Characterization of the 

Benthos, Marine Debris and Bottom Fish at 

Gray’s Reef National Marine Sanctuary

by Matthew S. Kendall, Laurie J. Bauer and Christopher F.G. Jeffrey

In partnership with Gray’s Reef National Marine Sanctuary

March 2007

NOAA Technical Memorandum NOS NCCOS 50
Mention of trade names or commercial products does not constitute endorsement or recommendation for their 

use by the United States Government.

Citation:

Kendall, M.S., L.J. Bauer and C.F.G. Jeffrey. 2007. Characterization of the Benthos, Marine Debris and Bottom 

Fish at Gray’s Reef National Marine Sanctuary. Prepared by National Centers for Coastal Ocean Science (NCCOS) Biogeography Team in cooperation with the National Marine Sanctuary Program. Silver Spring, MD. NOAA 

Technical Memorandum NOS NCCOS 50. 82 pp. + Appendices.
Characterization of the Benthos, Marine Debris and Bottom Fish at 

Gray’s Reef National Marine Sanctuary

Center for Coastal Monitoring and Assessment (CCMA)

NOAA/NOS/NCCOS

1305 East-West Highway, SSMC-4, N/SCI 1

Silver Spring, MD 20910

NOAA Technical Memorandum NOS NCCOS 50

March 2007

Authors:

Matthew S. Kendall (NCCOS)

Laurie J. Bauer (NCCOS)

Christopher F.G. Jeffrey (NCCOS)

United States Department National Oceanic and National Ocean Service

of Commerce Atmospheric Administration

Carlos M. Gutierrez Conrad C. Lautenbacher, Jr. John H. Dunnigan

Secretary Administrator Administrator 

page



ABOUT THIS DOCUMENT

This ecological characterization represents the continuation of an ongoing partnership between the National 

Marine Sanctuary Program (NMSP) and the National Centers for Coastal Ocean Science (NCCOS), Center for 

Coastal Monitoring and Assessment (CCMA). The purpose of this collaboration is to apply a biogeographical 

approach to the management of marine resources within the National Marine Sanctuaries. This particular work, 

conducted in consultation with Gray’s Reef National Marine Sanctuary (GRNMS) and scientists conducting research within the South Atlantic Bight region, builds on and advances biogeographic techniques developed by 

CCMA’s Biogeography Team for other National Marine Sanctuaries including Channel Islands, Cordell Bank, 

Gulf of Farallones, Monterey Bay, and Stellwagen Bank. At the onset of the project, CCMA, GRNMS, and NMSP 

staff identified a set of targeted research topics to fill existing gaps in baseline data, and enhance the understanding of key ecological patterns and processes to support the Sanctuary.

The characterization consists of two complementary components: a text report that includes a suite of quantitative spatial and statistical analyses that characterize physical and biological features of GRNMS; and the raw database of all spatial data analyzed to conduct the characterization. The report provides essential information on 

the distribution of modeled and observed species and features needed to support the development of monitoring 

and scientific studies, the development of educational material, and support of other spatially-explicit management decisions. The results of this ecological characterization are available via website. For more information 

on this effort please visit the NCCOS Biogeography Team webpage dedicated to this project at http://ccma.nos.

noaa.gov/ecosystems/sanctuaries/grays_nms.html or direct questions and comments to:

Mark E. Monaco, Ph.D.

Biogeography Team Lead

National Oceanic and Atmospheric Administration

1305 East West Highway

SSMC 4, N/SCI-1

Silver Spring, MD 20910

Phone: (301) 713-3028 x 160

Email: Mark.Monaco@noaa.gov

Or

Greg McFall

Research Coordinator

Gray’s Reef National Marine Sanctuary

National Oceanic and Atmospheric Administration

10 Ocean Science Circle

Savannah, GA 31411

Phone: (912) 598-2345

Email: Greg.McFall@noaa.gov
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Executive Summary

Baseline characterization of resources is an essential part of marine protected area (MPA) management and 

is critical to inform adaptive management. Gray’s Reef National Marine Sanctuary (GRNMS) currently lacks 

adequate characterization of several key resources as identified in the 2006 Final Management Plan. The objectives of this characterization were to fulfill this need by characterizing the bottom fish, benthic features, marine 

debris, and the relationships among them for the different bottom types within the sanctuary: ledges, sparse live 

bottom, rippled sand, and flat sand. Particular attention was given to characterizing the different ledge types, 

their fish communities, and the marine debris associated with them given the importance of this bottom type to 

the sanctuary. 

The characterization has been divided into 

four sections. Section 1 provides a brief overview of the project, its relevance to sanctuary 

needs, methods of site selection, and general 

field procedures. Section 2 provides the survey methods, results, discussion, and recommendations for monitoring specific to the benthic characterization. Section 3 describes the 

characterization of marine debris. Section 4 is 

specific to the characterization of bottom fish. 

Field surveys were conducted during August 

2004, May 2005, and August 2005. A total of 

179 surveys were completed over ledge bottom (n=92), sparse live bottom (n=51), flat 

sand (n=20), and rippled sand (n=16). There 

were three components to each field survey: 

fish counting, benthic assessment, and quantification of marine debris. All components occurred within a 25 x 4 m belt transect. Two 

divers performed the transect at each survey 

site. One diver was responsible for identification of fish species, size, and abundance using a visual survey. The second diver was responsible for characterization of benthic features using five randomly placed 1 m2

 quadrats, measuring ledge height and other benthic 

structures, and quantifying marine debris within the entire transect. 

GRNMS is composed of four main bottom types: flat sand, rippled sand, sparsely colonized live bottom, and 

densely colonized live bottom (ledges). Independent evaluation of the thematic accuracy of the GRNMS benthic 

map produced by Kendall et al. (2005) revealed high overall accuracy (93%). Most discrepancies between map 

and diver classification occurred during August 2004 and likely can be attributed to several factors, including 

actual map or diver errors, and changes in the bottom type due to physical forces. 

The four bottom types have distinct physical and biological characteristics. Flat and rippled sand bottom types 

were composed primarily of sand substrate and secondarily shell rubble. Flat sand and rippled sand bottom 

types were characterized by low percent cover (0-2%) of benthic organisms at all sites. Although the sand bottom 

types were largely devoid of epifauna, numerous burrows indicate the presence of infaunal organisms. Sparse 

live bottom and ledges were colonized by macroalgae and numerous invertebrates, including coral, gorgonians, 

sponges, and “other” benthic species (such as tunicates, anemones, and bryozoans). Ledges and sparse live 

bottom were similar in terms of diversity (H’) given the level of classification used here. However, percent cover 

of benthic species, with the exception of gorgonians, was significantly greater on ledge than on sparse live bottom. Percent biotic cover at sparse live bottom ranged from 0.7-26.3%, but was greater than 10% at only 7 out of 

51 sites. Colonization on sparse live bottom is likely inhibited by shifting sands, as most sites were covered in a 

layer of sediment up to several centimeters thick. On ledge bottom type, percent cover ranged from 0.42-100%, 

with the highest percent cover at ledges in the central and south-central region of GRNMS. 

Image 1a. Fish schooling around densely colonized live bottom in GRNMS.
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Biotic cover on ledges is influenced by local ledge 

characteristics. Cluster analysis of ledge dimensions (total height, undercut height, undercut width) 

resulted in three main categories of ledges, which 

were classified as short, medium, and tall. Median 

total percent cover was 97.6%, 75.1%, and 17.7% 

on tall, medium, and short ledges, respectively. Total percent cover and cover of macroalgae, sponges, and other organisms was significantly lower on 

short ledges compared to medium and tall ledges, 

but did not vary significantly between medium and 

tall ledges. Like sparse live bottom, short ledges 

may be susceptible to burial by sand, however the 

results indicate that ledge height may only be important to a certain threshold. There are likely other factors not considered here that also influence 

spatial distribution and community structure (e.g., 

small scale complexity, ocean currents, differential 

settlement patterns, and biological interactions). 

GRNMS is a popular site for recreational fishing and boating, and there has been increased concern about the 

accumulation of debris in the sanctuary and potential effects on sanctuary resources. Understanding the types, 

abundance, and distribution of debris is essential to improving debris removal and education efforts. Approximately two-thirds of all observed debris items found during the field surveys were fishing gear, and about half of the 

fishing related debris was monofilament fishing line. Other fishing related debris included leaders and spear gun 

parts, and non-gear debris included cans, bottles, and rope. The spatial distribution of debris was concentrated 

in the center of the sanctuary and was most frequently associated with ledges rather than at other bottom types. 

Several factors may contribute to this observation. Ledges are often targeted by fishermen due to the association 

of recreationally important fish species with this bottom type. In addition, ledges are structurally complex and are 

often densely colonized by biota, providing numerous places for debris to become stuck or entangled. Analysis of 

observed boat locations indicated that higher boat activity, which is an indication of fishing, occurs in the center 

of the sanctuary. On ledges, the presence and abundance of debris was significantly related to observed boat 

density and physiographic features including ledge height, ledge area, and percent cover. While it is likely that 

most fishing related debris originates from boats inside the sanctuary, preliminary investigation of ocean current 

data indicate that currents may influence the distribution and local retention of more mobile items. 

Fish communities at GRNMS are closely linked to benthic habitats. A list of species encountered, probability of 

occurrence, abundance, and biomass by habitat is provided. Species richness, diversity, composition, abundance, and biomass of fish all showed striking differences depending on bottom type with ledges showing the 

highest values of nearly all metrics. Species membership was distinctly separated by bottom type as well, although very short, sparsely colonized ledges often had a similar community composition to that of sparse live 

bottom. Analysis of fish communities at ledges alone indicated that species richness and total abundance of fish 

were positively related to total percent cover of sessile invertebrates and ledge height. Either ledge attribute was 

sufficient to result in high abundance or species richness of fish. Fish diversity (H`) was negatively correlated with 

undercut height due to schools of fish species that utilize ledge undercuts such as Pareques species. Concurrent 

analysis of ledge types and fish communities indicated that there are five distinct combinations of ledge type and 

species assemblage. These include, 1) short ledges with little or no undercut that lacked many of the undercut 

associated species except Urophycis earlii; 2) tall, heavily colonized, deeply undercut ledges typically with Archosargus probatocephalus, Mycteroperca sp., and Pareques sp.; 3) tall, heavily colonized but less undercut 

with high occurrence of Lagodon rhomboides and Balistes capriscus; 4) short, heavily colonized ledges typically 

with Centropristis ocyurus, Halichoeres caudalis, and Stenotomus sp.; and 5) tall, heavily colonized, less undercut typically with Archosargus probatocephalus, Caranx crysos and Seriola sp.. Higher levels of boating activity 

and presumably fishing pressure did not appear to influence species composition or abundance at the community level although individual species appeared affected. These results indicate that merely knowing the basic 

Image 1b. Large school of tomtates.
page

characteristics of a ledge such as total height, undercut width, and percent cover of sessile invertebrates would 

allow good prediction of not only species richness and abundance of fish but also which particular fish species 

assemblages are likely to occur there. Comparisons with prior studies indicate some major changes in the fish 

community at GRNMS over the last two decades although the causes of the changes are unknown.

Species of interest to recreational fishermen including Centropristis striata, Mycteroperca microlepis, and Mycteroperca phenax were examined in relation to bottom features, areas of assumed high versus low fishing pressure, and spatial dispersion. Both Mycteroperca species were found more frequently when undercut height of 

ledges was taller. They often were found together in small mixed species groups at ledges in the north central 

and southwest central regions of the sanctuary. Both had lower mode size and proportion of fish above the fishery size limit in heavily fished areas of the sanctuary (i.e. high boat density) despite the presence of better habitat 

in that region. Black sea bass, C. striata, occurred at 98% of the ledges surveyed and appeared to be evenly 

distributed throughout the sanctuary. Abundance was best explained by a positive relationship with percent cover 

of sessile biota but was also negatively related to presence of either Mycteroperca species. This may be due to 

predation by the Mycteroperca species or avoidance of sites where they are present by C. striata.

Suggestions for monitoring bottom features, marine debris, and bottom fish at GRNMS are provided at the end of 

each chapter. The present assessment has established quantitative baseline characteristics of many of the key 

resources and use issues at GRNMS. The methods can be used as a model for future assessments to track the 

trajectory of GRNMS resources. Belt transects are ideally suited to providing efficient and quantitative assessment of bottom features, debris, and fish at GRNMS. The limited visibility, sensitivity of sessile biota, and linear 

nature of ledge habitats greatly diminish the utility of other sampling techniques. Ledges should receive the bulk 

of future characterization effort due to their importance to the sanctuary and high variability in physical structure, 

benthic composition, and fish assemblages.
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TABLE OF CONTENTS

CHAPTER 1 JUSTIFICATION, OBJECTIVES AND APPROACH ....................................................................1

1.1 Introduction..........................................................................................................................................1

1.2 Justification...........................................................................................................................................1

1.3 Methods....................................................................................................................................................4

Site Selection ..........................................................................................................................................4

Field Methods..........................................................................................................................................4

REFERENCES...................................................................................................................................................6

CHAPTER 2 CHARACTERIZATION OF THE BENTHIC COMMUNITIES .......................................................7

2.1 Introduction..........................................................................................................................................7

2.2 Methods for Benthic Surveys........................................................................................................8

2.3 Methods for Data Analysis............................................................................................................10

Thematic accuracy of benthic habitat maps ......................................................................................10

Abiotic features.....................................................................................................................................10

Biotic cover ...........................................................................................................................................11

Abiotic effects on biotic composition.................................................................................................12

2.4 Results...................................................................................................................................................14

Thematic accuracy................................................................................................................................14

Abiotic features.....................................................................................................................................14

Biotic cover ...........................................................................................................................................17

Abiotic effects on biotic composition.................................................................................................20

2.5 Discussion .............................................................................................................................................23

2.6 Recommendations for management and monitoring .........................................................33

REFERENCES.................................................................................................................................................35

Chapter 3: Characterization of the Marine Debris..................................................................37

3.1 Introduction........................................................................................................................................37

3.2 Methods for Marine Debris Surveys.........................................................................................38

3.3 Methods for Data Analysis ...........................................................................................................39

Quantity, types, and spatial distribution of debris.............................................................................39

Effect of bottom type ............................................................................................................................39

Influence of ledge characteristics and boat density on debris ........................................................39

Predicting debris density .....................................................................................................................40

Ocean currents at GRNMS...................................................................................................................40

3.4 Results...................................................................................................................................................41

Quantity, types, and spatial distribution of debris.............................................................................41

Effect of bottom type ............................................................................................................................41

Influence of ledge characteristics and boat density on debris ........................................................41

Predicting debris density .....................................................................................................................44

Ocean currents at GRNMS...................................................................................................................44

3.5 Discussion .............................................................................................................................................46

3.6 Recommendations for management and monitoring .........................................................50

REFERENCES.................................................................................................................................................52

CHAPTER 4: CHARACTERIZATION OF THE BOTTOM FISH ......................................................................55

4.1 Introduction........................................................................................................................................55

4.2 Methods for Fish Surveys..............................................................................................................56

4.3 Methods for Data Analysis............................................................................................................57

4.4 Results...................................................................................................................................................59

4.5 Discussion .............................................................................................................................................69

4.6 Targeted Species ...............................................................................................................................76

4.7 Recommendations for management and monitoring .........................................................78

REFERENCES.................................................................................................................................................80

APPENDIX .......................................................................................................................................................83

Acknowledgements................................................................................................................................91
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List of Tables

Table 1.1. Number of surveys within each bottom type. .....................................................................................4

Table 2.1. Variables measured to characterize benthic composition along fish transects. .................................9

Table 2.2. Error matrix for habitat classification from diver surveys at the GRNMS .........................................12

Table 2.3. A list of misclassified sites based on diver surveys..........................................................................12

Table 2.4. Spearman coefficients (rho) computed for pair-wise correlations among ledge dimensions

for 92 ledge sites. ............................................................................................................................14

Table 2.5. Mean values and S.E. for dimensions of GRNMS ledge clusters determined from hierarchical

clustering. ........................................................................................................................................14

Table 2.6. Summary statistics for biotic composition by bottom types..............................................................16

Table 3.1. Frequency of debris types pooled across all GRNMS survey sites..................................................41

Table 3.2. Presence and average number of debris items per 100m2 by bottom type......................................43

Table 3.3. Contrast estimates, standard errors, and chi-square statistics from logistic regression 

of presence of marine debris in GRNMS by bottom type. ...............................................................43

Table 3.4. Two part conditional model for ledge bottom type............................................................................44

List of Figures

Figure 1.1. Location of Gray’s Reef National Marine Sanctuary.....................................................................2

Figure 1.2. Spatial distribution of GRNMS bottom types and survey locations. .............................................3

Figure 2.1. Schematic representation of the placement of the 1-m2-quadrat along a 25-m transect 

tape during fish and benthic substrate surveys at GRNMS. ........................................................8

Figure 2.2. Schematic representation of the physical ledge dimensions measured during benthic surveys. 9

Figure 2.3. Stacked histogram plot of average abiotic substrate composition by substrate bottom type.....13

Figure 2.4. Ledge dimensions (total height, undercut height, undercut width) at ledge sites in GRNMS. ...13

Figure 2.5. Comparison of maximum ledge height measured in situ and determined through

 GIS analysis of sonar data. .......................................................................................................14

Figure 2.6. Dendrogram produced from hierarchical clustering of ledge sites based on three mean

ledge dimensions. ......................................................................................................................14

Figure 2.7. Three-dimensional plot of ledge clusters against ledge dimensions..........................................15

Figure 2.8. Histogram of average sand thickness on sparse live bottom sites. ...........................................15

Figure 2.9. Means of ledge variables in areas of high and low boat density. ...............................................15

Figure 2.10. Box plots of percent cover of benthic organisms on four bottom substrates at GRNMS. .........17

Figure 2.11. Percent cover of biotic cover groups on flat and rippled sand bottom sites. ..............................18

Figure 2.12. Percent cover of biotic cover groups at sparse live bottom sites. ..............................................19

Figure 2.13. Percent cover of biotic cover groups at ledge sites....................................................................20

Figure 2.14. Box plots of number of gorgonians and sponges on four bottom substrates at GRNMS...........21

Figure 2.15. Box plots of height of gorgonians and sponges on four bottom substrates at GRNMS. ............21

Figure 2.16. Box plots of Shannon diversity index (H) by bottom type...........................................................21

Figure 2.17. Percent cover of coral types at ledge sites. ...............................................................................22

Figure 2.18. Percent cover of gorgonian types at ledge sites. .......................................................................23

Figure 2.19. Percent cover of macroalgae types at ledge sites. ....................................................................24

Figure 2.20. Percent cover of sponge types at ledge sites. ...........................................................................25

Figure 2.21. Percent cover of tunicates at ledge sites. ..................................................................................26

Figure 2.22. Percent cover of “other” benthic cover types at ledge sites. ......................................................27

Figure 2.23. Dendrogram from cluster analysis of all sites based on percent cover for aggregated

cover types.................................................................................................................................28

Figure 2.24. Mean percent cover of benthic organisms by clusters determined from hierarchal

cluster analysis in Figure 2.23....................................................................................................29

Figure 2.25. Dendrogram for cluster analysis of the 92 ledge sites based on percent cover for 

aggregated cover types..............................................................................................................29

Figure 2.26. Mean percent cover by cluster determined from hierarchal clustering of ledges. ......................30

Figure 2.27. Box plots of percent cover of benthic organisms on three ledge groups determined

by cluster analysis......................................................................................................................30
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Figure 2.28. Box plots of mean number of gorgonians and sponges on three ledge groups determined

by cluster analysis of abiotic ledge variables. ............................................................................31

Figure 2.29. Box plots of mean height of gorgonians and sponges on three ledge groups determined

by cluster analysis......................................................................................................................31

Figure 3.1a. Spatial distribution of total debris. ..............................................................................................42

Figure 3.1b. Spatial distribution of fishing gear. .............................................................................................42

Figure 3.1c. Spatial distribution of non-gear...................................................................................................43

Figure 3.2. Average number debris items per 100 m2 transect by bottom type............................................44

Figure 3.3. Frequency of debris at ledge sites by area and height class combinations. ..............................45

Figure 3.4. Relationship of observed number of debris items with ledge height. .........................................45

Figure 3.5. Relationship of observed number of debris items with percent cover of benthic organisms......45

Figure 3.6. Relationship of observed number of debris items with undercut width. .....................................45

Figure 3.7. Relationship of observed number of debris items with ledge area.............................................45

Figure 3.8. Locations of observed boats and density of boats per 0.25 km2 cell. .......................................46

Figure 3.9a. Frequency histogram of the number of boats per cell in GRNMS. ............................................46

Figure 3.9b. Regions of low and high boat density.........................................................................................46

Figure 3.10. For each 0.25 km2 cell, the relationship of average number of debris items with the

number of observed boats. ........................................................................................................47

Figure 3.11. Interpolated debris density in GRNMS from ordinary kriging. ...................................................48

Figure 3.12a. Frequency of current direction observations at 15m depth. .......................................................49

Figure 3.12b. Tidal current direction overlaid on map of GRNMS....................................................................49

Figure 3.13. Recommended strategy to prioritize ledge sites within GRNMS for debris removal..................50

Figure 4.1. Species richness, Shannon Diversity, abundance, and biomass of fish within each

bottom type. ...............................................................................................................................61

Figure 4.2. Size frequency histograms of fish by bottom type. ...................................................................62

Figure 4.3. Dendrogram for cluster analysis of all 179 sites based on species composition. ......................63

Figure 4.4. Multiple regression model of species richness of fish at ledge sites..........................................64

Figure 4.5. Multiple regression model of (log) abundance of fish at ledge sites. .........................................64

Figure 4.6. Multiple regression model of diversity of fish at ledge sites. .....................................................65

Figure 4.7. Dendrogram for cluster analysis of the 92 ledge sites based on species composition. .............66

Figure 4.8. Dendrogram for cluster analysis of the 92 ledge sites based on ledge measurements.............67

Figure 4.9. Intersection of sites clustered based on ledge measurements and fish communities. ..............68

Figure 4.10. Size-frequency histograms for selected bottom fish targeted by the recreational fishery. .........69

Figure 4.11. Size-frequency plots of Centropristis striata observed at ledge sites. .......................................70

Figure 4.12. Size-frequency plots of Mycteroperca phenax observed at ledge sites. ....................................71

Figure 4.13. Size-frequency plots of Mycteroperca microlepis observed at ledge sites.................................72

Figure 4.14. Regression model of Centropristis striata abundance at ledge sites. .......................................73

Figure 4.15. Logistic regression model of grouper species at ledge sites......................................................74

Figure 4.16. Logistic regression model of grouper species at ledge sites......................................................75
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Chapter 1 - Justification, Objectives and Approach 

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CHAPTER 1 JUSTIFICATION, OBJECTIVES AND APPROACH

1.1 Introduction

Baseline characterization of resources and patterns of use is an essential part of marine protected area (MPA) 

management (Kendall et al. 2004, Quattrini and Ross 2006). An inventory of the bottom features and the abundance, size structure, and habitat associations of organisms is needed to catalog and understand the environment within the MPA. In addition, human use patterns must be understood to support development of management strategies designed to minimize user impacts. Once the MPA’s resources and use patterns are adequately 

characterized, changes can be quantified and monitored through time. Such assessment and regular monitoring 

are critical to support adaptive management. Gray’s Reef National Marine Sanctuary (GRNMS) currently lacks 

adequate characterization of several topics including bottom fish, benthic invertebrates, and human use patterns 

(NOAA 2006).

GRNMS is located on the inner continental shelf of the southeastern United States, 32.4 km offshore of Sapelo 

Island, Georgia (Figure 1.1, NOAA 2006). The ecological importance of this area is related to its location at the 

transition between tropical and temperate waters, and the existence of a topographically complex system of 

ledges. The inner shelf area in the Georgia Bight is dominated by tidal currents and riverine runoff and is subject 

to seasonal variations in temperature, salinity and water clarity (Hanson et al. 1981; NOAA 2006). GRNMS is 

also influenced by the Gulf Stream along the outer shelf area, which transports deep nutrient-rich and temperate waters as well as tropical fish species to the area. Commonly referred to as “live bottom” areas, the rocky 

outcroppings within GRNMS support about 300 species of marine invertebrates (Gleason et al. 2005) and about 

65 species of macroalgae (Searles 1988). In turn, these benthic communities provide habitat for as many as 

150 fish species including several of interest to recreational and commercial fishermen (Sedberry and Van Dolah 

1984; Kennedy 1993).

Prior to designation of the sanctuary in 1981, simple characterizations had been conducted in the area (e.g. Hunt 

1974). Since then, the sanctuary has been steadily building an inventory of its biological resources and human 

use patterns. In addition, field guides of the local fish (Gilligan 1989, Parker et al. 1994), algae (Searles 1988), 

and invertebrates (Gleason et al. 2005) have been produced. While limited in their spatial scope and quantitative 

rigor, these efforts have been instrumental in furthering knowledge of sanctuary resources. The recent completion of detailed benthic maps of GRNMS (Figure 1.2, Kendall et al. 2005) has led to a more robust and spatially 

explicit biological inventory and ecological characterization as reported here. Building on these earlier inventory 

activities, the present assessment was designed to address priority resource management issues as identified 

in the 2006 Final Management Plan for the sanctuary (NOAA 2006). This plan identified the management needs 

and goals for the next 5 years at GRNMS and specifically addressed the need for a quantitative, spatial characterization of marine debris, sessile invertebrates, benthic features, and the bottom fish associated with them. 

1.2 Justification

Below are listed the GRNMS action plan items that 

were addressed by the present assessment followed by a description of how each item was met.

STRATEGY MRP-3: REMOVE MARINE DEBRIS 

FROM THE SANCTUARY AND PREVENT NEW 

DEBRIS FROM ACCUMULATING

Activity A: Clarify regulatory authority to address 

materials discharged or deposited outside the 

Sanctuary.

Activity B: Develop and implement a marine debris 

education and outreach program.

Activity C: Develop and implement a debris assessment and monitoring study.

The present assessment addressed Activities B 

and C of this strategy. The assessment character- Image 2. Diver studying benthic community with sponge species in the 

foreground.
Chapter 1 - Justification, Objectives and Approach 

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82°W

82°W

81°W

81°W 80°W

80°W

30°N 30°N

31°N 31°N

32°N 32°N

33°N 33°N

2

0 25 50 12.5

kilometers

SC

GA

FL

Gray’s Reef NMS

ized the type and quantity of marine debris at GRNMS, the type of bottom features it is routinely associated with, 

and proposed a strategy for continued assessment and monitoring. An understanding of the types and spatial 

distribution of marine debris in the sanctuary are necessary prerequisites to conduct clean-up and education activities efficiently. Knowledge of the spatial distribution of debris enables the sanctuary to focus clean-up efforts 

on the most affected areas given limited resources. Knowledge of the types of debris and potential mechanisms 

of transport and deposition allow education and outreach activities to focus their efforts on primary sources of 

debris. 

STRATEGY RM-3: ASSESS AND CHARACTERIZE SANCTUARY RESOURCES

Activity A: Develop and update the GIS database.

Activity B: Characterize benthic habitat.

Activity C: Develop an invertebrate identification guide.

Activity D: Develop the sanctuary characterization.

Figure 1.1. Location of Gray’s Reef National Marine Sanctuary.
Chapter 1 - Justification, Objectives and Approach 

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The present assessment addresses Activities A, B, and D of the strategy. Bottom types were evaluated by several variables including biotic and abiotic cover types as well as ledge dimensions and sand characteristics. By 

linking the characterization of marine debris, benthic habitat, and bottom fish to spatial coordinates and the seafloor map, the data can be easily incorporated into the GIS under development at the sanctuary. This will provide 

the most current, spatially explicit characterization of sanctuary resources available. 

STRATEGY RM-4: MAINTAIN AND ENHANCE MONITORING PROGRAMS

Activity A: Monitor the status and health of fish.

Activity B: Design and implement an invertebrate monitoring program.

Activity C: Develop a comprehensive water quality monitoring program.

Activity D: Develop and implement a sediment analysis and monitoring program.

Activity E: Support and enhance regional ocean observation systems.

Activity F: Expand and update socioeconomic assessment.

Activity G: Synthesize and characterize paleo-environmental information.

The present assessment addresses Activities A and B of this strategy. Quantitative, spatially explicit characterization of fish communities and their associated habitats was a primary goal of this project. This includes 

species composition, size distribution, and density of each species by bottom type. In addition, recommenda-

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2

0 10.5

Kilometers

Flat sand

Rippled sand

Sparsely colonized live bottom

Densely colonized live bottom

GRNMS Boundary

! Survey locations

Figure 1.2. Spatial distribution of GRNMS bottom types (classified by Kendall et al. 2005) and survey locations. 
Chapter 1 - Justification, Objectives and Approach 

page



tions for periodic assessment of bottom fish and 

sessile invertebrates are included to enable long 

term monitoring. 

The objectives of this characterization were to characterize the bottom fish, benthic features, marine 

debris, and the relationships among them for the 

different bottom types within the sanctuary: ledges, sparse live bottom, rippled sand, and flat sand. 

Particular attention was given to characterizing the 

different ledge types, fish communities of ledges, 

and marine debris associated with them given the 

importance of this bottom type to the sanctuary. 

1.3 Methods

Site Selection

Field surveys were conducted in August 2004, May 

2005, and August 2005 to coincide with the availability of a research vessel and favorable weather conditions. Since sampling periods were separated by only 

four months, seasonal differences in benthic communities were not explored in depth. Despite big differences in 

water temperature during May and August, even greater temporal separation of samples and more samples per 

year than used here would be better suited to characterizing seasonal differences. As a result, data are pooled 

for all analyses except where noted and the scope of inference is limited to the summer time period. 

Most survey effort was devoted to the ledge bottom due to its high diversity and its importance to 

the sanctuary (Table 1.1). Less effort was devoted 

to the less diverse, lower complexity, and lower 

variability bottom types such as sparse live bottom 

and less still was devoted to the sand areas. Sites 

were selected randomly from within four bottom 

categories (flat sand, rippled sand, sparse live bottom, and densely colonized live bottom or ledges) identified in the recently completed benthic maps of GRNMS 

(Kendall et al. 2005). Sites in the sparse live bottom and sand categories were buffered such that they were a 

minimum of 30 m from other habitat types to ensure that a 25 m transect conducted along a random heading 

was contained within a single bottom type. Ledge sites are typically only a few meters wide, therefore surveys in 

that habitat were not conducted along a random heading. Ledge surveys were instead conducted along the lip 

of the ledge. Only ledges a minimum of 60 m long were allowed during site selection. A ledge 60 m long was the 

minimum size (+10 m) to accommodate a 25 m transect assuming that it was begun in the middle of the ledge 

and then conducted in a randomly chosen direction (i.e. left or right) along the ledge. If the random site selection process resulted in a point on a ledge smaller than 60 m, the nearest ledge of suitable length was surveyed 

instead. However, this was not a common occurrence, as <5% of the randomly selected sites were located on 

ledges of unsuitable length. Number of surveys by bottom type and sampling period are provided in Table 1.1, 

and the spatial distribution of survey locations is shown in Figure 1.2.

