returns 

to GoM to spawn

-

Estuary in early spring;

Returns to GoM 

spring/early summer 

Rock 

shrimp All life stages GoM

Depths: 18–82 m

Substrate: calcareous 

sediments (FL); sandy 

bottoms

Spawning continuous; 

peaks Oct–Jan 31 (FL)

Pink 

shrimp Spawning GoM

Depths: 22–48 m

Substrate: calcareous 

sediments (FL); firm mud, silt, 

sands with shells

FL Keys: most intense 

spring to fall (year-round) 

FL / Tampa: summer

Pink 

shrimp

Free-swimming 

larvae GoM - -

Pink 

shrimp Postlarvae Recruits to estuaries -

FL Keys: Peaks April–

June 1 & July–Oct 31 

(year-round)

MS: May–Dec 31

TX: peak Aug–Sept 1

Pink 

shrimp Adult Moves to GoM - -

Seabobs All life stages GoM

Depths: 0–9 m, mostly at 1.8–

3.6 m

Substrate: mud, silt, silt/s and 

bottoms (TX–AL)

Spawning July–Dec 1
55

Craig (2012) investigated the effect of hypoxic conditions on brown shrimp. During hypoxic conditions 

(DO < 2.0 mg L-1

), brown shrimp were concentrated along the edge of the hypoxic zone (i.e., farther 

inshore and offshore), particularly within 0–5 km of the hypoxia. The shrimping fishery also showed a 

pattern of moving farther inshore and offshore near the edge of the hypoxic zone (Purcell et al. 2017). 

Craig (2012) also showed the strength of association between DO and brown shrimp decreased in years 

with less hypoxia. Notably, strong gradients of DO were found across relatively short distances of 1–5 

km, as oxygenated waters near shoals were surrounded by hypoxic waters (Craig 2012). During years of 

moderate hypoxia, Craig and Crowder (2005) found brown shrimp abundance peaked with the following: 

depths of 40–100 m, in relatively close proximity to the shoreline, temperatures of 18–27°C, DO >2 mg 

L-1

, and salinities >32 psu. For years with severe hypoxia, brown shrimp habitat associations changed. 

They shifted in latitude to a more narrow depth range (peaking at 30–70 m), to both cooler and warmer 

waters, and to salinities < 20 psu (i.e., characterizing inshore movements) (Craig and Crowder 2005).

In addition to annual variation resulting from hypoxic conditions, brown shrimp habitat use has been 

shown to vary with their density offshore of Texas (Craig et al. 2005). In years with relatively high 

abundance, Craig et al. (2005) found brown shrimp used waters farther offshore, which extended their 

distribution beyond their more consistently used shallow waters. Furthermore, annual SST may affect 

brown shrimp abundance. Li and Clarke (2005) found a strong relationship of annual trawl catches and 

SST of shelf waters in April and May, as brown shrimp catches increased in years with relatively high 

temperature during this time. They concluded the SST anomalies measured in April and May likely affect 

brown shrimp because of its high variability and its link to growth and survivorship of brown shrimp in 

estuaries.

3.1.8.2 Pink Shrimp

Drexler and Ainsworth (2013) modeled the distribution of pink shrimp for the nGoM. Importantly, they 

found that aggregating the predictions into broad polygons, rather than the finer resolution of the original 

data, improved model validation. Drexler and Ainsworth (2013) documented the following habitat 

relationships:

• Peak abundance of pink shrimp was at bottom temperatures of 17–32°C; few samples were 

beyond this range, but a negative association was observed at < 15°C.

• Sand and rock substrates had greater densities of pink shrimp compared to mud or gravel.

• A slight peak in abundance was observed at depths of ~25–40 m, abundance was relatively high 

at 41–119 m, then a sharp decline in abundance was predicted at depths > 120 m.

• There was a slight decrease in predicted pink shrimp abundance as chlorophyll increased, then a

sharp decline was predicted when chlorophyll was > 15 mg m-3

.

• An increase in abundance was predicted when DO was > 5 mL L-1

, but very few samples were 

observed in this range.

Rubec et al. (2016) created qualitative habitat suitability maps based on pink shrimp fishing vessels on the 

western Florida continental shelf during the spring and fall. Depths up to 50 m were trawled. They noted 

that shrimping mostly occurred on offshore sand ridges because those areas were most trawlable; nontrawlable substrates included hard bottom or mixed hard bottom habitats. Relatively high pink shrimp 

CPUE occurred in waters with a northwest origin of bottom currents in the spring, higher current 

velocities, and bottom types of fine sand and mud (compared to medium sand, coarse sand, and gravel). 

In support of the relationship with current velocity, simulations of pink shrimp larvae transport have 

found that larvae and postlarvae migration to estuaries is likely dependent on a combination of tidal and 

wind-driven currents (Criales et al. 2006). Zink et al. (2017) reviewed the relationship of salinity to pink 

shrimp postlarval, juveniles, and subadults; they found a wide tolerance to salinity. Pink shrimp 
56

survivorship remained high at 15–40 psu and a majority of the reviewed studies reported maximal 

abundance between ~20–35 psu (Zink et al. 2017).

3.1.8.3 White Shrimp

Adult white shrimp habitat relationships are poorly studied in the GoM, although white shrimp have been 

found to be abundant on and adjacent to sand shoals offshore of the Atlantic Coast of Florida (Zarillo et 

al. 2009). In a study offshore of Louisiana, Diop et al. (2007) analyzed white shrimp landings over time 

and found landings were positively, but weakly, correlated with late juvenile abundances. Furthermore, a 

moderately strong, negative relationship was found between adult white shrimp CPUE and temperature 

during the late juvenile phase (Diop et al. 2007). Other relationships discovered included a weak 

relationship of a reduced adult CPUE when river flow was high and when fishing effort was high (Diop et 

al. 2007). Overall, the availability and condition of juvenile habitats may affect offshore abundance and 

subsequent habitat use of white shrimp.

The density of juvenile white shrimp are similar at marsh edges and subaquatic vegetation habitats (Rozas 

and Minello 2006). In terms of juvenile white shrimp density, growth, and secondary production, an 

analysis of estuarine habitats showed that edges of saline and brackish marshes of Louisiana had a higher 

value (per area) compared to oligohaline marsh edges (Mace and Rozas 2017). As expected, density of 

white shrimp was highest ≤ 1 m from the marsh edge and declined as distance from the edge increased 

(Mace and Rozas 2017). Baker et al. (2014) analyzed a stage-based population model for white shrimp 

and concluded that the population was most sensitive to survival rates in the early life stages.

3.1.8.4 Royal Red, Seabob, and Rock Shrimp Habitat Associations

Royal red, seabob, and rock shrimp do not have an estuary-dependent life stage. Royal red shrimp are 

found and harvested from approximately 180–730 m in depth (Gulf of Mexico Fishery Management 

Council 1981; Stiles et al. 2007). Because this depth range is well beyond our study area (≤ 50 m), we do 

not provide further information on royal red shrimp.

Rock shrimp are primarily harvested from Florida’s sandy bottoms, although their range extends across

the entire nGoM (Figure 3-8) (Gulf of Mexico Fishery Management Council 1981; Stiles et al. 2007). 

They are nocturnal and present year-round but are suggested to be most available for harvest July–

November 30, with the greatest abundance being in September (Stiles et al. 2007). After a particularly 

large harvest of rock shrimp in 1996, trawling vessels began to enter juvenile grounds, including the deepsea coral ecosystem of Oculina Reef in the South Atlantic (Stiles et al. 2007). At this time, much of the 

juvenile population was thought to have been harvested, and damage has occurred to the deep-sea reef 

(South Atlantic Fishery Management Council 1996).
57

Figure 3-8. Potential distribution of rock shrimp based on interviews, FMC documents, and 

scientific literature where available.

Source: Adapted from Stiles et al. (2007) with open access permissions.

Seabob shrimp are harvested alongside white shrimp October–December 31 as they migrate from deeper 

waters to the shoreline in response to cold fronts (Gulf of Mexico Fishery Management Council 1981). 

Little is known about seabob shrimp. In the southern GoM, seabob shrimp were found in the shallowest 

of waters spanning ~0–20 m (Castrejon et al. 2005).
58

3.2 South Atlantic Fish Habitat Associations and Seasonality

3.2.1 Introduction to the South Atlantic Physical Setting and Fish

Two broad geographies are recognized in the South Atlantic: the South Atlantic Bight extends from Cape 

Hatteras, North Carolina to Cape Canaveral, Florida; and the southeastern Florida Coast extends from 

Cape Canaveral to the Florida Keys (Dame et al. 2000). The most prominent feature of the South Atlantic 

is the Gulf Stream current (Figure 3-9), which creates a cross-shelf mixing of waters and strong water 

stratification (Castelao 2011).The Gulf Stream can intrude upon shelf waters by surface water intrusion, 

interlayering, or via a bottom water intrusion (Atkinson 1977). Surface and bottom water intrusions are 

most frequent in the summer months, are influenced by wind stress, interact with salinity, and are more 

frequent at the extreme northern and southern waters of the South Atlantic Bight (Castelao 2011).

The coast of the South Atlantic has several series of barrier islands with associated inlets, estuaries, and 

wetlands that contribute to the high productivity of the coastal environment (Dame et al. 2000). These 

estuarine waters support marine fish by acting as a nursery and as juvenile habitats for species such as 

sharks (Castro 1993; Curtis et al. 2013; McCallister et al. 2013), red drum, shrimp, and flounder. 

Estuaries are also used by prey species like crabs and menhaden. Estuarine waters in the South Atlantic 

are threatened by modifications to freshwater inflow, runoff, and pollution (Dame et al. 2000).

Sediments in the shallow waters (≤ 50 m) of the South Atlantic primarily range from very fine sand to 

very coarse sand (Conley et al. 2017). Shoals and shoal complexes of the South Atlantic include large 

cape-associated shoals such as Cape Lookout Shoals (NC), Frying Pan Shoals (NC), and Canaveral 

Shoals (FL) (Rutecki et al. 2014) (see Volume 2 for details of geoform classification). Hundreds to 

thousands of bedform shoals are also present in the region. Natural hard bottom reefs, or live bottom 

reefs, consist of rocky outcrops that support sessile invertebrates such as sponges and sea fans (Miller and 

Richards 1980). These habitats support a diverse reef fish assemblage.

Key Points and Knowledge Gaps (gaps are in italics)

• In the South Atlantic, federally managed species that are socio-economically important and likely to 

be common on sand shoals include king mackerel, Spanish mackerel, summer flounder, shark 

species, black sea bass, and possibly other hard bottom fish. 

• Most research on South Atlantic marine fish has focused on depth and latitude, whereas relationships 

with geomorphic features and oceanographic conditions are poorly known. Because of the lack of 

surveys over soft substrates, the differences in fish species composition between hard bottom and 

soft substrates has not been well documented for reef fish.

• Juvenile reef-associated fish do use unconsolidated sediments extensively, although research and 

monitoring specific to shoals is limited. Black sea bass, bank sea bass, and grunts have been 

documented in unconsolidated sediment habitats. Little is known about the use of sandy habitats by 

juvenile red snapper and lane snapper, which have been linked to those habitats in the Gulf of 

Mexico.

• Adult reef-associated fish that use natural and artificial structures likely forage in surrounding soft 

substrates, but the extent of this remains uncertain.

• The shallow water habitats of the coastal migratory pelagic species (Spanish and king mackerel) are 

primarily unstudied.
59

Figure 3-9. The Gulf Stream current depicted in red moving from southern Florida toward the 

northeast.

Source: Adapted from NASA Jet Propulsion Laboratory.

In regard to prey species, Okey et al. (2014) simulated changes in forage species, or species groups, that 

were identified through a South Atlantic Marine Bight "Ecopath with Ecosim" food web model. They 

tested the effect of changes in anchovies, Atlantic menhaden, Atlantic silverside, halfbeaks, mullets, 

sardines, scads, shad, thread herring, pelagic-oceanic planktivores, squid, and shrimp. The results showed 

Atlantic menhaden and squids had the largest effects on higher trophic levels, although the effects varied 

by predatory species (Okey et al. 2014). As an example, they found an increase in Atlantic menhaden was 

projected to strongly increase biomass of striped bass, bluefish, large coastal sharks, small coastal sharks, 

and highly migratory species.

The National Marine Fisheries Service (2017) provides a fisheries summary for the South Atlantic region, 

and we provide a synopsis of the report here. For commercial fisheries of the South Atlantic, key 

federally managed species include groupers, snappers, flounders, king mackerel, swordfish, and tuna. For 

recreational fisheries, federally managed species of economic importance include black sea bass, bluefish, 

dolphinfish, king mackerel, sharks, sheepshead porgy, and Spanish mackerel. Within individual states, 

additional federally managed species of economic importance include gray snapper, porgies (sheepshead), 

summer flounder, striped bass, and tilefish. As of 2018 in the South Atlantic, the stocks, or stock 

complexes, of red snapper, red porgy, red grouper, snowy grouper, Warsaw grouper, speckled hind, 

hogfish (southeast Florida), tilefish, and blueline tilefish are considered overfished, or overfishing is 

occurring (NOAA Fisheries 2019). The seasonality of reef fish spawning is highly variable, but peak 
60

spawning is usually April–August (Figure 3-10). The overlap of EFH with our South Atlantic study area 

is summarized in Table 3-3 and includes the broad snapper-grouper complex.

In addition to mapped EFH, the South Atlantic FMC has designated the federally managed dolphinfish 

and wahoo as having EFH that includes the Gulf Stream, Charleston Gyre, Florida Current, and pelagic 

Sargassum. These features are not within our specific study area and do not overlap with past sand 

dredging locations. Therefore, we not review dolphinfish and wahoo further.

Figure 3-10. Timing of spawning (gray) and peak spawning (black) for selected species in the 

snapper-group EFH group of the southeastern Atlantic Ocean of the US.

Source: Adapted from Farmer et al. (2017). Reproduced with open access permission from PlosOne.
61

Table 3-3. For federally managed species within the South Atlantic, the proportion of area 

designated as EFH within each study area.

Notes: Species with EFH overlapping with > 50% of the study area are bolded. Study area is defined in Figure 1-3. 

All life stages were included. NA = information not applicable

3.2.2 Snapper-Grouper Complex of the South Atlantic

Containing 55 species of snapper, grouper, and related species, the snapper-grouper complex (also known 

as “reef fish”) is most closely associated with hard bottom and artificial reefs in the South Atlantic. With 

hard bottom scattered from nearshore to the outer slope, the EFH designations include a broad geographic 

coverage.

Walsh et al. (2006) studied reef fish habitat use of unconsolidated sediment substrates by conducting 

trawl surveys offshore of Georgia. They classified 121 of 181 fish species as being a juvenile life stage, 

and they suggest that unconsolidated sediments of the continental shelf are important for early life stages 

of reef-associated species. In particular, Walsh et al. (2006) reported consistent occurrence of bank sea 

bass sand perch, and Stenotomous spp. (scup, porgy). Reef-associated species with irregular use of soft 

Fishery Management Council Species or EFH 

species group

Proportion of South 

Atlantic study area

designated as EFH

Fishbase description

of habitat

South Atlantic Snapper-grouper 0.95 reef-associated

South Atlantic Corals 0.08 NA

South Atlantic Spiny lobster 0.95 NA

South Atlantic Coastal migratory 

pelagics 0.48 -

South Atlantic - Spanish mackerel - pelagic

10–35 m

South Atlantic - King mackerel -

reef-associated

5–140 m

usually 5–15 m

South Atlantic - Cobia - reef-associated

0–1,200 m

Mid-Atlantic Atlantic butterfish 0.24

benthopelagic

15–420 m

usually ≤55 m

Mid-Atlantic Atlantic mackerel 0.07

pelagic

0–1,000 m

usually 0–200 m

Mid-Atlantic Atlantic surfclam 0.04 NA

Mid-Atlantic Black sea bass 0.15 reef-associated

≥1 m

Mid-Atlantic Bluefish 0.95 pelagic

0–200 m

Mid-Atlantic Longfin inshore squid 0.17 NA

Mid-Atlantic Northern shortfin squid 0.03 NA

Mid-Atlantic Ocean quahog 0.01 NA

Mid-Atlantic Scup 0.17 demersal

≥15 m

Mid-Atlantic Spiny dogfish 0.10

benthopelagic

0–1,460 m

usually 50–300 m

Mid-Atlantic Summer flounder 0.92

demersal

10–183 m

usually ≤37 m
62

sediments included black sea bass, snowy grouper, short bigeye, Atlantic bigyeye, and mutton snapper. In 

the sampling of 0–70 m depths, the most common federally managed species had the following depth and 

season associations:

• Bank sea bass were associated with 40–70 m depths during spring, 0–70 m depths during summer, 

and 40-70 m depths during winter. They were not observed in fall. Among those recorded, 

juveniles were common.

• Black sea bass were associated with 0–40 m depths during spring and 0–70 m depths during 

summer. They were not observed in fall or winter. Juveniles and adults were recorded.

• Stenotomus spp. (scup or porgy) were observed over 0–40 m depths during spring, 0–70 m during 

summer, and 20–40 m depths during fall and winter. Among those recorded, juveniles were 

common.

• Mutton snapper were only observed during summer, were observed over the entire 0–70 m 

depths, and were mostly composed of juveniles.

Zarillo et al. (2009) conducted trawl surveys within and adjacent to sand shoals offshore of Florida’s 

Atlantic Coast. The most common (≥ eight occurrences) federally managed snapper-grouper species were 

rock sea bass, porgy, juvenile grunt, and Atlantic spadefish. The next most common species (two to three

occurrences) were white grunt and black sea bass (Zarillo et al. 2009). Gilmore (2008) notes that goliath 

grouper have been observed in spawning aggregations on shoals or adjacent to shoals. From wide-ranging 

fish sampling, Miller and Richards (1980) note that subtropical reef-associated fish species extend in a 

narrow productive zone of 33–40 m depths from North Carolina to Florida. They suggested that both 

inshore and farther offshore cooling occurred in winter and limited species’ distributions. Their data 

showed catch of commercial hard bottom fish species occurred primarily at 33–41 m depths (69% of total 

weight) with 24–32 m depths having a high catch rate as well (16% of total weight).

Two separate stocks of black sea bass have been identified, with the population splitting into a MidAtlantic/northeast Atlantic population and a South Atlantic population that occurs south of Cape Hatteras, 

North Carolina (McCartney et al. 2013; Roy et al. 2012). In an extensive analysis of trap data, Bacheler 

and Ballenger (2015) found small and large black sea bass were similarly related to depth (peak CPUE at 

< 30 m) and bottom temperature (peaked at 12–15°C, but had a broad temperature range). However, the 

proportion of small individuals was greater in the most shallow waters. Although surveys were 

concentrated solely on hard bottom habitats (i.e., no locations surveyed were away from hard bottoms), 

black sea bass were not associated with rugosity, slope of slope, or available hard bottom maps (Bacheler 

and Ballenger 2015).

The red snapper has been poorly studied in the South Atlantic, as 94% of red snapper studies are from the 

GoM, and no research on juveniles has been published in the US Atlantic waters (Rindone et al. 2015). 

Regarding the nGoM, sand shoals and unconsolidated substrates with some structural complexity provide 

habitats where juvenile red snapper can be abundant (see Gulf of Mexico section above). In the South 

Atlantic, the red snapper fishery has been closed since 2010. From trap and camera data of hard bottom 

locations offshore of Florida and Georgia, Coggins Jr et al. (2014) found red snapper were related to 

depth and latitude. In their study, red snapper were estimated to be present at 45% of the sites surveyed, 

were most abundant at 20 m depths, and declined in abundance through 60 m depths. The predictive 

model showed probability of presence and abundance to be highest surrounding Cape Canaveral (and its 

shoals) and northward to the Florida-Georgia state boundary (Coggins Jr et al. 2014).

Fish habitat use of sand sediments adjacent to reef structures was examined offshore of southeast North 

Carolina (Rosemond et al. 2018). Rosemond et al. (2018) found that daytime fish habitat use of sand 

substrate declined in terms of abundance, species richness, and biomass as distance to reef increased up to 

a maximum of 90 m from the reef. Community composition changes were due to decreases of common

planktivores and increases in transient pelagic predators. Benthic carnivores included black sea bass, 
63

scup, and slippery dick (Rosemond et al. 2018). Regarding the effect of survey timing, Wenner (1983)

found twice as many reef-associated species were captured at night over sandy substrate compared to 

daytime surveys. Species such as tomtate moved away from live bottom habitats during night hours. 

Black sea bass have been primarily caught at night as well (Sedberry and Van Dolah 1984).

Bacheler et al. (2016) examined hard bottom fish in the South Atlantic, and the most common fish 

surveyed by video were white grunt, black sea bass, red snapper, red porgy, Vermilion snapper, Almaco 

jack, gray triggerfish, greater amberjack, scamp, lionfish, gag, gray snapper, and hogfish. All other 

species were observed in < 5% of surveys. Relationships among fish species presence were quantified 

with the variables of longitude/latitude, depth, amount of hard bottom present in videos, water clarity, and 

current direction in relation to the camera (Bacheler et al. 2016). Latitude/longitude (factor in 69% of fish 

species’ statistical models), depth (66%), and amount of hard bottom (72%) were most commonly factors 

in species’ distributions; currents (38%) and water clarity (22%) were less common factors (Bacheler et 

al. 2016). Of particular relevance to unconsolidated sediment habitats, the following trends were 

observed:

• Black sea bass were most common with depths of < 30 m, but were present in waters up to

80 m in depth.

• Red snapper were found at all depths surveyed (10–110 m) with higher abundances with 

< 60 m depths.

• White grunt were most common with 20–40 m depths and only ranged 10–60 m depths.

• Lane snapper were much more common with depths < 30 m but were present in depths up to 

60 m.

Spatial models were developed using only the latitude/longitude variables, as the other variables were not 

spatially explicit. The deviation explained varied widely from 4.2% to 75% of variation explained in fish 

species distribution (Bacheler et al. 2016).

Farmer et al. (2017) synthesized spawning reef fish locations and timing in the South Atlantic (Figure 

3-10, Table 3-4). Most of the variability in the prediction of spawning locations was explained by the 

temporal variables of month, year, and lunar phase. Red snapper had 5% of the variation explained by 

substrate curvature, and black sea bass were strongly related to temperature and latitude (58% deviance 

explained). White grunt spawning was related to latitude and depth (23% deviance explained).

Table 3-4. Summary statistics of spawning females of reef-associated species in the South 

Atlantic

Species Mean depth (range) Mean salinity 

(range)

Mean water 

temperature 

(range)

Black sea 

bass

25.4 m

(15–66 m) 

35.7 psu

(34–40 psu)

19.5°C

(11–27°C)

Red snapper 43.2 m

(23–66 m)

36.2 psu

(35–37 psu)

22.6°C

(17–28°C)

White grunt 33.3 m

(22–52 m)

36.1 psu

(35–37 psu)

23.7°C

(18–27°C)

Notes: Reported from Farmer et al. (2017).
64

3.2.3 Coastal Migratory Pelagics of the South Atlantic: Spanish Mackerel 

and King Mackerel

3.2.3.1 Spanish Mackerel

The Spanish mackerel is a piscivorous, pelagic species that commonly use depths of 10–35 m (Froese and 

Pauly 2018), although only a few studies have focused on them in the South Atlantic. As in the GoM

population, Spanish mackerel in the South Atlantic likely winter in south Florida. Schmidt et al. (1993)

showed the reproductive chronology of female Spanish mackerel, as sampled from Beaufort, North 

Carolina, to Riviera Beach, Florida. They found the spawning season ranged primarily from May–

August 31. Similarly, Spanish mackerel are common in the Chesapeake Bay from late April to early 

October (Chittenden et al. 1993). In the South Atlantic, the Marine Resources Monitoring, Assessment, 

and Prediction (MARMAP) program (Collins and Stender 1987) sampled ichthyoplankton in depth 

ranges spanning 9 to 3,490 m and found Spanish mackerel larvae in waters ranging 11–29 m in depth. In 

their limited sampling of Spanish mackerel larvae, there was no overlap with the smallest of king

mackerel larvae. No latitudinal changes in abundance were observed in the South Atlantic (Collins and 

Stender 1987). Adding to the sparse data on habitat use, larvae and juvenile Spanish mackerel have been 

captured at Breach Inlet near Charleston, South Carolina, and along the nearshore of North Carolina, 

South Carolina, and Georgia from May–October (Peters and Schmidt 1997). Studies of more specific 

South Atlantic habitat occurrence of Spanish mackerel juveniles and adults are lacking.

3.2.3.2 King Mackerel

The king mackerel is a piscivorous species that generally uses a depth range of 5–140 m (Froese and 

Pauly 2018). In their wintering grounds, Atlantic king mackerel overlap with GoM king mackerel 

beginning near St. Augustine, Florida, and extending southwest of Naples, Florida (Clardy 2008). Studies 

of more specific habitat use of king mackerel juveniles and adults are lacking.

King mackerel tagged near Ft. Pierce, Florida, provided evidence that king mackerel migrated northward 

from Florida as far as North Carolina (Sutter et al. 1991). Primarily supporting these results, Trent et al. 

(1987) examined CPUE of king mackerel from charter boats and did not observe a temperature-dependent 

migration. Instead, they showed the approximate peak captures as represented in Figure 3-11.

In the South Atlantic, serial spawning occurs from April through early October, with a peak in September 

(Collins and Stender 1987; Finucane et al. 1986; McEachran et al. 1980). Larvae have also been collected

in November in these waters (Collins and Stender 1987). Through the MARMAP program, king mackerel 

larvae were more abundant at depth ranges of 21–200 m compared to more shallow waters (only 2 of 175 

surveys in depths ≤ 20 m had king larvae). Larvae were also abundant between the latitudes of 32–33oN, 

which may be attributed to a region of upwelling produced by the “Charleston Bump” topographic ridge 

(Collins and Stender 1987).
65

Figure 3-11. King mackerel seasonality with dates matching to approximate locations depicted by 

Trent et al. (1987).
66

3.3 Greater Atlantic Fish Habitat Associations and Seasonality

3.3.1 Introduction to the Greater Atlantic Physical Setting and Fish

The Greater Atlantic region can be described in three major divisions: the Mid-Atlantic Bight (Cape 

Hatteras, North Carolina–New Jersey), Southern New England (New York–southern Massachusetts), and 

the Gulf of Maine (north Massachusetts–Maine) (Greene et al. 2010). Each division differs in their major 

features, oceanographic characteristics, and geological origins. As described by Greene et al. (2010) these 

features include:

1) Mid-Atlantic Bight: The Chesapeake Bay and Delaware Bay provide extensive estuarine habitats. 

Warm water intrusions from the Gulf Stream move across the shelf and creates upwelling that 

enhances productivity. A series of barrier island inlets also provide spawning and estuarine 

habitats.

2) Southern New England: Rivers are an important aspect of this geography because of their 

historical support of large populations of American shad, Atlantic salmon, and eel. Shallow 

estuaries and sounds are important as well as the shellfish industry.

3) Gulf of Maine: One of the most productive marine ecosystems on Earth. These waters are near 

both the Labrador Current to the north and the Gulf Stream to the south. When these currents 

meet, ideal conditions are met for productivity of phytoplankton.