Field Methods

There were three components to the field survey: fish counting, benthic assessment, and quantification of marine 

debris, all of which occurred within a 25 x 4 m belt transect for a total survey area of 100 m2

. Two divers surveyed 

the transect at each survey site. One diver was responsible for visual counts and size estimation of fish species. 

The second diver characterized benthic features with five randomly placed 1 m2

 quadrats and quantified marine 

debris within the entire transect. More details on field methods will be provided in subsequent sections. 

For all bottom types except ledge, the divers selected a random compass heading (0-360°) along which to conduct the survey. Exceptions were made at sites with a strong current or surge, where the survey was conducted 

Image 3. Jackknife fish, Equetus lanceolatus and coral. 

Table 1.1. Number of surveys within each bottom type.

Bottom Type 

Aug

2004 

May 

2005 

Aug

2005 Total Surveys

Ledge 15 35 42 92

Sparse live bottom 17 20 14 51

Flat sand 4 8 8 20

Rippled sand 8 5 3 16
Chapter 1 - Justification, Objectives and Approach 

page



into the current to ease the physical demands on 

divers. Surveys over ledge habitat were conducted 

along the ledge face or lip (if undercut) and followed any turns or curves along the ledge. This 

ensured that the entire survey would be conducted 

within the ledge bottom type. Once at a site, the 

fish surveyor attached a tape measure to the substrate or weighted line that was used to mark the 

site and began the survey. The entire length of the 

transect survey was conducted at a relatively constant speed and fixed time period (~15 minutes) regardless of bottom type or number of fish present. 

Detailed survey methods and protocols for data 

collection for bottom fish, benthic assessment, and 

quantification of marine debris are provided in the 

subsequent chapters respectively.

Image 4. Diver conducting habitat survey.
Chapter 1 - Justification, Objectives and Approach 

page



REFERENCES

Gilligan, MR. 1989. An Illustrated Field Guide to the Fishes of Gray’s Reef National Marine Sanctuary. NOAA Technical 

Memorandum, NOS MEMD 25. Marine and Estuarine Management Division, OOCRM, NOS, NOAA, U.S. Department of 

Commerce, Washington, D.C. February 1989. 77 p.

Gleason, DF, AW Harvey, SP Vives. 2005. A guide to the benthic invertebrates and cryptic fishes of Gray’s Reef. Georgia 

Southern University. Statesboro GA. http://www.bio.georgiasouthern.edu/GR-inverts/

Hanson, RB, KR Tenore, S Bishop, C Chamberlain, MM Pamatmat, and J Tietjen. 1981. Benthic enrichment in the Georgia 

Bight related to Gulf Stream intrusions and estuarine outwelling. Journal of Marine Research 39(3): 417-441.

Hunt, JL. 1974. The geology and origin of Gray’s Reef, Georgia continental shelf. Master’s Thesis, Athens, Georgia.

Kendall, MS, JD Christensen, C Caldow, M Coyne, C Jeffrey, ME Monaco, W Morrison Z Hillis-Starr. 2004. The influence of 

bottom type and shelf position on biodiversity of tropical fish inside a recently enlarged marine reserve. Aquatic Conservation: Marine and Freshwater Ecosystems 14:113-132.

Kendall, MS, OP Jensen, C Alexander, D Field, G McFall, R Bohne, and ME Monaco. 2005. Benthic mapping using sonar, 

video transects, and an innovative approach to accuracy assessment: A characterization of bottom features in the Georgia 

Bight. Journal of Coastal Research 21(6): 1154-1165.

Kennedy, J. 1993. Gray’s Reef National Marine Sanctuary: after a decade in the sanctuary program, it still has secrets to 

share. Mariner’s Weather Log 37:2a-8a.

NOAA. 2006. Gray’s Reef National Marine Sanctuary Final Management Plan/Final Environmental Impact Statement. 

NOAA NOS NMSP, Savannah, GA.

Parker Jr., RO, AJ Chester, and RS Nelson. 1994. A video transect method for estimating reef fish abundance, composition, 

and habitat utilization at Gray’s Reef National Marine Sanctuary, Georgia. Fishery Bulletin 92:787-799.

Quattrini, AM, SW Ross. 2006. Fishes associated with North Carolina shelf-edge hardbottoms and initial assessment of a 

marine protected area. Bulletin of Marine Science 79:137-163.

Searles, RB. 1988. An Illustrated Field Guide to the Seaweeds of Gray’s Reef National Marine Sanctuary. NOAA technical 

memorandum NOS MEMD 22, Silver Spring, MD.

Sedberry, GR, and RF Van Dolah. 1984. Demersal fish assemblages associated with hard bottom habitat in the South Atlantic Bight of the USA. Environmental Biology of Fishes 11(4): 241-258.
Chapter 2 - Benthic Communities

page 

CHAPTER 2 CHARACTERIZATION OF BENTHIC COMMUNITIES

2.1 Introduction

Morphologically complex hardbottom outcroppings are interspersed throughout the continental shelf from the 

coast to the shelf edge along the South Atlantic Bight (Parker et al. 1983; Van Dolah et al. 1994). These rocky 

features vary from flat, smooth surfaces to exposed vertical scarps and ledges with numerous overhangs, crev-

ices, and slopes (Riggs et al. 1996). Exposed surfaces are colonized to varying extents by algae and sessile 

and burrowing invertebrates, which in turn provide shelter, foraging, and nursery areas for a large diversity of 

fish. In addition to providing important habitat, hardbottom substrate is an important source of sand production 

on sediment-starved areas on the continental shelf (Riggs et al. 1998). Although several studies have examined 

the distribution (Henry and Giles 1979; Parker et al. 1983; Van Dolah et al. 1994) and geological origins (Riggs 

et al. 1996) of hardbottom habitats in the South Atlantic Bight, less is known about the benthic flora and fauna 

communities that inhabit these substrates (but see Wenner et al. 1983; Peckol and Searles 1984).

Gray’s Reef National Marine Sanctuary (GRNMS) encompasses approximately 58 km

2

, about 75% of which is 

comprised of unconsolidated sediments, including flat sand plains and rippled sand (Kendall et al. 2005). The 

remaining substrate consists of outcroppings of carbonate hardbottom formed during the Pliocene era. The 

hardbottom ranges from areas with little or no vertical relief to areas of irregular, high-relief rocky ledges (> 2 m) 

where invertebrate growth is abundant (Henry and Giles 1979; Van Dolah et al. 1994; Kendall et al. 2005). The 

vast majority (~97%) of the hardbottom areas at GRNMS are flat, contain a thin veneer of sand overlying sand-

stone or limestone rock, and are sparsely colonized by sessile invertebrates. Densely colonized ledges account 

for <1% of the total bottom.

A recent review of the GRNMS management plan identified the need to assess and characterize sanctuary re-

sources to understand the associations among biological, physical and geological components of the ecosystem 

being protected (NOAA 2006). To this end, one focus of sanctuary management has been to classify and charac-

terize benthic habitats through mapping activities (Kendall et al. 2005). Benthic maps provide an understanding 

of the spatial distribution of benthic habitats within GRNMS and a spatial framework for addressing research and 

management questions such as identifying and protecting essential fish habitats. The premise here is that strong 

linkages exist between fishes and their habitats and these linkages affect the spatial distribution of these ani-

mals. However, benthic maps may be somewhat limited because they comprise a mosaic of habitat patches that 

are static point-in-time estimates of dynamic properties of the ecosystem they represent (Weins 1976). In addi-

tion, habitat patches are assumed to be areas of homogeneous ecological and environmental conditions with 

discrete boundaries at a specific spatial and temporal scale. For example, all map features labeled as densely 

colonized ledges are assumed to be identical at the mapped scale but may contain complex spatial patterns in 

the distribution of resources that are not only variable when examined at the finer scales but also are temporally 

dynamic. Finer-scale 

in situ assessments and characterizations of benthic substrates are needed to quantify 1) 

the accuracy of hardbottom delineations, and 2) within and between substrate variability that may be affecting 

benthic communities and fish assemblages within 

GRNMS (Kendall et al. 2005).

In addition, the recent management plan calls for 

activities to maintain and enhance monitoring pro-

grams (NOAA 2006). At GRNMS, invertebrates 

comprise the most diverse, abundant, and conspic-

uous component on hardbottom habitats, but pre-

vious assessments and monitoring attempts have 

not yielded appropriate data to detect changes 

in abundance, density or presence/absence over 

time (NOAA 2006). To fill this data gap, the current 

study provides a consistent and comprehensive in situ assessment of benthic communities, which 

can be used to design, implement, and maintain 

an invertebrate monitoring program at GRNMS. 

Moreover, this study provides data that will help 

quantify detailed habitat-fish associations, which 

will be useful in developing quantitative estimates Image 5. A close-up look at a diverse benthic community.
Chapter 2 - Benthic Communities

page 

of habitat utilization by fishes and provide the spatial framework 

needed to address management goals relative to protecting es-

sential fish habitats. 

Finally, data collected during this study will be used to evaluate 

independently the thematic accuracy of the GRNMS benthic 

map produced by Kendall et al. (2005). Kendall et al. (2005) 

evaluated the accuracy of the benthic habitat map with video 

transect data, which showed a very high degree of thematic 

accuracy. However, due to limitations of the accuracy assess-

ment data set, they were restricted to evaluating only two of 

the four bottom types, sparsely colonized live bottom and un-

consolidated sediment. Utilizing 

in situ classifications of habitat 

type will enable us to evaluate the accuracy of all four bottom 

types, including ledges.

This study provides baseline estimates of the composition of 

four mapped benthic substrates within GRNMS as identified by 

Kendall et al. (2005). The overall goal of this study was to pro-

vide detailed and complete assessments of benthic substrates 

within GRNMS, especially the ledge bottom types. Specific ob-

jectives were as follows:

1.Evaluate independently the thematic accuracy of the 

GRNMS benthic map produced by Kendall et al. (2005);

2. Characterize the abiotic features of the benthos within each mapped bottom substrate;

3. Characterize the types, distribution, abundance of benthic flora and fauna within mapped substrates; and, 

4. Identify abiotic features that may be influencing the spatial distribution, composition and abundance of inver-

tebrate assemblages at ledges.

2.2 Methods for Benthic Surveys

Using the same classification scheme that was used in benthic maps (Kendall et al., 2005), the diver indepen-

dently assigned an overall bottom type to each transect based on 

in situ observation. Bottom types were ledge, 

sparse live bottom, flat sand, or rippled sand. The bottom type was assigned independently of that expected 

based on the benthic map so that the 

in situ data could be used for map validation. Data on the percent cover 

of abiotic and biotic composition at each survey site were recorded within five 1 m

2

 quadrats along the 25 x 4 m 

transect. Some sites in August 2005 had only four quadrats evaluated due to scuba diving time limits. The quad-

rat was placed at each randomly chosen meter mark and systematically alternated from side to side along the 

transect tape, except on ledges (Figure 2.1). When characterizing the narrow ledges, the quadrats were placed 

entirely on the ledge rather than on the often bare substrate below it. It is important to note that beyond the scarp 

and first 1-2 meters of the top of the ledge, the bottom transitions into sparse live bottom. Transects at ledge 

sites were conducted solely along this ledge edge and not the sparse live bottom behind it. At all sites, several 

variables were measured to characterize benthic composition and structure (Table 2.1). The quadrat was divided 

into 100 smaller 10 x 10 cm squares with string (1 small square = 1% cover) to help the diver with estimation of 0 5 10 15 20 25 m

1-m

2 quadrat Transect tape

4 m

Figure 2.1. Schematic representation of the placement of the 1-m

2

-quadrat along a 25-m transect tape during fish and benthic sub-

strate surveys at GRNMS. Broken line represents the total area surveyed (100 m

2

).

Image 6. Diver measuring ledge dimensions.
Chapter 2 - Benthic Communities

page 

Table 2.1. Variables measured to characterize benthic composition along fish transects.

Benthic Composition % Cover Height (cm) Abundance (#) 

Abiotic

Hard x 

Sandx

Shell rubble x

Fine sediment x

Biotic

Corals 

 Branching 

 Cup 

 Ball 

xxx

 Encrusting 

 Other 

xx

Gorgonians 

 Sea rods / plumes 

 Sea fans 

xxxxxx

 Sea whips xxx

Macroalgae 

 Filamentous 

 Fleshy 

 Other 

/ Turf xxx

Sponges 

 Ball xxx

 Vase xxx

 Tube xxx

 Finger 

 Rope 

 Encrusting 

xxxxxxx

Other benthic macrofauna 

 Anemones x

 Tunicates (Encrusting) 

 Tunicates (Lobate) 

 Zoanthids 

xxx

 Other x

percent cover. Percent cover was determined by looking at the quadrat from above and visually estimating percent cover in a two dimensional plane. The percent cover (to the nearest 1%) of four abiotic substrate categories 

was determined first. The categories of abiotic substrates were hardbottom, sand, shell rubble, and fine sedi-

ments (Table 2.1). Hardbottom referred to consolidated substrates including those that were covered in a thin 

veneer of sand less than 10 cm thick and immovable, consolidated shell rock substrates. Shell rubble referred 

to loose shells or shell fragments that were moveable. Fine sediments were substrates consisting of unconsolidated silt that was easily resuspended and remained in the water column. 

Percent cover (to the nearest 0.1 %) of the sessile biota was also determined for major taxonomic groups, which 

were further subdivided into categories based on morphology (Table 2.1). The maximum height and number of 

individual colonies were recorded for each sponge 

and gorgonian morphology. Uncolonized substrata 

were recorded as bare substrate.

Other specific measurements were made on ledge, 

sparse live bottom, and sandy bottom types. For 

ledge sites, the dimensions of ledges were recorded at each quadrat position. Total height was measured from the base of the ledge to the top of the 

substrate behind it but excluded the height of ses-

sile organisms that were attached to the substrate 

(Figure 2.2). Undercut width – the distance from 

Total ledge 

height

Undercut width

Undercut height

igure 2.2. Schematic representation of the physical ledge dimensions 

easured during benthic surveys.

Fm
Chapter 2 - Benthic Communities

page 10

the leading face of the ledge to the farthest recess 

under the ledge – was visually estimated either 

by using the tape as a reference or by inserting 

the quadrat under the ledge (Figure 2.2). Under-

cut height – the height under the ledge – also was 

estimated visually with the length of the quadrat 

as a reference (Figure 2.2). Undercut width and 

height were recorded as zero for ledges that were 

not undercut. On sparse live bottom, the depths of 

sand, shell rubble, or fine sediment (hereafter sand 

thickness) were measured from the surface to the 

underlying limestone bottom up to 30 cm deep. On 

flat and rippled sand, the number of holes from bur-

rowing organisms that occurred within each quad-

rat was recorded. In addition, on rippled sand, the 

wavelength and height of sand ripples within each 

quadrat were recorded.

Sites were used as independent sample units and 

were considered replicates within each bottom type. Multiple quadrat measurements within each site transect 

were averaged using the equation: ∑(Qi-n)/n, where Q

i

 = quadrat i, and n = total number of quadrats. Average site 

values were then used to calculate means and standard errors of measured variables for each bottom type.

2.3 Methods for Data Analysis

Thematic accuracy of benthic habitat maps

Thematic accuracy of the benthic map of GRNMS was estimated using diver surveys at all 179 locations. Re-

sults including overall accuracy, user’s and producer’s accuracy, and Kappa statistic (Congalton and Green 

1999) are summarized in an error matrix for all four mapped bottom types: flat sand, ledge, rippled sand, and 

sparse live bottom. 

Abiotic features

The mean percent cover of each abiotic substrate category was plotted by bottom type. The hypotheses that 

1) the amount of hardbottom varied significantly between ledges and sparse live bottom and 2) the amount of 

sand and shell rubble varied significantly among flat sand and rippled sand were tested non-parametrically with 

a Wilcoxon Rank Test (JMP v5.1).

The physical dimensions (total height, undercut height, and undercut width) at all ledge sites were entered into 

a geographic information system (GIS) and mapped to examine any spatial patterns of these ledge character-

istics in the sanctuary. In addition, Spearman’s Rho was computed on the ranks of data values to determine if 

significant pair-wise correlations existed among ledge dimension variables. Rho is a non-parametric correlation 

parameter that ranges from 1 to -1 with a value of 1 indicating a strong positive relationship and -1 indicating 

a strong negative relationship between paired variables (Sokal and Rohlf 1995). Then hierarchical clustering 

(Ward’s minimum variance method) was used to identify groups of ledge sites with similar 

in situ physical dimen-

sions (JMP v5.1). The stability of the clusters was checked by running the cluster analysis with multiple fusion 

strategies. The sites and their corresponding clusters were plotted against total height, undercut height, and 

undercut width.

Estimates of ledge height for all ledges within GRNMS were derived previously from a GIS analysis of sonar 

bathymetry by Kendall and Eschelbach (2006). Briefly, they determined ledge height for each polygon using 

a 2 meter resolution bathymetry grid of the sanctuary. All depth values from the bathymetry grid around each 

polygon were extracted from the bathymetry data and the deepest and shallowest values were subtracted to 

determine the maximum depth change or height for each ledge. To assess the accuracy of the GIS height esti-

mates, the maximum ledge height determined by Kendall and Eschelbach (2006) was compared with maximum 

ledge height recorded 

in situ. In situ estimates of ledge height from the present study were plotted against GIS 

Image 7. Example of sand bottom in GRNMS. 
Chapter 2 - Benthic Communities

page

11

estimates from Kendall and Eschelbach (2006) and a nonparametric Spearman’s Rho correlation was computed 

on the ranks of the data.

A frequency histogram was used to determine the 

distribution of data on sand thickness from sparse 

live bottom sites. Data on sand characteristics 

(sand ripple height and wavelength) were calcu-

lated for rippled sand. The number of burrows on 

flat and rippled sand was compared with a oneway ANOVA test.

Finally, we examined how ledge characteristics 

differ across regions of varying human use. Dis-

cussions with recreational fishermen indicated 

that they tend to target ledges that are tall and 

large in area. Indeed, larger and taller ledges also 

contained significantly greater amounts of ma-

rine debris, which is additional evidence of fishing 

(Chapter 3). Several ledge metrics, such as area 

(measured through GIS analysis) ledge height, un-

dercut height, undercut width, and percent cover 

of benthic organisms, were therefore quantified 

and compared between areas of high and low boat 

density, as identified in Chapter 3, using paramet-

ric one-way ANOVA tests (JMP v5.1). 

Biotic cover

The percent cover of each biotic category was plotted by bottom type and interquantile (25th, 75th) statistics were 

calculated. The relative total percent cover was the sum of all quadrat measurements (converted to a percent-

age) of each biotic category across all sites within each bottom type. The hypotheses that the biotic cover varied 

among bottom types was tested non-parametrically with Kruskal-Wallis Tests (JMP v5.1) and Dunn’s multiple 

comparison tests. Similarly, height and abundance of sponges and gorgonians were plotted by bottom type and 

interquantile (25th, 75th) statistics were calculated. The hypothesis that height and number of individuals varied 

among bottom type was tested non-parametrically with Kruskal-Wallis Tests (JMP v5.1) and Dunn’s multiple 

comparison tests. 

Percent cover of main cover types at all sites was 

mapped in a geographic information system (GIS). 

Pie chart symbology was used to depict percent 

cover of corals, gorgonians, macroalgae, sponge, 

“other”, and bare substrate at each site. Loca-

tion of sites was occasionally adjusted slightly to 

prevent chart overlap. In addition, detailed maps 

were made for ledge bottom to show percent cov-

er of subcategories within each main cover type. 

For each main cover type, bar charts were used 

to depict percent cover of subcategories at each 

ledge site. Two maps of this type were made for 

the “other” category. Due to the importance of tuni-

cates as a sanctuary resource, the subcategories 

devoted to tunicates (encrusting and lobate) were 

mapped separately to better discern spatial pat-

terns of tunicate cover. The second map included 

the remaining subcategories (anemones, benthic 

dwelling zoanthids, and other).

Image 8. Sparsely colonized live bottom (with bat fish) in GRNMS.

Image 9. Short ledge with 1x1 m quadrat.
Chapter 2 - Benthic Communities

page

12

Shannon-Weiner diversity (H’) of biotic cover for 

each site was calculated using the detailed (nonaggregated) morphology types as:

 x

H’= - ∑pi ln(p

i

) 

 i=1

where x is the total number of morphological types 

(see Table 2.1 for list of all types) and p

i

 is the pro-

portion of area covered by morphological type i. 

The hypothesis that diversity varied among bottom 

types was tested non-parametrically with a Krus-

kal-Wallis Test (JMP v5.1) and Dunn’s multiple 

comparisons tests. 

Biotic data were also analyzed by hierarchal clus-

tering using Ward’s minimum variance method. 

The percent cover variables, aggregated by cover 

type (corals, gorgonians, macroalgae, sponge, 

and other), were the basis for the analysis. A scree 

plot was used to help determine 

the number of well-separated 

clusters from the dendrogram. 

The stability of the clusters was 

checked by running the cluster 

analysis with multiple fusion 

strategies. In addition, the clus-

tering procedure was conducted 

for ledges only using the same 

methods. 

Abiotic effects on biotic 

composition

Finally, the relationship be-

tween the benthic community 

structure and abiotic ledge 

characteristics was assessed. 

Graphical methods and nonparametric tests were utilized 

to examine whether the percent 

cover, number of individuals, 

and height of benthic organ-

isms varied among the three 

ledge categories (short, medi-

um, and tall), which were deter-

mined from the cluster analysis 

of abiotic variables (see Re-

sults- Abiotic features). Percent 

cover of each biotic type (total, 

coral, gorgonians, macroalgae, 

sponge, and other organisms) 

was plotted by ledge category 

and interquantile (25th, 75th) 

statistics were calculated. The 

Table 2.2. Error matrix for habitat classification from diver surveys at the Grays’ Reef National 

Marine Sanctuary. U = user’s accuracy and P = producer’s accuracy.

Image 10. Example of a tall, undercut ledge in GRNMS. A 1x1 m quad-

rat is shown for scale.

Diver assessed 

bottom type

Count Flat sand Ledge Rippled sand Sparse live 

bottom

Total

Flat sand 18

100% (U) 

85.7% (P) 

00 0 18

Ledge 0 92

91.1% (U) 

100 % (P) 

2 7 101

Rippled sand 30 13

81.3% (U) 

86.7% (P) 

0 16

Sparse live 

bottom

000 44

 100 % (U) 

 86.3 % (P) 

44

Total 21 92 15 51 179

Mapped

bottom type 

*Overall accuracy = (167/179)*100% = 93.3%

 Kappa = 0.89 ± 0.03 

Table 2.3. A list of misclassified sites based on diver surveys (n = 12).

Site Date 

Mapped

Bottom Type 

Diver Assessed 

Bottom Type Diver Notes 

D19 Aug 04 Ledge Rippled sand change in depth, sparse, 

surveyed sand nearby 

D22 Aug 04 Ledge Sparse live bottom None 

D23 Aug 04 Ledge Sparse live bottom small ledge nearby 

D27 Aug 04 Ledge Rippled sand change in depth, surveyed 

rippled sand nearby 

D30 Aug 04 Ledge Sparse live bottom None 

D4 Aug 04 Ledge Sparse live bottom None 

D42 Aug 04 Ledge Sparse live bottom None 

D8 Aug 04 Ledge Sparse live bottom small ledge nearby 

D9 Aug 04 Ledge Sparse live bottom None 

RS1 Aug 05 Rippled sand Flat sand None 

RS7 Aug 05 Rippled sand Flat sand many echinoderms, >15 

RS9 Aug 05 Rippled sand Flat sand None 
Chapter 2 - Benthic Communities

page

13

hypothesis that the percent cover 

varied among ledge categories 

was tested non-parametrically with 

Kruskal-Wallis Tests (JMP v5.1) and 

Dunn’s multiple comparison tests. 

Similarly, height and abundance of 

sponges and gorgonians were plot-

ted by ledge category and interquan-

tile (25th, 75th) statistics were calcu

-

lated. The hypothesis that height and 

number of individuals varied among 

ledge category was tested nonparametrically with Kruskal-Wallis 

Tests (JMP v5.1) and Dunn’s multi-

ple comparison tests. The mean and 

standard error of Shannon diver-

sity was calculated for each ledge 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Ledge Sparse live

bottom

Flat sand Rippled

sand

Mean percent cover (%)

Shell rubble

Sand

Hard bottom

n=92 n=51 n=21 n=15

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Ledge Sparse live

bottom

Flat sand Rippled

sand

Mean percent cover (%)

Shell rubble

Sand

Hard bottom

n=92 n=51 n=21 n=15

Figure 2.3. Stacked histogram plot of average abiotic substrate composition (relative 

total percent cover) by substrate bottom type.

2

0 10.5

Kilometers

Flat sand

Rippled sand

Sparsely colonized live bottom

Densely colonized live bottom

GRNMS Boundary

Ledge dimensions (cm)

150

Mean total height

Mean undercut height

Mean undercut width

Figure 2.4. Ledge dimensions (total height, undercut height, undercut width) at ledge sites in GRNMS. The tallest bar in the legend 

represents 150 cm.
Chapter 2 - Benthic Communities

page

14

height category and the hypothesis that diversity varied between ledge height classes was tested using a one-way ANOVA test 

(JMP v5.1).

2.4 RESULTS

Thematic accuracy

Overall map accuracy was quite high 

(93.3%), with 167 of 179 sites being cor-

rectly classified (Table 2.2). Kappa was 

0.89 ± 0.03 indicating that the classification 

in the map is ~89% better than that expect-

ed if bottom types were randomly assigned 

to each polygon. A user of the benthic map 

can expect nearly 100% accuracy for flat 

sand and sparse live bottom, 81.3% for 

rippled sand, and 91.1% for ledge. Three 

sites classified in the map as rippled sand 

were identified as flat sand by divers. Nine 

of the sites classified as ledges in the map 

were identified as other bottom types by 

divers. Two such ledge sites were identified 

as rippled sand and the other seven were 

identified as sparse live bottom. A list of all 

misclassified sites along with relevant diver 

notes is given in Table 2.3.

Abiotic features

Ledge and sparse live bottom sites were 

dominated by hardbottom substrates, with 

very small amounts of sand or shell rubble 

cover (Figure 2.3). Mean cover of hardbottom did not vary significantly between ledge 

and sparse live bottom sites (X

2

 = 0.5991, 

df = 1, p = 0.44). No hardbottom was observed at either flat or rippled sand sites 0

50

100

150

200

250

300

0 50 100 150 200 250 300

GIS height (cm)

In stu height (cm)

Rho = 0.63

p <0.0001

Figure 2.5. Comparison of maximum ledge 

height measured in situ and determined through 

GIS analysis of sonar data. The red dashed line 

represents the theoretical 1:1 ratio. Statistics for 

Spearman’s rank correlation are provided.

Table 2.4. Spearman coefficients (rho) computed for pair-wise correlations 

among ledge dimensions for 92 ledge sites.

variable by variable Rho

Undercut width (cm) Total height (cm) 0.7029

Pr>Rho 

<.0001 

Ave. Undercut height (cm) Total height (cm) 0.7269 <.0001 

Ave. Undercut height (cm) Undercut width (cm) 0.9673 <.0001 

Table 2.5. Mean values and S.E. for dimensions of GRNMS ledge clusters 

determined from hierarchical clustering (Ward’s minimum variance).

ClusterN Group Total Height Undercut width Undercut Height

1 60 Short 12.3 (1.0) 3.0 (0.7) 1.4 (0.3 2 26 Medium 45.5 (2.9) 34.0 (6.9) 15.5 (1.3) 3 6 Tall 115.8 (18.9) 175.1 (38.5) 38.4 (2.1) 

Figure 2.6. Dendrogram produced from hierarchical clustering of ledge 

sites based on three mean ledge dimensions (total height, undercut 

height, and undercut width).

Cluster 3

(Tall)

Cluster 2

(Medium)

Cluster 1

(Short)

3 well separated clusters

Dissimilarity
Chapter 2 - Benthic Communities

page

15

(Figure 2.3). Sand and shell rubble dominated flat 

and rippled sand bottom sites (Figure 2.3). Shell 

rubble generally occurred in the troughs of sand 

waves on rippled sand sites. Ranking of mean 

sand and shell rubble values was not significantly 

different between flat sand and rippled sand sites 

(X

2

 = 0.56, df = 1, p = 0.45). Fine sediment was not 

observed at any site surveyed. 

The physical dimensions of the ledges surveyed 

exhibited wide variation and did not exhibit distinct 

spatial patterns (Figure 2.4). Strong positive pairwise correlations occurred among ledge dimensions such that an increase in total height also 

correlated with an increase in undercut height and 

undercut width (Table 2.4). In particular, undercut 

height was highly correlated with undercut width 

(Table 2.4) Total height explained 70 and 72% of 

the variability in average undercut width and height, 

respectively. In stu maximum height correlated 

with maximum ledge height determined from the 

GIS analysis of sonar bathymetry (rho = 0.63, p < 

0.0001). However, with the exception of one case, 

GIS derived heights were always higher than n stu

field measurements (Figure 2.5).

Hierarchical clustering identified three groups of 

ledge sites based on their physical dimensions (Figure 2.6). Cluster one (hereafter “short”) was the largest group and contained 60 short ledge sites with 

little or no undercut (Figure 2.6, Table 2.5). Cluster 

two (hereafter “medium”) contained 23 ledge sites 

that were of medium height and moderate undercut, 

while Cluster 3 (hereafter “tall”) had six tall ledges. 

A three dimensional plot of ledge sites against their 

Mean undercut height (cm)

1700

57

113

29.3

44.0

Mean total height (cm)

14.7

0 0.0

Mean undercut width (cm)

296

197

99

Short

Medium

Tall

Mean undercut height (cm)

1700

57

113

29.3

44.0

Mean total height (cm)

14.7

0 0.0

Mean undercut width (cm)

296

197

99

Short

Medium

Tall

Figure 2.7. Three-dimensional plot of ledge clusters against ledge dimensions. Ledge sites from GRNMS were classified into three groups 

by hierarchical clustering using the Ward’s minimum variance method. 02468

10

12

14

16

5 10 15

Sand thickness (cm)

Frequency

Figure 2.8. Histogram of average sand thickness on sparse live bottom 

sites.