In terms of temporal variability, the North Atlantic Oscillation Index and the Atlantic Mulidecadal 

Oscillation both drive SST dynamics at annual and decadal timeframes (Ecosystem Assessment Program 

2009). Climate change is affecting the present and future of fish in the Greater Atlantic. The Greater 

Atlantic is projected to undergo the most dramatic shifts in fish species compared to the South Atlantic 

and GoM (Morley et al. 2018). Kleisner et al. (2017) showed fall SSTs in the Greater Atlantic have 

increased since 1968, and water temperatures are projected to keep increasing. An analysis of select 

demersal and pelagic fish shows the thermal shift will result in species moving northward or to deeper 

waters, as some species lose habitat and the range of other species expands (Kleisner et al. 2017). For 

example, black sea bass are projected to lose habitat, while summer flounder are projected to gain habitat. 

Northern species, such as Atlantic cod, haddock, American plaice, and thorny skate are projected to lose 

habitat (Kleisner et al. 2017). The center of biomass for black sea bass and scup have also moved 

northward due to temperature changes (Bell et al. 2014). The effects of a warming ocean are important in 

the Greater Atlantic, but we do emphasize that our review is focused on species-habitat associations rather 

than any evaluation of future change.

In terms of sand resources, the Greater Atlantic region has cape-associated and sorted bedform shoals. 

Research has focused on various aspects of sand shoals in the Mid-Atlantic (Diaz et al. 2004; Maa et al. 

2004; Slacum Jr et al. 2010a). New England sand shoals have been recognized in the literature (Smith 

1969; Twichell 1983), but these shoals are rarely explicit in fisheries research. Sediments in shallow 

waters of the Greater Atlantic are diverse, ranging from silt to sand and gravel (Greene et al. 2010). Hard 

bottoms have a patchy distribution throughout much of the Greater Atlantic but are common in shallow 

waters of the Gulf of Maine (unpublished data, Matthew Poti, NOAA NCCOS).

The importance of prey species is highly varied in the Greater Atlantic. Bluefin tuna have been identified 

as feeding on species such as silver hake, Atlantic herring, and sand lance (Estrada et al. 2005). 

Phytoplankton, zooplankton, and benthic invertebrates have been proposed as important prey for fisheries 

(Ecosystem Assessment Program 2009). Bowman et al (2000) assessed stomach contents of 180 species 

and found common prey were sand lance, hakes, herrings, mackerels, butterfish, anchovies, scup, 

flatfishes, sculpins, longfin inshore squid, and northern shortfin squid.
67

The National Marine Fisheries Service (2017) provides a fisheries summary for the New England 

(Connecticut–Maine) and the Mid-Atlantic (Virginia–New York), and we provide a synopsis of the report 

here. For New England commercial fisheries, key federally managed fish are Atlantic herring, Atlantic 

mackerel, cod, haddock, various flounders, and monkfish (also referred to as goosefish). Although our 

focus is not on invertebrates, key federally managed species are quahog clam, sea scallop, and squid. For 

New England recreational fisheries, federally managed species of socioeconomic importance are Atlantic 

cod, Atlantic mackerel, bluefin tuna, bluefish, little tunny, scup, and summer flounder. Concerning MidAtlantic commercial fisheries, the key federally managed fish is summer flounder and key invertebrates 

are Atlantic surfclam, quahog clam, sea scallop, and squid. Concerning Mid-Atlantic recreational 

fisheries, key federally managed fish are black sea bass, bluefish, scup, summer flounder, and winter 

flounder.

The overlap of EFH with our Greater Atlantic study area is summarized in Table 3-5. As of 2018, 

overfishing is occurring for summer flounder in the Mid-Atlantic (NOAA Fisheries 2019). The following 

stocks, or stock complexes, have the status of being overfished or have overfishing occurring in New 

England (NOAA Fisheries 2019):

• Atlantic cod – Georges Bank

• Atlantic cod – Gulf of Maine

• Windowpane – Gulf of Maine/ Georges 

Bank

• Witch flounder

• Yellowtail flounder – Cape Cod/Gulf of 

Maine

• Yellowtail flounder – Georges Bank

• Yellowtail flounder – Southern New 

England/Mid-Atlantic

• Thorny skate – Gulf of Maine

• Atlantic halibut

• Atlantic salmon

• Atlantic wolfish

• Ocean pout

• Winter flounder – Southern New 

England

• Red hake – Southern Georges 

Bank/Mid-Atlantic

• Atlantic mackerel – Gulf of Maine/ Cape 

Hatteras

Given that the Greater Atlantic has 38 federally managed species and each have their individually 

designated EFH, we selected a subset of these to review the literature on habitat associations. We selected 

species based on the amount of overlap between a species' EFH designation and our study area, plus the 

commercial economic value of species. Of the highest ranked species, we selected:

• Atlantic herring

• Summer flounder

• Monkfish

• Black sea bass

• Atlantic surfclam

• Longfin inshore squid
68

3.3.2 Review of Select Finfish Species in the Greater Atlantic

3.3.2.1 Atlantic Herring

Pelagic fish are not well sampled with typical bottom trawl surveys (Wang et al. 2018); therefore, habitat 

relationships of these fish are poorly known. Wang et al. (2018) used fishery-dependent data to 

investigate Atlantic herring in the Greater Atlantic. Their presence-only modeling produced monthly 

distribution models, and showed the following habitat relationships with Atlantic herring:

• The effect of environmental variables on Atlantic herring differed by month.

• SST of the current and previous month had the strongest influence overall. In particular, these 

variables accounted for 78% of the model explanatory power in spring (March–May).

• Chlorophyll-a concentration most influenced Atlantic herring in June–August, and chlorophyll-a

had a moderate influence September–November.

• Bathymetry accounted for a moderate level of explanatory power overall. Bathymetry was most 

influential in the winter and spring when the species was in deeper waters.

• Geomorphology habitat variables were not tested.

• Offshore of New Jersey, Palamara et al. (2012) investigated Atlantic herring habitat with a 

multivariate approach. They found Atlantic herring (winter to spring seasons) were related to the 

combined environmental variables of more shallow depths, cooler SST anomalies, cooler bottom 

temperatures, coarse sediment grain size, and a remote sensing variable similar to chlorophyll.

• Atlantic herring spawn in the Gulf of Maine July–November; spawning then initiates 

progressively to the south (Sinclair and Tremblay 1984). The species tends to be located 

relatively close to the shoreline in the spring, farthest inshore in summer, and are found over a

wide-ranging area in the fall/winter (Wang et al. 2018).

Table 3-5. For federally managed species within the Greater Atlantic, the proportion of area 

designated as EFH within the study area.

Fishery Management 

Council

Species or EFH species 

group

Proportion of Greater 

Atlantic study area

designated as EFH

Fishbase description of 

habitat

Mid-Atlantic Atlantic butterfish 0.87

benthopelagic

15–420 m

usually ≤55 m

Mid-Atlantic Atlantic mackerel 0.87

pelagic

0–1,000 m

usually 0–200 m

Mid-Atlantic Atlantic surfclam* 0.64 NA

Mid-Atlantic Black sea bass* 0.80 reef-associated

1 m–unknown

Mid-Atlantic Bluefish 0.82 pelagic

0–200 m

Mid-Atlantic Golden tilefish 0.00 demersal

80–540 m

Mid-Atlantic Longfin inshore squid* 0.79 NA

Mid-Atlantic Northern shortfin squid 0.24 NA

Mid-Atlantic Ocean quahog 0.42 NA

Mid-Atlantic Scup 0.89 demersal

15 m–unknown 

Mid-Atlantic Spiny dogfish 0.87

benthopelagic

0–1,460 m

usually 50–300 m
69

Fishery Management 

Council

Species or EFH species 

group

Proportion of Greater 

Atlantic study area

designated as EFH

Fishbase description of 

habitat

Mid-Atlantic Summer flounder* 0.92

demersal

10–183 m

usually ≤37 m

New England Acadian redfish 0.02

pelagic

100–1,000 m

usually 100–500 m

New England American plaice 0.05

demersal

10–3,000 m

usually 90–250 m

New England Atlantic cod 0.54

benthopelagic

0–600 m

usually 150–200 m

New England Atlantic halibut 0.00 demersal

50–2,000 m

New England

Atlantic herring* 0.86 benthopelagic

0–364

usually 0–200 m 

New England

Atlantic wolffish 0.11 demersal

1–600 m

18–110 m

New England

Barndoor skate 0.03 demersal

0–750 m

usually 0–150 m

New England

Clearnose skate 0.42 demersal

0–330 m

usually 0–50 m

New England Haddock 0.30

demersal

10–450 m

usually 10–200 m

New England Little skate 0.55 demersal

0–329 m

New England Monkfish* 0.81 bathydemersal

20–1,000 m

New England Ocean pout 0.34 demersal

0–388 m

New England Offshore hake 0.01

bathydemersal

80–1,170 m

usually 160–640 m

New England Pollock 0.20 demersal

37–364 m

New England Red hake 0.67

demersal

35–1,152 m

usually 110–130 m

New England Rosette skate 0.00 reef-associated

55–530 m

New England Sea scallop 0.61 NA

New England Silver hake 0.35 demersal

55–914 m

New England Smooth skate 0.00 bathydemersal

46–914 m

New England Thorny skate 0.01

demersal

20–1,000 m

usually 50–100 m

New England White hake 0.16

demersal

100–1,000 m

usually 100–247 m
70

Fishery Management 

Council

Species or EFH species 

group

Proportion of Greater 

Atlantic study area

designated as EFH

Fishbase description of 

habitat

New England Windowpane flounder 0.89 demersal

55–73 m

New England Winter flounder 0.43 demersal

5–143 m

New England Winter skate 0.62 demersal

0–120 m

New England Witch flounder 0.54

demersal

18–1,570 m

usually 45–366 m

New England Yellowtail flounder 0.67

demersal

27–364 m

usually 36–91 m

Notes: * = Literature reviews were conducted on these species

Those overlapping with > 50% of the study area are bolded. Study area is defined in Figure 1-4. Includes all life 

stages.

3.3.2.2 Summer Flounder

In a Mid-Atlantic Bight study, Manderson et al. (2011) investigated relationships of summer flounder 

with a plethora of habitat characteristics. In comparison to other species, they found summer flounder 

were more responsive to benthic characteristics and prey abundance. Manderson et al. (2011) found the 

following habitat associations:

• Peak summer flounder abundance with a bottom temperature of 7–12.5°C. Summer flounder were 

rare in waters of < 6°C and temperature selection did not change with season.

• Highest abundance with depths of < 150 m (winter–spring).

• A positive relationship with standard deviation of depth (winter–spring); in our study, shoals have 

a high standard deviation of depth compared to flat substrates (also similar to rugosity measures).

• A positive relationship with a divergence index (i.e., vertical water velocity), which indicates 

potential upwelling. The authors suggest that upwelling and enhanced productivity near estuarine 

plumes entering the ocean may serve as valuable spawning sites.

• A positive relationship with Simpson's potential energy anomaly (strength of stratification) .

• High summer flounder abundance with a frontal index.

• A negative correlation with chlorophyll-a (winter–spring) and a positive correlation with 

chlorophyll-a in the fall.

• Summer flounder were positively correlated with a potential prey, squid, in the winter–spring, but 

a negative correlation was found in the fall.

Offshore of New Jersey, Palamara et al. (2012) investigated summer flounder habitat with a multivariate 

approach. They found summer flounder (winter to spring seasons) were related to the combined 

environmental variables of more shallow depths, cooler SST anomalies, cooler bottom temperatures, 

coarse sediment grain size, and a remote sensing variable similar to chlorophyll. In waters near Maryland, 

Slacum Jr et al. (2008) focused on substrate associations with summer flounder and found poor 

correlations. Summer flounder were often, but inconsistently, found in the 1–20 m depth range, which 

was often represented as the troughs of sand shoals (Slacum Jr et al. 2008).

Summer flounder spawning peaks in the fall for the Mid-Atlantic Bight waters, and spawning ranges from 

September–January 31 (Able et al. 1990). Able et al. (1990) found larvae abundance peaked in 

November, and Smith (1973) reported spawning waters were 12–19°C. Migration corridors between 

estuaries and the continental shelf may be important. Recent analyses have shown summer flounder 

juveniles (year 2 and 3) emigrate to the shelf in the fall and return to the estuary in the spring (Sackett et
71

al. 2007). Even young-of-the-year summer flounder migrate out of estuaries in the late fall to the shallow 

continental shelf (Able et al. 1990).

3.3.2.3 Monkfish

The monkfish, also referred to as goosefish, is a benthic species of the Greater Atlantic (Armstrong et al. 

1992). Monkfish ecology has been poorly studied (Richards et al. 2008), but some insights can be gained 

from known prey species, depth, and water temperatures used by the species. Teleost fish are an 

important prey across all monkfish size classes, but smaller size classes frequently prey on crustaceans 

(Armstrong et al. 1996). Sand lance, long-finned inshore squid, red hake, and little skate (for the largest 

monkfish) were the primary fish species fed on across all size classes (Armstrong et al. 1996). Richards et 

al. (2008) provides a detailed analysis of monkfish in relation to depths and seasonal distributions. They 

found the following:

• Across all seasons, 90% of monkfish in trawl surveys were in depths of 32–339 m (surveyed 

waters included 24–346 m depths).

• Winter and spring monkfish were in relatively deeper water; in the summer, they were equally 

widespread across depths.

• Fall distribution of monkfish showed no depth preference in the North Atlantic, but were in 

relatively deeper water in the Mid-Atlantic Bight.

• Compared to depths, monkfish were more selective of bottom temperatures. Across all seasons, 

90% of monkfish were found with bottom temperatures of 4.5–13.0°C (surveyed waters included 

3.8–19.3°C). This resulted in their distribution within relatively warmer waters for winter/spring 

and relatively cooler waters in summer/fall.

3.3.2.4 Black Sea Bass

Black sea bass is a demersal, temperate reef-associated species that associates with hard bottom structures 

such as rock outcroppings, reefs, and artificial reefs (Fabrizio et al. 2013). The black sea bass north of 

Cape Hatteras, North Carolina, is a separate stock from the other populations in the Atlantic (McCartney 

et al. 2013; Roy et al. 2012). The species overwinters in waters of the middle- to outer continental shelf 

and then migrates inshore as temperatures increase in the spring (Moser and Shepherd 2009; Musick and 

Mercer 1977). In the Mid-Atlantic, black sea bass spawn April–October ranging from depths of 18–45 m 

(Musick and Mercer 1977), while the southern New England population spawns May–June (Steimle 

1999). The Greater Atlantic black sea bass population has been shown to be moving northward in 

response to warming water temperatures (Bell et al. 2014), and their range is projected to contract in the 

future (Kleisner et al. 2017).

Miller et al. (2016a) studied black sea bass in relation to oceanographic variables in the Mid-Atlantic 

Bight and southern New England. Juveniles and adults had similar habitat characteristics with the 

following results:

• Juveniles and adults together selected salinities ranging 32.6–35 psu.

• A positive relationship was found with temperature and juveniles.

• Adults black sea bass selected bottom temperatures of > 7.9°C.

Fabrizio et al. (2013) studied black sea bass near a dredge disposal site. They found black sea bass started 

to move away from an inshore site beginning in June. By early November, 74% of individuals had 

presumably moved to offshore locations; this generally coincided with the time when shallow, inshore 

waters were becoming cooler than offshore waters (Fabrizio et al. 2013). Habitat use of black sea bass 

showed a selection for coarse substrate material, depth, and variance of the slope (i.e., substrate 

complexity) (Fabrizio et al. 2013). However, they found that black sea bass did not select for substrates, 

or substrate complexity, when waters were > 27.5 m in depth.
72

3.3.2.5 Atlantic Surfclam

The Atlantic surfclam is a filter-feeding bivalve that is common in sandy sediments of the northeast 

Atlantic (Weinberg 2005), although most of its habitat associations are related to oceanographic 

conditions. Changes in the distribution of Atlantic surfclam have been attributed to climate change and 

the associated increase in bottom temperatures (Weinberg 2005; Weinberg et al. 2002). Atlantic surfclam 

are typically in waters < 50 in depth (Weinberg and Helser 1996). A study in the southern portion of their 

range (offshore of Virginia) showed the species declining, but the decline was lessened in deeper waters 

(Weinberg 2005). The authors suggest this is because warming was more severe in shallow waters. 

Likewise, a physiology-based simulation study for the Mid-Atlantic Bight showed that years of above 

average water conditions (+2°C) would result in starvation mortality and declines in the population 

(Narváez et al. 2015). Overall, the southern portion of the Atlantic surfclam range is likely the most atrisk. However, a study of Georges Bank (offshore Massachusetts) also shows a movement into deeper 

waters (Powell et al. 2017).

In a study encompassing a shoal complex (Beach Haven Ridge) offshore of New Jersey, Savage et al. 

(1976) found Atlantic surfclam had a much greater abundance in troughs compared to shoal crests. We do 

note that substantial numbers were still found on crests (Savage 1976). In an experimental study of larvae 

settlement, Snelgrove et al. (1998) found larvae selected sand substrates over mud when water flow was 

present. In still water experiments, Atlantic surfclam larvae had inconsistent selection, possibly because 

their movement was limited (Snelgrove et al. 1998).

3.3.2.6 Longfin Inshore Squid

Longfin inshore squid (hereby, “squid”) migrate between lower latitudes or farther offshore wintering 

habitats to higher latitudes and inshore waters during summer (Manderson et al. 2011). A detailed 

assessment of these shifts in abundance was described by Hatfield and Cadrin (2002), who provides 

detailed information on squid frequency by depth zones and broad latitudinal zones. In a comprehensive 

study of the Mid-Atlantic Bight, Manderson et al. (2011) quantified habitat relationships of longfin 

inshore squid. They found squid distribution was best explained by remote sensing oceanographic data 

followed by in situ oceanographic variables and then benthic habitat data (Manderson et al. 2011). The 

habitat variables that explained > 5% of the null deviance are given below with a qualitative interpretation 

of the relationship (1 being the most important variable, 5 the least important):

1) Squid were most abundant with bottom temperatures > 7°C (fall–spring).

2) Squid were most abundant at ≤ 200 m depths (winter–spring).

3) A positive relationship with standard deviation of depth was found (winter–spring). In our study, 

shoals have a high standard deviation of depth compared to flat substrates.

4) Squid were related to water mass classifications (unsupervised classification characterized by 

moderate temperature, salinity, and primary productivity over intermediate depths).

5) Squid were negatively related to Simpson's potential energy anomaly (strength of stratification) in 

the fall.
73

3.4 Shark Habitat Associations

3.4.1 Introduction to Sharks

As predators, sharks are vulnerable to overfishing of prey species, mortality as part of fisheries bycatch, 

and direct harvests (Musick et al. 2000). Shark populations are particularly vulnerable because of their 

natural history characteristics of having a late age to maturity and low fecundity (Musick et al. 2000). In a 

study including the Atlantic Ocean of North America, GoM, and Caribbean Sea, Baum et al. (2003) found 

that all coastal and oceanic shark species recorded in longline fisheries (except makos) had decline by 

> 50% in the last 8 to 15 years. For commercial fisheries, the blacktip shark and sandbar shark are the 

most valuable (Castro 1996), although the sandbar shark fishery is now closed. Other shark species are 

harvested, but the value of individual species are not well documented.

Generally, sharks can be divided into oceanic and coastal species. The coastal species have a relatively 

high proportion of their EFH designations in our study areas (waters ≤ 50 m), while more oceanic species 

have a lower proportion of their distribution in the study areas (Table 3-6). Oceanic species also tend to 

be larger. Furthermore, sharks are classified into those that are reef-associated, pelagic, demersal, or 

benthopelagic (Table 3-6). We note that the Fishbase classifications of "reef-associated" is particularly 

broad; species like blacknose shark, spinner shark, and blacktip shark are not strongly associated with 

reefs in the Atlantic. Benthopelagic species are defined as those that live and feed near the bottom, in

midwaters, or near the surface (Froese and Pauly 2018). Although our focus is on shark-habitat 

relationships, we note that density dependence can regulate shark populations; both positive and negative 

interactions occur among shark species (Peterson et al. 2017). Importantly, recent studies have shown that 

sharks are not simply transient in nature and often reside in specific waters for several weeks or months at 

a time (Conrath and Musick 2008; Haulsee et al. 2018; Reyier et al. 2014); this has even been 

demonstrated for highly pelagic species (Lea et al. 2015).

Key Points (Gaps are in italics)

• Shark species can be categorized into demersal, pelagic, benthopelagic, and reef-associated, as 

well as coastal or oceanic pelagic.

• Commonly derived habitat associations of sharks include depth, water temperature, chlorophyll, 

dissolved oxygen, and salinity. Although infrequently tested, sharks are also associated with SST 

gradients, fronts, and eddies. In turn, these oceanic features are likely related to prey, but this 

connection has been poorly studied. 

• Sharks may migrate vast distances but use relatively localized areas, where they either stopover 

briefly during migration or reside for weeks to months at a time. In case studies, migratory and 

wintering areas are used consistently, but our knowledge of such areas among shark species is 

sparse.

• Sharks may interact with sand shoals because of their narrow migration paths along nearshore 

waters. 

• Waters near Cape Hatteras, North Carolina, and Cape Canaveral, Florida, are particularly 

important nearshore areas for a variety of shark species. These locations offer the northernmost 

waters where temperate and subtropical temperatures, respectively, are consistently maintained. 
74

Table 3-6. For federally managed shark species, the proportion of area designated as EFH within 

each study area.

Species

Proportion of GoM

study area

designated as EFH

Proportion of 

South Atlantic 

study area

designated as EFH

Proportion of 

Greater Atlantic 

study area

designated as EFH

Fishbase description 

of habitat

Angel shark 0.03 0.11 0.48

Bathydemersal

1–1,375 m

usually ≤128 m

Atlantic 

sharpnose 

shark

1.00 0.97 0.24

Demersal

0–280 m

usually 0–10 m

Basking 

shark* 0.00 0.05 0.49 Pelagic0–2,000m

Bignose shark 0.02 0.17 0.16

Reef-associated

12-810 m

usually 80–220 m

Bigeye 

thresher 0.00 0.00 0.00 Pelagic-oceanic0–730 m

Blacknose 

shark 0.76 0.66 0.00

Reef-asscoiated

9–64 m

usually ≥ 9 m

Blacktip shark 0.58 0.61 0.04

Reef-associated

0–100 m

usually 0–30 m

Blue shark 0.00 0.07 0.55

Pelagic

1–1,000 m

usually 1–220 m

Bonnethead 

shark 0.15 0.27 0.00

Reef-associated

10–80 m

usually 10–25 m

Bull shark 0.74 0.32 0.00

Reef-associated

1–152 m

usually 1–30 m

Caribbean 

reef shark 0.03 0.04 0.00

Reef-associated

1–65 m

usually 1–35 m

Common 

Thresher 

shark

0.00 0.35 0.82

Pelagic

0–650 m

usually 0–200 m

Dusky shark 0.04 0.75 0.88

Reef-associated

0–400 m

usually 200–400 m

Finetooth 

shark 0.23 0.33 0.00 Demersal≤10 m

Great 

hammerhead 

shark

0.72 0.72 0.24

Pelagic

1–300 m

usually 1–100 m

Lemon shark 0.48 0.23 0.00 Reef-associated

0–92 m

Longfin mako 0.00 0.00 0.00 NA

Night shark 0.00 0.00 0.00

benthopelagic

0–600 m

50–100 m

Nurse shark 0.45 0.26 0.00

reef-associated

0–130 m

1–35 m

Oceanic 

whitetip shark 0.00 0.00 0.00

pelagic

0–230 m

usually 0–152 m
75

Species

Proportion of GoM

study area

designated as EFH

Proportion of 

South Atlantic 

study area

designated as EFH

Proportion of 

Greater Atlantic 

study area

designated as EFH

Fishbase description 

of habitat

Porbeagle 

shark 0.00 0.01 0.10 pelagic0–715 m

Sandbar shark 0.43 0.93 0.86

benthopelagic

0–500 m

usually 20–65 m

Sand tiger 

shark 0.00 0.49 0.55

reef-associated

1–191 m

usually 15–25 m

Scalloped 

hammerhead 

shark

0.64 0.93 0.71

pelagic

0–1,000 m

usually 0–25 m

Shortfin mako 

shark 0.00 0.12 0.81

pelagic

0–750 m

usually 100–150 m

Silky shark 0.21 0.66 0.16

reef-associated

0–4,000 m

usually 0–500 m

Spinner shark 0.71 0.70 0.18 reef-associated

0–100 m

Tiger shark 0.49 0.97 0.82

benthopelagic

0–800 m

usually 0–140 m

Whale shark 0.04 0.00 0.00

pelagic

0–1,928

usually 0–100 m

White shark 0.16 0.44 0.68

pelagic

0–1,200 m

usually 0–250 m

Notes: * The GoM and South Atlantic both extend through the Florida Keys. The Greater Atlantic extends from Maine 

to Cape Hatteras, North Carolina.

Those overlapping with > 50% of the study area are bolded. Study areas are defined in Figures 1-2, 1-3, and 1-4. All 

life stages were included.

Because our focus is on offshore marine habitats, we do not directly address the rivers, estuaries, and bays 

that commonly act as nursey habitat for juvenile sharks (Heupel et al. 2007). For further information on 

these topics, resources are available for the GoM and Atlantic coasts (e.g., Curtis et al. 2013; Froeschke et 

al. 2010; Heupel et al. 2007; McCallister et al. 2013; Ulrich et al. 2007).

3.4.2 GoM Sharks

Of the 30 highly migratory Atlantic shark species with designated EFH, seven species had EFH 

overlapping with > 50% of our GoM study area (Table 3-6). These species were the Atlantic sharpnose, 

blacknose, blacktip, bull, great hammerhead, scalloped hammerhead, and spinner sharks. These species 

commonly overlapped with sand dredging locations offshore of Louisiana and Florida. Additionally, 

lemon, nurse, sandbar, and tiger sharks had EFH overlapping with 40–50% of the study area, and EFH 

overlap for them was primarily on the western shelf of Florida. Eight shark species’ EFH designations 

had no overlap with the GoM study area, and we did not consider these species further for the GoM.

Below, we synthesize species-habitat relationships of relevant shark species.

Drymon et al. (2013) tested the relationships of shark abundance (CPUE) with depth, water temperature, 

chlorophyll, dissolved oxygen, salinity, crustacean biomass, and fish biomass over the entire nGoM. They 

found blacknose shark distribution was best explained by temperature, although temperature was 

negatively correlated with depth; this generally supports a depth preference of 10–30 m for blacknose 
76

shark (Drymon et al. 2013; Drymon et al. 2010). Atlantic sharpnose shark distribution was best correlated 

with chorlophyll concentration, and blacktip shark distribution was best explained by crustacean biomass 

(Drymon et al. 2013). However, blacktip sharks prey primarily on fish, so the relationship was likely a 

proxy for another environmental or biotic factor (Drymon et al. 2013). They also found Atlantic 

sharpnose and spinner sharks were negatively related to dissolved oxygen for unknown reasons. Spinner 

shark were correlated with a combination of depth and chlorophyll. Bull sharks were not well correlated 

with the variables tested.