0

500

1000

1500

2000

2500

3000

High Low

Area (m2)

05

10

15

20

25

30

35

40

45

50

High Low

Mean ledge height (cm)

02468

10

12

14

High Low

Mean undercut height (cm)

05

10

15

20

25

30

35

40

45

High Low

Mean undercut width (cm)

F Ratio = 11.97, 

p = 0.0008

F Ratio = 9.01, 

p = 0.0035

F Ratio = 5.0, 

p = 0.0279

F Ratio = 2.22, 

p = 0.1395

0

500

1000

1500

2000

2500

3000

High Low

Area (m2)

05

10

15

20

25

30

35

40

45

50

High Low

Mean ledge height (cm)

02468

10

12

14

High Low

Mean undercut height (cm)

05

10

15

20

25

30

35

40

45

High Low

Mean undercut width (cm)

F Ratio = 11.97, 

p = 0.0008

F Ratio = 9.01, 

p = 0.0035

F Ratio = 5.0, 

p = 0.0279

F Ratio = 2.22, 

p = 0.1395

0

10

20

30

40

50

60

70

High Low

Mean percent cover (%)

F Ratio = 16.0, 

p = 0.0001

0

10

20

30

40

50

60

70

High Low

Mean percent cover (%)

F Ratio = 16.0, 

p = 0.0001

Figure 2.9. Means (± SEM) of ledge variables in areas of high and low boat density. Results of one-way ANOVA tests are provided 

(df = 91, α = 0.05). 

30

40

50

60

70

rcent cover (%)

F Ratio = 16.0, 

p = 0.0001

30

40

50

60

70

rcent cover (%)

F Ratio = 16.0, 

p = 0.0001

0

500

1000

1500

2000

2500

3000

High Low

Area (m2)

05

10

15

20

25

30

35

40

45

50

Mean ledge height (cm)

02468

10

12

14

High Low

Mean undercut height (cm)

05

10

15

20

25

30

35

40

45

Mean undercut width (cm)

F Ratio = 11.97, 

p = 0.0008

F Ratio = 5.0, 

p = 0.0279

0

500

1000

1500

2000

2500

3000

High Low

Area (m2)

05

10

15

20

25

30

35

40

45

50

Mean ledge height (cm)

02468

10

12

14

High Low

Mean undercut height (cm)

05

10

15

20

25

30

35

40

45

Mean undercut width (cm)

F Ratio = 11.97, 

p = 0.0008

F Ratio = 5.0, 

p = 0.0279
Chapter 2 - Benthic Communities

page

16

Table 2.6. Summary statistics for biotic composition by bottom types. Blank cells indicate that zero organ-

isms were observed. 

COVER TYPE Morphology Biotic variable Mean S.E. Mean S.E. Mean S.E. Mean S.E.

Corals Ball 

% Cover 

 

Branching 

% Cover 1.2 0.2 <0.1 <0.1 

Cup 

% Cover <0.1 <0.1 <0.1 <0.1 

Encrusting

% Cover <0.1 <0.1 <0.1 <0.1 

Other

% Cover 0.1 0.1 <0.1 <0.1 

Gorgonians Sea fans # Individuals <0.1 <0.1 <0.1 <0.1 

% Cover <0.1 <0.1 <0.1 <0.1 

Ht (cm) 1.4 0.5 1.1 0.4 

Sea rod/plume # Individuals 3.3 0.4 4.9 0.5 <0.1 <0.1 <0.1 <0.1

% Cover 1.3 0.2 1.5 0.2 <0.1 <0.1 <0.1 <0.1

Ht (cm) 13.5 1.4 19.4 1.5 0.7 0.8 1.2 0.9

Sea whips # Individuals 0.1 <0.1 0.1 <0.1 <0.1 <0.1 

% Cover <0.1 <0.1 <0.1 <0.1 <0.1 <0.1 

Ht (cm) 2.0 0.6 3.6 1.1 <0.1 <0.1 

Macroalgae Filamentous/turf 

% Cover 18.1 2.5 0.3 0.1 <0.1 <0.1 

 

Fleshy 

% Cover <0.1 <0.1 0.1 <0.1 

Other

% Cover <0.1 <0.1 

 

Other Anemones 

% Cover <0.1 <0.1 <0.1 <0.1 <0.1 <0.1 <0.1 <0.1

 

Other

% Cover 4.6 1.2 0.5 0.2 <0.1 <0.1 <0.1 <0.1

 

Tunicates 

(Encrusting) % Cover 2.9 1.0 0.3 0.2 

Tunicates

(lobate) % Cover 6.3 1.1 0.7 0.1 

Zoanthids

(benthic dwelling) % Cover 0.4 <0.1 <0.1 <0.1 

 

Sponge Ball # Individuals 0.8 0.1 0.4 <0.1 

% Cover 0.3 <0.1 0.1 <0.1 

Ht (cm) 1.7 0.2 0.9 0.2 

Encrusting 

% Cover 2.4 0.4 0.4 0.2 

 

Finger # Individuals 0.4 <0.1 1.5 0.5 

% Cover 0.2 <0.1 0.3 <0.1 

Ht (cm) 1.3 0.2 2.0 0.4 

Rope # Individuals 0.2 <0.1 0.3 <0.1 

% Cover 0.1 <0.1 0.1 <0.1 

Ht (cm) 2.1 0.4 2.3 0.6 

Tube # Individuals 0.9 0.1 0.8 0.2 

% Cover 0.7 0.1 0.2 <0.1 

Ht (cm) 3.4 0.5 2.7 0.9 

Vase # Individuals 2.2 0.3 0.5 <0.1 

% Cover 3.5 0.4 0.4 0.1 

Ht (cm) 12.1 1.0 2.6 0.5 

Ledge Sparse live bottom Flat sand Rippled sand
Chapter 2 - Benthic Communities

page

17

dimensions revealed interesting 

differences among the ledge clus-

ters (Figure 2.7). The three ledge 

clusters were well separated along 

the mean undercut height axis 

such that the tallest ledges gen-

erally had the highest undercut 

height and the shortest ledges had 

the shortest undercuts. The tallest 

ledges generally also had the larg-

est average undercut width, but 

there were exceptions to this trend 

(Figure 2.7).

The mean numbers of burrows in 

flat and rippled sand were 0.92/m

2

± 0.20 SE and 0.74/m

2

 ± 0.23 SE, 

respectively. There was not a sig-

nificant difference in the number 

of burrows between the two bot-

tom types (F = 0.3553, df = 35, p 

= 0.55). Mean sand thickness on 

sparse live bottom was 5.4 cm ± 

0.4 SE and ranged from 1.6 cm 

to 14.4 cm (Figure 2.8). The mea-

sured sand thickness (i.e. within 

quadrats) ranged from 0 cm to 

30 cm, which was the maximum 

depth of sand measured. There 

was a large mode in mean thick-

ness at 4 cm, with 50% of the sites 

having a mean thickness between 

3.6 and 6.6 cm. 

Ledges in the area of high boat density were sig-

nificantly larger in area, taller, more undercut, and 

more densely covered by benthic organisms than 

ledges in the low boat density area (Figure 2.9). 

Undercut width was more variable, as exhibited by 

the high standard error, and was not statistically 

different between the two regions. 

Biotic cover

Summary statistics for biotic composition by bot-

tom type are displayed in Table 2.6. Multiple com-

parison tests indicated that cover of coral, mac-

roalgae, sponges, and other benthic organisms 

was significantly greater on ledges than the other 

bottom types (Figure 2.10). Flat sand and rippled 

sand bottom types were characterized by low per-

cent cover (0-2%) of benthic organisms at all sites 

(Figure 2.10, Figure 2.11). Percent biotic cover at 

sparse live bottom ranged from 0.7-26.3%, but was 

only greater than 10% at 7 out of 51 sites (Figure 

2.12). On ledge bottom type, percent cover ranged 0

10

20

30

40

50

60

70

80

90

100

| | | |

Ledge Sparse live Flat Rippled

bottom sand sand

Total F2 = 120.8, 

p<0.0001

05

10

15

20 Coral F2 = 78.6, 

p<0.0001

048

12

16

20 Gorgonians F2 = 67.7, 

p<0.0001

Macroalgae F2 = 72.0, 

p<0.0001

0

10

20

30

40

50

60

70

80

90

1000

10

20

30

40

50

60

70

80

90

100

Other benthic organisms F2 = 96.5, 

p<0.0001

05

10

15

20

25

30

35

40 Sponges F2 = 109.4, 

p<0.0001

| | | |

Ledge Sparse live Flat Rippled

bottom sand sand

Mean percent areal cover (%)

Figure 2.10. Box plots of percent cover of benthic organisms on four bottom substrates 

at GRNMS. Results of nonparametric ANOVAs (Kruskal-Wallis tests) and Dunn’s multiple 

comparison tests to determine significant differences among mean ranks are provided (df 

= 3, alpha = 0.05). Solid horizontal lines join groups that are not significantly different from 

each other.

Image 11. Undercut ledge densely colonized by tunicates and other 

benthic organisms.
Chapter 2 - Benthic Communities

page

18

2

0 10.5

Kilometers

Percent cover (Flat and Rippled Sand)

Coral

Gorgonians

Macroalgae

Sponge

Other

Bare

Flat sand

Rippled sand

Sparsely colonized live bottom

Densely colonized live bottom

GRNMS Boundary

from 0.42-100%, with the highest percent cover at ledges in the central and south-central region of GRNMS 

(Figure 2.13). However, percent cover of gorgonians and mean number of gorgonians did not vary significantly 

different between ledge and sparse live bottom sites (Figure 2.10, Figure 2.14). Although a significant difference 

was detected in the height of gorgonians among the four bottom types, no pair-wise tests were significant (Figure 

2.15). In contrast, sponges were significantly more numerous and taller on ledges than on sparse live bottom 

(no sponges were found on either sand bottom type) (Figure 2.15). Shannon-Weiner diversity of biotic types was 

significantly greater at ledge and sparse live bottom than at either sand bottom type (Figure 2.16).

Cover of corals and gorgonians were generally low (range = 0-18%, mean = 1.35%). Branching coral was the 

most frequently encountered coral type (Figure 2.17), and sea rod/plumes were the most frequently encountered 

gorgonians (Figure 2.18). A high cover of filamentous macroalgae was typical at many of the densely colonized 

ledges (Figure 2.13, Figure 2.19), while several of the northernmost ledges were characterized by high cover of 

sponges, tunicates, and miscellaneous species (including bryozoans, molluscs, barnacles, and other unclassi-

fied taxa) within the “other” category (Figure 2.20-2.22). Numerous sponge types were observed throughout the 

sanctuary, including encrusting, tube, and vase sponges (Figure 2.20). 

Figure 2.11. Percent cover of biotic cover groups on flat and rippled sand bottom sites.
Chapter 2 - Benthic Communities

page

19

2

0 10.5

Kilometers

Percent cover (Sparse Live Bottom)

Coral

Gorgonians

Macroalgae

Sponge

Other

Bare

Flat sand

Rippled sand

Sparsely colonized live bottom

Densely colonized live bottom

GRNMS Boundary

Cluster analysis of percent cover data for all sites revealed the presence of eight distinct groups of sites (Figure 

2.23). Mean percent cover of the aggregated cover types for each cluster is displayed in Figure 2.24. Cluster 1 

was composed primarily of ledge sites and was characterized by the highest mean percent cover of sponges 

(mean = 23 ± 3% SE). In addition, it has a moderately high (mean = 22 ± 6% SE) mean cover of species within 

the “other” category. Cluster 2 was a larger cluster, again composed of mostly ledges. Total percent cover aver-

aged just under 30% and was typified by macroalgae, sponges, and other species. Clusters 3-6 contained only 

ledge sites. Clusters 3 and 5 were “outliers” as they contained only one site each and were characterized by 

highest percent cover of coral and gorgonians, respectively. Sites within Cluster 4 typically were characterized 

by the highest percent macroalgal cover. Sites within Cluster 6 were also highly colonized, but these sites were 

dominated by organisms within the “other” category such as tunicates. All of the flat sand and rippled sand sites 

were included in Cluster 7, in addition to several ledges and numerous sparse live bottom sites. Sites within this 

cluster were characterized by low percent cover of all organism types (mean total cover = 1.8 ± 0.3% SE). Mean 

total cover in Cluster 8, which contained ledge and sparse live bottom sites, was the second lowest among the 

clusters (mean total cover = 8 ± 1% SE) but mean gorgonian cover was higher than many of the other clusters. 

There were no obvious spatial patterns in the distribution of clusters.

Figure 2.12. Percent cover of biotic cover groups at sparse live bottom sites.
Chapter 2 - Benthic Communities

page 20

2

0 10.5

Kilometers

Percent cover (Ledge)

Coral

Gorgonians

Macroalgae

Sponge

Other

Bare

Flat sand

Rippled sand

Sparsely colonized live bottom

Densely colonized live bottom

GRNMS Boundary

Cluster analysis of only ledges resulted in six well separated clusters (Figure 2.25). The resulting clusters were 

very similar to those in the first analysis that encompassed all bottom types, with the primary difference being a 

larger cluster containing sites that had been previously dispersed among a few clusters. Similar to the first analy-

sis, two clusters (4 and 5) contained only one site each that had high percent cover of corals and gorgonians, 

respectively (Figure 2.26). Cluster 1 was characterized by the highest mean percent cover of sponges of all clus-

ters, as well as moderate-high cover of species within the other category. Cluster 2, containing 52 ledges, was 

characterized by a mean total cover of 28.6 % (± 4.4 SE). Sites in Cluster 3 were densely colonized, particularly 

by macroalgae. Cluster 6 was characterized by the highest mean cover of “other” species, primarily tunicates. 

Abiotic effects on biotic composition

The effect of ledge height on individual cover types was examined through the use of nonparametric KruskalWallis tests. Results revealed significant differences in total biotic cover for all cover types among ledge size 

categories determined from cluster analysis of abiotic variables (Figure 2.27). Median total percent cover was 

97.6%, 75.1%, and 17.7% on tall, medium, and short ledges, respectively. The majority of the ledges classified 

as medium or tall tended to have high overall percent cover. All tall ledges (n=6) had >50% total cover, com-

pared with 65% of medium ledges (n=26), and 22% of short ledges (n=60). Short ledges had significantly lower 

percent cover than medium or tall ledges, but there was not a significant difference between medium and tall 

Figure 2.13. Percent cover of biotic cover groups at ledge sites.
Chapter 2 - Benthic Communities

page

21

05

10

15

20

| | | |

Ledge Sparse live Flat Rippled

bottom sand sand

Mean number of individuals per site

Gorgonians F2 = 65.7, df=3,

p<0.0001

Sponges F2 = 8.8, df=1,

p=0.003

05

10

15

20

25

30

||

Ledge Sparse live 

bottom 

05

10

15

20

| | | |

Ledge Sparse live Flat Rippled

bottom sand sand

Mean number of individuals per site

Gorgonians F2 = 65.7, df=3,

p<0.0001

Sponges F2 = 8.8, df=1,

p=0.003

05

10

15

20

25

30

||

Ledge Sparse live 

bottom 

Figure 2.14. Box plots of number of gorgonians and sponges 

on four bottom substrates at GRNMS. Results of nonparametric 

ANOVAs (Kruskal-Wallis tests) and Dunn’s multiple comparison 

tests to determine significant differences among mean ranks 

are provided (alpha = 0.05). Sponges were not observed on flat 

or rippled sand bottom types. Solid horizontal lines join groups 

that are not significantly different from each other.

0

10

20

30

40

50

60 Gorgonians F2 = 9.8, df=3,

p=0.02

05

10

15

20

25

30

| | | |

Ledge Sparse live Flat Rippled

bottom sand sand

| |

Ledge Sparse live bottom

Sponges F2 = 28.4, df=1,

p<0.0001

Mean height (cm)

0

10

20

30

40

50

60 Gorgonians F2 = 9.8, df=3,

p=0.02

05

10

15

20

25

30

| | | |

Ledge Sparse live Flat Rippled

bottom sand sand

| |

Ledge Sparse live bottom

Sponges F2 = 28.4, df=1,

p<0.0001

0

10

20

30

40

50

60 Gorgonians F2 = 9.8, df=3,

p=0.02

05

10

15

20

25

30

| | | |

Ledge Sparse live Flat Rippled

bottom sand sand

| |

Ledge Sparse live bottom

Sponges F2 = 28.4, df=1,

p<0.0001

Mean height (cm)

Figure 2.15. Box plots of height of gorgonians and sponges on 

four bottom substrates at GRNMS. Results of nonparametric 

ANOVAs (Kruskal-Wallis tests) and Dunn’s multiple comparison 

tests to determine significant differences among mean ranks 

are provided (alpha = 0.05). Sponges were not observed on flat 

or rippled sand bottom types. Solid horizontal lines join groups 

that are not significantly different from each other. Although the 

overall test was significant for gorgonian height, no pairwise 

comparison tests were significant.

H

0

0.51

1.52

2.5

| | ||

Ledge Sparse live Flat Rippled

bottom sand sand F2 = 88.1, 

p<0.0001

H

0

0.51

1.52

2.5

| | ||

Ledge Sparse live Flat Rippled

bottom sand sand F2 = 88.1, 

p<0.0001

H

0

0.51

1.52

2.5

| | ||

Ledge Sparse live Flat Rippled

bottom sand sand

H

0

0.51

1.52

2.5

| | ||

Ledge Sparse live Flat Rippled

bottom sand sand F2 = 88.1, 

p<0.0001

Figure 2.16. Box plots of Shannon diversity index (H) by bottom type. Results 

of nonparametric ANOVAs (Kruskal-Wallis tests) and Dunn’s multiple compari-

son tests to determine significant differences among mean ranks are provided 

(df = 3, alpha = 0.05). Solid horizontal lines join groups that are not signifi-

cantly different from each other. 
Chapter 2 - Benthic Communities

page

22

2

0 10.5

Kilometers

Percent cover (Coral) 6Branching

Cup

Encrusting

Other

Flat sand

Rippled sand

Sparsely colonized live bottom

Densely colonized live bottom

GRNMS Boundary

Figure 2.17. Percent cover of coral types at ledge sites. The tallest bar in the legend represents 6% cover.

ledges. Median coral cover was low (<2%) for all ledge types, but was significantly higher on medium than short 

ledges. Percent cover of gorgonians was significantly higher at short ledges when compared with medium or tall 

ledges. Macroalgal cover ranged from 0-76.2% (median = 0.8%) on short ledges, 0-88.4% (median = 21.44%) 

on medium ledges, and 7.6-64.5% (median = 29.9%) on tall ledges. Sponge cover ranged from 0-21.6% (median 

= 4.35%) on short ledges, 2.84-39.4% (median = 7.9%) on medium ledges, and 8.2-15.2% (median = 10.0%) 

on tall ledges. Cover of other benthic species ranged from 0-33.3% (median = 4.6%) on short ledges, 0.8-86.7% 

(median = 13.2%) on medium ledges, and 21.8-80.3% (median = 38.4%) on tall ledges. Cover of macroalgae, 

sponges, and other benthic organisms was significantly higher at medium or tall ledges when compared with 

short ledges, but there was not a significant difference between medium and tall ledges. 

The number of individual gorgonians was greater at short ledges than at medium or tall ledges (Figure 2.28). 

Short ledges were also characterized by significantly fewer sponges. The number of sponges or gorgonians was 

similar on medium and tall ledges. There was no significant difference in the height of gorgonians between short 

and medium ledges (Figure 2.29); tall ledges were excluded from this analysis because gorgonians were only 

present on one tall ledge. In contrast, sponges were significantly shorter on short ledges compared with the other 

ledge types (Figure 2.29). Shannon diversity did not vary significantly between short, medium, and tall ledges (F 

= 0.24, df = 91, p = 0.79).
Chapter 2 - Benthic Communities

page

23

2

0 10.5

Kilometers

Percent cover (Gorgonians)

10

Sea whip

Sea fan

Sea rod/plume

Flat sand

Rippled sand

Sparsely colonized live bottom

Densely colonized live bottom

GRNMS Boundary

Figure 2.18. Percent cover of gorgonian types at ledge sites. The tallest bar in the legend represents 10% cover.

2.5 Discussion

GRNMS is composed of four main bottom types that have distinct physical and biological characteristics. In sup-

port of ongoing management of sanctuary resources, the overall goal of this component of the characterization 

was to quantify abiotic features and cover of benthic species. This study builds on previous work of benthic habi-

tats in GRNMS, which includes benthic habitat mapping (Kendall et al. 2005), a guide to invertebrates (Gleason 

et al.), macroalgae (Searles 1988), a characterization of soft-bottom macrobenthos (Hyland et al. 2006) and 

several studies that surveyed individual species or communities associated with live bottom within GRNMS 

(Hopkinson et al. 1991; Ruzicka 2005; Wagner 2006). The primary difference between this characterization and 

prior surveys is that the present study encompassed all habitat types and quantified both abiotic characteristics 

and epibenthic communities at a large number of site locations throughout the sanctuary.

Our first objective was to assess the accuracy of the habitat maps by Kendall et al. (2005). Overall map ac-

curacy was excellent at 93.3% as measured in the present study. Kendall et al. (2005) previously estimated a 

similarly high level of overall thematic accuracy at 94.8% correct although quite different methods were used. 

While the present assessment used random stratified points and diver based assessments, Kendall et al. (2005) 

used randomly placed video transects and spatial statistics. Kendall et al. (2005) were limited to assessing sand 

(both rippled and flat combined) and sparse live bottom due to the nature of the transect and video based data. 

Quantitative assessment of the accuracy of the ledge category was not possible. Considering only sparse live 
Chapter 2 - Benthic Communities

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24

2

0 10.5

Kilometers

Percent cover (Macroalgae)

50

Filamentous

Fleshy

Other

Flat sand

Rippled sand

Sparsely colonized live bottom

Densely colonized live bottom

GRNMS Boundary

bottom and sand, similarly high values were found by Kendall et al. (2005) and in the present study despite quite 

different methods. For sparse live bottom, Kendall et al. (2005) found user’s and producer’s accuracies of 90.9% 

and 93.0% respectively, compared to 100% and 86.3% in the present study. For sand, Kendall et al. (2005) 

found user’s and producer’s accuracies of 96.7% and 95.7% respectively whereas in the present study we found 

values of 100% and 94.4% (when results from rippled and flat sand were combined). In the present study, user’s 

and producer’s accuracies of the ledge category were 91.1% and 100%, respectively. The combined findings of 

these two studies demonstrate a robust and complete accuracy assessment of all the bottom types at GRNMS. 

All nine errors in the ledge category of the present assessment occurred in the August 2004 sampling period. At 

two sites which were surrounded by large areas of sand on all sides, divers’ notes indicated the presence of a 

deflection in bathymetry that was sparsely colonized with sessile benthic invertebrates. These ledges were prob-

ably better defined in 2001 when the sonar data were collected, but were now in the process of being covered 

by shifting sands. Further along the length of these features, the ledge may have been better defined. However, 

because a ledge was not readily observable at either of these two sites, the divers noted that they moved off a 

short distance and did a survey over rippled sand in order to not waste the dives. At two other sites identified 

as sparse live bottom, the divers noted that ledges were nearby but that the survey was conducted on sparse 

live bottom. No special notes were made for five other ledge sites identified as sparse live bottom. After this first 

sampling period it became apparent that some small ledges could be missed by divers searching for them un-

Figure 2.19. Percent cover of macroalgae types at ledge sites. The tallest bar in the legend represents 50% cover.
Chapter 2 - Benthic Communities

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25

2

0 10.5

Kilometers

Flat sand

Rippled sand

Sparsely colonized live bottom

Densely colonized live bottom

GRNMS Boundary

Percent cover (Sponge)

10

Ball

Encrusting

Finger

Rope

Tube

Vase

Figure 2.20. Percent cover of sponge types at ledge sites. The tallest bar in the legend represents 10% cover.

derwater due to the often limited visibility at GRNMS. For later sampling trips, the starting point of each randomly 

selected ledge was moved based on sonar images to a segment of the ledge that was likely to be more easily 

located underwater. Once on a ledge it can be followed more easily through areas even where it is less promi-

nent. Indeed no other ledges were missed in the subsequent two sampling periods once this modification to site 

selection was made. Some combination of actual map errors, ledges being covered by sand, and diver error is 

probably responsible for the large number of discrepancies between map and diver opinion in the August 2004 

sampling period. 

All three remaining errors were in the sand category and occurred during the August 2005 sampling period. This 

most recent of the sampling periods was four years and two months after the sonar data used to develop the 

benthic maps were collected in June 2001. This time gap would allow time for bottom altering forces such as 

bioturbation to rework the surficial sediments (Sisson et al. 2002) and for localized water movements to create 

or remove sand ripples. Indeed, the presence of many echinoderms reworking the surface was noted at one site 

that was mapped based as rippled sand (2001 data) but was later identified as flat sand in 2005. 

In situ ledge height estimates confirmed sonar derived estimated of ledge heights. Ledge height estimated from 

GIS analysis was positively correlated with maximum ledge height measured 

in situ although GIS height was 

always higher than 

in situ estimates. There are two possible explanations for this discrepancy. First, in situ ledge 
Chapter 2 - Benthic Communities

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26

2

0 10.5

Kilometers

Flat sand

Rippled sand

Sparsely colonized live bottom

Densely colonized live bottom

GRNMS Boundary

Percent cover (Other-Tunicates)

50

Tunicates (Encrusting)

Tunicates (Lobate)

height was only measured at five points along a 25 m transect. Many ledges were longer than this and it is quite 

possible that the tallest part of the ledge simply wasn’t measured. Second, differences in height estimates may 

be a product of the way GIS height was determined. Height was calculated as the difference between the deep-

est and shallowest bathymetry values. In the case of a broadly sloping ledge, it is likely that these points would 

be located at opposite ends of the ledge. In such a case, height measured by a diver at any location along the 

ledge would be lower than the maximum GIS height.

Biological and physical processes work to continually shape the sand and hardbottom features. The sand and 

shell rubble observed on sparse live bottom sites may have been deposited through sand movement from 

nearby sandy areas or from weathering of hardbottom. Fine sediments were not observed, which is consistent 

with previous records of sediment distribution on the mid-continental shelf in the South Atlantic Bight (Milliman et 

al. 1972; Riggs et al. 1996; Hyland et al. 2006). Large storms and seasonal storm patterns can cause sediments 

to shift, which may alter benthic communities or result in an import/export of sediments to the system (Riggs et 

al. 1998). This constant shifting of sediments likely prevents the flat hardbottom from becoming more densely 

colonized by epibenthic fauna. The sediment layer covering the bottom was usually several centimeters thick 

(up to 18 cm) which would prevent larvae from settling, or bury recent recruits. The most common cover type on 

sparse live bottom was sea rods/plumes, which are often quite tall (mean height = 19.4 cm), making them less 

vulnerable to burial. Furthermore, these gorgonians may have colonized the sparse live bottom areas when such 

Figure 2.21. Percent cover of tunicates at ledge sites. The tallest bar in the legend represents 50% cover.
Chapter 2 - Benthic Communities

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27

2

0 10.5

Kilometers

Flat sand

Rippled sand

Sparsely colonized live bottom

Densely colonized live bottom

GRNMS Boundary

Percent cover (Other)

50

Anemones

Other

Zoanthids

Figure 2.22. Percent cover of “other” benthic cover types at ledge sites. The tallest bar in the legend represents 50% cover.

areas were uncovered by sand or may have growth rates that exceed the rate of sand deposition on hardbottom. 

At some sites, divers cleared away sand that covered sparse live bottom and noted living sponges and ascidians 

that may have recently been buried by sand. In addition, the mean number of sponges on sparse live bottom 

was not significantly different from ledges, but sponges were smaller and covered a smaller percentage of the 

substrate on sparse live bottom. Sponge morphology also differs between different bottom types. In a recent 

study at GRNMS and nearby J Reef, Ruzika (2005) documented distinct sponge communities at ledge “scarps” 

(ledges in the present study) and “plateaus” (similar to sparse live bottom in the present study). The majority of 

species occurring on the scarps were amorphous or encrusting species, while the plateaus were characterized 

by branching, pendunculate, or digitate sponges (Ruzika 2005).

Numerous cover types were observed on ledges, including macroalgae, sponges, tunicates, coral, and gorgoni-

ans. High diversity of macrofauna in Gray’s Reef and other stations in the inner and mid-shelf was also observed 

by Wenner et al. (1983). Although that survey was conducted using dredge and trawl methods, the taxonomic 

groups of major importance included sponges, bryozoans, corals, anemones, tunicates, and echinoderms. Hop-

kinson et al. (1991) documented similar taxa in GRNMS in association with a survey of community metabolism 

at an east-central site along the northern rim of ledges and hardbottom. Dominant morphology types included 

sponges, corals, and miscellaneous species (bryozoans, hydroids, ascidians, and mussels). Hopkinson et al. 

(1991) also found higher coral cover than was observed at any site in the present study; however, the one area 
Chapter 2 - Benthic Communities

page

28

sampled may have been a hot spot for coral cover. Thus, it may be inappropriate to compare our average esti-

mates of coral cover to that reported by Hopkinson et al. (1991). In addition, in the previous study, mean mac-

roalgal cover did not exceed 9% at low, medium, and high density areas (Hopkinson et al. 1991).

Linking biological community structure to the environment is a major goal of ecology but often is difficult to as-

sess (Clarke and Ainsworth 1993). Geological differences in substrate type or morphological complexity have 

been linked to community structure, but the patterns are not always universal (Davis et al. 2003; Beaman et 

Cluster 3: 1 L

Highest % coral cover

Cluster 1: 6 L, 1 SLB

High sponge cover

Cluster 6: 6 L

Highest mean total cover

Dominated by other organisms

Cluster 5: 1 L

Highest % gorgonian cover

Cluster 4: 27 L

Highest % macroalgal cover

benthic organisms

Cluster 2: 27 L, 2 SLB

Low-moderate cover, composed of 

macroalgae , sponges, and other 

benthic organisms

Cluster 7: 21 FS, 15 RS,

11 L, 32 SLB

Very low overall cover

Cluster 8: 13 L, 16 SLB

Low overall cover

8 well separated clusters

Dissimilarity

Figure 2.23. Dendrogram from cluster analysis of all sites based on percent cover for aggregated cover types (corals, gorgo-

nians, macroalgae, sponge, other). The scree plot indicates that there are eight well separated clusters. Sites are color coded 

by bottom type (dark blue=ledge, light blue=sparse live bottom, yellow=flat sand, orange=rippled sand).
Chapter 2 - Benthic Communities

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29

al. 2005). The majority of ledges 

surveyed in GRNMS were short 

with little or no undercut, while 

ledges classified as medium or 

tall exhibited varying amounts 

of undercut. Although undercut 

dimensions were positively cor-

related with ledge height, there 

were exceptions to this trend 

(Figure 2.7). For example, the 

tallest ledge surveyed (mean 

height = 170 cm) had relatively 

little undercut (mean undercut 

height = 33.8 cm, mean under-

cut width = 25 cm). Tall ledges 

with small undercuts may result 

from physical and bioerosional 

processes that may weaken por-

tions of the ledge overhangs over 

time, eventually causing them to 

fall off and form rock rubble in 

the ledge openings (Riggs et al. 

1996; Riggs et al. 1998). 

In general, medium and tall ledg-

es did not differ in total biotic cover 

or the cover of individual morpho-

logical groups. It is possible that 

height may only be important to a 

certain threshold and that ledges 

above a certain height are sim-

ply less likely to be routinely bur-

ied and unburied by sediments. 

There was also a large degree 

of variability in cover on short 

ledges. Despite the low median 

cover, a few short ledges had to-

tal cover exceeding 50%. How-

ever, excluding gorgonians and 

coral, cover of other groups was 

significantly less on short ledges. 