Drymon et al. (2010) examined the relationship between shark species CPUE and water depth (Table 

3-7). Additional findings included:

• Atlantic sharpnose sharks were more abundant at 10–29.9 m depths compared to depths of 

> 30 m.

• Atlantic sharpnose females were disproportionately more abundant than males in waters > 30 m 

depth, whereas males were more common in waters of 0–9.9 m depths. Likewise, Parsons and 

Hoffmayer (2005) captured 718 males and only 9 female sharpnose sharks in shallow waters, 

presumably because females do not use shallow waters for mating or pupping.

• Blacknose shark males were disproportionately more abundant than females in waters of 0–

29.9 m depths.

• Blacktip sharks were more abundant at 0–9.9 m depths compared to waters of > 30 m depths. 

These sharks were biased toward mature females at 0–29.9 m compared to depths of > 30 m.

Data for other species were not robust enough for statistical analyses, but the authors noted that scalloped 

hammerhead, sandbar, and silky sharks were rarely encountered at shallow or mid depths but were found

in waters with depths of > 30 m. Finetooth sharks were mostly observed at shallow depths. Of species 

caught in nearshore surveys (in the vicinity of classified sand shoals), 10 of 12 species occurred during 

the spring, summer, and fall. Bonnethead and nurse sharks were absent during the spring months. Peak 

periods for Atlantic sharpnose, finetooth, and spinner sharks occurred during the fall (Drymon et al. 

2010).
77

Table 3-7. Reported CPUE (sharks hooks-100 h-1) across depth categories (range 2–366 m) for 

sharks on GoM surveys 1995–2008.

Species Depth:

0–9.9 m

Depth:

10–29.9 m

Depth:

> 30 m

Atlantic sharpnose shark 2.01 2.82 5.22

Bignose shark 0 0 0.01

Blacknose shark 0.77 2.11 0.51

Blacktip shark 1.36 1.01 0.53

Bonnethead shark 0.01 0.01 0.01

Bull shark 0.33 0.15 0.08

Dusky shark 0 0 0.01

Finetooth shark 0.24 0.02 0

Great hammerhead 0.04 0.08 0

Night shark 0 0 0.01

Nurse shark 0.01 0.07 0.02

Sandbar shark 0.01 0.04 0.22

Scalloped hammerhead 0.02 0.06 0.22

Shortfin mako 0 0 0.01

Silky shark 0 0 0.11

Spinner shark 0.31 0.36 0.23

Tiger shark 0.02 0.1 0.06

Only sharks with designated EFH are shown. Source: Modified based 

on information from Drymon et al. (2010).

Gruss et al. (2018) grouped all large coastal sharks together (sandbar, blacktip, silky, tiger, bull, spinner, 

lemon, nurse, smooth hammerhead, scalloped hammerhead, and great hammerhead) and found the 

following habitat relationships for nGoM waters offshore of Florida:

• Peak probability of presence occurred at depths of 75–150 m.

• Probability of large shark occurrence decreased as distance from shoreline increased.

• Peak probability of occurrence occurred at 15–22°C.

• Bottom salinity differences of < 2 psu were identified as having an extremely large effect on shark 

distribution for unknown reasons.

In the same study, an analysis of small coastal sharks as a group (Atlantic sharpnose, blacknose, finetooth, 

and bonnethead) showed the following relationships (Grüss et al. 2018):

• High probability of presence at ≤ 50 m of depth

• High probability of presence with lower chlorophyll concentration

• High probability of presence with surface salinity > 35 psu

Wells et al. (2018) tracked scalloped hammerheads throughout the GoM, and they selected for relatively 

high salinity waters (~35 psu), depths of < 1,500 m, less chlorophyll concentration, relatively high sea 

surface height anomalies, and for waters within 20 km of artificial reef or natural hard bottom. In their 

study, artificial reefs included oil and gas platforms, and the substrate variables had a much stronger 

influence than the oceanographic variables (e.g., SST was not a factor). Movements of scalloped 

hammerheads showed discrete areas were used for foraging and only a few movements were purely 
78

transitory (Wells et al. 2018). In their study, the 50% kernel density home ranges described overlap with 

Ship Shoal, St. Bernard Shoals, and other shoals near Mississippi and Alabama.

Dusky sharks captured and tagged near the Mississippi River Delta moved 9–31 km per day but did not 

appear to track chlorophyll concentrations (Hoffmayer et al. 2014). The depth of dusky shark individuals 

within the water column ranged from 0–573 m, with high frequency of use for 20–50 m depths in the 

water column. The maps generally showed dusky sharks using waters > 50 m in total depth, although 

those measures were not reported. Temperatures of the water column utilized by dusky sharks ranged 

from 9–32°C, with 24–26°C being the most selected (Hoffmayer et al. 2014).

Subadult bull sharks offshore of Louisiana and Florida were initially captured in water depths ranging 5–

96 m with a mean depth of 12 ± 14 m (Carlson et al. 2010). Some sharks remained in the general area of 

capture, while others migrated long distances (range of daily movements of individuals: 0.1–27 km). 

Water temperature used by bull sharks ranged from 16 to > 32°C, with a peak at 26–33°C. Water depths 

of bull shark ranged from 2 to > 50 m. Of 15 bull sharks analyzed, five spent all of their time at < 20 m 

depths of the water column, although specific depths of the location were not reported.

3.4.3 South Atlantic Sharks

Of the 30 shark species with designated EFH, 10 species had EFH overlapping with > 50% of the South 

Atlantic study area (Table 3-6). These species were Atlantic sharpnose, blacknose, blacktip, dusky, great 

hammerhead, sandbar, scalloped hammerhead, silky, spinner, and tiger shark. They were strongly 

associated with South Atlantic waters, where sand dredging has occurred. Notably, finetooth shark EFH 

overlapped by only 33% of the study area, but the overlap occurred in locations with past dredging 

events. Bull, common thresher, sand tiger, and white sharks had EFH overlapping with 30–50% of the 

study area, and these areas commonly overlapped with past sand dredging events. Of the remaining shark 

species, nurse and lemon sharks are geographically limited to Florida, but overlap strongly with the study 

area there. Because of the highly migratory nature of sharks moving between the Greater Atlantic and 

South Atlantic regions, the section below on Greater Atlantic sharks covers migratory movement and 

wintering studies that overlap both the South Atlantic and Greater Atlantic regions.

Studies conducted in the shallow waters offshore of North Carolina (Thorpe et al. 2004) and South 

Carolina (Ulrich et al. 2007) investigated waters of 3–15 m depths and found the following federally 

managed shark species to be common:

• Atlantic sharpnose

• Blacknose

• Blacktip

• Bonnethead

• Finetooth

• Sandbar

• Spinner

In a study of tiger, blue, shortfin mako, and great hammerhead sharks, Queiroz et al. (2016) found 

predictable hotspots of high habitat use based on a coarse selection for SST and a fine-scale selection for 

strong SST/productivity gradients (i.e., fronts). Blue and shortfin mako sharks were wide-ranging along 

the North American, Atlantic Coast in spring/summer, but were primarily found in waters near Florida 

and deep offshore waters of the central Atlantic during the fall and winter. Hammerhead sharks remained 

on the continental shelf year-round, and tiger sharks used the Gulf Stream during the warmer months. 

Specific aggregations of multiple species highlighted use of the Gulf Stream and the North Atlantic 

Current/Labrador Current convergence zone; the low productivity Sargasso Sea was generally absent of 

locations.
79

At hard bottom survey sites, Bacheler et al. (2016) observed the following depth associations:

• Atlantic sharpnose shark had no association with depth and were present at 10–60 m depths.

• Tiger shark showed no association with depth and were present at 10–79 m depths.

• Sandbar shark had no association with depth and spanned 20–69 m depths.

• Nurse shark were most common at depths of 10–19 m and declined in occurrence in deeper water 

with a maximum depth of 59 m.

Cape Canaveral waters are considered a winter nursery area for juvenile lemon sharks (Reyier et al. 2008; 

Reyier et al. 2014). These sharks aggregate with densities as high as 21.8 sharks km-1 of survey in the 

waters near Cape Canaveral and its associated shoals (Reyier et al. 2008). Monitoring the movements of 

54 juvenile lemon sharks with a telemetry array, Reyier et al. (2014) observed the following:

• Strong site fidelity of individuals at Cape Canaveral spanning November 23–February 28 with

some movements to another nearby aggregation.

• Juvenile lemon sharks inhabited waters ranging 12–30°C, but spent >70% of their time at 15–

20°C.

• Greater abundance was observed with shorter day lengths and cooler water temperatures, which 

suggests Cape Canaveral is a warm water refuge in the region. During one cold spell when water 

temperature averaged < 16°C, individuals temporarily moved southward 62–191 km.

• Sand shoals were observed to deflect south-flowing nearshore currents to the east, which allowed 

warmer north-flowing currents to flow to the nearshore. Water temperatures were 2–3°C warmer 

because of this phenomenon.

• During late February through April, juvenile lemon sharks migrated northward to waters near 

Georgia and Charleston Harbor, South Carolina.

• In addition to juveniles, Cape Canaveral monitoring has observed 60 tagged adult lemon sharks 

migrating through in spring and several adults that remained into the summer.

Overall, sand shoals are thought to be either a refuge from predators or a productive feeding ground for 

sharks in the Cape Canaveral region (Reyier et al. 2008; Reyier et al. 2014). Observations of juvenile 

lemon sharks offshore of Brazil showed foraging by “substrate inspection” near rocky and reef bottoms, 

whereas adults foraged for sardines (Garla et al. 2017). Such behavior may explain the association with 

Cape Canaveral’s shoals. In addition to lemon sharks, the nearshore shoal habitats surrounding Cape 

Canaveral appear to be nursery areas for scalloped hammerhead (Adams and Paperno 2007). For 

scalloped hammerheads, Adams and Paperno (2007) found neonates in water depths of 3.8–9.7 m 

from late May through June. From February to June, they also captured juvenile nurse sharks, 

juvenile blacktip sharks, neonate/juvenile/adult Atlantic sharpnose, and juvenile/adult bonnetheads. 

The authors suggest that the shallow waters may protect these sharks from large predators (Adams 

and Paperno 2007).

Sandbar sharks are common from Long Island to West Palm Beach, Florida, during the summer and range

from the Carolinas to the southern tip of Florida during winter (Springer 1960). Conrath and Musick

(2008) tracked sharks offshore of eastern Virginia during the summer, and all seven individuals wintered 

offshore of North Carolina. The authors suggest that central North Carolina could be important waters for 

wintering sharks because of its proximity to the warm Gulf Stream. Conrath and Musick (2008) reported 

the following habitat associations:

• During the summer, 80% of sandbar shark locations in the water column were < 12 m in depth 

(range of 0–24 m), but winter observations ranged 0–172 m. Total depth of the water column was 

not reported.
80

• Water temperatures that sharks experienced ranged from 10–26°C during winter with peak use at 

18–22°C.

• During summer, sharks tended to be in waters of 20–28°C.

3.4.4 Greater Atlantic Sharks

Of the 30 shark species with designated EFH, nine species had EFH overlapping with > 50% of our 

Greater Atlantic study area (Table 3-6). These species were blue, common thresher, dusky, sandbar, sand 

tiger, scalloped hammerhead, shortfin mako, tiger, and white sharks. Because current sand dredging is 

limited to the Mid-Atlantic, blue shark EFH does not overlap with current sand dredging leases. In 

addition, angel, Atlantic sharpnose, great hammerhead, and spinner shark EFH did overlap with past sand 

and gravel leases in the Mid-Atlantic.

Sand tiger sharks (hereby, sand tigers) near Fenwick Island, Delaware, and its shoals, selected waters that 

were closer to shore (range 0–20 km), less saline (< 32 psu), and higher in dissolved organic matter

compared to other areas (Haulsee et al. 2015). Water temperature, DO, and chlorophyll were not a 

predictor of habitat use in their study. In addition, sand tigers were found to migrate northward along a 

narrow, nearshore band of shallow water in the spring (Haulsee et al. 2015). Haulsee et al. (2018) further 

suggest that sand tigers may use the shoreline and shoals as landmarks for their migration in the fall. In 

the same region, adult and juvenile sand tiger sharks were related to depth, day of year, and raw 

reflectance from satellite imagery (related to chlorophyll). Habitat selection of sand tiger sharks showed 

peak use at 10–23 m in depth in this study.

The seasonality of sand tiger shark distribution provides a case study for other sharks that remain 

unstudied in the Mid-Atlantic. Juvenile sand tigers are known to use waters extending from Cape 

Hatteras, North Carolina, northward to New England from June to October 1, and then can be found only 

south of Cape Hatteras to central Florida December to April (Kneebone et al. 2014). Kneebone et al. 

(2014) showed juvenile sand tiger sharks used water temperatures of 9.8–26.9°C, with the most common 

temperatures being 12–20°C. They also found juveniles used depths of 0–80 m and were frequently found 

at depths < 35 m. Similar to juvenile migration patterns, adult sand tigers often arrive offshore of

Delaware Bay by May, and they migrate southward by mid-October (Haulsee et al. 2018). Importantly, 

Kneebone et al. (2014) concluded the following:

• Twenty-seven sand tigers (43% of individuals studied) were detected briefly near Cape Hatteras, 

North Carolina. This is likely a consistent part of their migration route.

• During winter (December–April), approximately 20% of individuals had a period of residency in 

the vicinity of Cape Hatteras, North Carolina, and another 9% had residency in Florida, 

particularly near Cape Canaveral.

• Because of the locations of passive telemetry locators, sand tiger sharks were known to use 

waters < 10 m in depth along their migration paths. The lack of passive transmitters in deeper 

waters means a comparison could not be made.

Furthermore, a study in Delaware Bay showed all seven tagged male sand tigers migrated south to the 

vicinity of Cape Hatteras, North Carolina, whereas females moved outward to the edge of the continental 

shelf (Teter et al. 2015). As noted with sandbar sharks (Conrath and Musick 2008), this area is thought to 

be important to wintering sharks because of relatively high temperatures of the Gulf Stream. Five of the 

seven male sand tigers also spent 2–4 weeks at “rest-stops” offshore of the North Carolina/Virginia 

border (Teter et al. 2015). During migration, sand tigers used depths of 18–73 m with larger sharks using 

deeper waters (Teter et al. 2015).

Basking sharks, a planktivorous species, were captured and satellite tagged offshore of Cape Cod, 

Massachusetts, from June to October, and were found to migrate southward in late autumn (e.g., Sept 30);
81

individuals spent the winter in a range spanning from South Carolina to Brazil (Skomal et al. 2009). 

Eighty-one percent of the tracked sharks overwintered in tropical waters. Skomal et al. (2009) note that 

the North Atlantic waters undergo dramatic seasonal temperature fluctuations, whereas other studies of 

basking sharks in more stable environments have not shown such long-distance movements.

For white shark, Curtis et al. (2014) and Skomal et al. (2017) showed white shark spend winter (January–

March) restricted to waters south of Cape Hatteras, North Carolina, and extending into the eastern GoM. 

Curtis et al. (2014) also found the following:

• During summer (July–September), white sharks were primarily observed north of Virginia.

• During spring and fall, white shark were spread throughout the Atlantic and eastern GoM.

• White sharks migrated northward when SST was > 14°C.

• Young-of-the year and neonates were most frequently observed between the central coast of New 

Jersey to Massachusetts Bay.

Skomal et al. (2017) tagged white sharks near Cape Cod, Massachusetts, and Jacksonville, Florida. They 

found juveniles and most subadults spent their time in coastal environments; some subadults and most 

mature adults spent at least some time much farther offshore. Although the total depth of the waters used 

were not reported, white sharks themselves spent almost all of their time < 50 m in depth and more than 

half of their time was ≤ 20 m deep the water column (Skomal et al. 2017). White sharks were observed in 

waters ranging from 4–28°C, indicating that movements are likely based on foraging or reproductive 

potential rather than temperature tolerance (Skomal et al. 2017). Near the Gulf Stream, mature white 

sharks have been reported as focusing on the interior of clockwise-rotating anticyclonic eddies 

characterized as warm temperature anomalies (Gaube et al. 2018).

Shortfin mako were tagged near Long Island, New York and the Yucatan Peninsula of Mexico for studies 

of horizontal and vertical movements (Vaudo et al. 2017; Vaudo et al. 2016). Shortfin mako were found 

in water temperatures ranging 5.2–31.1°C, and frequently spent time in waters 22–27°C (Vaudo et al. 

2016). In this study, shortfin mako adjusted to temperature changes by diving less deeply in colder waters 

and more deeply in warmer waters. Depths of shortfin mako ranged from 28–866 m, and waters of < 15°C

created a lower depth limit to diving behaviors (Vaudo et al. 2016). During summer and autumn, shortfin 

mako had core home ranges extending along the US Atlantic Coast north of the Gulf Stream from the 

Carolinas, USA to Newfoundland, Canada (Vaudo et al. 2017). Winter and spring distributions expanded 

toward the south into the Caribbean Sea and northern South America, where offshore waters were 

frequently used (Vaudo et al. 2017).

For the blue shark, Howey et al. (2017) tagged individuals offshore of Massachusetts. They found blue 

sharks were on the continental shelf May–November 1 and used a maximum daily water temperature 

range of 12.1–23.1°C and an average daily maximum depth of 46 m (Howey et al. 2017). In addition 

Vandeperre et al. (2016) observed the following habitat associations:

• For small juveniles of both sexes, few blue sharks were observed with SST of < 15°C and in 

waters with the lowest primary productivity; more individuals were observed relatively close 

to the shelf break.

• Large juveniles/subadult females were associated with 1) a combination of warm SST in 

areas of high primary productivity and 2) areas of high primary productivity in a close 

proximity to the shelf.

• Large juvenile male blue shark were associated with 1) distance to 1-km isobaths and 2) a 

combination of warm SST plus high primary productivity.
82

3.5 Tuna, Swordfish, and Billfish Habitat Associations

The designated EFH for the Atlantic highly migratory species of tuna, swordfish, and billfish overlap only 

small amounts with our study areas in the GoM and South Atlantic. Therefore, species in those regions

are not further addressed here. In contrast, skipjack tuna, albacore tuna, yellowfin tuna, and bluefin tuna 

do have substantial overlap in the Greater Atlantic study area. However, direct linkages of these species 

to shoal habitats are not well defined (Rutecki et al. 2014), and we found in next section that few 

studies of large pelagic fish have considered testing for geomorphology relationships. Because of 

these characteristics, plus the complexity involved with research on these species, we have limited 

our review here to bluefin tuna in the Greater Atlantic. The EFH designation of bluefin tuna overlaps 

with 99% of our Greater Atlantic study area (Table 3-8). More specifically, designated EFH for 

juvenile bluefin tuna is throughout our Greater Atlantic study area, and EFH for adults is restricted to 

offshore Virginia, northern New Jersey, as well as waters east of Long Island Sound, New York.

Table 3-8. For Highly Migratory Species (excluding sharks), the proportion of area designated as 

EFH within each study area.

Species 

group Species

Proportion of 

GoM study area

designated as 

EFH

Proportion of 

South Atlantic 

study area

designated as 

EFH

Proportion of 

Greater Atlantic 

study area

designated as 

EFH

Fishbase 

description of 

habitat

Tuna Albacore 

tuna 0.00 0.06 0.52 pelagic0–600 m

Tuna Bigeye tuna 0.00 0.09 0.14

pelagic

0–1,500 m

usualy 0–500 m

Tuna Bluefin tuna 0.00 0.23 0.99

pelagic

0–985 m

usually 0–100 m

Tuna Skipjack 

tuna 0.01 0.14 0.82 pelagic0–260 m

Tuna Yellowfin 

tuna 0.00 0.23 0.60

pelagic

1–250 m

usually 1–100 m

Swordfish Swordfish 0.02 0.29 0.16

pelagic

0–2,878 m

usually 0–550 m

Billfish Blue marlin 0.00 0.00 0.00 pelagic

0–1,000 m

Billfish Longbill 

spearfish 0.00 0.06 0.17

pelagic

0–200 m

usually ≥ 100 m

Billfish Roundscale 

spearfish 0.00 0.00 0.00 pelagic0–200 m

Billfish Sailfish 0.05 0.36 0.08

pelagic

0–200 m

usually ≥ 30 m

Billfish White marlin 0.00 0.00 0.00

pelagic

0–150 m

usually 0–100 m

Notes: Those overlapping with > 50% of the study area are bolded. Study areas are defined in Figures 1-2, 1-3, and 

1-4. All life stages were included.
83

Within the US, bluefin tuna primarily spawn in the deeper waters of the GoM and their main feeding 

areas are the extremely productive Atlantic shelf (Druon et al. 2016). Worldwide, bluefin tuna seasonal 

migrations track changes in chlorophyll, SST, and temperature fronts (Druon et al. 2011; Royer et al. 

2004). Research has also found that bluefin tuna in the North Atlantic are associated with anticyclonic 

eddies that downwell water and effectively mix the water column (Hsu et al. 2015). Within the North 

Atlantic region, Walli et al. (2009) found bluefin tuna have long residence times (mean= 167 days per 

year), and their diving behavior was correlated with the depth of the thermocline (i.e., deeper dives were 

made if thermocline was deeper). Marcek et al. (2016) also found juvenile bluefin tuna diving behavior 

was related to thermocline depth. Water temperatures of bluefin tuna range widely as 0–31°C, but 87% of 

their time were in waters 10–23°C (Walli et al. 2009). Walli et al. (2009) and Galuardi et al. (2012) both 

found that bluefin tuna used waters near the surface (< 20 m depth) the vast majority of time, but the 

depth of the water column was not reported.

After a literature review of habitat associations that affect bluefin tuna distribution, Druon et al. (2016)

developed a habitat suitability index model based the range of four habitat variables (Table 3-9).

Table 3-9. Parameters used to develop habitat suitability models for bluefin tuna.

Size class of 

bluefin tuna

Variable Minimum 

value

Intermediate 

value

Maximum 

value

Small (5–25 kg) Chlorophyll (mg m-3) 0.10 0.25 1.95

Small (5–25 kg Gradient of chlorophyll 

(mg m-3 km-1)

0.0008 0.0030 NA

Small (5–25 kg SST (°C) 13 NA 26.1

Small (5–25 kg Sea surface height 

anomaly (m)

NA NA -0.10

Large (5–25 kg) Chlorophyll (mg m-3) 0.14 0.25 4.42

Large (5–25 kg) Gradient of chlorophyll 

(mg m-3 km-1)

0.0008 0.0030 NA

Large (5–25 kg) SST (°C) 7.5 NA 24

Large (5–25 kg) Sea surface height 

anomaly (m)

NA NA -0.10

Source: Druon et al. (2016)

In regard to seasonal movements in the Atlantic, juvenile bluefin tuna were often located south of 

Delaware waters during November–April 30, and then moved northward May–October 31 (Galuardi and 

Lutcavage 2012). The diet of bluefin tuna does provide some inference into their habitat use. Chase et al. 

(2002) found sand lance, Atlantic herring, Atlantic mackerel, squid, and bluefish were the most common 

prey of juvenile and adult bluefin tuna.
84

4 Where are the Marine Fish? A Literature Review of Spatially 

Explicit Habitat Associations and Models of Fish Distribution

4.1 Introduction

Species distribution models, and similar methods of spatial modeling, have proliferated in the last two 

decades due to rapid improvements in Geographic Information Systems (GIS) technologies, remote 

sensing, availability of spatial data, and computation capacity. However, modeling of marine species has 

lagged far behind the terrestrial counterpart (Robinson et al. 2011). For terrestrial ecosystems, landscape 

ecology has been defined as the study of heterogeneous spatial patterns and processes, and it has a history 

of merging the fields of ecology and geography (Turner 1989). These concepts are now being applied to 

marine ecosystems in the form of “seascape ecology” (Pittman 2011). The distribution of marine fish are 

particularly important because of their commercial and recreational economic value, as well as their value 

for subsistence fishing. Recent distribution models in marine ecosystems have been applied to ecosystembased management (Gruss et al. 2018; Moore et al. 2016), marine spatial planning (Hattab et al. 2013), 

scenario assessments (Delevaux et al. 2018), stock assessments (Saul et al. 2013), and climate change 

scenarios (Morley et al. 2018; Su et al. 2013).

As marine spatial modeling continues to expand, a synthesis of studies has the potential to inform best 

modeling practices, identify knowledge gaps, assess research trends, identify the most useful datasets, and 

improve the efficiency of future studies by identifying environmental variables commonly invoked. Here, 

we focus on marine fish species because they are economically and socially important, represent diverse 

marine environments, occupy multiple trophic levels, and likely to respond to a similar suite of 

environmental characteristics. Spatial modeling approaches have changed dramatically from habitat 

suitability indices initially developed in the 1970s (U.S. Fish and Wildlife Service 1981) to correlative 

species distribution models (Guisan and Zimmermann 2000), individual-based models (Okunishi et al. 

2009), and other data-driven techniques. Simultaneously, statistical methods in ecology have continued to 

evolve, as machine-learning techniques and flexible general additive models (GAMs) continue to gain 

popularity in ecology.

Key Points and Knowledge Gaps (gaps are in italics)

• Spatially explicit predictive models of marine fish have increased rapidly in recent years. 

Worldwide, only two such studies were published in 2007, and the trend increased to an annual 

high of 42 predictive studies published in 2018. 

• A total of 7 predictive marine fish studies have been published in the Gulf of Mexico, 4 in the 

South Atlantic, and 19 in the Greater Atlantic. 

• Nonlinear statistical techniques are common, showing that species-habitat relationships are often 

complex.

• Oceanographic and water chemistry habitat characteristics tend to dominate modeling, while 

substrate associations are poorly studied for 8 of 10 fish guilds. The effects of soft bottom 

complexity on fish distribution remains largely unknown.

• Further progress can be made between marine fish ecology and predictive modeling to determine 

where spatial models can be improved for management and conservation applications.
85

A vast of array of predictor variables have now been developed for marine environments. For example, 

the online database “Bio-Oracle” provides worldwide data on 23 types of predictor variables (Tyberghein 

et al. 2012). Predictive modeling studies have spanned from those using no predictor variables by 

applying kriging techniques (Rambo et al. 2017) to using > 30 types of predictor variables (Bouchet et al. 

2017; Manderson et al. 2011). Additionally, each variable may be computed at multiple spatial (e.g., 

Pittman et al. 2009) and temporal scales (Mannocci et al. 2017), which can result in a bewildering array 

of options. In regard to spatial scale of analyses, Mannocci et al. (2017) recognizes a hierarchy where 

marine prey patches determine fine-scale habitat associations (1 m–1 km), eddies/fronts/oceanographic 

features occur at an intermediate scale (1–100 km), and water masses/currents dictate broad-scale (100–

1,000 km) distributions of organisms. However, the mobility and guild of fish species are likely to play a 

role in their responses to scale. Robinson et al. (2011b) suggests that pelagic species may be well 

represented by coarse data because heterogeneity of their environment occurs at a broad scale (e.g., 10–

100 km scale), whereas coastal and benthic species may respond to a relatively fine scale because of the 

existence of local heterogeneity. Therefore, predictors of pelagic fish species are likely to differ from 

demersal or reef-associated species. Understanding these differences and patterns will be help further 

develop models for fish species and will help elucidate knowledge gaps.