Compared to tall ledges, low re-

lief ledges would likely be more 

subject to burial by shifting sedi-

ments, which could inhibit coloni-

zation by organisms. Several of 

these short ledges with low cov-

er are located in the southeast 

corner of the sanctuary and are 

otherwise surrounded by sand. 

This would be a good area to 

investigate sand migration rates 

and the associated impacts on 

ledges. Gorgonians and several 

sponge types (finger, rope, and 

0

10

20

30

40

50

60

70

80

90

100

1 2 3 4 5 6 7 8

Cluster

Mean percent cover (%)

Other

Sponge

Macroalgae

Gorgonians

Coral

Figure 2.24. Mean percent cover of benthic organisms (±SEM) by clusters determined 

from hierarchal cluster analysis in Figure 2.23.

Cluster 1: 6 L

Highest mean % cover of 

sponges

Cluster 6: 8 L

Highest mean % cover of 

“other ” benthic species

Cluster 3: 24 L

Highest mean % cover of 

macroalgae

Cluster 2: 52 L

Low mean total cover

Cluster 4: 1 L

Highest % coral

cover

Highest % 

Cluster 5: 1 L

Highest % gorgonian

cover

Highest %

Dissimilarity

6 well separated clusters 6 clusters

Figure 2.25. Dendrogram for cluster analysis of the 92 ledge sites based on percent 

cover for aggregated cover types (corals, gorgonians, macroalgae, sponge, other). The 

scree plot indicates that there are six well separated clusters. The numbers representing 

each site denote total mean percent cover.
Chapter 2 - Benthic Communities

page 30

tube) appear to be less limited than 

other invertebrates and were found 

on sparse live bottom or short ledges 

with similar or greater frequency than 

on medium or tall ledges. While many 

of the ledges located in the southeast 

region were characterized by low 

overall cover, gorgonians and a low 

density of sponges were nearly al-

ways present in this area.

Conversely, taller ledges and slopes 

would be less susceptible to sand 

burial and can support shorter colo-

nies. Total cover increased with ledge 

height, but examination of spatial pat-

terns and cluster analyses by biotic 

type indicate that a diversity of benthic 

community combinations occur re-

gardless of ledge height. The 

most densely colonized ledge 

sites were generally situated 

in the central region of the 

sanctuary, particularly sites 

located among the northern 

rim of ledges and a group of 

ledges to the south (Figure 

2.13). Highest cover of coral, 

macroalgae, sponges, and tu-

nicates tended to be located 

in these regions as well (Fig-

ures 2.13, 2.17-2.21). 

The reasons for these spatial 

patterns are not clear. Many of 

the densely colonized ledges 

were also tall in height, but this 

was not always the case. Why 

some ledges were dominated 

by macroalgae, and others of 

similar height by tunicates is 

unknown. It is likely that other 

factors not considered in this 

study, such as small scale 

rugosity or complexity, ocean 

currents, other environmen-

tal variables, and biological 

interactions (e.g., differential 

grazing or settlement pat-

terns) work in concert to influ-

ence spatial distribution and 

community structure. Osman 

(1977) notes five major fac-

tors important to the develop-

ment of an epifaunal benthic 

0

20

40

60

80

100

120

1 2 3 4 5 6

Cluster

Mean percent cover (%)

Other

Sponge

Macroalgae

Gorgonians

Coral

Figure 2.26. Mean percent cover (±SEM) by cluster determined from hierarchal cluster-

ing of ledges (Figure 2.24). | |

|

Short Medium Tall

Mean percent areal cover (%)

Total F2 = 33.7, 

p<0.0001

Macroalgae F2 = 10.8, 

p=0.0046

Coral F2 = 16.4, p=0.0003

Gorgonians F2 = 24.4, 

p<0.0001

Sponges F2 = 18.3, 

p=0.0001

Other benthic organisms F2 = 30.9, 

p<0.0001

048

12

16

2005

10

15

20

05

10

15

20

25

30

35

400

10

20

30

40

50

60

70

80

90

1000

10

20

30

40

50

60

70

80

90

100

0

10

20

30

40

50

60

70

80

90

100

|||

Short Medium Tall

|||

Short Medium Tall

Mean percent areal cover (%)

Total F2 = 33.7, 

p<0.0001

Macroalgae F2 = 10.8, 

p=0.0046

Coral F2 = 16.4, p=0.0003

Gorgonians F2 = 24.4, 

p<0.0001

Sponges F2 = 18.3, 

p=0.0001

Other benthic organisms F2 = 30.9, 

p<0.0001

048

12

16

2005

10

15

20

05

10

15

20

25

30

35

400

10

20

30

40

50

60

70

80

90

1000

10

20

30

40

50

60

70

80

90

100

0

10

20

30

40

50

60

70

80

90

100

|||

Short Medium Tall

Figure 2.27. Box plots of percent cover of benthic organisms on three ledge groups determined 

by cluster analysis. Results of nonparametric ANOVAs (Kruskal-Wallis tests) and Dunn’s multiple 

comparison tests to determine significant differences among mean ranks are provided (df = 2, al-

pha = 0.05). Solid horizontal lines join groups that are not significantly different from each other. 
Chapter 2 - Benthic Communities

page

31

0

0.51

1.52

2.53

3.5

Gorgonians F2 = 21.6, p<0.0001

Sponges F2 = 15.1, p = 0.0005

Mean number of individuals per site

|||

Short Medium Tall

01234560

0.51

1.52

2.53

3.5

Gorgonians F2 = 21.6, p<0.0001

Sponges F2 = 15.1, p=0.0005

Mean number of individuals per site

|||

Short Medium Tall

0123456

Figure 2.28. Box plots of mean number of gorgonians 

and sponges on three ledge groups determined by clus-

ter analysis of abiotic ledge variables. Results of non-

parametric ANOVAs (Kruskal-Wallis tests) and Dunn’s 

multiple comparison tests to determine significant differ-

ences among mean ranks are provided (df = 2, alpha 

= 0.05). Solid horizontal lines join groups that are not 

significantly different from each other. 

Gorgonians F2 = 1.6, df=1 p=0.21

Sponges F2 = 9.2, df=2 p=0.01

Mean height (cm)

|||

Short Medium Tall

0

10

20

30

40

50

6005

10

15

20

25

30

| | 

Short Medium 

Gorgonians F2 = 1.6, df=1 p=0.21

Sponges F2 = 9.2, df=2 p=0.01

Mean height (cm)

|||

Short Medium Tall

0

10

20

30

40

50

6005

10

15

20

25

30

| | 

Short Medium 

Figure 2.29. Box plots of mean height of gorgonians and 

sponges on three ledge groups determined by cluster analysis. 

Results of nonparametric ANOVAs (Kruskal-Wallis tests) and 

Dunn’s multiple comparison tests to determine significant dif-

ferences among mean ranks are provided (alpha = 0.05). Only 

short and medium ledges were considered for the analysis of 

gorgonian height. Solid horizontal lines join groups that are not 

significantly different from each other. 

community and species distribution, including larval selection of settlement location, seasonal fluctuation in the 

climate and larval abundance, biological interactions (predation, intra and inter-specific competition), substrate 

size, and physical disturbance. In particular, the structural complexity of ledges is difficult to quantify, but it may 

contribute greatly to species diversity and variability between different patches. For example, within a small area 

(e.g. < 100 m

2

) numerous microhabitats may exist (e.g., crevices, overhangs, bare rock, sand patches) that may 

allow a large number of species to co-exist due to differential larval settlement and survival patterns (Wenner et 

al. 1983). 

Although flat and rippled sand bottom types were largely devoid of epibenthic invertebrates and macroalgae 

that settle on hardbottom, this does not mean that this habitat is not important ecologically. Numerous burrows 

indicate the presence of benthic infauna. Indeed, Hyland et al. (2006) documented 349 different infaunal taxa, 

including polychaetes, crustaceans, echinoderms, and mollusks from sediment grabs in GRNMS. Patterns in 

species composition and abundance varied among bottom types. The diversity and densities of macroinfauna 

were higher in rippled sand than flat sand, and were also high in sediments close to or overlying live bottom (Hy-

land et al. 2006). Macroinfauna serve as important prey items for numerous fish in GRNMS, including grunts and 

Pareques species. Fish such as these are closely associated with ledges but often forage over adjacent sand 

bottoms, resulting in a “halo effect” of decreased infaunal abundance closer to reef ledges (Posey and Ambrose 

1994).

Although this study was not designed to detect seasonal patterns, invertebrates and particularly macroalgae are 

prone to both seasonal and year-to-year variability (Peckol and Searles 1984). Seasonal and inter-annual differ-

ences can be attributed to multiple sources, including fluctuating environmental conditions (Peckol and Searles 

1984), storm events (Renaud et al. 1997), and success in larval settlement and growth (Osman 1977). GRNMS 

lies at the boundary between the inner and middle continental shelf and tropical and temperate water masses, 

and hence experiences seasonal fluctuations in temperature, salinity, and water clarity (NOAA 2006). Coastal 
Chapter 2 - Benthic Communities

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32

circulation patterns in the South Atlantic Bight are 

also prone to seasonal variation (Bumpus 1955). 

Mean water temperature at the GRNMS data buoy 

was 30°C in August 2005, two degrees higher than 

the previous August. Mean water temperature in 

May 2005 was much cooler at 23.4°C (data ob-

tained from the NOAA Station 41008 buoy located 

in GRNMS, http://www.ndbc.noaa.gov/, accessed 

Sept. 30, 2006). Although mean cover for most 

groups was similar across the three sampling peri-

ods, there is some indication of temporal patterns 

for macroalgae. At Gray’s Reef, macroalgae gen-

erally reaches peak abundance in July and August 

before dying back in the fall and winter (Searles 

1988). In the present study, mean macroalgal cov-

er on ledges varied from 0.6% in August 2004 to 

1.3% in May 2005 to 11.6% in August 2005. No 

ledges sampled in August 2004 or May 2005 had a 

mean macroalgal coverage exceeding 25%. Con-

versely, 16 ledge sites sampled in August 2005 

were covered by macroalgae in excess of this amount. Results from cluster analysis also indicated some tempo-

ral differences, as all but one site located in the “macroalgae” cluster was surveyed in August 2005. Due in part 

to this macroalgal bloom, the seven most densely colonized ledges (99-100% total cover) were documented in 

August 2005. The causes for the large difference in macroalgae in the two successive summers are unknown, 

but in addition to interannual variation in environmental conditions and storm events, macroalgal growth is also 

sensitive to variability in nutrient levels and grazing pressure (Miller and Hay 1996). Similarly, Peckol and Searles 

(1984) detected strong interannual differences in macroalgal cover on North Carolina reefs, but less so for in-

vertebrates. In addition, it cannot be ruled out that observed differences in macroalgal cover in the present study 

were partially attributed to the random sampling design as the same sites were not characterized across all three 

surveys. More work is needed to accurately assess temporal patterns in macroalgae and epifaunal cover and 

responsible control mechanisms. 

Temperate reefs such as those in GRNMS differ from coral reefs in other National Marine Sanctuaries (Florida 

Keys, Flower Garden Banks, NWHI) in numerous ways, including geologic origin (Harding and Henry 1990) and 

dominant biota (Miller and Hay 1996). Unlike tropical reefs, temperate reefs consist of pre-existing, submerged 

rocky outcrops that are colonized by epibenthic organisms (Harding and Henry 1990). Corals are less common 

on temperate reefs and tend to form smaller colonies then in tropical regions (Miller and Hay 1996). The latitudi-

nal limits of coral are thought to be attributed not only to lower temperatures but also increased competition with 

macroalgae, which are favored in higher nutrient waters (Johannes et al. 1983). Oculina arbuscula, the primary 

coral species in GRNMS, ranges from the Carolinas to Florida (Humann 1993) and has a wide temperature 

tolerance, although highest growth occurs in warm water under high light conditions (Miller 1995). The distribu-

tion of Oculina on temperate reefs in North Carolina is limited by macroalgae through both direct competition 

and indirectly by restricting the coral to deeper, lower lit environments where Oculina growth is not as favorable 

(Miller and Hay 1996). Recent work by Wagner (2006) on the population genetics of O. arbuscula in GRNMS and 

surrounding hardbottoms provide evidence for local recruitment, perhaps due to the nature of the patchy reef 

environment. In the present study, coral was commonly observed at 75% of all ledge sites, however, it generally 

contributed a small percentage to total percent cover. 

In contrast, sponges represent an important component of the benthic community in GRNMS, accounting for as 

high as 39% cover. Usually multiple morphological types and species were present in a single quadrat at an in-

dividual ledge. Although less studied, sponges often exceed corals and algae in terms of diversity on coral reefs 

(Diaz and Rutzler 2001), and some species may compete with coral for space (Aerts 1998). Compared to tropical 

reefs, temperate SAB reefs appear to have lower species diversity, but higher density of species and individuals, 

particularly for encrusting species (Ruzika 2005).

Image 12. Flamingo tongue and gorgonian.
Chapter 2 - Benthic Communities

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33

The South Atlantic Bight is occasionally affected by tropical cyclones and strong winter storms, which can result 

in strong current overflow and turbulence over ledge outcrops, particularly near the lip of the ledge (Peckol and 

Searles 1984). Although Peckol and Searles (1984) found reduced colonization by perennial macroalgae in this 

environment compared to several meters back from the face of the ledge, sessile invertebrates appeared to be 

less restricted and colonized this region heavily. In addition, surveys of North Carolina reefs following Hurricane 

Diana found little damage to the benthic invertebrate communities, which suggests that benthic communities 

are resilient to the impacts of strong storms and bottom currents (Kirby-Smith and Ustach 1986; Vaughan et 

al. 1987). Furthermore, although storms can negatively affect algae and invertebrates through dislodgement or 

scouring, they may also create favorable conditions for settlement by exposing hardbottom that had previously 

been covered by sediments (Renaud et al. 1997). Although insufficient data is available to investigate this further, 

differential storm patterns between years is one possible factor that may have contributed to the differences in 

cover of macroalgae in 2004 and 2005.

Concerns were raised about potential human impacts on the sanctuary resources in the recently updated GRNMS 

management plan (NOAA 2006). Compared to other hardbottom habitats, regulations afford the sanctuary pro-

tection from trawling and dredging, which have been shown to damage sponges, gorgonians and corals (Van 

Dolah et al. 1987). However, recreational activities can also negatively impact benthic fauna. For example, our 

surveys found fishing line entangled in oculinid coral (Chapter 3). Ledges within the area of high boat density 

were on average larger in area, taller, and had a higher percent cover than in the area where less boat activity 

was observed. This is not surprising as fishermen are more likely to detect larger, taller ledges on their depth 

finder (personal communication), and these ledges are more likely to harbor larger fish densities (Chapter 4). 

However, this finding is significant to management because it indicates that areas with the highest amount of live 

bottom may be disproportionately more vulnerable to human impacts (e.g., anchoring, derelict fishing gear). 

2.6 Recommendations for management and monitoring

Monitoring sanctuary resources on an annual basis is crucial to understand year-to-year variations in abundance 

and presence or absence of epibenthic flora and fauna, and to assess any changes in condition of biological 

communities over time. This work provides a baseline assessment and a foundation for long-term monitoring of 

the benthic community in GRNMS. Surveys of the benthos can be conducted in conjunction with the transect 

surveys for fish using the procedures outlined in the field methods of this document. We recommend conducting 

the annual survey in the summer of each year, as this is when annual species such as macroalgae are likely to 

be highest. Should additional resources remain, a survey could also be conducted during another time of the 

year to address seasonal variations. Ledges should receive a majority, if not all, of the effort due to the high 

abundance and diversity of sessile flora and fauna and associated fish (Chapter 4). Ledges of all sizes and 

heights should be surveyed to further characterize 

the relationship of invertebrate communities with 

their environment. Although diversity was also high 

on sparse live bottom, cover of most biota, with the 

exception of gorgonians, was significantly lower on 

this bottom type. 

Information from this baseline survey can be used 

to adjust field methods in the future. For example, 

due to the prevalence of tunicates on ledges, tu-

nicates could be given their own category (with 

subcategories lobate and encrusting) rather than 

subcategories under “other” benthic cover types. 

Other potential categories/subcategories that were 

not included on data sheets, but were occasionally 

noted, include barnacles and particularly bryozo-

ans. One factor that was not measured at ledge 

sites was the thickness of overlying sediment, but 

as this is important to community development and 

Image 13. Undercut ledge densely colonized by coral, sponges, and 

other benthic organisms.
Chapter 2 - Benthic Communities

page

34

maintenance, quantifying this variable may shed additional light on the dynamics of benthic community pat-

terns. 

The field survey method applied in this study is advantageous in that it allows researchers to survey a large num-

ber of sites within a short period of time. To date, this was the most spatially comprehensive characterization of 

benthic communities at GRNMS and yielded a substantial amount of information on the abundance and distribu-

tion of benthic invertebrates within GRNMS and their association with major bottom types. However, the method 

is not without drawbacks. For example, due to time constraints, invertebrates and macroalgae were identified 

by morphology rather than species. Additional surveys could focus on a subset of sites and specific cover types 

(e.g. sponges, corals) to identify individuals to a lower taxonomic level. In addition, studies pertaining to recruit-

ment, settlement, and population dynamics of invertebrate species in GRNMS (e.g., Wagner 2006) should be 

continued. The invertebrate species database (http://www.bio.georgiasouthern.edu/GR-inverts/, Gleason et al.) 

is a valuable public-accessible resource that should continue to be updated over time. 
Chapter 2 - Benthic Communities

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35

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Chapter 3 - Marine Debris

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37

Chapter 3: Characterization of the Marine Debris

3.1 Introduction

The accumulation of debris in the marine environment is an increasing problem worldwide. Marine debris is aesthetically displeasing, can be a nuisance to boaters and the shipping industry, and can negatively impact marine 

biota (Derraik 2002). The abundance and spatial distribution of marine debris is dependent upon several factors, 

including its origin/source (e.g., terrestrial vs. maritime), ocean currents, wind patterns, and physiographic characteristics (Galgani et al. 2000; Donohue et al. 2001). Depending upon their composition, individual debris items 

may persist for a long time in the marine environment. In particular, plastics, which are the dominant debris type 

in numerous marine systems (Derraik 2002), tend to break down slower in the ocean than items on land due to 

lower temperatures and fouling by marine organisms (Andrady 2000).

Derelict fishing gear is a common debris type of maritime origin, and areas with concentrated fishing activity may 

contain elevated amounts of debris (Hess et al. 1999; Galgani et al. 2000). Derelict fishing gear and other marine 

debris can impact environments in several ways. Floating debris may facilitate the spread of non-native species 

to new areas by providing a means of transportation (Aliani and Molcard 2003). Plastic items are often ingested 

by or entangle marine organisms, including fish, seabirds, sea turtles and marine mammals (Laist 1997). Lost 

fishing gear, such as monofilament nets and traps, may affect marine organisms both by direct injury to benthic 

habitats and organisms (Donohue et al. 2001) and by continuing to catch fish and invertebrates (“ghost fishing”, 

Dayton et al. 1995). 

Although smaller in size than large monofilament nets, hook and line is a prevalent gear type, particularly among 

recreational fisheries, and can also be detrimental to marine organisms (Chiappone et al. 2005). Effort is often 

concentrated at popular fishing sites, and consequently hook and line fishing may affect small areas but also 

inflict a high amount of damage within the affected areas (Asoh et al. 2004). Fishing line entangles readily in 

coral, which may lead to progressive fouling by algae and eventually, coral death (Schleyer and Tomalin 2000; 

Yoshikawa and Asoh 2004). Chiappone et al. (2005) documented numerous cases of tissue abrasion in branching gorgonians, milleporid hydrocorals, and sponges in the Florida Keys NMS.

A key challenge to marine debris mitigation is effectively assessing the distribution and density of debris in order 

to prioritize removal efforts. Traditional methods such as beach surveys are economical, widespread, and easy 

to implement (Rees and Pond 1995), but are limited to intertidal areas. Other survey techniques, including in 

situ underwater surveys, trawling, aerial flyovers, and remote sensing represent innovative approaches that are 

gaining increasing utilization. For example, SCUBA diving surveys have been employed to describe the distribution of derelict fishing gear in coral reefs within the Florida Keys National Marine Sanctuary (Chiappone et 

al. 2004) and in the Northwestern Hawaiian Islands in conjunction with cleanup efforts (Donohue et al. 2001). 

Using oceanographic modeling and remote sensing techniques, the “GhostNet” program in the Pacific Ocean is developing tools to track debris and 

identify likely locations of accumulation of derelict 

fishing nets and other marine debris (http://www.

highseasghost.net, accessed May 16, 2006). 

Understanding the sources and processes that 

drive marine debris distribution patterns is crucial 

to remediation efforts. High concentrations of debris are often found in areas of concentrated fishing activity, shipping channels, or riverine outflows 

(Galgani et al. 2000). Further, wind and currents 

can transport debris to areas far from the original 

location of loss or dispersal. For example, oceanic 

convergence zones in the North Pacific Ocean are 

thought to contribute to the accumulation of debris in the NWHI (Donohue et al. 2001; Donohue 

2005). Image 14. Net entangled in live bottom.
Chapter 3 - Marine Debris

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Recently, there has been increased concern about the potential accumulation of marine debris in Gray’s Reef 

National Marine Sanctuary (GRNMS). Since the establishment of GRNMS in 1981, the population of neighboring 

coastal counties has increased substantially (~40% from 1980-2000), and has been forecast to increase an additional 32% by 2015 (CGRDC 2006). Coincident with this population increase, the use of the sanctuary for boating 

and fishing activities has also increased. In 1983, aerial flyovers documented 106 vessels in the sanctuary during 

62 overflights (1.7 boats/flight); in 1999, 527 boats were observed during 90 overflights (5.9 boats/flight) (NOAA 

2006). While most commercially employed gear has been prohibited in the sanctuary, Gray’s Reef is a popular 

recreational fishing site both for king mackerel and bottom fish such as red snapper, grouper, amberjack, and 

especially black sea bass. Hook and line is the dominant gear type to target these species, although spearfishing 

with non-power spearheads is also conducted (NOAA 2006). Several sport fishing tournaments take place off 

of the Georgia coast each year, with Gray’s Reef being a premier location (Ehler and Leeworthy 2002). Current 

regulations prohibit the deposition of most materials in the sanctuary, with the exception of fish parts, bait and 

chumming materials, effluent from marine sanitation devices, and vessel cooling water (NOAA 2006). The extent 

of external inputs from sources outside the sanctuary is unknown but also of concern. 

The characteristics of bottom features in Gray’s Reef may influence the accumulation and spatial distribution of 

debris in the sanctuary. Kendall et al. (2005) estimated that the GRNMS seafloor is comprised of approximately 

75% sand, 25% sparsely colonized live bottom, and less than 1% densely colonized rock outcroppings or ledges. 

However, despite their limited area, ledges may be most vulnerable to debris accumulation. The abundance of 

sessile benthic organisms and structurally complex features such as overhangs and caves provide ample opportunities for debris items to become lodged or entangled. In addition, ledge features are targeted by fishermen 

due to the high abundance and diversity of target fishes that reside there.

The most recent management plan calls for specific measures to assess, monitor, and remove debris from 

targeted areas within the sanctuary (NOAA 2006). Activities were proposed to a) clarify regulatory authority to 

address materials discharged or deposited outside the sanctuary, b) develop and implement a marine debris 

education and outreach program, and c) develop and implement a debris monitoring and assessment study. In 

addition, GRNMS organizes divers to remove debris from targeted locations within the sanctuary through the 

annual “Sweep the Reef, Sweep the Beach” World Ocean Day Cleanup.

Understanding the amounts, distribution, and types of debris in the sanctuary is the first step to improving cleanup 

efforts and will aid managers in prevention and education efforts. To date, the types of debris, and its distribution 

and abundance have not been quantified. The objectives of this component of the characterization were to:

1. Describe the abundance, types, and spatial distribution of marine debris in GRNMS, 

2. Determine whether debris presence is associated with bottom type, 

3. Determine the causes of observed spatial patterns of debris at ledges by identifying what factors, such 

as ledge height and area and observed boat activity, are related to debris presence and abundance, 

4. Predict debris densities at unsampled locations within GRNMS, and 

5. Explore relevance of ocean currents in GRNMS to debris accumulation patterns. 

Finally, the results will be used to recommend a strategy for identifying high priority sites for targeted debris 

removal, and conducting periodic monitoring to determine rates of debris accumulation in different areas and 

bottom types within the sanctuary.

3.2 Methods for Marine Debris Surveys

Sampling for marine debris occurred along a 100 m2 transect at randomly selected sites as outlined in Chapter 1. 

Marine debris was recorded for the entire 100 m2

 transect. Debris was defined as any man-made object and was 

separated into two main categories, fishing gear and non-gear. Subcategories of fishing gear were not always 

noted but included monofilament line, leaders, spear gun parts, and other/undescribed (e.g. jigs or lead weights). 

Subcategories of non-gear marine debris included cans, bottles, and other (e.g. clothing, twine, tennis ball, wood 

plank, lift bag). Rope and mesh bags were found at a few sites and were scored as non-gear even though they 

may have been associated with fishing (e.g., rope could be used to mark ledge sites and mesh bags could be 

used for chumming). Fishing line that crossed the transect, but was not completely within it, was counted as a 

single item. Monofilament line with a leader attached was counted as a single piece of gear in the leader category. 
Chapter 3 - Marine Debris

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3.3 Methods for Data Analysis

Quantity, types, and spatial distribution of 

debris

Survey statistics for the quantity and types of debris 

were calculated for the entire survey domain and 

according to bottom types (Table 3.1, Table 3.2). 

Observed density of total debris, fishing gear, and 

non-gear items were entered into a Geographic Information System (GIS) and mapped according to 

geographic position of survey transect in ArcView 

v9.1.

Effect of bottom type 

First, the hypothesis that presence of debris varies significantly by bottom type was tested. For this 

analysis, flat sand and rippled sand were combined 

into a single “sand” category due to the low number of sampling locations in these bottom types 

compared to ledge and sparse live bottom. Bottom 

type (ledge, sand, sparse live bottom) was identified for each site based on diver observations. Debris was classified as “present” or “absent” at each site and the presence/absence data were modeled using logistic regression (Proc Logistic, SAS v9.1). Bottom type was included as a class variable. If the main effect was significant at 

the alpha =0.05 level, contrast statements were then constructed in Proc Logistic to test for differences in debris 

density among each pair of bottom types.

Influence of ledge characteristics and boat density on debris

Given that 90% of the observed debris was found on ledges, additional descriptive statistics were calculated and 

tests were performed to identify ledge characteristics that were associated with higher amounts of marine debris. 

The occurrence of debris on ledges according to the ledge area and height categories previously classified by 

GIS analysis (Kendall and Eschelbach 2006) were summarized in pie charts. Briefly, ledges were categorized as 

short (<58.5 cm), medium (58.5-89.2 cm), or tall (>89.2 cm) by rank ordering their heights and assigning 1/3 of 

the ledges to each category (Kendall and Eschelbach 2006). Area calculations were used to categorize ledges 

as small (<316.5 m2

), medium (316.5-731.4 m2), or large (>731.4 m2), again by assigning 1/3 of the ledges to 

each category (Kendall and Eschelbach 2006). 

Ledge characteristics that were suspected to be positively associated with debris accumulation were identified 

and included mean ledge height measured in situ, ledge area (m2

) based on GIS analysis, mean undercut width 

(m) measured in situ, and percent cover of benthic organisms measured in situ. Total debris (per 100 m2

) was 

plotted against each of these ledge variables and a non-parametric Spearman Rho rank order correlation statistic was calculated (SAS v9.1, Proc Corr). 

An additional factor that may influence the distribution and abundance of debris is the level of fishing and boating 

activity. Positions of boats in GRNMS from 1998 to 2004 were determined from multiple sources including national 

reconnaissance systems and entered into a GIS. Positional accuracy was within 26 m and boats were classified 

as either moving or stationary. Both stationary and moving boats from all seasons were included for two reasons. 

First, to eliminate potential bias from king mackerel fishing tournaments, the boat data was initially separated by 

season and status (stationary vs. moving) to determine whether use patterns differed among bottom and pelagic 

fishers. However, regardless of how data was partitioned, the overall spatial patterns in boat density were consistent. Second, any boat could dispose of debris at sea, regardless of whether they were bottom fishing, trolling 

for pelagics or bait, or not fishing at all. To determine how the intensity of activity varies over space, the sanctuary 

was divided into 0.25 km2

 cells (500 m x 500 m) and the number of boats within each cell was calculated. Spearman correlation was used to examine the association between the number of boats and the average number of 

debris in each cell (SAS v9.1, Proc Corr). After exploring several spatial scales (1 km2

, 0.25 km2, 0.09 km2, and 

0.01 km2

), the 0.25 km2 scale was considered most appropriate for GRNMS. The 1 km2 scale was found to be 

too coarse to capture potentially meaningful small scale variability. Conversely, the finest scale options resulted 

Image 15. Miscellaneous debris items.
Chapter 3 - Marine Debris

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in too many cells that contained no sample locations. We used the information on boat distribution 

patterns to divide the sanctuary into areas of “low” 

versus “high” boat density. A frequency histogram 

of boats per cell was used to help determine a cutoff between low and high density areas. 

Next, we modeled debris data to determine if 

ledge characteristics and boat density were significant predictors of the presence and abundance of 

debris at ledge sites. Due to the presence of numerous sites with zero debris items, the data was 

analyzed using a two-step conditional model that 

is often used for zero-inflated data (Cunningham 

and Lindenmayer 2005). This approach separates 

variables that determine whether or not debris is 

present from variables that determine the amount 

of debris, given presence. The variables included 

the boat density (low, high), mean ledge height (m), 

ledge area (m2

), mean undercut width (m), and percent cover of benthic species. In the first step, the debris was treated as present or absent and the presence/absence data were modeled using logistic regression (Proc Logistic, SAS v9.1). In the second step, only sites in 

which debris was present were considered. At sites where debris was present, the number of debris items was 

modeled with a generalized linear model (Proc Genmod, SAS v9.1) with a negative binomial distribution and a 

log link. The negative binomial variance distribution was chosen because it requires fewer assumptions than the 

normal or Poisson distribution and is appropriate for modeling skewed count distributions (White and Bennetts 

1996). A Pearson’s Chi-Square test was used to assess the goodness of fit of the negative binomial model to 

the data. At both stages, only main effects were considered, and parsimonious models were selected by using 

backward elimination of non-significant variables (α=0.05). 

Predicting debris density

Ideally, it would be most beneficial to use the two-part conditional model to generate a map of expected debris 

density at ledges throughout GRNMS. However, estimates of the covariates undercut width and percent cover 

were not available for all ledges. Instead, we used ordinary kriging, an interpolation method, of observed debris 

data to predict debris density at ledges throughout GRNMS. The procedure is based only on the observed values 

and does not explicitly take into account other variables that were found to influence debris patterns. In this approach, the spatial covariation among all possible sample points is used to develop an estimate of debris density 

at unsampled locations, based on appropriate weightings of observed values at neighboring sites. Variogram fitting and ordinary kriging were conducted in SAS to make predictions over a 25 m grid scale, which was deemed 

to be an appropriate scale based on the size distribution of ledges within GRNMS. The interpolated debris density was mapped in ArcView v9.1 for cells that were completely within or crossed the boundary of a ledge. In 

addition, a 25 m buffer was added to each ledge to aid in visualization of the predicted values. 

Ocean currents at GRNMS

Finally, the potential influence of tidal currents on debris distribution was explored. Tidal currents may not only 

affect the patterns of debris that originated within GRNMS, but may also be responsible for depositing debris at 

GRNMS that originated elsewhere. Ocean current profile data from September 1, 2005 to February 28, 2006 

were obtained from the NOAA Station 41008 buoy located in GRNMS (http://www.ndbc.noaa.gov/, accessed 

March 23, 2006). Current speed and direction was measured at hourly intervals with a surface Acoustic Doppler Current Profiler (ADCP) mounted to the buoy. The direction the current is flowing is measured from 0-360o, 

where 360o is due north, and 0o means that no current was measurable. Measurements were made at 1 m intervals from surface to bottom. The general direction of bottom currents were examined by plotting the frequency of 

direction measurements at 15 m depth, the deepest reliable measurements available for GRNMS. 

Image 16. Aluminum beverage container.
Chapter 3 - Marine Debris

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41

3.4 Results

Quantity, types, and spatial distribution of 

debris

A total of 93 items were found during field surveys at GRNMS. Debris was present at 32 out of 

the 179 survey sites. The number of debris items 

found within a 100 m2 transect ranged from 0 to 10 

items. Approximately two-thirds of all observed debris items were fishing gear, and about half of the 

fishing-related debris was fishing line (Table 3.1). 

Other fishing related debris included leaders and 

spear gun parts. Non-gear debris included cans, 

bottles, and rope. Other debris, classified as nongear, included such items as wood, electrical wire, 

and a pair of pants pockets. The spatial location 

of total observed debris and fishing gear density 

are shown in Figure 3.1a-c. Highest incidence of 

debris occurred at ledges in the center of the sanctuary. 

Effect of bottom type 

Out of the 32 sites where debris was present, all except three sites were classified as ledge bottom type (Table 

3.2, Figure 3.2). Only non-fishing gear items were found on sand bottom types. Results from the logistic regression indicated that the presence of debris varied significantly by bottom type (Table 3.3). The probability of debris 

presence on ledges was 0.32 (n = 92), which was significantly greater than the probability of presence on sparse 

live bottom (0.02, n = 50) and sand bottom types (0.06, n = 37). There was no significant difference in the presence of debris between sparse live bottom and sand. 

Influence of ledge characteristics and boat density on debris

The highest debris counts were encountered on ledges that had previously been classified as “tall” in height 

and “large” in area (Figure 3.3). The number of debris items present was positively correlated with ledge height 

(Spearman’s Rho = 0.44, Figure 3.4). Debris was present at all of the five tallest ledges surveyed. In addition, the 

number of debris items increased with increasing percent cover (Spearman’s Rho = 0.48, Figure 3.5), undercut 

width (Spearman’s Rho = 0.41, Figure 3.6) and ledge area (Spearman’s Rho = 0.40, Figure 3.7). 

The number of boats per 0.25 km2

 cell ranged from 0-99, with higher boat densities observed in the central part 

of the sanctuary (Figure 3.8). In nearly half of the sanctuary (107 out of 234 cells), no boats were recorded, while 

in much of the remaining cells, only a few boats were observed. A natural break in frequency of cells occurred 

between density classes 4 and 5 (Figure 3.9a). Only 33 cells had an estimated density of ≥5 boats, and further, 

these cells were clustered in the center of the sanctuary. Therefore, cells with <5 boats were labeled as having 

low boat density and cells with ≥5 boats were labeled as having high boat density (Figure 3.9b).

The number of boats per 0.25 km2 cell 

was positively correlated with the observed average debris density (number/100 m2

) (Spearman’s Rho = 0.42, 

Figure 3.10). The majority of debris 

items (80 out of 93) were found in the 

area that was defined as having high 

boat density (≥5 boats/0.25 km2 cell), 

even though more than twice as many 

sites were sampled in the region of low 

boat density (n = 122 compared to n = 

57, respectively). In addition, the comImage 17. Weight belt used for SCUBA diving.

Table 3.1. Frequency of debris types pooled across all GRNMS survey sites (n=179).

Debris type Total number % of total debris 

Fish line 31 33.3

Leader 10 10.8

Spear gun parts 1 1.1

Non-descript/Other gear 21 22.6

Total gear pieces 63 67.7

Cans 14 15.1

Bottles 2 2.2

Rope 4 4.3

Other 10 10.8

Total non-gear 30 32.3

Total debris 93 100
Chapter 3 - Marine Debris

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2

0 10.5

Kilometers

Flat sand

Rippled sand

Sparsely colonized live bottom

Densely colonized live bottom

GRNMS Boundary

Observed # total debris

0

1 - 2

3 - 4

5 - 6

7 - 10

Figure 3.1a. Spatial distribution of total debris (number per 100 m2).

2

0 10.5

Kilometers

Flat sand

Rippled sand

Sparsely colonized live bottom

Densely colonized live bottom

GRNMS Boundary

Observed # gear

0

1 - 2

3 - 4

5 - 6

7 - 9

Figure 3.1b. Spatial distribution of fishing gear (number per 100 m2).
Chapter 3 - Marine Debris

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43

2

0 10.5

Kilometers

Flat sand

Rippled sand

Sparsely colonized live bottom

Densely colonized live bottom

GRNMS Boundary

Observed # non-gear

0

1 - 2

3

Figure 3.1c. Spatial distribution of non-gear (number per 100 m2).

Table 3.2. Presence and average number of debris items per 100 m2

 by bottom type.

Bottom type Total Sites Number of sites 

with debris 

Total # 

debris

Average # debris 

(±SE) /100m2 transect

Ledge 92 29 89 0.97 (±0.21)

Sparse live bottom 50 1 2 0.04 (±0.094) 

Flat sand 22 0 0 0.00

Rippled sand 15 2 2 0.13 (±0.09) 

All 179 32 93 0.52 (±0.11)

Table 3.3. Contrast estimates, standard errors, and chi-square statistics from logistic regression of presence of marine debris in 

GRNMS by bottom type.

variable df Parameter

Estimate 

SE 95% wald Confidence 

Limits

wald Chi-Square Pr>ChiSq 

Lower Upper

Bottom type 2 - - - 15.5 0.0004

 Ledge vs. Sparse 

 Lve Bottom 

1 3.14 1.03 1.11 5.16 9.2 0.002

 Ledge vs. Sand 1 2.06 0.76 0.57 3.55 7.3 0.007

 Sand vs. Sparse Lve

 Bottom 

1 1.08 1.24 -1.36 3.52 0.8 0.386
Chapter 3 - Marine Debris

page

44

position of debris varied between the two 

regions. For example, 75% (60 out of 80 

items) of the debris in the high boat density 

area was fishing gear. In comparison, 23% 

(3 out of 13 items) of the debris found in the 

low boat density area was fishing gear. Two 

out of the three fishing gear items found in 

this region were observed at a site just outside the boundary of the high density boat 

area. 

Results from the two-part conditional model indicate that multiple characteristics of 

ledge features and boat density influence 

observed distribution patterns of debris. 

Boat density was a significant predictor for 

presence of debris and abundance of debris, given presence (Table 3.4). Additionally, 

ledge area and percent cover were significant predictors of presence of debris, and mean ledge height was a 

significant predictor of abundance of debris, given presence. There was a higher probability of encountering 

debris with increasing ledge height. However, at both stages of the model, the estimates for the significant ledge 

variables were small. The Pearson Chi-Square test statistic indicated that the negative binomial distribution was 

appropriate. The null hypothesis of this test was that the data fit the model, and we were unable to reject this 

hypothesis (X2

 = 28.05, df = 26, p = 0.356).

Predicting debris density

Interpolated density of debris at ledges is displayed in Figure 3.11. Highest predicted densities of up to 8.4 pieces/100 m2

 occurs in the center of the sanctuary where highest numbers of debris items were observed. Moderate 

amounts of debris are expected to occur in regions east, west, and south of this area. In much of the sanctuary, 

a density of zero items/ 100 m2

 is predicted. 

Ocean currents at GRNMS 

Direction of bottom currents was bimodal (Figure 3.12a), consistent with an ebb and flow tidal cycle. Arrows 

pointing in the direction of the two modes, 305 and 125 degrees, were overlaid on the GRNMS map (Figure 

3.12b). Thus, the tidal currents appear to flow in a SE-NW direction, perpendicular to the orientation of the northernmost line of ledges. Although we did not have enough information to test for the significance of currents on 

debris distribution, this pattern indicates that currents may be favorable to accumulation of debris at some locations where densities were highest. For example, debris originating north of the northernmost ledge line could 

travel south over sandy bottom with the tide until it encounters a ledge. The potential role of currents in mitigating 

distribution of debris will be discussed further in the following section.

Figure 3.2. Average # debris items (±SE) per 100 m2 transect by bottom 

type.

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

Ledge Sparse Live Bottom Sand

Average # debris per 100m 2

Table 3.4. Two part conditional model for ledge bottom type to test for the effects of boat density (low, high) and ledge characteristics 

(ledge area (m2

), mean ledge height, mean undercut width, and percent cover of benthic organisms) on presence and abundance, given 

presence, of marine debris in GRNMS. The first stage models presence-absence with logistic regression, while the second stage predicts 

density, given presence, with a generalized linear model with a negative binomial distribution. P-values less than 0.05 were considered to 

show a significant effect, and models were reduced by backward elimination to remove non-significant variables. 

variable Parameter

Estimate 

SE wald Chi-square Pr>ChiSq 

Stage 1 Boat density (high vs. low) 0.65 0.29 5.1 0.024

Ledge area 0.0004 0.00021 3.94 0.047

Percent cover 0.024 0.0089 7.79 0.006

Stage 2 Boat density (high vs. low) 0.82 0.35 5.41 0.020

Ledge height 0.006 0.002 6.29 0.012
Chapter 3 - Marine Debris

page

45

0

1

2

3

4

5

6

10

Short

(<58.5)

Medium

(58.5-89.2)

Small

(<316.5)

Large

(>731.4)

Medium

(316.5-731.4)

Tall

(>89.3)

n=1

n=1

n=2 n=18 n=7

n=6 n=7

n=50

Observed # 

debris/100m2

Height (cm)

n=0

Area (m2)

Figure 3.3. Frequency of debris at ledge sites by area and height class combinations as determined by Kendall and Eschelbach (2006). See methods for further description. The number 

of ledges within each area-height combination is noted by n=i.

0

2

4

6

8

10

12

0 50 100 150 200

Mean ledge height (cm)

# debris per 100 m 2 Figure 3.4. Relationship of observed number of debris items (per 

100 m2

) with ledge height (measured in situ).

0

2

4

6

8

10

12

0 20 40 60 80 100

% Cover

# debris per 100 m 2

Figure 3.5. Relationship of observed number of debris items (per 

100 m2

) with percent cover of benthic organisms (measured in 

situ).

0

2

4

6

8

10

12

0 50 100 150 200 250 300

Mean undercut width (cm)

# debris per 100 m 2 Figure 3.6. Relationship of observed number of debris items 

(per 100 m2

) with undercut width (measured in situ).

0

2

4

6

8

10

12

0 2000 4000 6000 8000

Ledge area (m2

)

# debris per 100 m 2

Figure 3.7. Relationship of observed number of debris items 

(per 100 m2

) with ledge area (determined from GIS analysis).
Chapter 3 - Marine Debris

page

46

3.5 Discussion

The purpose of this study was to characterize debris 

patterns in GRNMS to support cleanup and monitoring of debris in the sanctuary. To our knowledge, this 

was the first study to quantify the types and amount 

of debris in offshore waters Georgia. A variety of debris items, including plastics, Styrofoam products, 

metal, glass, and fishing-related items, have been 

observed and removed during beach surveys in 

coastal Georgia (Gilligan et al. 1992). While fishing 

gear constituted a small portion of the total debris 

found on the beaches, Gilligan et al. (1992) noted 

that the impact of small items such as fishing line 

and string may have a disproportionately large effect 

due to the potential for entanglement of the benthic 

substrate, organisms, and other debris items. In contrast, in terms of number of debris items, fishing gear 

was more common than consumer related items 

(e.g., bottles, cans, packaging, etc.) in GRNMS, 

which is not surprising given the popularity of recreational fishing in the sanctuary. The types of debris 

observed in GRNMS are similar to those found in 

coral reef habitats in the Florida Keys National Marine Sanctuary (FKNMS). Both sanctuaries have a 

large recreational fishing contingent. Lost hook and 

line gear is the dominant debris type in both sanctuaries, although lost lobster traps also are common in 

FKNMS (Chiappone et al. 2004). 

The distribution and abundance of marine debris 

in GRNMS is related to the bottom type, the level 

of boating/fishing activity, and local characteristics 

of benthic features. There is a significantly greater 

probability of presence of debris at ledges compared 

to other bottom types. Several factors may contribute to this observation. First, the abiotic features of 

ledges (e.g., crevices, changes in relief, overhangs) 

provide numerous places for fishing line and other 

debris to snag or become trapped. Ledges also tend 

to be densely colonized with corals, sponges, and 

other biota (Chapter 2), creating further opportunities 

for debris entanglement. For example, although association with corals was not routinely recorded, divers noted several instances where fishing line was 

found tangled in branches of oculinid coral. Second, 

due to the association of recreationally important 

fish species with ledges (Chapter 4), these bottom 

features are often targeted by fishermen. Even in areas with many boat observations, there were almost 

no occurrences of debris at sand and sparse live 

bottom sites. This is probably due to the concentration of fishing effort at ledges, and because the low 

complexity of sand bottom types is less conducive to 

debris entanglement and accumulation.

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2

0 10.5

Kilometers

GRNMS Boundary

! Boat locations

# Boats / cell

0

1-4

5-8

9-12

13-16

17-20

>20

Figure 3.8. Locations of observed boats and density of boats per 0.25 

km2

 cell. 

0

20

40

60

80

100

120

0 10 20 30 40 50 60 70 80 90

# boats per 0.25 km2

 cell

Frequency of cells

Figure 3.9a. Frequency histogram of the number of boats per cell 

in GRNMS. The red line represents the selected cutoff between low 

and high boat density. 

2

0 10.5

Kilometers

GRNMS Boundary

Low boat density (0-4)

High boat density (5-99)

Figure 3.9b. Regions of low (0-4 boats/ 0.25 km2 cell) and high 

(5-99 boats/ 0.25 km2

 cell) boat density.
Chapter 3 - Marine Debris

page

47

Of all tested variables, boat density had the strongest association with both presence and abundance of debris at 

ledges. Boat density is highest in the center of the sanctuary on a SW-NE axis, with the largest concentration occurring in the vicinity of the data buoy (NOAA station 41008); 

99 boats were observed in the cell that included the buoy. 

The high density of boats in this region is likely attributed 

to several factors. Recreational fishermen noted that the 

buoy is a popular location to catch bait and troll for king 

mackerel, and a nearby ledge attracts bottom fishers (Captain Judy Helmey and William H. “Bing” Phillips, personal 

communication). Slightly further away from the buoy, boat 

activity is less dense but still high. Fewer boats were observed in the southern portion of the sanctuary, despite the 

presence of numerous ledges, which indicates less fishing 

occurs here compared to areas of high boat density. This is 

further supported by the difference in debris types between 

the two areas. Three-quarters of the debris items found in the region of high boat density were fishing gear, while 

debris items observed in the low density region were primarily non-fishing related.

In addition to the strong link with boat density, patterns of debris occurrence were also related to physiographic 

features of ledges. The presence of debris significantly increased with increasing area and percent cover of 

ledges; and given presence, the abundance of debris was positively related to ledge height. It is not surprising 

that ledge area is a significant predictor of presence of debris because extensive ledges are more likely to be targeted by recreational fishermen who closely monitor their depth sounder (John Duren, personal communication). 

Once good fishing spots have been located, fishermen often return to those locations. Thus, high boat density in 

the center of the sanctuary, where many large ledges, including the five with the largest area, are located, may be 

indicative of preferred fishing spots. Fishing charts that include the GPS location of bottom features and fishing 

“hot spots” can be purchased in marina stores. Several of the features on such fishing charts (www.sstcharts.

com, accessed June 4, 2006) are in close proximity to areas where both a large number of boats were observed 

and fishing debris was found. 

There are a couple of reasons that ledge height may not have been significant in the first stage of the analysis. 

First, numerous tall ledges occur in the southern portion of the sanctuary, and they may experience lower fishing 

pressure based on boat sighting data. Such ledges may not be well known or have not been “discovered” at all, 

which may partially explain why little debris was found on them (Captain Judy Helmey and William H. “Bing” Phillips, personal communication). The importance of 

ledge height is confirmed in the second stage of 

the model; among ledges with debris, taller ledges 

have greater concentrations. 

Interpolation (ordinary kriging) of observed debris 

data was used to predict debris density at ledges 

throughout GRNMS. Highest densities were predicted in the central region where the most debris was found, whereas little to no debris would 

be expected in the southern region. Although this 

method appears to be an effective way to estimate 

spatial density patterns, additional independent 

samples would be necessary to perform cross-validation analysis. Potential future work could refine 

the predictive model to include information on boat 

density and bottom feature characteristics, since 

these were significantly related to debris presence 

0

1

2

3

4

5

6

0 20 40 60 80 100

# of boats per 0.25 km2 cell

Average # of debris per 100 m 2

Figure 3.10. For each 0.25 km2 cell, the relationship of average number of debris items (per 100 m2

) with the number of 

observed boats.

Image 18. Lead weight used for fishing.
Chapter 3 - Marine Debris

page

48

2

0 10.5

Kilometers

Flat sand

Rippled sand

Sparsely colonized live bottom

Densely colonized live bottom

GRNMS Boundary

Predicted # debris

0

0.1-1

1.1-2

2.1-3

3.1-4

4.1-5

5.1-6

6.1-7

7.1-8

8.1-9

and abundance. As stated previously, this was not carried out here as estimates of some variables (e.g., percent 

cover) are not available for all ledges.

The potential for outside sources of debris to enter the sanctuary was a subject of concern in the recent draft management plan (GRNMS 2006). Although there is no way to verify the origin of debris items found within GRNMS, 

we examined ocean current data to evaluate the potential for debris from outside the sanctuary to contribute to 

debris accumulation in GRNMS. Currents in GRNMS are primarily tidal, as indicated by the bimodal frequency in 

direction of currents recorded by the NOAA data buoy. The directions of the dominant currents, 305o and 125o, 

suggest that debris originating in the north-central part of the sanctuary could roll easily over featureless sand 

areas during a tidal cycle until it encounters the line of ledges where highest debris densities were observed. 

This is also the portion of the sanctuary nearest land, further suggesting that currents could bring in land-based 

debris items from outside the sanctuary. However, the highest densities of debris were not distributed evenly 

along this line of ledges, as would have been expected were tidal currents depositing marine debris from outside 

the sanctuary. It is likely that most fishing-related debris originates from boats inside the sanctuary. All observed 

fishing gear consisted of permitted gear types that are known to be used in the sanctuary. The prevalence of gear 

that is not used locally is often an indication that it has traveled from elsewhere, as has been observed in the 

NWHI (Donohue et al. 2001). The net movement of water could not be evaluated from available data; however, 

Figure 3.11. Interpolated debris density (per 100 m2) in GRNMS from ordinary kriging. 
Chapter 3 - Marine Debris

page

49

Seim and Edwards (Seim and Edwards 

in press) demonstrated that the NOAA 

buoy-mounted ADCP in GRNMS underestimated current velocity at depth.

The influence of ocean currents on debris 

accumulation in GRNMS warrants further 

study, particularly in relation to items that 

may be more easily transported. For example, many of the non-fishing related 

debris items found in GRNMS consisted 

of beer and soda cans/bottles. While the 

highest concentration of non-fishing debris was also located in the center of the 

sanctuary, these items were often present at sites with no fishing debris and in 

the area of lower boat density. Marking 

debris items and tracking their movement 

over time is one possible way to assess 

0 50 100 150 200

0

35

70

105

140

175

210

245

280

315

350

Direction (degrees)

Frequency of observations

Figure 3.12a. Frequency of current direction observations at 15m depth at the 

NOAA buoy located in GRNMS. Observations were taken at hourly intervals 

from 9/1/05 to 2/28/06.

^

2

0 10.5

Kilometers

Flat sand

Rippled sand

Sparsely colonized live bottom

Densely colonized live bottom

GRNMS Boundary

Predicted # debris

0

0.1-1

1.1-2

2.1-3

3.1-4

4.1-5

5.1-6

6.1-7

7.1-8

8.1-9

^ Buoy

Figure 3.12b. Tidal current direction overlaid on map of GRNMS. The arrows represent the bimodal peaks from the frequency 

distribution (125o and 305o). 360o is due north.
Chapter 3 - Marine Debris

page

50

the influence of currents on debris movement and 

accumulation. 

Debris density at GRNMS can be compared to 

estimated densities in the Florida Keys National 

Marine Sanctuary (FKNMS) found during a study 

using similar methodology (Chiappone et al. 

2004). Total marine debris in the high relief spur 

and groove and low relief bottom types in FKNMS 

were estimated as 1.15 (±0.14 SE) and 1.22 

(±0.20 SE) per 100 m2

, respectively (Chiappone 

et al. 2004), which is slightly higher than the mean 

density observed on ledge bottom type (0.97) and 

twice as high as overall mean density (0.52) in 

GRNMS. Furthermore, the distribution of debris in 

FKNMS appears to be more widespread; debris 

was recorded at 92% of sites sampled in FKNMS 

(Chiappone et al. 2004). The differences between 

the two sanctuaries may be a reflection of the disparities in accessibility; GRNMS is further from shore and likely receives fewer fishing trips than FKNMS. However, due to the differences in the bottom types that were sampled, it is difficult to directly compare our results 

to those in the Florida Keys. Chiappone et al. (2004) also compared hook and line density between regions of 

varying fishing pressure (no fishing, fished, and catch and release zones) but surprisingly found no significant 

differences between the three areas. The authors hypothesized that this may be due to noncompliance with 

regulations and/or the deposition of debris prior to enactment of regulations in protected zones in 1997. Similarly, it is unknown when debris that we observed 

in GRNMS was deposited. Periodic monitoring 

and removal of debris at designated sites would 

greatly improve our understanding of debris accumulation rates in GRNMS. 

3.6 Recommendations for management and monitoring

Information gleaned from the current analysis was 

used to devise a strategy for prioritizing cleanup 

efforts (Figure 3.13). Because the overwhelming 

majority of debris was located in densely colonized ledge habitat, ledges should be considered 

a higher priority for debris mitigation and removal. 

Second, due to the significant difference in presence and abundance of debris between regions 

of high versus low boat density, ledges positioned 

within the area of more intense fishing pressure 

are more likely to have debris. Of the 436 mapped 

ledges in GRNMS, 156 are located within the region of high boat density. The number of ledges 

receiving top priority for cleanup can be reduced 

further by accounting for ledge height and area, 

since the results of this study demonstrated that 

presence and abundance of debris are positively 

correlated with these variables. As described in 

the methods section, all of the ledges in GRNMS 

were classified as short, medium, or tall in height 

and small, medium or large in area. Within the 

region of high boat density, 34 ledges are clas- Figure 3.13. Recommended strategy to prioritize ledge sites within 

GRNMS for debris removal.

Image 19. Fishing line entangled in coral.

436 ledges in 

GRNMS 

156 ledges 

within high 

boat density 

region

Selection Scheme #1

34 ledges in upper quantile for height and 

size (tall/lg)

Selection Scheme #2

85 ledges in upper 2/3 quantiles for height and 

size (tall/lg, tall/med, med/lg, med/med)
Chapter 3 - Marine Debris

page

51

sified as both “tall” and “large” and would be recommended as the first sites to target. During the 

recent “Sweep the Reef, Sweep the Beach” World 

Ocean Day Cleanup 2006, divers removed numerous debris items, including fishing gear and beverage cans/bottles, from several sites within this 

category (Gail Krueger, personal communication). 

If sufficient resources are available, an additional 

54 ledges that fall into both the upper two quantiles 

for height and area (tall/medium, medium/large, 

medium/medium) are additional recommended 

sites for debris removal, bringing the total to 85 

ledges. After debris is removed, sites should be 

monitored periodically to measure rates of new 

debris accumulation. In addition, we would recommend expanding long-term monitoring efforts to include several ledges that are located in the areas 

of lower observed boat densities to compare accumulation rates. Periodic updates of boat sighting 

data will allow managers to detect any changes in 

recreational boating patterns in GRNMS.

Marine debris may inflict both direct and indirect damage to biota in GRNMS. Although impacts on biota were not 

quantified as part of our study, in several instances fishing line was observed to be entangled with benthic organisms, particularly the branching corals in the family Oculinidae. Fishing line, wire, hooks, and leaders can cause 

tissue abrasion when they snag on reef organisms. Chiappone et al. (2005) documented significant relationships 

between the density of lost hook-and-line gear with the density of damaged sponges, gorgonians, and milleporid 

hydrocorals, and found a positive correlation between length of fishing line and densities of damaged gorgonians. Once entangled, fishing line may become incorporated into the reef matrix if it is overgrown by individual 

organisms (Chiappone et al. 2005). In our study, fishing line was often fouled by algae. In time, progressive algal 

fouling of fishing line entangled in coral may lead to coral death (Schleyer and Tomalin 2000; Asoh et al. 2004; 

Yoshikawa and Asoh 2004). In GRNMS, taller ledges in particular may be most susceptible to damage because 

they tend to be most densely colonized with benthic organisms (this document). The impacts of hook-and-line 

fishing gear and other debris on benthic organisms in GRNMS and elsewhere need further study because negative effects are likely to become more severe as use of the sanctuary increases.

Image 20. Example of fouled fishing line (out of water).
Chapter 3 - Marine Debris

page

52

REFERENCES

Aliani, S, and A Molcard. 2003. Hitch-hiking on floating marine debris: macrobenthic species in the Western Mediterranean 

Sea. Hydrobiologia 503(1-3): 59-67.

Andrady, AL. 2000. Plastics and their impact in the marine environment. In: Carbajal S (ed) Proceedings of the International 

Marine Debris Conference on Derelict Fishing Gear and the Marine Environment, Honolulu, HI, pp 137-143.

Asoh, K, T Yoshikawa, R Kosaki, and EA Marschall. 2004. Damage to cauliflower coral by monofilament fishing lines in 

Hawaii. Conservation Biology 18(6): 1645-1650.

Chiappone, M, H Dienes, DW Swanson, and SL Miller. 2005. Impacts of lost fishing gear on coral reef sessile invertebrates 

in the Florida Keys National Marine Sanctuary. Biological Conservation 121(2): 221-230.

Chiappone, M, DW Swanson, SL Miller, and H Dienes. 2004. Spatial distribution of lost fishing gear on fished and protected 

offshore reefs in the Florida Keys National Marine Sanctuary. Caribbean Journal of Science 40(3): 312-326.

CGRDC (Coastal Georgia Regional Development Center). 2006. Georgia Coast 2030: Population Projections for the 10-

county Coastal Region. Prepared by the Center for Quality Growth and Regional Development at the Georgia Institute of 

Technology.

Cunningham, RB, and DB Lindenmayer. 2005. Modeling count data of rare species: some statistical issues. Ecology 86(5): 

1135-1142.

Dayton, PK, SF Thrush, MT Agardy, and RJ Hofman. 1995. Environmental effects of marine fishing. Aquatic Conservation: 

Marine and Freshwater Ecosystems 5(3): 205-232.

Derraik, JGB. 2002. The pollution of the marine environment by plastic debris: a review. Marine Pollution Bulletin 44(9): 

842-852.

Donohue, MJ. 2005. Eastern Pacific Ocean source of Northwestern Hawaiian Islands marine debris supported by errant fish 

aggregating device. Marine Pollution Bulletin 50(8): 886-888.

Donohue, MJ, RC Boland, CM Sramek, and GA Antonelis. 2001. Derelict fishing gear in the Northwestern Hawaiian Islands: 

diving surveys and debris removal in 1999 confirm threat to coral reef ecosystems. Marine Pollution Bulletin 42(12): 1301-

1312.

Ehler, R, and VR Leeworthy. 2002. Gray’s Reef National Marine Sanctuary (GRNMS): A Socioeconomic Review of Georgia’s 

Marine Related Industries and Activities. U.S. Department of Commerce, National Oceanic and Atmospheric Administration, 

National Ocean Service, Silver Spring, MD.

Galgani, F, JP Leaute, P Moguedet, A Souplet, Y Verin, A Carpentier, H Goraguer, D Latrouite, B Andral, Y Cadiou, JC Mahe, 

JC Poulard, and P Nerisson. 2000. Litter on the sea floor along European coasts. Marine Pollution Bulletin 40(6): 516-527.

Gilligan, MR, RS Pitts, JP Richardson, and TR Kozel. 1992. Rates of accumulation of marine debris in Chatham County, 

Georgia. Marine Pollution Bulletin 24(9): 436-441.

Hess, NA, CA Ribic, and I Vining. 1999. Benthic marine debris, with an emphasis on fishery-related items, surrounding Kodiak Island, Alaska, 1994-1996. Marine Pollution Bulletin 38(10): 885-890.

Kendall, MS, and KA Eschelbach. 2006. Boundary Options for a Research Area within Gray’s Reef National Marine Sanctuary. NOAA NOS NCCOS, Silver Spring.

Kendall, MS, OP Jensen, C Alexander, D Field, G McFall, R Bohne, and ME Monaco. 2005. Benthic mapping using sonar, 

video transects, and an innovative approach to accuracy assessment: A characterization of bottom features in the Georgia 

Bight. Journal of Coastal Research 21(6): 1154-1165.

Laist, DW. 1997. Impacts of marine debris: entanglement of marine life in marine debris including a comprehensive list of 

species with entanglement and ingestion records. In: Rogers DB (ed) Marine Debris: Sources, Impacts, and Solutions. 

Springer-Verlag, New York, pp 99-139.

NOAA. 2006. Gray’s Reef National Marine Sanctuary Final Management Plan / Final Environmental Impact Statement. 

NOAA NOS NMSP, Savannah, GA.
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Rees, G, and K Pond. 1995. Marine litter monitoring programs - a review of methods with special reference to national surveys. Marine Pollution Bulletin 30(2): 103-108.

Schleyer, MH, and BJ Tomalin. 2000. Damage on South African coral reefs and an assessment of their sustainable diving 

capacity using a fisheries approach. Bulletin of Marine Science 67(3): 1025-1042.