Given the nascent nature of marine spatial modeling, a complex array of potential predictor variables, 

high diversity of marine fish species and data sources, and evolving statistical methods, we sought to 

provide clarity to the state of predictive modeling of marine fish. We conducted a literature review with 

the following objectives:

• Identify the most and least frequently studied geographies and fish functional groups

• Describe the frequency of various statistical techniques

• Quantify differences in fisheries data sources used for fish functional groups

• Determine how predictor variables that are tested differ by functional group

• Identify and discuss knowledge gaps

4.2 Literature Search Methods

We conducted a literature search of peer-reviewed research articles with the Web of Science database. 

Articles from 2007–2018 were included, and the search was conducted January 16, 2019. The following 

keywords were searched within the title and abstract: TS = ("species distribution" OR "ecological niche*" 

OR "bioclimatic envelope" OR "habitat suitability" OR "habitat model*" OR "spatial distribution" OR 

"seascape") AND TS = fish AND TS= ("marine" OR "ocean"). After finding notable articles on shark 

spatial distribution modeling were missing, we conducted a second search by simply replacing “fish” with 

“shark.” From the two searches, a total of 1,648 articles were obtained.

4.3 Review Scope and Protocol

The scope of the review included only fish species; therefore, taxonomic groups such as squid, shrimp, 

crabs, other crustaceans, corals, and bivalves, are not covered here. We limited the review to those 

research articles that resulted in a spatial prediction of fish distribution beyond fish survey locations. 

Although localized studies may greatly improve our understanding of fish ecology and distribution, 

ultimately we were interested in predicting and mapping the distribution of fish over relatively broad 

areas. No limitations were placed on the dependent variables, as predictions of individual species 

presence/relative abundance/relative biomass, species richness, species diversity, or other measures of 

community composition were all considered relevant. Given our focus and objectives, we removed the 

following types of studies from the initial keyword search:

• Studies that did not include predictions of fish distribution (e.g., whales, invertebrates, seabirds)
86

• Studies focused solely on the distribution of fishing effort

• Genetic or evolutionary studies that addressed long-term, broad-scale connectivity

• Study areas focused solely on salt marsh, mangrove, intertidal, estuary, or freshwater 

environments (< 8 ppt)

• Simulations of larval dispersal; studies of larvae were included when spawning was inferred

• Review or discussion articles

• Center of gravity studies, as they do not predict the full distribution of species

• Reserve design or conservation planning studies that used existing models

• Marine Protected Area (MPA) studies were examined to ensure those that predicted the 

distribution of fish were included. Those studies that compared MPA with non-MPA sites were 

often excluded as spatial predictions and analyses were not developed

The geography of each study area was recorded as its ocean, nearest continent (hereby, “continent”), 

nearest nation (hereby, “nation”), specific geography name (if given), and year published. For each study, 

we categorized the type of fish species analyzed into one of 10 guild categories (Table 4-1). A few 

studies included only the juvenile stage of fish, and thus, these studies were characterized by the habitat 

of those juvenile fish regardless of adult stage habitat associations. To improve our sample size of shark 

studies, the four studies that included both sharks and other species (elasmobranch diversity, grouper, and 

large pelagics) were categorized as shark studies. Otherwise, we used a “general” category to include 

studies that included more than one fish guild. These general studies often examined species richness, 

species diversity, or all of the species captured in a particular survey. Fish data sources were categorized 

as fishery independent, fishery dependent, both fishery independent and dependent, international 

database, previous research/museum specimens, and interviews. The specific method of fisheryindependent surveys was also recorded. Fishery-dependent data included logbooks, landings, catch 

observations, incidental catch observations, and recreational catch. International databases included “Sea 

Around Us,” “SeaLifebase” geographic range polygons as well as databases of raw species data such as 

the “Ocean Biogeographic Information System.”

For statistical methods, we recorded the type and total number of statistical methods applied for each 

study and the total number of machine-learning methods. Machine-learning methods are relatively new 

statistical methods in ecology and are based on iterative learning. These techniques included artificial 

neural networks, classification and regression trees, random forest, boosted regression trees, and 

multivariate adaptive regression splines. We also summarized the different methods that applied 

bioclimatic envelopes (surface range envelopes, AquaMaps, Sea Around Us, bioclimatic envelope). We 

did not distinguish models with only fixed effects and those with random effects included. Spatial 

autocorrelation was not documented, though geographic variables were included with the variables tested.

We recorded each variable tested, or otherwise utilized (e.g., in habitat suitability indices), in models of 

fish distribution. However, we did not include temporal factors (year, month, day of year, day/night) or 

factors primarily affecting detectability (moon phase, lunar illumination, wind, clouds, precipitation) 

because of our primary interest in habitat relationships and best predictors for mapping fish distribution.

We further summarized variables into categories of fish-based oceanographic, physical oceanographic, 

geography, substrate, and biological. Fish-based oceanographic variables included those that have direct 

influence on fish, such as temperature, salinity, and nutrients. In contrast, physical oceanographic factors 

are hypothesized to have an indirect effect on fish through enhanced productivity via sea-level anomalies, 

chlorophyll-a, upwelling events, and ocean currents. Geographic depicted variables focused on location 

or proximity to surrounding ecosystems, such as latitude/longitude, distance to shoreline, distance to 

shelf, and distance to other ecosystems like mangroves or estuaries. Substrate variables characterized 

components like sediment type, bottom types, and topography.
87

Table 4-1. Fish functional group, examples of common species, and the number of predictive modeling papers obtained from the Web of 

Science database from 2007–2018.

Fish functional 

group Common example species

Number 

of 

studies

Fishery 

independent

Fishery 

dependent

Fishery 

independent and 

dependent

International 

database

Museum or 

previous 

research

Demersal

(not associated 

with hard bottom)

cod, stingrays, hake, whiting, 

sole, flounder, halibut, and other 

flatfish 59 55 (93%) 5 (8%) 5 (8%) 4 (7%) 2 (3%)

General

species richness, species 

diversity, or a combination of the 

groups; seahorse, aquaculture 

species

40 15 (38%) 6 (15%) 1 (3%) 17 (43%) 2 (5%)

Large pelagics tuna, swordfish, marlin, billfish, 

sailfish, and ocean sunfish 29 10 (34%) 15 (52%)** 0 1 (3%) 1 (3%)

Sharks - 21 10 (48%) 9 (43%)** 2 (10%) 2 (10%) 2 (10%)

Forage fish

Atlantic herring, capelin, sardine, 

anchovy, lanternfish, and sand 

lance 18 11 (61%) 4

(22%) 2 (11%) 2 (11%) 0

Coral reef - 16 13 (81%) 1 (6%) 1 (6%) 1 (6%) 2 (13%)

Hard bottom grouper, snapper 14 14 (100%) 3 (21%) 3 (21%) 0 0

Medium pelagics

mackerel, dolphinfish, eel 

species, yellow kingfish 15 3 (20%) 10 (67%)** 0 2 (13%) 0

Diadromous shad, salmon 7 4 (57%) 2 (29%) 1 (14%) 1 (14%) 1 (14%)

Invasive - 7 2 (29%) 0 0 3 (43%) 2 (29%)

Totals - 226 137 (61%) 55 (24%) 15 (7%) 33 (15%) 12 (5%)

Notes: The source of fish data is described as a percent of studies within each defined guild. The category of fisheries-independent + fisheries-dependent data is 

not exclusive of individual categories.
88

4.4 Statistical Analysis

We reviewed 226 peer-reviewed scientific papers. We tested the trend in number of studies across years 

with a generalized linear model (GLM) with a Gaussian distribution. We used a multinomial logistic 

regression with fish functional group as a dependent variable and separately tested for differences in data 

sources (simplified to fishery-independent, fishery-dependent, and international databases), statistical 

method, and type of predictor variables (fish-based oceanographic, physical oceanographic, geographic, 

substrate, and biological). For multinomial logistic regression, we used the R package “nnet” and Wald 

tests to calculate p-values (∞ =0.05). Because demersal species were the most common fish functional 

group, we used them as a reference group in the analyses. A single study, Gruss et al. (2018), was not 

included in the analyses of habitat variables because they studied 51 functional groups of fish and 

invertebrates and each one had a differing, consolidated number of predictor variables. All values 

reported are ± 1 standard error (SE).

To further consider habitat variables tested by functional group, we invoked linear discriminant analysis 

(LDA), which is a statistical, machine-learning method for identifying linear combinations of variables 

that maximizes the separation of known data groupings (Rao 1948; Ripley and Hjort 1996). Here, we 

utilized LDA to identify the linear combinations of study characteristics that separate the specified fish 

functional groups. We performed LDA with the “MASS” package “lda” function (Ripley 2002; Ripley 

and Hjort 1996) in RStudio (Version 1.1.383). Additional packages used “irr” (Gamer et al. 2019) and 

“scatterplot3d” (Ligges and Mächler 2002). For this analysis, we discarded those variables in < 1% of 

studies. We condensed the classes of medium and large pelagics into “pelagics” and hard bottom and 

coral reef fish into a category of “reef fish.”

We first fit an LDA model with a set seed that considered the set of habitat characteristics and examined 

the percentage of the trace explained by each of the linear discriminants. We included the appropriate 

number of discriminants to maintain interpretability and still lead to good separation of the groupings by 

explaining a sum of at least 75% of the trace. Next, we examined the separation of the groups from plots 

of the discriminants to understand where the groups lie based on the linear combinations of discriminants. 

We examined the discriminant coefficients to identify the study characteristics that are likely to lead to 

this separation. Finally, we examined agreement statistics including percent agreement and Cohen’s 

kappa between the estimated and true groupings to understand how well the model fits the data.

4.5 Results and Discussion

The number of marine fish predictive modeling papers had a strong upward trend from 2007–2018 (GLM, 

t-value = 7.91, β = 3.02 (± 1 SE, 0.38), p < 0.0001) (Figure 4-1). Only two such studies were published in 

2007, but 42 predictive modeling papers were published in 2018. Of the studies focused on a single 

ocean, the Atlantic Ocean was most studied (88 studies), followed by the Pacific (71), Mediterranean Sea 

(27), Indian (12), Southern (8), and Arctic (2) (Figure 4-2). In regard to continents of study, North 

America (70 studies), Europe (38), and Australia (29) were most studied, followed by Asia (19), South 

America (6), Africa (3), and Antarctica (1). Taken together, the Atlantic Ocean coasts of North America 

and Europe, plus the Mediterranean Sea encompassed 46% of all predictive modeling published research 

on marine fish. Twenty-eight studies (12%) covered multiple oceans, including 11 that were worldwide in 

scope.
89

Figure 4-1. Number of peer-reviewed marine fish spatially explicit predictive model studies 2007–

2018.

Figure 4-2. Geography of studies that predicted the distribution of marine fish (2007–2018) 

(n=226).

Studies that depicted multiple oceans, or covered extremely large geographies, are shown in orange and are located 

near the center of their extent. Single ocean studies are shown in black. Eleven studies were worldwide (not shown),

and one study is counted in both the entire Atlantic and Indian/Southern Ocean.
90

Studies of demersal species were most common, followed by general studies and large pelagic fish (Table 

4-1). Shark, forage fish, coral reef, hard bottom, and medium pelagic fish were moderately studied, while 

the distribution of diadromous and invasive species were rarely modeled. Fish data sources used for 

modeling were dominated by fishery-independent surveys (61%), followed by fishery-dependent surveys 

(24%), international databases (15%), fishery-independent and dependent surveys (7%), and previous 

research or museum specimens (5%). Of the 137 studies that utilized fishery-independent data sources, 

51% were by trawl, 20% video, 12% field survey (e.g., scuba diving), 8% ichthyoplankton survey, 8% 

bottom/vertical longline, 7% acoustic survey, 4% satellite tag, 3% traps, 3% gill/seine net, and 2% citizen 

science.

GAMs were the most common modeling method (Table 4-2). The high frequency of GAMs and the 

emergence of machine-learning statistics emphasizes the importance of nonlinear habitat relationships. 

Together, these statistical techniques were present in 40% of all predictive marine fish studies. Of the 

machine-learning methods, boosted regression trees (6% of all studies), classification trees (5%), and 

random forests (4%) were most common, followed by mulivariate adaptive regression splines (3%) and 

artificial neural networks (3%). Seventeen percent of all studies used more than one statistical method. 

Individual-based models, quantile regression, occupancy models, and ordinary least squares were each 

used in < 2% of studies.

As the statistical reference, demersal studies used GAMs (34%), machine learning (10%), GLMs (29%), 

Maxent (12%), habitat suitability indices (5%), multivariate (5%), envelopes (3%), and geostatistics 

(15%). As expected from studies that typically analyzed tens to hundreds of species (e.g., species 

richness), envelope methods were more common in general species studies (p < 0.005, β = 2.96 ± 1.05). 

Maxent models, based on presence-only modeling methods, were more common with invasive species (p 

= 0.019 β = 2.73 ± 1.16). Habitat suitability indices were more common with invasive species (p = 0.07 β

= 2.30 ± 1.28) and pelagics (p = 0.003 β = 2.32 ± 0.77). The use of geostatistic techniques were 

negatively associated with pelagics (p = 0.08, β = -1.93 ± 1.11), reef fish (p = 0.09 β = -1.88 ± 1.11), and 

geostatistics were not used with studies of invasive fish, diadromous, general species. For pelagics, GLMs 

(p = 0.064 β = -1.37 ± 0.74) were less common and GAMs were more commonly used (p = 0.04 β = 1.06 

± 0.52). Machine-learning methods were more common with reef fish (p = 0.08, β = 1.17 ± 0.67). 

Multivariate methods were not related to fish guild.