Seim, HE, and CR Edwards. in press. Comparison of buoy-mounted and bottom-moored Acoustic Doppler Current Profiler 

performance at Gray’s Reef. Journal of Atmospheric and Oceanic Technology. 

White, GC, and RE Bennetts. 1996. Analysis of frequency count data using the negative binomial distribution. Ecology 

77(8): 2549-2557.

Yoshikawa, T, and K Asoh. 2004. Entanglement of monofilament fishing lines and coral death. Biological Conservation 

117(5): 557-560.
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Chapter 4 - Bottom Fish

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55

CHAPTER 4: CHARACTERIZATION OF THE BOTTOM FISH

4.1 Introduction

A comprehensive characterization of the density 

and size distribution of bottom fish and their associated habitats has not yet been conducted for 

Gray’s Reef National Marine Sanctuary (GRNMS). 

Not long after the sanctuary was designated in 

1981, Gilligan (1989) began compiling a list of 91 

fish species that were characteristic of the area 

and noted the general habitats of each species 

such as hardbottom, sand, or in the pelagic environment. Video and scuba surveys over selected 

ledges and other bottom types in recent years 

have greatly expanded this species list and have 

also estimated the relative abundance of some 

taxa (Parker et al. 1994, Reef Environmental Education Foundation). Other studies have had sites 

on ledges or hardbottom within GRNMS and have 

further characterized fish communities, general 

bottom associations, fish densities, and some seasonal differences among fish assemblages (Sedberry and Van Dolah 1984, Parker et al. 1994, Barkoukis 2006). 

Many additional studies have been conducted elsewhere in the South Atlantic Bight on fish communities over 

live bottom (Struhsacker 1969, Huntsman 1976, Miller and Richards 1980, Powles and Barans 1980, Grimes et 

al. 1982, Wenner 1983, Chester et al. 1984, Sedberry and Van Dolah 1984), sand bottom (Struhsacker 1969, 

Wenner et al. 1979a, 1979b), and shelf edge environments (Struhsacker 1969, Grimes et al. 1982, Barans and 

Henry 1984, Parker and Ross 1986, Gilmore and Jones 1992, Parker and Mays 1998, Sedberry et al. 2004, 

Quattrini and Ross 2006). Most of these studies were conducted at broad scales covering much of the South 

Atlantic Bight and examined differences in assemblage structure between inshore and offshore communities or 

latitudinal changes in biogeography. 

Despite this abundance of studies in the South Atlantic Bight, very little quantitative analysis has been done on 

mid-shelf fish and their specific habitat associations (but see Parker et al. 1994). Hardbottom and limestone 

ledges are known to be key habitats for bottom fish in the region; however, the factors that make them attractive to various components of the fish community have not been quantified. Even studies that have focused on 

individual species of bottom fish often do not quantify their fine-scale habitat preferences (Matheson et al. 1986, 

Mercer 1989, Gilmore and Jones 1992, Harris et al. 2002, McGovern et al. 2005, Barkoukis, 2006). At best, gross 

ledge height has been categorized as small, medium, and large or sparsely, moderately, or densely colonized 

by sessile invertebrates and then related to fish assemblages (Parker et al. 1994, Riggs et al. 1996, Quattrini 

and Ross 2006). Although all of these studies have provided a wealth of biogeographic information on the South 

Atlantic Bight and an understanding of the general habitat associations of bottom fish, the more detailed structural attributes of benthic habitat that control the variability in the fish community at locations like GRNMS have 

remained unknown. 

In contrast to the lack of detailed studies on hardbottom habitats in the South Atlantic Bight, much research has 

been focused on defining fine-scale habitat associations of fish in coral reef environments although often with 

conflicting results (Risk 1972, Luckhurst and Luckhurst 1978, Molles 1978, Roberts and Ormond 1987, Chabanet et al. 1997, Öhman and Rajasuriya 1998, Friedlander and Parrish 1998, Gratwicke and Speight 2005). In 

these studies, the total abundance, species richness, diversity, and trophic structure of reef fish have variously 

been correlated with benthic characteristics such as rugosity, vertical relief, coral cover, and other variables. 

Various reef types and regions have been shown to have quite different relationships between fish community 

parameters and benthic characteristics and have demonstrated the need to identify the factors that drive community structure of fish that are specific to each reef type and locale.

Image 21. Various species of fish.
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Bottom type at GRNMS has been coarsely defined as sand, flat live bottom, and ledges. A key precursor to the 

present study was conducted by Parker et al. (1994) who examined the fish communities associated with these 

general bottom types as mapped by Hunt (1974), and further categorized live bottom as covered with sparse 

(<25%), moderate (25-50%), or dense (>50%) cover of sessile benthic invertebrates. An important next step 

is analysis of how fish community structure varies with habitat features measured on continuous rather than 

categorical scales. For example, how ledge height, area, undercut, percent cover of hard or soft substrate, and 

percent cover of sessile biota relate to resident fish communities at GRNMS has not been quantified.

With the recent availability of much more detailed benthic maps of GRNMS (Kendall et al. 2005), a spatially 

comprehensive inventory of fish and their associated bottom features is now possible. Necessary improvements 

to the existing inventory include comprehensive surveys of fish associated with all bottom types, estimation of 

the size structure of fish communities, analysis of the fine-scale benthic features that are associated with the fish 

community, and detailed analysis of the spatial distribution of key species. 

In addition to a detailed characterization of fish and their associated bottom types, the impact of other forces 

that shape fish communities remain largely unknown. For example, recreational fishing is a key user activity to 

understand and sustain at GRNMS. Fishing effort has presumably been on the rise at GRNMS in recent decades 

given increasing human populations for coastal Georgia, higher numbers of recreational anglers and days spent 

fishing (Ehler and Leeworthy 2002), and increasing boat density observed within the sanctuary (NOAA 2006). 

Recreational fishing with hook and line is the dominant approach at GRNMS although some spear fishing occurs 

as well. Both methods selectively target fish such as black sea bass (Centropristis striata), gag (Mycteroperca 

microlepis), and scamp (M. phenax) (Huntsman 1976, Mercer 1989). Trap fishing and bottom trawling are not 

allowed in the sanctuary (NOAA 2006). Direct and indirect effects on fish communities of several forms of fishing 

(e.g. commercial, recreational, artisanal) have been demonstrated in many parts of the world (Russ and Alcala 

1989, McClanahan 1994, Watson and Ormond 1994, Grigg 1994, Jennings et al. 1995, Jennings and Polunin 

1996, Jennings and Polunin 1997, Wantiez et al. 1997, Chiappone et al. 2000, Westera et al. 2003, Dulvy et al. 

2004). Despite an abundance of studies elsewhere, little is known about the effects of recreational fishing on the 

overall species richness, diversity, and abundance of benthic fish communities in the South Atlantic Bight. The 

impact of recreational fishing directly on target species has been considered at broader scales but less is known 

about impacts at discrete localities such as GRNMS. Similarly, indirect effects of such activities on fish resources 

are not well understood. The spatial distribution of fishing effort is not uniform throughout the sanctuary. Patterns 

of boat use and marine debris such as fishing line and lures indicate that the central area of the sanctuary receives more fishing pressure than surrounding areas (Chapter 3). This offers the potential to look for differences 

in the fish community between the heavily used and less used areas.

The objectives of this component of the study were 

to: 1) conduct comprehensive surveys of bottom 

fish associated with all bottom types in the sanctuary using a random stratified sampling approach 

and the best available bottom maps; 2) describe 

the size structure of fish communities and key 

species of interest to the recreational fishery; 3) 

identify the fine-scale benthic features that are associated with the fish community and key species; 

4) compare fish communities and key species in 

heavily fished versus less intensively fished areas 

of the sanctuary; 5) map the spatial distribution of 

key species; and 6) offer suggestions for future 

monitoring of bottom fish at GRNMS. 

4.2 Methods for Fish Surveys

Visual fish surveys occurred along a 100 m2 transect at randomly selected sites as outlined in Chapter 1. Once at a site, the fish surveyor attached a 

tape measure to the substrate or weighted line that Image 22. Diver conducting fish survey.
Chapter 4 - Bottom Fish

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57

was used to mark the site from the surface and 

began the survey. Recall that surveys were conducted in a random direction on all bottom types 

except for ledges. On dives over ledge habitat, the 

survey was conducted along the ledge face or lip if 

undercut. This allowed fish on the underside, face, 

and top of the ledge to be surveyed. As the tape 

rolled out, the diver looked forward toward the end 

of the transect and recorded all fish species to the 

lowest taxonomic level possible within the survey 

area. To maximize time spent observing fish and 

minimize the time spent writing the data, four letter codes were used that consisted of the first two 

letters of the genus name followed by the first two 

letters of the species name. In the rare case that 

two species had the same four-letter code, letters 

were added to the species name until a difference 

occurred. If the fish could only be identified to the 

family or genus level then this was all that was recorded. The number of individuals per species was 

tallied in 10 cm size class increments up to 70 cm using visual estimation of fork length. If an individual fish was 

greater than 70 cm, then a visual estimate of the actual fork length was recorded. Although the benthos was 

not altered by lifting or moving rocks or other objects, the fish surveyor moved off the center line of the transect 

temporarily to identify, enumerate, or observe fish in holes and under ledges. 

Several similar looking pairs or groups of species that were observed often moved too quickly, kept a distance 

from divers, or remained far under recesses of ledges to allow consistent identification to the species level and 

were therefore identified only to the genus level. Those species were Seriola dumerili and S. rivoliana, Pareques 

umbrosus and P. acuminatus, and Decapterus maculatus and D. punctatus.

4.3 Methods for Data Analysis

A summary of all species observed in this characterization was created in tabular format. The probability of encounter, mean abundance (+/- standard error), and biomass (+/- standard error) within a 100 m2

 transect were 

provided for each species within the four bottom types surveyed. Probability of encounter is the proportion of 

surveys in a given habitat type on which a species was observed. For species that had zero values for probability 

of encounter, abundance and biomass were left blank. No standard error is given when a species was seen on 

less than three surveys although mean abundance and biomass were calculated. Mean values are rounded to 

the nearest whole number and SE is rounded to tenths. Biomass was calculated using the length-weight relationship W = aLb

, where L is length in centimeters and weight is in grams. The mid point of each size class was used 

as the value of L. For example, if a fish were in the 10-20 cm size class its length (L) for biomass estimation was 

assumed to be 15 cm. Values of the terms (a) and (b) were obtained from FishBase (Froese and Pauly 2005) for 

each species. For species with more than one length-weight relationship defined, values for the study nearest 

GRNMS were used. For species with no length-weight relationship published, terms for a morphologically similar 

species were used. Analysis of seasonal differences in the fish community is limited to noting presence/absence 

by sampling month (i.e., May, August, or both).

General differences in the fish communities among bottom types were evaluated by comparing species richness (number of species), diversity (Shannon Index), abundance, and biomass for each bottom type. Data were 

grouped by bottom type: flat sand, rippled sand, sparse live bottom, or ledge as identified by divers at each site. 

All data were log transformed to meet normality and homogeneity of variance assumptions. ANOVA was used to 

determine if multiple means comparisons were warranted followed by Tukey tests to identify which bottom types 

differed for each variable. 

Differences in the size distribution of fish among bottom types were examined with size frequency histograms. 

For these, fish abundance was averaged across all species within each 10 cm size class. 

Image 23. Triggerfish and tomtates.
Chapter 4 - Bottom Fish

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To further evaluate the differences in community structure among bottom types, the differences in the particular fish species that were present at each site were examined using cluster analysis. Sites were hierarchically 

clustered based on presence/absence of the 78 species found in the study using Ward’s Minimum Variance 

technique. Presence/absence was used to focus the analysis on simple community membership. Patterns were 

checked for stability using other clustering procedures and by clustering based on abundances of species at 

each site with extreme outliers removed.

The fish communities at ledges were examined in greater detail through regression, cluster analyses, multiple 

means comparisons, and GIS plots. Relationships between community structure of fish and ledge characteristics were investigated with multiple regression of the 92 ledge sites. Response variables were species richness, 

abundance, and diversity of fish. Explanatory variables included mean percent cover of sessile invertebrates, 

total height, undercut height, undercut width, and total area of ledges from benthic maps (Kendall et al. 2005). 

In the event that percent cover was significant in explaining fish community variables, abiotic cover (hardbottom, sand, etc.) and biotic cover groups (coral, sponges, etc.) were examined further in a separate analysis. In 

addition, to evaluate any differences in fish community structure due to fishing pressure, fish data associated 

with ledges located in areas of high boat density were compared with low boat density areas (see Chapter 3). 

Preliminary analysis of boat count data likely to be engaged in bottom fishing and marine debris data revealed 

that the high and low boat density areas used in Chapter 3 were good surrogates for areas receiving relatively 

higher versus lower bottom fishing pressure respectively. Backwards selection in regression models was used to 

ensure that only the most influential variables were retained in the model. Analyses were performed on untransformed data except for fish abundance which was log transformed to meet statistical assumptions for multiple 

linear regression.

The 92 ledge sites were further examined for differences in their fish communities through cluster analysis. 

These 92 sites were hierarchically clustered based on presence/absence of the 72 species found on ledges in 

the study using Ward’s minimum variance technique. Patterns were checked for stability using other clustering 

procedures and clustering based on abundance with removal of extreme values (e.g. school of 40,000 Haemulon 

aurolineatum). The 92 ledge sites were also clustered based on ledge characteristics including percent cover, 

total height, undercut height, and undercut width. 

The association between fish communities and ledge characteristics was examined further by considering the 

types of ledges with which each fish community was found. This was done by comparing the results of the cluster 

analysis for sites based on their fish community with the clusters based on ledge measurements. To facilitate this 

comparison, a scatter plot was created in which the sites clustered based on the fish data were placed on the X 

axis and the sites clustered based on the ledge data were placed on the Y axis. The intersection of each site was 

then plotted in chart space. The results from earlier 

analyses indicated four clusters based on the fish 

community analysis and four based on the ledge 

measurement analysis respectively. These clusters were used to separate the chart space into 

sixteen intersecting regions. This enabled both the 

fish community, hereafter called fish clusters, and 

the corresponding physical characteristics of ledge 

sites, hereafter called ledge clusters, to be described relative to each other. To check the stability of the resulting coincidence of fish clusters and 

ledge clusters, an additional clustering procedure 

was conducted wherein ledge sites were clustered 

based on both their ledge characteristics and fish 

species present in the same analysis.

An additional summary of all species observed on 

ledges was created in tabular format. The probability of encounter, mean abundance (+/- standard Image 24. Sea robin on sparsely colonized live bottom.
Chapter 4 - Bottom Fish

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59

error), and biomass (+/- standard error) within each 100 m2 transect are provided for each species within the four 

ledge types identified by the cluster analysis. 

Simple plots of species richness, abundance, biomass, diversity, and ledge clusters were produced in a GIS 

using each survey location’s latitude and longitude. Sites were overlaid on benthic maps of the sanctuary and 

visually assessed for spatial patterns.

Finally, selected fish species were examined further due to their importance to the recreational fishery including black sea bass (Centropristis striata), gag (Mycteroperca microlepis), and scamp (M. phenax) (Huntsman 

1976). Only those surveys conducted on ledges (n=92) were used in the analyses for these species since that is 

their preferred bottom type and is where fishermen most often target them (Duren and Helmey, pers. comm.). In 

particular, Mycteroperca species are rarely seen apart from ledges. Size frequency histograms were created for 

each species with sightings designated as either in more intensively fished or less fished areas according to boat 

density (Chapter 3). Average number of fish in each size class +/- SE was calculated. The size bin containing the 

size limit of the recreational fishery (South Atlantic Fishery Management Council 2006) is noted on these plots 

and the proportion of fish above and below this value was calculated for high versus low boat density areas of 

the sanctuary respectively. Where size limits fell within our size classes, the number of fish in that class was split 

proportionally above and below the value. For example, the size limit for gag is 61 cm (24 inches total length) 

and our size bin ranged from 60 to 70 cm. Therefore, 10% of the fish observed within that bin were assumed to 

be below and 90% were assumed to be above the size limit. In addition, size frequency histograms of these three 

species were plotted for each survey site and overlaid onto the benthic map of GRNMS to examine the spatial 

distribution of the various size classes. 

In addition, these three fish species of interest were examined further for relevant relationships to ledge characteristics and other variables using data from the 92 ledge sites. The abundance and mean body length of 

C. striata was examined for relationships with ledge and other relevant variables through multiple regression. 

Variables tested were total percent cover of sessile biota, total ledge height, ledge area, and location (high or 

low boat density region). In addition, all possible two-way interactions with boat density (high versus low) were 

examined. Undercut variables were not considered in this analysis since C. striata is not observed to utilize ledge 

undercuts. Abundance was log transformed to meet statistical assumptions.

Probability of occurrence for the two Mycteroperca species respectively, as related to ledge variables, was examined through logistic regression. Abundances were too low to enable analysis beyond simple presence/absence. 

Observations of these species made while diving indicate that these species utilize undercut ledges. Therefore, 

relationships between the presence/absence of these species and undercut height, undercut width, ledge area, 

and location (high or low boat density region) were considered along with all possible two-way interactions with 

boat density. Mean size of these species was also examined through multiple regression with these same variables.

The relationship between occurrences of the two grouper species and Centropristis striata relative to each other 

was also evaluated with multiple regression. For this analysis the presence/absence of both Mycteroperca species was combined because they often occur together. The abundance of C. striata was analyzed as a response 

variable with presence/absence of Mycteroperca species and the significant variables predicting C.striata abundance and Mycteroperca species occurrence as independent variables (ledge area, percent cover, and undercut 

height). 

4.4 Results

Visual censuses recorded 78 fish species (or species groups) from 61 genera (Appendix A). Of the 78 species, 

45 were observed in both May (68 total surveys) and August (111 total surveys) sampling periods, 8 were only 

observed in May, and 25 were only observed in August. 

On ledge habitat, 72 of the 78 species observed in the study were found with Centropristis striata (seen on 98% 

of ledge surveys), Halichoeres bivittatus (89%), Serranus subligarius (88%), and Stenotomus species (80%) 

encountered most frequently (Appendix A). The most numerically abundant species on ledges were schooling juvenile fish such as Haemulon aurolineatum (mean abundance/100 m2

 transect 931 +/- 495 SE) and De-
Chapter 4 - Bottom Fish

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capterus species (195 +/- 119 SE) which were 

seen in great abundance during August surveys 

in particular. Also quite abundant at all times were 

Pareques species (55 +/- 23 SE), C. striata (28 

+/- 2 SE), and Stenotomus species (24 +/- 3 SE). 

Pelagic schooling fish such as Caranx crysos had 

by far the highest biomass (mean biomass (g)/100 

m2

 14278 +/- 9082 SE). Bottom associated fish 

with high biomass were Pareques species (6013 

+/- 3411 SE), C. straita (4111 +/- 524 SE), Archosargus probatocephalus (3041 +/- 840 SE), Mycteroperca phenax (3035 +/- 884 SE), and M. microlepis (2586 +/- 1073 SE).

Thirty-five out of the 78 fish species were observed 

over sparse live bottom (Appendix A). The species 

most commonly encountered were C. striata (seen 

on 98% of surveys over sparse live bottom) and 

Stenotomus species (90%). These were also the 

most numerically abundant and had the highest 

biomass of bottom associated species on this habitat type although Caranx crysos, considered a pelagic fish, 

had the highest biomass (3026 +/- 1560 SE) among all species.

Seventeen of the 78 species observed in the study were seen over flat sand habitat (Appendix A). The most commonly encountered species was Xyrichtys novacula (seen on 75% of surveys over flat sand). Pelagic schooling 

fish such as Decapterus species, Caranx crysos, and Scomberomorus maculatus were the most numerically 

abundant and had the highest biomass. Eighteen fish species were seen over rippled sand, with Xyrichtys novacula again being the most frequently encountered (seen on 88% of the surveys over rippled sand) (Appendix 

A). Flat and rippled sand shared 13 species comprised mostly of pelagics and those specializing in sand habitat 

such as flatfish and razorfish. The most numerically abundant bottom species on rippled sand were X. novacula 

(9 +/- 6.8 SE) and also Stenotomus species (69 +/- 62.4 SE) due to a large number of juveniles observed at 

some sites during the May sampling period. Stenotomus species also had high biomass on rippled sand (257 g 

+/- 161.6 SE), second only to Decapterus species (628 +/- 524.8 SE), a pelagic schooling species.

Significant differences occurred in fish species richness, diversity, abundance, and biomass among bottom types 

(Figure 4.1). Flat and rippled sand sites had lowest values for all four variables. Ledge sites had significantly 

higher species richness, abundance, and biomass than all other bottom types. Fish diversity at sparse live bottom habitat was not significantly different from that at ledge sites. The spatial distribution of species richness, 

abundance, biomass, diversity, and ledge clusters based on the fish species present at each site all showed no 

clear clumping or other non-random pattern when plotted and visually examined within the sanctuary boundaries.

Size frequency of all fish by bottom type revealed large differences in abundance and size structure of fish communities among the four bottom types (Figure 4.2). Flat and rippled sand were populated exclusively with the 

smaller size classes of fish with virtually all benthic associated fish in the 0-20 cm size classes. Fish in the 20-40 

cm size classes that were observed over sand habitats were almost exclusively pelagic schooling species such 

as Caranx crysos, Decapterus species, and Scomberomorus maculatus. Also of note, rippled sand had a large 

but highly variable occurrence of fish in the smallest size class. This was due to large schools of Stenotomus

species observed at some sites in May 2005. Sparse live bottom sites were also dominated by the smaller size 

classes of fish although occasional observations of individual longer fish, up to 70 cm FL, such as Gymnothorax saxicola were made. Ledges had much higher abundance of fish in all size classes. Of note were immense 

schools of fish in the smallest size class, primarily juvenile Haemulon aurolineatum, and highest abundances of 

the very largest fish such as serranids and Lutjanus campechanus in the 60-90 cm size classes as well as Ginglymostoma cirratum up to 160 cm. 

Image 25. Black seabass.
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Abundance

0

10000

20000

30000

40000

Biomass (g)

-100000

100000

300000

500000

700000

900000

1100000

Flat Sand Rippled Sand Sparse Live Ledge

Bottom

Species Richness

0

5

10

15

20

25

Diversity H'

-0.1

0.1

0.3

0.5

0.7

0.9

1.1

Figure 4.1. Species richness, Shannon Diversity (H’), abundance, and biomass of fish on 

100 m2

 surveys within each bottom type. Box plots denote median and interquartile range. 

Bars denote groups that are not significantly different from each other based on Tukey 

multiple means comparisons.

The clustering procedure on all 179 of the fish survey sites identified groups of sites with similar species composition (Figure 4.3). The inflection point on the scree plot at the bottom of the clustering dendrogram indicated the 

presence of 4 well separated clusters. Two clusters included only ledge sites with 26 and 20 sites respectively. 

Sites in these two clusters typically had a similarly high number of species (16-17) (although obviously with different membership). One large cluster consisted primarily of sparse live bottom sites (49 of 94) although a large 

number of low relief ledges were also included. These sites had fewer species with an average of only 8.8 per 

site. The final cluster consisted primarily of flat and rippled sand which together accounted for 32 of its 39 sites. 
Chapter 4 - Bottom Fish

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An average of only 3.4 species was present at these sites. Resulting patterns were similar whether based on 

fish abundance or other clustering techniques, adding confidence that the groupings are reliable. Plots of site 

clusters overlaid on the benthic maps revealed no spatial patterns.

The regression of ledge variables on fish community metrics indicated that only a few ledge characteristics influenced the overall fish assemblage. Species richness and abundance of fish had significant positive relationships 

0

10

20

30

40

50

60

70

80

90

100

Flat Sand

0

10

20

30

40

50

60

70

80

140

150

Rippled Sand

0

10

20

30

40

50

60

70

80

90

100

Sparse Live 

Bottom

0

10

20

30

40

50

60

70

80

90

100

10- 20- 30- 40- 50- 60- 70- 80- 90- 100- 110- 120- 130- 140- 150-

Ledge

1100

1200

0-

Mean Abundance/100m2

0.04

0.03

0.02

0.6

0.9

0.06

0.02

0.2

0.02

Fork Length (cm)

Figure 4.2. Size frequency histograms of fish by bottom type. Columns denote average number of fish (+/- 

SE) within each size class. Numbers within the sparse live bottom and ledge plots denote low mean occurrence values which were not discernable at this scale. 
Chapter 4 - Bottom Fish

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to both average percent cover and total height (Figures 4.4-4.5). These two-variable models for fish richness 

and abundance explained 65% and 70% of the variability in the data. Analysis of abiotic and biotic cover groups 

indicated a significant relationship for these two measures of the fish community occurred with only macroalgae 

and other (mostly tunicates) cover types, the two most dominant colonizers on ledges. Fish diversity (H’) had a 

significant, positive, and linear relationship with ledge area and a significant negative relationship with average 

undercut height (Figure 4.6), however, these variables explained only 12% of the variability in the data. Undercut 

width was not a significant variable in predicting values for any fish community metric. Ledges under high boat 

Cluster 1

26 Ledges

Avg. = 17

species

Cluster 2

20 Ledges

Avg = 16 sp. 

Cluster 3

41 Ledges

49 Sparse LB

3 Flat Sand

1 Rippled 

Avg. = 9 sp.

Cluster 4

5 Ledges

2 Sparse LB

18 Flat Sand

14 Rippled

Avg. = 3.4 sp. 

4 well separated clusters

Dissimilarity

Figure 4.3. Dendrogram for cluster analysis of all 179 sites based on species composition. The scree plot at bottom 

indicates that there are four well separated clusters. Dark blue denotes ledge, light blue denotes sparse live bottom, 

flat sand is represented by brown, and rippled sand is yellow.
Chapter 4 - Bottom Fish

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64

density (presumed to be more intensively fished) 

versus low boat density areas did not have a significant relationship with fish community structure 

when added to the final models except for overall 

fish abundance. Even then, while the relationship 

was significant with less fished ledges having higher 

fish abundance it only explained an additional 1.7% 

of the variability in the data (Adjusted R2

=0.713 relative to 0.696 for the two variable model). 

The clustering procedure on the 92 ledge sites 

based on occurrence of fish species identified 

groups of ledges with similar species composition 

(Appendix B, Figure 4.7). The inflection point on the 

scree plot below the cluster dendrogram (Figure 

4.7) indicated the presence of four clusters. Several 

clustering procedures were tested and yielded similar patterns as did clustering based on abundance. 

Clusters 1 and 3 were typically made up of sites 

with a higher number of species (average species 

richness 17 and 16 respectively). Cluster 2 typically 

had sites with a lower number of species (average 

8.5). Cluster 4 was made up of a single site with 

nine species and had the only observations of a 

couple of rare species, Pomacanthus paru and Ogcocephalus nasutus. This lone site/cluster was not 

considered further. Unlike the other clusters, sites 

in Cluster 1 appeared separated from others by virtue of never having a record of Lagodon rhomboides but often having observations of other species 

such as Chaetodipterus faber, Haemulon plumieri, 

Holacanthus bermudensis, Mycteroperca microlepis, and Mycteroperca phenax. Sites in Cluster 2 

were separated from others by typically having no 

occurrence of Apogon pseudomaculatus, Archosargus probatocephalus, Balistes capriscus, Haemulon aurolineatum, Pareques sp., and Rypticus 

maculatus but often with occurrence of Diplectrum 

formosum. Cluster 3 often had Apogon pseudomaculatus, Centropristus ocyurus, and Haemulon 

aurolineatum. 

The clustering procedure on the 92 ledge sites 

based on ledge measurements identified groups of 

ledges with similar physical characteristics (Figure 

4.8). Ledge sites in Cluster 1 were tall and heavily 

colonized but had little or no undercut. Ledges in 

Cluster 2 were tall, heavily colonized, and had deep 

undercuts. Ledges in Cluster 3 were short, sparsely 

colonized, and had little or no undercut. Ledges in 

Cluster 4 were also short and had little undercut but 

were heavily colonized. 

Species occurrence was markedly interrelated with 

ledge type (Figure 4.9). The chart of ledge sites 

Predicted Richness = 6.77 + 0.104(Percent Cover) + 0.044(Total Height (cm))

R2 = 0.66

Intercept

Mean Percent Cover

Mean Total Height (cm)

Term

6.77

0.10

0.04

Estimate

0.55

0.01

0.01

Std Error

12.29

7.88

3.07

t Ratio

<.0001

<.0001

0.0028

Prob>|t|

Parameter Estimates

Figure 4.4. Multiple regression model of species richness of fish

at ledge sites.

Predicted (log)abundance = 3.80 + 0.022(Mean Percent Cover) 

+0.021(Mean Total Height (cm))

Intercept

Mean Percent Cover

Mean Total Height (cm)

Term

3.80

0.02

0.02

Estimate

0.14

0.003

0.004

Std Error

26.84

6.61

5.76

t Ratio

<.0001

<.0001

<.0001

Prob>|t|

Parameter Estimates

R2 = 0.70

Figure 4.5. Multiple regression model of (log) abundance of fish at 

ledge sites.
Chapter 4 - Bottom Fish

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65

plotted using fish clusters versus ledge clusters revealed only 5 distinct combinations of fish community and ledge characteristics (Areas A-E in Figure 

4.9) out of a possible total of 16 (recall there were 

four clusters of sites based on fish community and 

four based on ledge measurements). The largest of 

these, Area A, represented the intersection of sites 

in Cluster 2 based on the fish community and Cluster 3 based on the ledge characteristics. Of the 48 

sites in fish Cluster 2, all but 3 were within Ledge 

Cluster 3. Similarly, of the 51 sites in Ledge Cluster 

3, all but 6 were within fish Cluster 2. Sites in this 

area had short ledges with little or no undercut and 

consequently lacked many of the undercut associated species such as Apogon pseudomaculatus, 

Pareques sp., and Rypticus maculatus. This area 

had high occurrence and/or biomass of Stenotomus sp. and Urophycis earlli but is perhaps better 

described by those species that were seldom present in contrast to other ledge clusters. In addition 

to those undercut associated species mentioned, 

Archosargus probatocephalus and Balistes capriscus also had low occurrence. Area B represented 

all of the sites from Ledge Cluster 2 and 6 of the 26 

sites in Fish Cluster 1. The tall, heavily colonized, 

and deeply undercut ledges of this area were 

characterized by some of the largest fish species. 