Demersal-, forage-, coral reef-, hard bottom- and diadromous fish studies were dominated by fisheryindependent data sources (Table 4-1). Compared to demersal species, pelagics (p = 0.03, β = 1.42 ± 0.65) 

and sharks (p = 0.09, β = 1.23 ± 0.73) had more research based on fishery-dependent data. International 

databases served as a major data source for invasive species and general species models, but they did not 

statistically differ from demersal species. Pelagic models less commonly used international databases (p= 

0.03, β = -2.37 ± 1.08), and no studies of invasive species used fishery-dependent data. With the 

exception of reef fish, all other fish guilds had fewer studies from fishery-independent data compared to 

demersal fish studies (all p < 0.02).
91

Table 4-2. Methods used to predict the distribution of marine fish in 

studies spanning 2007–2018 (n = 226).

Statistics for modeling

Percent of 

studies

General additive model 32

Generalized linear model 19

Maxent 16

Habitat suitability index 13

Machine learning 11

Multivariate statistics 9

Envelope models 8

Geostatistics/kriging 8

Bayesian 5

Note: Methods used in < 2% of papers are not shown; studies may have used multiple methods.

LDA results (Figure 4-3, Table 4-3) showed fish groups could be separated based on what habitat 

variables were tested. Table 4-3 includes the discriminant coefficients from the three discriminants of the 

LDA model and indicator of the driving variables for separation of our most separable groups. The 

median values are evident from the scatterplots (Figure 4-3). The checkmarks indicate the characteristics 

that are most likely included in studies for the given fish group.

A 3D scatterplot and three pairwise scatterplots describe the three linear discriminants utilized for this 

separation as they together explain 77% of the trace (39%+20%+17%=77%). From these, we saw good 

separation for demersal, general, invasive, pelagic, and reef fish groups. Finally, the model fit the data 

fairly well with percent agreement of 67.6 and Cohen’s kappa of 0.60 (κκ = 0 is random agreement, κκ = 1

is perfect agreement).

Overall, the following results were observed from LDA:

• Reef fish were most distinguished because studies often examined substrates attributes, such as 

rugosity, standard deviation of depth, aspect, sessile biota, slope of slope (a derivative of slope), 

and reefs.

• Demersal studies were represented by depth, sediment grain size, bottom temperatures, soft 

bottom proportion, or distance to soft bottom. Substrate variables related to substrate complexity 

were not a characteristic of this group.

• Pelagic species were represented by sea surface height anomaly, DO, pH, phosphate, and prey.

• Invasive species were represented by common, broad-scale variables of SST, chlorophyll, and 

salinity.

• General species (e.g., species richness) were only distinguished by ice and salinity measures.

Overall, these patterns show how researchers have perceived these marine fish functional groups and 

demonstrates knowledge gaps that will require further testing.
92

Figure 4-3. LDA results that show habitat variables tested in predictive modeling studies differ 

with fish functional groups.
93

Table 4-3. Discriminant coefficients from the three discriminants of the LDA and indicators of the driving variables for separation of the

most separable groups.

Variable LD1 LD2 LD3

Demersal

(-0.56, 0.76,

0.73)

General

(0.64,

0.71, -0.90)

Invasive

(0.82, -0.14,

-2.73)

Pelagic

(1.81, -1.56, 

0.44)

Reef

(-3.08, -1.28,

-0.30)

Depth -0.43 0.61 0.54  - - - -

Ice 0.04 1.31 -1.75 -  - - -

SST anomaly, climatic variable -0.38 0.11 0.71  - - - -

Sea surface height anomaly 0.88 -1.73 1.75 - - -  -

Bottom temperature -0.06 0.16 0.59  - - - -

SST 0.98 -0.55 -0.53 - -  - -

Chlorophyll 0.39 -0.04 -0.49 - -  - -

Salinity at surface 0.42 0.28 -0.62 -   - -

Salinity at bottom 0.46 0.19 -1.43 -   - -

Dissolved oxygen 0.18 -0.40 0.93 - - -  -

Phosphate 2.39 -5.18 2.50 - - -  -

pH 1.93 -2.52 1.21 - - -  -

POC -0.57 1.65 0.76  - - - -

Prey 0.34 -0.25 0.02 - - -  -

Anthropogenic stress -0.96 -0.94 -1.17 - - - - 

Distance to shoreline -0.40 -0.51 -0.08 - - - - 

Sediment grain size -0.26 1.30 0.98  - - - -

Soft bottom (proportion or distance to) -0.06 1.65 0.90  - - - -

Sessile biota -1.41 -0.87 -0.15 - - - - 

Reef or hard bottom -0.58 -1.23 -0.30 - - - - 

Slope of slope -1.85 -2.40 -0.73 - - - - 

Rugosity -0.41 -0.61 -1.41 - - - - 

SD of depth -0.66 -1.03 -1.05 - - - - 

Aspect -0.43 -0.37 -1.17 - - - - 

Seagrass, macroalgae, algae 0.37 -0.12 -1.21 - -  - -

Habitat type or patch area -1.20 0.51 0.87  - - - -

Notes: The values in parentheses are the median positions for these groups on the three linear discriminants.
94

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Appendix A. Supplemental Tables

Table A-1. Common and scientific names of species cited in the text. 

Table modified from Rutecki et al. (2014).

Common Name Scientific Name Common Name Scientific Name 

Alewife Alosa pseudoharengus Flounder, winter Pseudopleuronectes americanus

AmberJack Seriola spp. Haddock Melanogrammus aeglefinus

AmberJack, greater Seriola dumerili Hake, white Urophycis tenuis 

AmberJack, lesser Seriola fasciata Halfbeak Hemiramphus brasiliensis

Anchovy Family: Engraulidae Halibut, Atlantic Hippoglossus hippoglossus 

Atlantic bigeye Priacanthus arenatus Herring, Atlantic Clupea harengus 

Atlantic bumper Chloroscombrus chrysurus Herring, Atlantic thread Opisthonema oglinum 

Atlantic spadefish Chaetodipterus faber Herring, blueback Alosa aestivalis 

Atlantic tomcod Microgadus tomcod Herrings Clupeidae

Anchovy, bay Anchoa mitchilli Herring, Pacific Clupea pallasii pallasii

Atlantic, silverside Menidia menidia Herring, thread Opisthonema oglinum

Bass, largemouth Micropterus salmoides Hind, red Epinephelus guttatus 

Bass, striped Morone saxatilis Hind, rock Epinephelus adscensionis 

Bermuda chub Kyphosus sectatrix Hind, speckled Epinephelus drummondhayi

Bluefish Pomatomus saltatrix Hogfish Lachnolaimus maximus

Bluegill Lepomis macrochirus Horse-eye jack Caranx latus

Blue runner Caranx crysos Lobster, American Homarus americanus

Butterfish Peprilus triacanthus Lobster, spiny Panulirus argus

Clam, Atlantic surf Spisula solidissima Lobster, slipper Scyllaridae

Clam, northern quahog Mercenaria mercenaria Longhorn sculpin Myoxocephalus octodecemspino

Clam, ocean quahog Arctica islandica Mackerel, Atlantic Scomber scombrus 

Clam, quahog Mercenaria campechiensis Mackerel, chub Scomber colias 

Cobia Rachycentron canadum Mackerel, king Scomberomorus cavalla 

Cod, Atlantic Gadus morhua Mackerel, king and cero Scomberomorus spp. 

Coney Cephalopholis fulva Mackerel, Spanish Scomberomorus maculatus 
120

Common Name Scientific Name Common Name Scientific Name 

Crab, horseshoe Limulus polyphemus Mako, shortfin Isurus oxyrinchus 

Crab, blue Callinectes sapidus Menhaden, Atlantic Brevoortia tyrannus 

Crab, Florida stone Menippe mercenaria Menhaden, Gulf Brevoortia patronus

Crab, Gulf stone Menippe adina Mullet Mugil spp.

Crab, Dungeness Metacarcinus magister Oyster Ostreidae

Croaker, Atlantic Micropogonias undulatus Pollock Pollachius virens 

Dogfish, smooth Mustelus canis Porgies Calamus spp.

Dogfish, spiny Squalus acanthias Porgy, red Pagrus pagrus 

Dolphinfish Coryphaena hippurus Pout, ocean Zoarces americanus 

Drum, red Sciaenops ocellatus Redfish, Acadian Sebastes fasciatus 

Dwarf sand perch Diplectrum bivittatum Red hind Epinephelus guttatus

Flounder, southern Paralichthys lethostigma Sheepshead Archosargus probatocephalus

Flounder, summer Paralichthys dentatus Sand perch Diplectrum formosum

Flounder, windowpane Scophthalmus aquosus Sardines Clupeidae

Flounder, winter Pseudopleuronectes Scads Carangidae

Flounder, witch Glyptocephalus cynoglossus Shrimp, seabob Xiphopenaeus kroyeri

Flounder, yellowtail Limanda ferruginea Shrimp, rock Sicyonia brevirostris

Flounder, American plaice Hippoglossoides 

platessoides

Shrimp, royal red Pleoticus robustus 

Gag Mycteroperca microlepis Shrimp, sand Crangon spp.

Goosefish (monkfish) Lophius americanus Shrimp, white Litopenaeus setiferus 

Gobies Suborder: Gobioidei Skates Rajidae

Graysby Cephalopholis cruentata Skate, barndoor Dipturus laevis 

Groupers Serranidae spp. Skate, little Leucoraja erinacea 

Grouper, goliath Epinephelus itajara Snapper, blackfin Lutjanus buccanella 

Grouper, warsaw Hyporthodus nigritus Snapper, cubera Lutjanus cyanopterus 

Grouper, orange-spotted Epinephelus coioides Snapper, gray Lutjanus griseus 

Grouper, red Epinephelus morio Snapper, lane Lutjanus synagris 

Grunts Haemulon spp. Snapper, mutton Lutjanus analis 
121

Common Name Scientific Name Common Name Scientific Name 

Grouper, yellowedge Hyporthodus flavolimbatus Snapper, pink Pagrus auratus

Grouper, yellowmouth Mycteroperca interstitialis Snapper, red Lutjanus campechanus 

Grouper, snowy Hyporthodus niveatus Snapper, gray Lutjanus griseus

Haddock Melanogrammus aeglefinus Snapper, silk Lutjanus vivanus 

Hagfish Myxine glutinosa Snapper, vermilion Rhomboplites aurorubens 

Hake, Atlantic, red/white Urophycis spp. Shark, bull Carcharhinus leucas 

Hake, offshore silver Merluccius albidus Shark, common thresher Alopias vulpinus 

Hake, red Urophycis chuss Shark, dusky Carcharhinus obscurus 

Hake, silver Merluccius bilinearis Shark, finetooth Carcharhinus isodon 

Rays superorder: Batoidea Shark, great Sphyrna mokarran 

Salmon Salmonidae Shark, lemon Negaprion brevirostris 

Salmon, Atlantic Salmo salar Shark, makos Isurus spp. 

Sand lance Ammodytes spp. Shark, porbeagle Lamna nasus 

Sand seatrout Cynoscion arenarius Shark, sand tiger Odontaspis taurus 

Scallop, bay Argopecten irradians Shark, sandbar Carcharhinus plumbeus 

Scallop, sea Placopecten magellanicus Shark, scalloped Sphyrna lewini 

Scamp Mycteroperca phenax Shark, silky Carcharhinus falciformis 

Scup Stenotomus chrysops Shark, smooth Sphyrna zygaena 

Scups or porgies Sparidae spp. Shark, spinner Carcharhinus brevipinna 

Sea bass, bank Centropristis ocyurus Shark, tiger Galeocerdo cuvier 

Sea bass, black Centropristis striata Shrimp, brown Farfantepenaeus aztecus 

Sea bass, rock Centropristis philadelphica Shrimp, pink Farfantepenaeus duorarum 

Seatrout, sand Cynoscion arenarius Shrimp, rock Sicyorzia brevirostris 

Shad, American Alosa sapidissima Short bigeye Pristigenys alta

Seatrout, spotted Cynoscion nebulosus Slippery dick Halichoeres bivittatus

Shark, Atlantic sharpnose Rhizoprionodon terraenovae Snappers Lutjaninae spp. 

Shark, blacknose Carcharhinus acronotus Snapper, gray Lutjanus griseus

Shark, blacktip Carcharhinus limbatus Snapper, mutton Lutjanus analis
122

Common Name Scientific Name Common Name Scientific Name 

Shark, blue Prionace glauca Snapper, silk Lutjanus vivanus

Shark, bonnethead Sphyrna tiburo Snapper, vermilion Rhomboplites aurorubens

Snapper, yellowtail Ocyurus chrysurus Snapper, blackfin Lutjanus buccanella

Spot Leiostomus xanthurus Tuna, albacore Thunnus alalunga 

Squid, longfin Loligo pealei Tuna, bigeye Thunnus obesus 

Squid, northern shortfin Ilex illecebrosus Tuna, blackfin Thunnus atlanticus 

Squids Squid spp. Tuna, bluefin Thunnus thynnus 

Speckled hind Epinephelus drummonhayi Tuna, skipjack Katsuwonus pelamis 

Sturgeon, Gulf Acipenser oxyrinchus Tuna, yellowfin Thunnus albacares 

Sturgeon, shortnose Acipenser brevirostrum Tunas Thunnus spp. 

Swordfish Xiphias gladius Tunny, little Euthynnus alletteratus 

Tautog Tautoga onitis Walleye Sander vitreus

Tilefish, blueline Caulolatilus microps Weakfish Cynoscion regalis 

Tilefish, golden Lopholatilus 

chamaeleonticeps 

Wenchman Pristipomoides aquilonaris

Tilefish, sand Malacanthus plumieri Wolffish, Atlantic Anarhichas lupus 

Tilefishes Malacanthidae spp. Weakfish Cynoscion regalis 

Triggerfish, gray Balistes capriscus Wahoo Acanthocybium solandri 

Tomtate Haemulon aurolineatum

Trout Subfamily: Salmoninae
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