Typically Archosargus probatocephalus, Calamus bajonado, Caranx crysos, Chaetodipterus faber, Diplodus holbrookii, Haemulon aurolineatum, Haemulon plumieri, Holacanthus bermudensis, Mycteroperca microlepis, M. 

phenax, Pareques sp., and Rypticus maculatus had high occurrence and/or biomass. Area C included all but 4 of 

the remaining 20 sites in Fish Cluster 1. The tall, heavily colonized, but less undercut ledges of Cluster 1 typically 

had high occurrence and/or biomass of Archosargus probatocephalus, Lagodon rhomboides, Balistes capriscus, 

Caranx crysos, Pareques sp., and Seriola sp.. Area D included half of the sites in fish Cluster 3 and two thirds 

of the sites in ledge Cluster 4. Sites in this area were characterized by short ledges with low or no undercut, 

low or moderate height, and high cover of sessile biota. The short but heavily colonized ledges of this area typically had high occurrence and/or biomass of Centropristus ocyurus, Halichoeres caudalis, Microgobius carri, 

and Stenotomus sp.. Last, area E included many 

sites in Fish Cluster 3 and Ledge Cluster 1. The 

tall, heavily colonized, but less undercut ledges of 

Cluster 1 typically had high occurrence and/or biomass of Archosargus probatocephalus, Lagodon 

rhomboides, Balistes capriscus, Caranx crysos, 

Pareques sp., and Seriola sp. The stability of these 

groups was checked by clustering sites based on 

both species present and ledge variables at the 

same time. The scree plot from this indicated the 

presence of five well separated clusters that corresponded well to areas A, B, C, D and E combined, 

plus one outlier site. 

Size-frequency histograms of the key species targeted by bottom fishermen, (Centropristis striata, 

Mycteroperca microlepis, and M. phenax) revealed 

that many fewer fish were observed in size classes above the size limit of the fishery (Figure 4.10). 

Predicted H' = 0.64 + -0.007 (Mean Undercut Height (cm)) + 0.000(Ledge Area)

Intercept

Mean Undercut Height (cm))

Area (m2)

Term

0.64

-0.007

0.00004

Estimate

0.04

0.002

0.00002

Std Error

17.75

-3.31

2.26

t Ratio

<.0001

0.0013

0.0261

Prob>|t|

Parameter Estimates

R2 = 0.12

Figure 4.6. Multiple regression model of diversity of fish at ledge sites. 

Image 26. School of Pareques sp.
Chapter 4 - Bottom Fish

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66

This was evident in both the high and low boat density portions of the sanctuary although the differences were 

markedly smaller in the low boat density areas for M. microlepis and M. phenax. The modal size for C. striatus

was ~20 cm for fish in both areas. In contrast, M. microlepis had a mode size of ~40 cm in areas with high boat 

density and a higher mode size of ~60-70 cm in areas with lower boat density. Similarly, M. phenax had a mode 

size of ~40 cm in areas with high boat density and a larger mode of ~50 cm in areas with low boat density although the distribution was much flatter with fewer fish in the low boat density areas. 

The size frequency plots of C. striata for each ledge site indicated no clear clumping of this species (Figure 

4.11). In contrast, M. phenax and M. microlepis were seen at only a few sites in two main areas of the sanctuary 

(Figures 4.12-4.13). Many were observed on the tall ledges in the north/central part of the sanctuary. Another 

17

17

15

8

10

10

10

16

7

8

13

10

9

9

18

20

13

17

17

21

11

10

17

5

22

20

15

5

19

12

23

14

15

8

10

19

6

6

13

11

16

18

14

23

14

12

10

22

13

16

4

22

8

17

8

26

8

14

8

4

4

16

8

13

5

11

12

7

15

5

16

8

12

12

20

11

13

21

10

8

8

26

8

12

4

8

5

11

5

14

3

6

Cluster 3: Average of 16 species per site 

Generally present:

Apogon pseudomaculatus

Centroprsts ocyurus

Haemulon aurolneatum

Cluster 1: Average of 17 species per site

Generally present:

Calamus calamus

Caranx ruber

Chaetodpterus faber

Decapterus sp.

Epnephelus moro

Haemulon plumer

Holacanthus bermudenss

Mycteroperca mcroleps

Mycteroperca phenax

Serola sp.

Generally absent:

Archosargus rhombodals

Stenotomus sp.

Cluster 2: Average of 8.5 species per site

Generally present:

Dplectrum formosum

Generally absent:

Apogon pseudomaculatus

Archosargus probatocephalus

Balstes caprscus

Haemulon aurolneatum

Pareques sp.

Parablennus marmoreus

Ryptcus maculatus

Cluster 4: 9 species at this site

Only occurrences of:

Pomacanthus paru

Ogcocephalus nasutus 4 well separated clusters Dissimilarity

Figure 4.7. Dendrogram for cluster analysis of the 92 ledge sites based on species composition. The scree 

plot at the bottom indicates that there are four well separated clusters. The numbers representing each site 

denote species richness. Species typically present or absent that separate each cluster from the others are 

noted at left.
Chapter 4 - Bottom Fish

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67

concentration was found on the tall ledges along or near the south/central boundary of the sanctuary. C. striata

was much more abundant than the Mycteroperca species. The abundance of C. striata was significantly related 

to only percent cover and ledge area. Abundance was positively related to percent cover and negatively related 

to ledge area (Figure 4.14) although these two variables accounted for only 15% of the variability in the data. 

None of the variables tested was a significant predictor of mean C. striata size.

The presence/absence of M. microlepis was significantly related only to the undercut height of ledges (Figure 

4.15). Presence/absence of the other grouper species, M. phenax, was significantly related to undercut height 

of ledges and ledge area although comparisons with a reduced model indicated that ledge area explained only 

Cluster 3: 51 sites

Low Biotic Cover (%) 16 +/- 2.6

Low Total Ht. (cm) 12 +/- 1.2

No Undercut Width (cm) 4 +/- 0.9

No Undercut Ht. (cm) 2 +/- 0.4

Cluster 1: 23 sites

High Biotic Cover (%) 74 +/- 4.9

Moderate Total Ht. (cm) 48 +/- 2.9

Small Undercut Width (cm) 37 +/- 7.6

Small Undercut Ht. (cm) 16 +/- 1.4 

Cluster 4: 12 sites

High Biotic Cover (%) 70 +/- 2.6

Low Total Ht. (cm) 17 +/- 2.3

Small Undercut Width (cm) 37 +/- 7.6

No Undercut Ht. (cm) 2 +/- 0.8

Cluster 2: 6 sites

High Biotic Cover (%) 89 +/- 7.0

Highest Total Ht. (cm) 115 +/- 18.9

Deep Undercut Width (cm) 175 +/- 38.5

Tall Undercut Ht. (cm) 38 +/- 2.1 4 well separated clusters Dissimilarity

Figure 4.8. At right is the dendrogram for cluster analysis of the 92 ledge sites based on ledge measurements. The scree plot at the bottom indicates that there are four well separated clusters. Descriptive and quantitative characteristics (mean +/- SE) typical of ledges within each cluster along with a 

cross-sectional cartoon are noted at left.
Chapter 4 - Bottom Fish

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68

8% of the variability in the data (Figure 4.15). No other variables or interactions were significantly related to 

the presence/absence of either species including heavily/less fished areas. None of the variables tested were 

significantly related to mean size of either species. The abundance of C. straita was significantly related to the 

presence/absence of the two grouper species (Figure 4.16). When Mycteroperca species are present, the abundance of C.straita is significantly lower although only 17% of the variability in abundance was explained by the 

model.

Sites clustered based on fish community

Sites clustered based on ledge measurements

1 4 3 2

1 2 3 4

A

B

C

D

E

Figure 4.9. Intersection of sites clustered based on ledge measurements and fish communities. Groups of sites 

have both the same ledge characteristics and species composition.
Chapter 4 - Bottom Fish

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69

4.5 Discussion

This study provides a comprehensive baseline assessment of the fish communities and their associated bottom 

types at GRNMS. Prior to this, knowledge of the fish community was based on a handful of sites within the sanctuary, a limited diversity of bottom types, and with marginal quantification of associations with fine scale benthic 

features. This baseline characterization provides the foundation for future monitoring to track the trajectory of 

fish communities in the sanctuary.

Fish communities at GRNMS are closely linked to the benthic structure within it (Gilligan 1989, Parker et al. 

1994). Species richness, diversity, composition, abundance, and biomass of fish all showed striking differences 

Mycteroperca mcroleps

Centroprsts strata

Fishery size limit = 25 cm

Mean abundance

Fork length (cm)

0

2

4

6

8

10

12

14

0

0.1

0.2

0.3

0.4

0.5

Mycteroperca phenax

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0- 10- 20- 30- 40- 50- 60- 70- 80- 90-

Mycteroperca mcroleps

Fishery size limit = 61 cm

Mycteroperca phenax

Fishery size limit = 51 cm

BH: 70%

AH: 30%

BL: 68%

AL: 32%

BH: 87%

AH: 13%

BL: 65%

AL: 35%

BH: 76%

AH: 24%

BL: 56%

AL: 44%

Figure 4.10. Size frequency histograms for selected bottom fish targeted by the recreational fishery 

for regions of low (blue) and high (red) boat density. Only ledge sites are included in the figure. Columns denote mean number of fish (per 100m2

) within each size class. Error bars represent standard 

error. The recreational fishery size limit for each species is noted and the shaded bars represent the 

size class in which the size limit occurs. The proportion of fish above and below the size limit of the 

fishery within the areas of high and low boat density respectively are noted; BH=fish below size limit 

within the area of high boat density, AH= fish above size limit within the area of high boat density, 

BL= fish below size limit within the area of low boat density, and AL= fish above the size limit within 

the area of low boat density. Note the differences in abundance scale.
Chapter 4 - Bottom Fish

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70

2

0 10.5

Kilometers

Flat sand

Rippled sand

Sparsely colonized live bottom

Densely colonized live bottom

GRNMS Boundary

Frequency 0-10

10-20

20-30

30-40

40-50

50-60

60-70

70-80

80-90

39

Figure 4.11. Size-frequency plots of Centropristis striata observed at ledge sites. The tallest bar in the legend represents 39 

depending on bottom type. As expected, flat and rippled sand had the lowest values for these aspects of the 

fish community relative to sparse live bottom and ledge habitats. There were no differences in species richness, 

diversity, biomass, abundance, or species composition between fish over flat and rippled sand in any analysis. 

In contrast, a recent study of benthic infauna at GRNMS did not find such equality between sand types. Rippled 

sand had significantly higher diversity, species richness, and density of infauna in grab samples (Hyland et al. 

2006). It was hypothesized that differing hydrologic and disturbance regimes between these bottom types could 

result in differing infaunal assemblages. Why this would not translate to fish communities as well is not clear. 

Perhaps the greater mobility of fish relative to infauna results in a more even distribution between sand types. 

The largest difference between sand types was observed in May sampling when large schools of juvenile Stenotomus species were observed at some rippled sand sites. Ledges, which have higher structural complexity, 

showed the highest values of nearly all metrics (except diversity which was the same for both ledges and sparse 

live bottom). 

Values of some fish community variables were comparable among studies. For example, Parker et al. (1994) 

recently conducted video surveys of fish within GRNMS. Their sampling design was similar to the present study 

in that survey sites were randomly placed and stratified by bottom type including ledge, three categories of live 

individuals.
Chapter 4 - Bottom Fish

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71

2

0 10.5

Kilometers

Flat sand

Rippled sand

Sparsely colonized live bottom

Densely colonized live bottom

GRNMS Boundary

Frequency 0-10

10-20

20-30

30-40

40-50

50-60

60-70

70-80

80-90

6

Figure 4.12. Size-frequency plots of Mycteroperca phenax observed at ledge sites. The tallest bar in the legend represents 6 

bottom based on colonization density, and sand (no distinction between rippled and flat). Site selection was 

guided by the best bottom map of GRNMS available at the time (Hunt 1974). Surveys were conducted in August 

1985, November 1985, May 1986, and August 1986. Apart from the November survey, this seasonal distribution 

of samples allowed excellent comparison to the present study which was conducted in August 2004, May 2005, 

and August 2005. Species richness, density, and community structure of fish were compared between the studies with November 1985 data excluded where possible. 

Overall fish density on ledges observed by Parker et al. (1994) was 8-20 fish/m2

 based on video surveys in August 1985 and 1986. The same month in the present study fell at the high end of this range with an average of 

21 fish/m2

 on ledges. This average included observations of very large schools of fish such as Haemulon aurolineatum juveniles and many pelagics. Similarly, 55 species were observed by Parker et al. (1994) and the present 

study identified 59. Despite a similar density and overall number of species on ledges that were seen by the two 

studies, the lists of particular species seen were quite different. Over 1/3 of the species identified in each survey 

technique were not seen by the other (these comparisons generously assumed probable matches for fish identified to species level in one study but only genus or family in the other). Specifically, visual surveys included 22 

species not encountered in the video approach, and conversely, the video surveys included 18 species not encountered in visual surveys. These discrepancies included not only rarely seen species, which is not surprising, 

individuals.
Chapter 4 - Bottom Fish

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72

2

0 10.5

Kilometers

Flat sand

Rippled sand

Sparsely colonized live bottom

Densely colonized live bottom

GRNMS Boundary

Frequency 0-10

10-20

20-30

30-40

40-50

50-60

60-70

70-80

80-90

3

Figure 4.13. Size-frequency plots of Mycteroperca microlepis observed at ledge sites. The tallest bar in the legend represents 

but also quite common ones. For example, two species of Acanthurids were routinely recorded on the video surveys but were never seen on visual transects. Similarly, on visual surveys, two very different species, the bottom 

dwelling Urophysis earlii and the pelagic Caranx crysos, were among the most common and abundant species 

encountered respectively. Differences in the bias of video versus visual survey techniques alone cannot account 

for these striking differences. The additional video survey in November may account for some of the 18 species 

seen in that study that were not encountered in the August and May visual surveys of the present assessment, 

however, this does not explain the 22 species seen with visual assessment but not video. Also of note, on flat 

bottom, 54 species were encountered in the video surveys but only 28 were found using visual assessments. 

This may be due to the more detailed stratification over flat live bottom that was used by Parker et al. (1994) in 

the video assessments. They surveyed sparse, moderate, and densely colonized (flat) live bottom whereas in 

the present study only sparsely colonized (flat) live bottom was sampled. It is also probable that the older, more 

general maps used to guide initial site selection by Parker et al. (1994) led to the sampling of somewhat different 

habitats and fish communities on ledges than the much more detailed maps used in the present study in which 

specific ledges were randomly chosen from the entire group within the sanctuary (Kendall et al. 2005). Despite 

these influences, the differences in species composition between these studies are striking. Rather than explained by the differences in survey technique, seasonal effort, or sampling design, some considerable change 

in community structure between the two studies appears present. Unlike a trend toward more tropical species 

3 individuals.
Chapter 4 - Bottom Fish

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73

RSquare

RSquare Adj

Root Mean Square Error

Summary of Fit

0.15

0.13

0.85

Model

Error

C. Total

Source

2

87

89

DF

10.801182

62.373963

73.175145

Sum of Squares

5.40059

0.71694

Mean Square

7.5328

F Ratio

0.0010

Prob > F

Analysis of Variance Whole Model

Intercept

Percent Cover

Ledge Area

Term

2.881797

0.0100302

-0.000151

Estimate

0.165383

0.002774

0.000063

Std Error

17.42

3.62

-2.40

t Ratio

<.0001

0.0005

0.0184

Prob>|t|

Parameter Estimates

0

1

2

3

4

5

log_abund Leverage Residuals

0 10 20 30 40 50 60 70 80 90 110

% Cover

Percent Cover

Leverage Plots

0

1

2

3

4

5

log_abund Leverage Residuals

0 1000 3000 5000 7000

Ledge Area

Ledge Area

100

found on deeper live bottom off North Carolina (Parker and Dixon, 1998), neither study had differing proportions 

of tropical versus temperate species nor pelagic versus benthic species, the assemblages were simply different. 

This could be due to some long term gradual changes between the 1985-1986 study and the present one which 

was based on 2004-2005 data. It could also be as a result of more random variation in recruitment success prior 

to the two studies, which may have resulted in different community composition at GRNMS on the scale of decades. Without quantitative observations during the interval between these studies and additional monitoring in 

the future, the variability and stability of fish community patterns at GRNMS cannot be known.

Another study that provided data for possible comparisons with the present study was conducted by Sedberry 

and Van Dolah (1984) using trawl surveys. They evaluated the influence of shelf position and season on fish assemblages in the South Atlantic Bight in 1980. One site fell within GRNMS and three others were within the same 

shelf zone. During summer they found 48 species over live bottom, a value similar to the present findings and 

those of Parker et al. (1994). In contrast, to these studies however, the trawls in GRNMS revealed much lower 

fish density of 0.1132/m2

. Differences in these values and those of other studies must be cautiously interpreted 

due to vastly different biases in trawl, trap, and visual assessment techniques.

In addition to evaluating species composition by bottom type as others had done previously, the present study 

quantified the size structure of fish assemblages, a metric not recorded by other studies at GRNMS. Not surprisingly, ledges harbored the greatest abundance of fish in all size classes. Like structurally complex habitats 

Figure 4.14. Regression model of Centropristis striata abundance at ledge sites. 
Chapter 4 - Bottom Fish

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74

Difference

Model

30.829540

-LogLikelihood

2

DF

61.65908

ChiSquare

<.0001

Prob>ChiSq

RSquare (U) 0.61

Whole Model Test

Ledge Area

Source

1

1

Nparm

1

1

DF

5.97305373

14.0116536

Wald ChiSquare

0.0145

0.0002

Prob>ChiSq

Effect Wald Tests P(p/a=1)

1

0

0.5

Ledge Area

211

7690

2940 0

44

13.4

Mean Undercut Height (cm)

Mean Undercut Height (cm)

Mycteroperca phenax

Difference

Model

14.425299

-LogLikelihood

1

DF

28.8506

ChiSquare

<.0001

Prob>ChiSq

RSquare (U) 0.32

Whole Model Test

Mean Undercut Height (cm)

Source

1

Nparm

1

DF

17.7609622

Wald ChiSquare

0.0000

Prob>ChiSq

Effect Wald Tests P(p/a=1)

1

0

0.5

Mean Undercut Height (cm)

0

44

21.2

Mean Undercut Height (cm)

Mycteroperca mcroleps

Figure 4.15. Logistic regression model of presence(1)/absence(0) of grouper species at 

ledge sites. Values at 50% probability of occurrence are highlighted.

elsewhere, ledges offer by far the greatest diversity of niche space to support a variety of fish sizes and species. Sparse live bottom and both sand types offer virtually no change in substrate relief which could be used as 

structural refugia. Sparse live bottom offers only modest additional protection with its low density of gorgonians, 

sponges, and other sessile biota. 

The species composition of fish communities was quite distinct for ledges, sparse live bottom, and sand sites 

as indicated by cluster analysis. This finding was similar to that of Parker et al. (1994) who also used cluster 

analysis of the overall fish communities in the area based on video data. A possible exception to this separation of fish community by bottom type was in Cluster 3 which was composed of a majority of sparse live bottom 

sites but also had a large number of ledge sites. Closer inspection indicated that these ledges were quite short 
Chapter 4 - Bottom Fish

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75

(average height = 5 cm) with little or no undercut 

and relatively sparse colonization of sessile invertebrates. In fact, many of these sites when viewed 

underwater appeared more as sparse live bottom 

abutting sandy areas than true ledges.

Many studies outside of the South Atlantic Bight 

have examined the relationship between structure 

of fish communities and benthic variables although 

with conflicting results. The relationships between 

fish abundance, species richness, diversity, and 

benthic characteristics appear to be highly localized phenomena. Different reef types among regions have been shown to have unique correlations 

between fish community parameters and benthic 

characteristics with few rules common to all systems (Roberts and Ormond 1987, Chabanet et al. 

1997, Öhman and Rajasuriya 1998). Working in a 

variety of reef types and regions worldwide, many 

have found species richness of fish to be positively correlated with rugosity or vertical relief of the 

substrate (Luckhurst and Luckhurst 1978, Molles 

1978, Öhman and Rajasuriya 1998, Gratwicke and 

Speight 2005) although not in all systems (Roberts 

and Ormond 1987, Öhman and Rajasuriya 1998). 

At GRNMS, species richness was positively correlated with ledge height and explained 66% of 

the variability in the data. This highlights the importance of vertical relief in adding niche space at 

GRNMS. The ledges are essentially the only hard 

vertical structure in the otherwise flat landscape of 

GRNMS. In other studies, species richness of fish 

has also been found related to diversity of benthic 

cover (Roberts and Ormond 1987, Gratwicke and 

Speight 2005) or particular bottom types such as 

hard bottom and live coral (Parker et al. 1994, Öhman and Rajasuriya 1998, Gratwicke and Speight 

2005) although, again results were inconsistent 

among regions (Luckhurst and Luckhurst 1978, 

Roberts and Ormond 1987). At GRNMS, species 

richness was also positively correlated with percent cover of sessile biota, namely macroalgae 

and ‘other’, the two dominant cover types. The 

three dimensional planar plot of this relationship 

indicates that either high percent cover or total 

height (or both) of ledges can be related to high 

richness values. Possible mechanisms are higher 

food resources for fish afforded by greater cover 

and enhanced structural refuge options and niche 

space offered by taller ledges.

Fish abundance at GRNMS was also significantly 

correlated with percent cover and ledge height, 

which together explained 70% of the variability 

in the data. Links between fish abundance and 

0

1

2

3

4

5

Log C.striata abund.

0 10 20 30 40 50 60 70 80 90 100

Percent Cover

Myct. sp. absent

present

Regression Plot

RSquare

RSquare Adj

Root Mean Square Error

0.17

0.15

0.84

Summary of Fit

Model

Error

C. Total

Source

2

87

89

DF

12.206558

60.968587

73.175145

Sum of Squares

6.10328

0.70079

Mean Square

8.7092

F Ratio

0.0004

Prob > F

Analysis of Variance Whole Model

Intercept

p/a Myct.sp,

Percent Cover

Term

2.3311367

0.3320177

0.0133319

Estimate

0.190883

0.118075

0.003218

Std Error

12.21

2.81

4.14

t Ratio

<.0001

0.0061

<.0001

Prob>|t|

Parameter Estimates

absent

present

Level

3.2326824

2.5686469

Least Sq Mean

0.11316164

0.18738277

Std Error

3.06279

2.96506

Mean

Least Squares Means Table

Figure 4.16. Logistic regression model of presence(1)/absence(0) of 

grouper species at ledge sites. Values at 50% probability of occurrence are highlighted.

Image 27. Sharksucker.
Chapter 4 - Bottom Fish

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76

benthic characteristics have been more difficult to 

identify in other studies with only weak or no correlation found with rugosity (Risk 1972, Luckhurst 

and Luckhurst 1978, Öhman and Rajasuriya 1998, 

Gratwicke and Speight 2005), cover of particular 

bottom types (Roberts and Ormond 1987, Öhman 

and Rajasuriya 1998), or other benthic variables 

(Risk 1972, Luckhurst and Luckhurst 1978, Roberts and Ormond 1987). Similar to the findings for 

species richness of fish at GRNMS, high abundance can be related to either high percent cover 

or total ledge height, both are not necessary.

Diversity (H’) of fish has also been elusive to link 

with benthic characteristics. Fish diversity has 

been positively correlated with benthic variables 

such as rugosity or reef height (Risk 1972, Molles 

1978, Öhman and Rajasuriya 1998) and live coral 

cover (Öhman and Rajasuriya 1998) but not with 

substrate diversity (Risk 1972) and not in all studies 

or reef types investigated (Luckhurst and Luckhurst 1978, Öhman and Rajasuriya 1998). Unlike fish abundance 

and species richness, fish diversity at GRNMS was not significantly related to ledge height and percent cover. 

Instead, ledge area had a positive relationship and undercut height had, counter to expectations, a negative relationship with fish diversity. A large undercut would presumably allow greater niche space, number of species, 

and equitable distribution of community membership and therefore higher diversity but this was not the case. 

More detailed evaluation of the data indicated that larger undercuts coincided with the presence of large schools 

of a few species such as Pareques sp., which are regularly observed utilizing undercut ledges, and Haemulon

sp. Such large monotypic schools actually lowered the overall values of fish diversity at undercut ledges even in 

the presence of a larger number of species.

Many studies have found fishing intensity to affect species richness, diversity, and abundance of fish communities in many parts of the world (Russ and Alcala 1989, McClanahan 1994, Watson and Ormond 1994, Grigg 

1994, Jennings et al. 1995, Jennings and Polunin 1996, Wantiez et al. 1997). At GRNMS, more intensively fished 

versus less fished (based on high versus low boat density) ledges did not have a significant relationship with fish 

community structure when added to the final models except for overall fish abundance. Even then, while the relationship was significant with less fished ledges having higher fish abundance it only explained an additional 1.7% 

of the variability in the data (Adjusted R2

=0.713 relative to 0.696 for the two variable model). While significant in a 

predictable direction, the influence is quite small and does not appear to be an important variable structuring fish 

abundance in this area. Based on the present findings, the relationships between fish abundance, species richness, diversity, and benthic characteristics can be predicted from just a few easily quantifiable variables: ledge 

height, undercut height, percent cover, and ledge area. 

Characterizations of the particular fish species associated with ledges revealed five distinct combinations of 

ledge characteristics and fish assemblages. The four ledge types and four fish community types identified in the 

cluster analysis could have resulted in sixteen unique combinations of ledge characteristics and fish community. 

However, only five accounted for nearly 90% of the survey sites over ledges indicating a strong relationship 

between fish community membership and ledge type. Merely knowing the basic characteristics of a ledge such 

as total height, undercut width, and percent cover would allow good prediction of not only species richness and 

abundance of fish, but also which particular fish species are likely to occur there. 

4.6 Targeted Species

This study provides a comprehensive assessment for species of interest to recreational fishermen such as C. 

striata, M. microlepis, and M. phenax at GRNMS. Densities of these species were 0.52, 0.04, and 0.02/m2 respectively on ledges as reported by Parker et al. (1994). Densities found on ledges in the present study were half 

as high at 0.28 for C.striata and 0.01 and 0.02/m2

 respectively for the Mycteroperca species. Again, differences 

Image 28. School of spadefish.
Chapter 4 - Bottom Fish

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77

in these estimates may be due to several factors including the respective biases of the sampling methods, the 

inclusion of November sampling by Parker et al. (1994), and the different base maps upon which sampling strategies were designed. However, if both of these assessments are considered to have adequately quantified fish at 

GRNMS, some real differences appear likely. 

The presence of both Mycteroperca species was most related to undercut height of ledges rather than any other 

variables such as total ledge height or biotic cover. Indeed. More M. phenax were observed in the fished area of 

the sanctuary which had better habitat for this species than the less fished area (Chapter 2). Fishing pressure 

also did not have a significant relationship with the simple presence/absence of these two species, however, it 

did appear to influence their size distributions. For example, modal size of M. microlepis was skewed by ~25 cm 

toward smaller individuals in the heavily fished area. Similarly, the mode size of M. phenax was ~15 cm smaller in 

the heavily fished area although the size distribution was flattened in the less fished area. This pattern emerged 

despite the apparent presence of better habitat in the form of more deeply undercut ledges at survey sites in the 

fished area (Chapter 2). In fact, many fewer M. microlepis, and M. phenax were observed in size classes above 

the size limit of the fishery in all areas of the sanctuary. This could be the result of selective removal of largest fish 

due to fishing, as has been observed in other areas (Chiappone et al. 2000, Westera et al. 2003), as well as ontogenetic migration out of the area by large fish (McGovern et al. 2005). Also of note, the proportion of fish larger 

than the size limit was higher in low boat density areas than in high boat density areas for both Mycteroperca

species. This suggests that, despite better habitat in fished areas, fish size in heavily fished areas of GRNMS 

appear to be lower than in unfished areas. 

The spatial distribution of both Mycteroperca species was quite clumped on ledges in the north central and south 

central regions of the sanctuary. Of the 92 ledges surveyed, only 20 had occurrences of these species with the 

majority only occurring on 10 ledges. Both species were often observed together at the same ledge and were 

rarely observed as lone individuals. 

In contrast, Centropristis striata occurred at 98% of the ledges surveyed and appeared evenly distributed throughout the sanctuary. Abundance was best explained by percent cover of sessile biota rather than ledge height or 

undercut variables. Indeed this species was never observed utilizing the undercut of a ledge. Interestingly, lower 

abundance of C. striata occurred when either of the large grouper (Mycteroperca) species were present. Lower 

abundance of C. striata at such sites could be due to predation by the large grouper (Matheson et al. 1986), 

avoidance of sites with large grouper, or some other mechanism correlated with these two variables. As with the 

grouper species, many fewer C. striata were observed in size classes above the size limit of the fishery. Emigration is not thought to reduce the abundance of C. striata which are thought to stay in the same specific area for 

much of their adult life (Mercer 1989, Parker 1990, Barkoukis 2006,).

There are several important caveats to consider in the present characterization. Surveys were conducted during 

the day. Some ledge associated species such as those in the family Haemulidae are known to undergo migrations away from ledges into surrounding sand habitats each night to feed. This will have the effect of inflating the 

biomass and species richness of sand areas each night to levels higher than those observed in the daytime surveys. Also, only visual surveys were used in this assessment and some fish species avoid divers. For example, 

Lutjanus campechanus and Lagodon rhomboides are often only seen at the limit of diver visibility, negatively 

biasing their counts, and other species probably move away prior to diver detection at all. Other sampling gear or 

survey techniques will evaluate such species better/differently (but not without their own set of biases). Only bottom fish or those pelagics that approached the bottom were surveyed. Among the most abundant species over 

all bottom types were pelagic species. However, the transect survey technique is not designed to sample pelagic 

fish effectively. Alternative techniques should be used to sample these species such as sonar, nets, and hook 

and line. Finally, the seasonal changes known to occur in the fish assemblage of this area (Sedberry and Van 

Dolah 1984, Parker et al. 1994) are not addressed in detail by this study. The higher number of species unique 

to August (25 on 111 surveys) relative to May (8 on 68 surveys) is not totally accounted for in the proportionally 

greater number of August surveys. Changes in proportional abundance are known to occur seasonally as well 

(Sedberry and Van Dolah 1984) but were not investigated here and should be evaluated with seasonally stratified sampling effort. 
Chapter 4 - Bottom Fish

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4.7 Recommendations for management

and monitoring

A long term strategy for quantitatively monitoring 

fish at GRNMS should be devised based on the 

present findings. A stratified-random sampling design can maximize inference to the entire sanctuary and optimize effort to allow key comparisons 

among regions within it. At a minimum, sampling 

should be focused on randomly selected ledges 

stratified by the four ledge types identified here, 

as well as in the heavily fished versus less fished 

areas of the sanctuary. With limited monitoring resources the sampling should be conducted annually within the same season. Given greater monitoring resources, additional sampling could be 

undertaken to quantify seasonal effects and other 

strata. Additional strata of interest may include the 

four major bottom types used here, or perhaps 

comparison areas outside the sanctuary to place 

the sanctuary in a regional context.

The same assessment technique must be used each sampling period to simplify analysis and reliably detect 

changes in community structure in response to fishing pressure or other influences such as range changes due 

to global warming (Parker and Dixon 1998). For assessment and monitoring of bottom fish at GRNMS, visual 

transects should be used. No other survey technique provides as effective an approach given the visibility, bottom features, data needs, and logistical constraints. The most robust approach to monitoring of bottom fishes 

requires quantitative data. Species, size, and number of fish per unit area are needed for monitoring. Roving 

diver and trap surveys can provide relative per unit area measures at best. Trawl surveys, while spatially quantitative, are ill suited to sampling ledges, the most important bottom type at GRNMS. Trawling can be done from 

the high to low side of a ledge, but is harmful to the encrusting benthic organisms, and fails to sample the substantial component of the fish community that utilizes the undercut of many ledges. Visual point surveys require 

7.5 m visibility (Bohnsack and Bannerot 1986), conditions that rarely occur at GRNMS. Point surveys also do not 

survey ledges efficiently, nor do findings extrapolate appropriately (see Parker et al. 1994). The ledge is essentially a linear feature that is best evaluated with a linear survey technique. In contrast, visual transects conducted 

along the axis of ledges meet the data requirements and logistical constraints imposed by the benthic features 

at GRNMS. Spatially quantitative, requiring only 2 m visibility, and simultaneous survey of fish above and below 

undercut ledges make transects ideally suited to assessing bottom fish communities in this area. Although not 

without their own biases as mentioned earlier, transects offer the best approach for assessment and monitoring 

of fish at GRNMS. While the rationale provided here suggests that visual transects should play the dominant 

role in quantitative monitoring of bottom fish, other techniques should be used to accomplish other objectives. 

For example, pelagic fish should be evaluated with alternative approaches such sonar, nets, or hook and line 

sampling. In addition, long term datasets such as Marine Resources Monitoring, Assessment, and Prediction 

Program (MARMAP) trap sampling must continue. Despite difficulties of comparing these data to other studies, 

such long term, consistently collected datasets continued in the future will provide comparative information on 

increases and decreases in fish community variables relative to their data collected previously. 

The densities and size structure of selected fish species can be monitored through time with effort optimized to 

test particular hypotheses of interest. For example, power analysis based on the variability of the density data 

for a species of interest (or other variable) can be performed. Specifically, the sample size needed to detect a 

particular change in the fish community can be calculated and field work prioritized to meet that goal. Many variations on monitoring and sampling design are possible. A reference describing monitoring options was recently 

completed for reef fish and provides a good place to begin such considerations (Menza et al. 2006).

Another assessment option to consider in addition to stratified random sampling is a more complete comprehensive survey of all ledges. There are 436 ledges in GRNMS of various height and dimension. It is possible to visit 

Image 29. Frogfish.
Chapter 4 - Bottom Fish

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every one over the course of a year or in a couple of field seasons. Ledges could all reasonably be surveyed 

without replacement (in the statistical sense) to obtain an understanding of the entire population of ledges. This 

would not necessarily need to include all of the variables and approaches here, but could instead focus on a 

subset of fish species or ledge variables such as those deemed significant in the present study. For example, 

evaluating a ledge’s percent cover, total height, undercut width, and undercut height would establish its characteristics relative to the four ledge types identified in this study (Figure 4.8). Based on those characteristics it is 

possible to infer the species richness (Figure 4.4), fish abundance (Figure 4.5), diversity (Figure 4.6), and even 

species composition (Figure 4.7 and 4.9) of every ledge in the sanctuary. Based on this it would even be possible 

to estimate population sizes of ledge associated species. 

Additional activities should also be initiated to quantify fishing effort in different parts of the sanctuary. At present 

only relative levels of fishing effort can be inferred from the boat count data. The central area has higher fishing 

effort than the rest of the sanctuary, however, exactly what that level of effort and impact to the resource or CPUE 

may be is not quantifiable given present monitoring activities.
Chapter 4 - Bottom Fish

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Appendix

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Appendix A. Fish species observed on all bottom types. Within each of the major bottom types at GRNMS, the percent of surveys 

on which the species was encountered and the average abundance and biomass (and standard error) are provided. Presence during 

the May and/or August survey periods is denoted by an X. For species which have zero values for probability of encounter, abundance and biomass are left blank. No standard error is given when a species was seen on less than three surveys although mean 

abundance and biomass are provided. Also note that mean values are rounded to the ones digit and SE is rounded to tenths which 

results in some low values appearing as zeros. 

Genus species Sparse live 

common name Flat sand Rippled sand bottom Ledge

May Aug variable value SE value SE value SE value SE

Abudefduf saxatls X percent of surveys 0 0 0 1

sergeant major mean abundance 0

mean biomass (g) 0

Acanthostracon quadrcorns X percent of surveys 0 0 0 1

scrawled cowfish mean abundance 0

mean biomass (g) 3

Antennarus sp. X percent of surveys 0 0 0 2

frogfish mean abundance 0

mean biomass (g) 0

Apogon pseudomaculatus X X percent of surveys 0 0 0 49

twospot cardinalfish mean abundance 3 0.5

mean biomass (g) 6 1.1

Archosargus probatocephalus X X percent of surveys 0 0 0 33

sheepshead mean abundance 2 0.7

mean biomass (g) 3041 839.7

Archosargus rhombodals X X percent of surveys 5 6 25 20

sea bream mean abundance 1 0 2 0.5 1 0.3

mean biomass (g) 138 10 139 47.8 652 507.7

Balstes caprscus X X percent of surveys 0 0 0 26

gray triggerfish mean abundance 1 0.3

mean biomass (g) 846 290.6

Bothus ocellatus X percent of surveys 20 13 0 0

eyed flounder mean abundance 0 0.1 0

mean biomass (g) 0 0.2 0

Calamus bajonado X X percent of surveys 0 0 0 3

jolthead porgy mean abundance 0 0.0

mean biomass (g) 176 120.5

Calamus calamus X X percent of surveys 0 0 0 10

saucereye porgy mean abundance 0 0.1

mean biomass (g) 229 98.5

Calamus penna X X percent of surveys 0 0 0 5

sheepshead porgy mean abundance 0 0.1

mean biomass (g) 49 33.0

Caranx bartholomae X percent of surveys 0 0 8 3

yellow jack mean abundance 0 0.1 0 0.0

mean biomass (g) 13 6.9 15 13.2

Caranx crysos X X percent of surveys 15 6 22 24

blue runner mean abundance 14 10.2 0 5 2.4 21 13.4

mean biomass (g) 7114 5322.2 5 3026 1560.2 14278 9082.1

Caranx ruber X percent of surveys 0 0 0 7

bar jack mean abundance 1 0.6

mean biomass (g) 290 200.5

Centroprsts ocyurus X X percent of surveys 5 6 33 41

bank sea bass mean abundance 0 0 1 0.5 1 0.3

mean biomass (g) 1 0 65 34.4 106 29.5

Centroprsts strata X X percent of surveys 5 6 98 98

black sea bass mean abundance 0 0 13 1.5 28 2.3

mean biomass (g) 9 38 1327 193.3 4111 524.0

Chaetodpterus faber X X percent of surveys 0 0 2 12

Atlantic spadefish mean abundance 1 7 3.4

mean biomass (g) 182 3070 1572.9

Chaetodon ocellatus X percent of surveys 0 0 0 2

spotfin butterflyfish mean abundance 0

mean biomass (g) 0

Chlomycterus schoepf X percent of surveys 0 0 0 1

stripped burrfish mean abundance 0

mean biomass (g) 1

Chloroscombrus chrysurus X percent of surveys 11 13 12 12

Atlantic bumper mean abundance 2 0 8 4.5 24 12.4

mean biomass (g) 146 8 578 289.2 5176 3023.0
Appendix

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Appendix A. Continued. Fish species observed on all bottom types. Within each of the major bottom types at GRNMS, the percent 

of surveys on which the species was encountered and the average abundance and biomass (and standard error) are provided. 

Presence during the May and/or August survey periods is denoted by an X. For species which have zero values for probability of 

encounter, abundance and biomass are left blank. No standard error is given when a species was seen on less than three surveys 

although mean abundance and biomass are provided. Also note that mean values are rounded to the ones digit and SE is rounded 

to tenths which results in some low values appearing as zeros. 

Genus species Sparse live 

common name Flat sand Rippled sand bottom Ledge

May Aug variable value SE value SE value SE value SE

Conger sp. X percent of surveys 0 0 0 1

conger eel mean abundance 0

mean biomass (g) 3

Coryphopterus glaucofraenum X percent of surveys 0 0 0 5

bridled goby mean abundance 0 0.0

mean biomass (g) 0 0.1

Decapterus sp. X X percent of surveys 20 38 6 10

scad mean abundance 14 12.6 8 3.9 1 0.5 195 118.7

mean biomass (g) 1814 1776.6 628 524.8 240 205.4 908 540.5

Dodon hystrx X percent of surveys 0 0 0 1.00

porcupinefish mean abundance 0

mean biomass (g) 19

Dplectrum formosum X X percent of surveys 11 38 43 33

sand perch mean abundance 0 1 0.3 4 0.9 1 0.4

mean biomass (g) 0 47 42.8 71 21.4 57 22.3

Dplodus holbrook X X percent of surveys 0 0 6.00 34.00

spottail pinfish mean abundance 0 0.4 4 1.0

mean biomass (g) 30 26.7 483 112.4

Echenes naucrates X percent of surveys 0 0 2 0

sharksucker mean abundance 0

mean biomass (g) 23

Epnephelus moro X X percent of surveys 0 0 0 7.00

red grouper mean abundance 0 0.0

mean biomass (g) 102 43.7

Equetus lanceolatus X X percent of surveys 0 0 6 16

jackknife fish mean abundance 0 0.1 0 0.1

mean biomass (g) 22 15.3 26 10.9

Gnglymostoma crratum X percent of surveys 0 0 0 3

nurse shark mean abundance 0 0.0

mean biomass (g) 1006 776.9

Gymnachrus melas X percent of surveys 0 6 0 0

naked sole mean abundance 0

mean biomass (g) 0

Gymnothorax saxcola X X percent of surveys 0 0 8 1

honeycomb moray mean abundance 0 0.0 0

mean biomass (g) 25 14.6 0

Haemulon aurolneatum X X percent of surveys 0 0 4 48

tomtate mean abundance 0 931 494.5

mean biomass (g) 4 1897 644.2

Haemulon plumer X X percent of surveys 0 0 0 11

white grunt mean abundance 0 0.2

mean biomass (g) 240 153.8

Haemulon sp. X percent of surveys 0 0 2 1

grunt mean abundance 0 0

mean biomass (g) 0 0

Halchoeres bvttatus X X percent of surveys 0 0 61 89

slippery dick mean abundance 6 1.1 15 1.6

mean biomass (g) 194 48.0 290 37.0

Halchoeres caudals X X percent of surveys 0 6 51 45

painted wrasse mean abundance 1 3 0.8 1 0.2

mean biomass (g) 55 115 30.8 57 12.6

Holacanthus bermudenss X X percent of surveys 0 0 0 20

blue angelfish mean abundance 1 0.2

mean biomass (g) 603 180.5

Hypleurochlus gemnatus X X percent of surveys 0 0 0 17

crested blenny mean abundance 0 0.1

mean biomass (g) 0 0.1

Lutjanus anals X percent of surveys 0 0 0 1

mutton snapper mean abundance 0

mean biomass (g) 12
Appendix

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85

Appendix A. Continued. Fish species observed on all bottom types. Within each of the major bottom types at GRNMS, the percent 

of surveys on which the species was encountered and the average abundance and biomass (and standard error) are provided. 

Presence during the May and/or August survey periods is denoted by an X. For species which have zero values for probability of 

encounter, abundance and biomass are left blank. No standard error is given when a species was seen on less than three surveys 

although mean abundance and biomass are provided. Also note that mean values are rounded to the ones digit and SE is rounded 

to tenths which results in some low values appearing as zeros. 

Genus species Sparse live 

common name Flat sand Rippled sand bottom Ledge

May Aug variable value SE value SE value SE value SE

Lutjanus campechanus X X percent of surveys 0 0 2 9

red snapper mean abundance 0 0 0.1

mean biomass (g) 29 473 256.9

Mcrogobus carr X X percent of surveys 20 31 12 12

seminole goby mean abundance 0 0.2 1 0.3 0 0.1 0 0.1

mean biomass (g) 0 0.2 1 0.3 0 0.1 1 0.6

Mcropogonas undulatus X percent of surveys 0 0 0 1

Atlantic croaker mean abundance 0

mean biomass (g) 2

Muraena retfera X X percent of surveys 0 0 0 11

reticulate moray mean abundance 0 0.0

mean biomass (g) 43 15.9

Mycteroperca mcroleps X X percent of surveys 0 0 0 20

gag grouper mean abundance 1 0.2

mean biomass (g) 2586 1073.1

Mycteroperca phenax X X percent of surveys 0 0 0 24

scamp mean abundance 2 0.5

mean biomass (g) 3035 883.7

Ncholsna usta X percent of surveys 0 0 8 0

emerald parrotfish mean abundance 0 0.1

mean biomass (g) 2 1.5

Ogcocephalus nasutus X percent of surveys 0 0 0 1

shortnose batfish mean abundance 0

mean biomass (g) 1

Ogcocephalus radatus X X percent of surveys 0 6 0 1

polka-dot batfish mean abundance 0 0

mean biomass (g) 0 3

Opsanus tau X X percent of surveys 0 0 25 61

oyster toadfish mean abundance 0 0.1 1 0.1

mean biomass (g) 56 17.7 188 27.1

Pagrus pagrus X X percent of surveys 0 0 0 2

red porgy mean abundance 0

mean biomass (g) 45

Parablennus marmoreus X X percent of surveys 0 0 0 28

seaweed blenny mean abundance 1 0.2

mean biomass (g) 1 0.3

Paralchthys albgutta X X percent of surveys 10 6 8 16

gulf flounder mean abundance 0 0 0 0.0 0 0.2

mean biomass (g) 9 2 31 21.0 309 150.3

Pareques sp. X X percent of surveys 0 0 2 55

cubbyu/high hat mean abundance 0 55 22.8

mean biomass (g) 0 6013 3411.1

Pomacanthus paru X percent of surveys 0 0 0 1

French angelfish mean abundance 0

mean biomass (g) 0

Pomacanthus sp. X percent of surveys 0 0 0 1

angelfish mean abundance 0

mean biomass (g) 0

Pronotus ophryas X percent of surveys 0 0 2 0

bandtail searobin mean abundance 0

mean biomass (g) 4

Pronotus sctulus X X percent of surveys 5 0 0 4

leopard searobin mean abundance 0 0 0.0

mean biomass (g) 2 6 3.0

Pronotus sp. X X percent of surveys 25 6 12 11

searobin mean abundance 0 0.1 0 0 0.0 0 0.0

mean biomass (g) 3 2.2 2 22 10.8 8 3.3

Ptereleotrs callurus X X percent of surveys 0 0 10 5

blue goby mean abundance 0 0.1 0 0.1

mean biomass (g) 1 1.0 0 0.3
Appendix

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Appendix A. Continued. Fish species observed on all bottom types. Within each of the major bottom types at GRNMS, the percent 

of surveys on which the species was encountered and the average abundance and biomass (and standard error) are provided. 

Presence during the May and/or August survey periods is denoted by an X. For species which have zero values for probability of 

encounter, abundance and biomass are left blank. No standard error is given when a species was seen on less than three surveys 

although mean abundance and biomass are provided. Also note that mean values are rounded to the ones digit and SE is rounded 

to tenths which results in some low values appearing as zeros. 

Genus species Sparse live 

common name Flat sand Rippled sand bottom Ledge

May Aug variable value SE value SE value SE value SE

Ptereleotrs helenae X percent of surveys 0 0 0 1

hovering goby mean abundance 0

mean biomass (g) 0

Raja eglantera X percent of surveys 0 0 2 0

clearnose skate mean abundance 0

mean biomass (g) 48

Rhnobatos lentgnosus X percent of surveys 0 0 0 1

Atlantic guitarfish mean abundance 0

mean biomass (g) 11

Ryptcus maculatus X X percent of surveys 0 0 2 39

whitespotted soapfish mean abundance 0 1 0.4

mean biomass (g) 1 167 71.8

Scomberomorus maculatus X percent of surveys 5 0 4 7

Spanish mackerel mean abundance 10 2 3 1.7

mean biomass (g) 3539 537 970 599.0

Scorpaena sp. X percent of surveys 0 0 0 1

scorpionfish mean abundance 0

mean biomass (g) 1

Serola sp. X percent of surveys 5 0 4 9

almaco/amberjack mean abundance 0 0 0 0.2

mean biomass (g) 35 149 276 198.7

Serranculus pumlo X percent of surveys 0 0 0 1

pygmy sea bass mean abundance 0

mean biomass (g) 0

Serranus sublgarus X X percent of surveys 0 0 47 88

belted sandfish mean abundance 2 0.6 13 1.4

mean biomass (g) 6 3.4 25 2.8

Sphyraena barracuda X X percent of surveys 0 0 0 7

great barracuda mean abundance 0 0.1

mean biomass (g) 162 81.5

Sphyraena sp. X percent of surveys 0 0 0 1

barracuda mean abundance 0

mean biomass (g) 0

Stegastes varabls X percent of surveys 0 0 0 10

cocoa variabilis mean abundance 0 0.2

mean biomass (g) 1 0.6

Stenotomus sp. X X percent of surveys 15 31 90 80

scup/longspine porgy mean abundance 8 7.5 69 62.4 20 4.1 24 2.8

mean biomass (g) 26 18.5 257 161.6 1677 313.0 3007 430.0

Stephanoleps hspdus X X percent of surveys 0 0 0 8.00

planehead filefish mean abundance 0 0.0

mean biomass (g) 14 5.5

Syngnathdae sp. X percent of surveys 0 13 0 1

pipefish mean abundance 0 0

mean biomass (g) 0 0

Synodus sp. X X percent of surveys 15.00 6.00 12.00 2.00

lizardfish mean abundance 0 0.1 0 0 0.1 0

mean biomass (g) 23 17.9 24 71 54.4 9

Urophycs earll X X percent of surveys 0 0 4.00 26.00

Carolina hake mean abundance 0 1 0.4

mean biomass (g) 2 283 79.4

Xyrchtys novacula X X percent of surveys 75 88 2 1

pearly razorfish mean abundance 3 0.7 9 6.8 0 0

mean biomass (g) 44 12.2 68 19.5 1 0
Appendix

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Appendix B. Fish species observed on ledges. Within each of the four ledge types based on cluster analysis, the percent of surveys 

on which the species was encountered and the average abundance and biomass (and standard error) are provided. For species 

which have zero values for probability of encounter, abundance and biomass are left blank. No standard error is given when a species 

was seen on less than three surveys although mean abundance and biomass are provided. Also note that mean values are rounded 

to the ones digit and SE is rounded to tenths which results in some low values appearing as zero’s.

Genus species

common name Cluster 1 Cluster 2 Cluster 3 Cluster 4

variable value SE value SE value SE value SE

Abudefduf saxatls percent of surveys 0 17 0 0

sergeant major mean abundance 0

mean biomass (g) 1

Acanthostracon quadrcorns percent of surveys 4 0 0 0

scrawled cowfish mean abundance 0

mean biomass (g) 11

Antennarus sp. percent of surveys 9 0 0 0

frogfish mean abundance 0

mean biomass (g) 1

Apogon pseudomaculatus percent of surveys 74 67 31 67

twospot cardinalfish mean abundance 4 0.7 5 1.7 1 0.7 3 1.1

mean biomass (g) 10 1.6 11 3.9 3 1.5 7 2.6

Archosargus probatocephalus percent of surveys 70 83 8 42

sheepshead mean abundance 6 2.4 6 2.1 0 0.3 1 0.4

mean biomass (g) 8745 2823.4 7117 3380.8 593 333.7 473 282.1

Archosargus rhombodals percent of surveys 17 0 20 33

sea bream mean abundance 2 1.1 1 0.3 2 1.0

mean biomass (g) 2293 2017.7 123 77.5 76 46.8

Balstes caprscus percent of surveys 61 67 6 25

gray triggerfish mean abundance 3 1.1 2 0.7 0 0.1 0

mean biomass (g) 2809 1052.3 952 385.6 123 87.6 104

Bothus ocellatus percent of surveys 0 0 0 0

eyed flounder mean abundance

mean biomass (g)

Calamus bajonado percent of surveys 4 33 0 0

jolthead porgy mean abundance 0 1 0.5

mean biomass (g) 43 2527 1684.6

Calamus calamus percent of surveys 17 33 2 17

saucereye porgy mean abundance 0 0.1 1 0.8 0 0

mean biomass (g) 313 180.2 1794 1221.7 25 151

Calamus penna percent of surveys 9 17 4 0

sheepshead porgy mean abundance 0 0 0 0.1

mean biomass (g) 35 492 14 10.1

Caranx bartholomae percent of surveys 4 0 2 8

yellow jack mean abundance 0 0.2 0 0

mean biomass (g) 52 52.3 3 6

Caranx crysos percent of surveys 26 50 22 17

blue runner mean abundance 58 52.0 62 48.2 5 1.9 2 1.7

mean biomass (g) 39360 34944.5 41552 36637.3 2824 1335.5 1250 1243.2

Caranx ruber percent of surveys 22 17 0 0

bar jack mean abundance 4 2.5 1

mean biomass (g) 1152 787.5 24

Centroprsts ocyurus percent of surveys 39 33 33 83

bank sea bass mean abundance 1 0.4 1 0.6 2 0.4 2 0.6

mean biomass (g) 107 58.2 145 141.9 96 40.5 123 75.7

Centroprsts strata percent of surveys 96 100 98 100

black sea bass mean abundance 35 6.8 30 11.7 23 1.9 35 5.2

mean biomass (g) 6702 1620.6 5368 3228.7 2879 320.0 3747 1077.3

Chaetodpterus faber percent of surveys 17 83 4 0

Atlantic spadefish mean abundance 12 8.6 56 34.0 2 1.4

mean biomass (g) 4667 3593.2 26331 18472.4 337 236.5

Chaetodon ocellatus percent of surveys 4 0 0 8

spotfin butterflyfish mean abundance 0 0

mean biomass (g) 0 0

Chlomycterus schoepf percent of surveys 0 0 2 0

stripped burrfish mean abundance 0

mean biomass (g) 2

Chloroscombrus chrysurus percent of surveys 9 17 12 17

Atlantic bumper mean abundance 44 43.4 83 83.3 11 5.2 7 4.5

mean biomass (g) 10923 10700.9 20525 20524.7 1756 1059.6 1024 827.5
Appendix

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Appendix B. Continued. Fish species observed on ledges. Within each of the four ledge types based on cluster analysis, the percent of surveys on which the species was encountered and the average abundance and biomass (and standard error) are provided. 

For species which have zero values for probability of encounter, abundance and biomass are left blank. No standard error is given 

when a species was seen on less than three surveys although mean abundance and biomass are provided. Also note that mean 

values are rounded to the ones digit and SE is rounded to tenths which results in some low values appearing as zero’s.

m

Genus species

common name Cluster 1 Cluster 2 Cluster 3 Cluster 4

variable value SE value SE value SE value SE

Conger sp. percent of surveys 0 0 0 8

conger eel mean abundance 0

mean biomass (g) 26

Coryphopterus glaucofraenu percent of surveys 9 0 4 8

bridled goby mean abundance 0 0 0

mean biomass (g) 0 0 0

Decapterus sp. percent of surveys 17 33 6 0

scad mean abundance 249 177.5 2000 1633.0 4 3.9

mean biomass (g) 1344 1142.0 1663 1358.0 836 819.6

Dodon hystrx percent of surveys 4 0 0 0

porcupinefish mean abundance 0

mean biomass (g) 76

Dplectrum formosum percent of surveys 13 0 43 42

sand perch mean abundance 0 0.3 2 0.6 1 0.2

mean biomass (g) 21 14.3 92 39.1 5 3.9

Dplodus holbrook percent of surveys 74 83 10 33

spottail pinfish mean abundance 10 3.3 11 4.5 1 0.5 3 1.5

mean biomass (g) 970 270.6 1848 982.5 143 73.3 312 157.6

Echenes naucrates percent of surveys 0 0 0 0

sharksucker mean abundance

mean biomass (g)

Epnephelus moro percent of surveys 22 0 0 8

red grouper mean abundance 0 0.1 0

mean biomass (g) 328 147.6 155

Equetus lanceolatus percent of surveys 17 33 12 25

jackknife fish mean abundance 0 0.3 1 0.5 0 0.2 0 0.3

mean biomass (g) 23 15.6 0 0.3 21 14.8 68 46.4

Gnglymostoma crratum percent of surveys 9 17 0 0

nurse shark mean abundance 0 0.1 0

mean biomass (g) 3944 3075.4 306

Gymnachrus melas percent of surveys 0 0 0 0

naked sole mean abundance

mean biomass (g)

Gymnothorax saxcola percent of surveys 0 0 2 0

honeycomb moray mean abundance 0

mean biomass (g) 1

Haemulon aurolneatum percent of surveys 87 67 24 67

tomtate mean abundance 873 445.4 10675 6645.1 10 6.1 80 50.1

mean biomass (g) 3068 709.0 14964 8069.3 243 114.1 151 94.6

Haemulon plumer percent of surveys 22 83 0 0

white grunt mean abundance 1 0.9 1 0.6

mean biomass (g) 720 591.1 925 564.0

Haemulon sp. percent of surveys 0 0 0 8

grunt mean abundance 3 2.5

mean biomass (g) 3 3.1

Halchoeres bvttatus percent of surveys 100 100 80 100

slippery dick mean abundance 18 3.0 12 4.6 10 1.5 32 6.3

mean biomass (g) 349 82.0 353 158.8 239 45.4 362 114.4

Halchoeres caudals percent of surveys 22 0 49 92

painted wrasse mean abundance 0 0.2 2 0.3 3 0.4

mean biomass (g) 19 11.5 82 21.4 47 10.1

Holacanthus bermudenss percent of surveys 52 83 2 0

blue angelfish mean abundance 2 0.5 4 1.4 0 0.1

mean biomass (g) 1221 487.9 3603 1152.6 114 113.7

Hypleurochlus gemnatus percent of surveys 17 33 12 33

crested blenny mean abundance 0 0.1 0 0 0.1 1 0.4

mean biomass (g) 0 0.2 1 0 0.1 1 0.6

Lutjanus anals percent of surveys 0 0 2 0

mutton snapper mean abundance 0

mean biomass (g) 21
Appendix

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Appendix B. Continued. Fish species observed on ledges. Within each of the four ledge types based on cluster analysis, the percent of surveys on which the species was encountered and the average abundance and biomass (and standard error) are provided. 

For species which have zero values for probability of encounter, abundance and biomass are left blank. No standard error is given 

when a species was seen on less than three surveys although mean abundance and biomass are provided. Also note that mean 

values are rounded to the ones digit and SE is rounded to tenths which results in some low values appearing as zero’s.

Genus species

common name Cluster 1 Cluster 2 Cluster 3 Cluster 4

variable value SE value SE value SE value SE

Lutjanus campechanus percent of surveys 22 33 0 8

red snapper mean abundance 0 0.2 2 1.1 0

mean biomass (g) 793 432.3 4219 3450.5 0

Mcrogobus carr percent of surveys 4 0 8 50

seminole goby mean abundance 0 0.2 0 0.1 1 0.3

mean biomass (g) 0 0.2 1 1.0 1 0.3

Mcropogonas undulatus percent of surveys 4 0 0 0

Atlantic croaker mean abundance 0

mean biomass (g) 9

Muraena retfera percent of surveys 17 50 2 17

reticulate moray mean abundance 0 0.1 1 0 0

mean biomass (g) 101 54.2 132 10 28

Mycteroperca mcroleps percent of surveys 43 83 4 8

gag grouper mean abundance 1 0.7 5 1.3 0 0.1 0

mean biomass (g) 5283 3700.8 13386 3824.1 689 670.3 79

Mycteroperca phenax percent of surveys 61 100 2 8

scamp mean abundance 4 1.1 11 3.3 0 1 1.3

mean biomass (g) 6141 1997.9 16701 6939.8 12 3095 3095.3

Ncholsna usta percent of surveys 0 0 0 0

emerald parrotfish mean abundance

mean biomass (g)

Ogcocephalus nasutus percent of surveys 0 0 2 0

shortnose batfish mean abundance 0

mean biomass (g) 1

Ogcocephalus radatus percent of surveys 0 0 2 0

polka-dot batfish mean abundance 0

mean biomass (g) 6

Opsanus tau percent of surveys 61 100 55 67

oyster toadfish mean abundance 1 0.3 3 0.8 1 0.1 1 0.3

mean biomass (g) 215 55.3 504 140.9 141 30.6 178 82.1

Pagrus pagrus percent of surveys 4 0 2 0

red porgy mean abundance 0 0 0.2

mean biomass (g) 14 75 75.4

Parablennus marmoreus percent of surveys 52 67 14 25

seaweed blenny mean abundance 2 0.5 3 1.2 0 0.1 1 0.4

mean biomass (g) 3 0.8 5 2.0 0 0.2 1 0.7

Paralchthys albgutta percent of surveys 35 50 4 17

gulf flounder mean abundance 1 0.3 4 2.1 0 0

mean biomass (g) 518 194.6 2338 2145.3 30 81

Pareques sp. percent of surveys 96 100 25 83

cubbyu/high hat mean abundance 154 85.3 190 73.8 6 3.1 8 1.7

mean biomass (g) 19444 13361.6 13366 3123.5 492 359.6 61 50.4

Pomacanthus paru percent of surveys 0 0 2 0

French angelfish mean abundance 0

mean biomass (g) 0

Pomacanthus sp. percent of surveys 4 0 0 0

angelfish mean abundance 0

mean biomass (g) 0

Pronotus ophryas percent of surveys 0 0 0 0

bandtail searobin mean abundance

mean biomass (g)

Pronotus sctulus percent of surveys 0 0 8 0

leopard searobin mean abundance 0 0.0

mean biomass (g) 10 5.4

Pronotus sp. percent of surveys 4 0 16 8

searobin mean abundance 0 0 0.1 0

mean biomass (g) 7 11 5.0 0

Ptereleotrs callurus percent of surveys 0 0 2 33

blue goby mean abundance 0 0.1 0 0.2

mean biomass (g) 0 0.1 3 2.2
Appendix

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Appendix B. Continued. Fish species observed on ledges. Within each of the four ledge types based on cluster analysis, the percent of surveys on which the species was encountered and the average abundance and biomass (and standard error) are provided. 

For species which have zero values for probability of encounter, abundance and biomass are left blank. No standard error is given 

when a species was seen on less than three surveys although mean abundance and biomass are provided. Also note that mean 

values are rounded to the ones digit and SE is rounded to tenths which results in some low values appearing as zero’s.

Genus species

common name Cluster 1 Cluster 2 Cluster 3 Cluster 4

variable value SE value SE value SE value SE

Ptereleotrs helenae percent of surveys 0 0 2 0

hovering goby mean abundance 0

mean biomass (g) 0

Raja eglantera percent of surveys 0 0 0 0

clearnose skate mean abundance

mean biomass (g)

Rhnobatos lentgnosus percent of surveys 0 0 2 0

Atlantic guitarfish mean abundance 0

mean biomass (g) 19

Ryptcus maculatus percent of surveys 65 100 16 58

whitespotted soapfish mean abundance 2 0.5 10 4.3 0 0.1 1 0.3

mean biomass (g) 213 63.6 1442 995.1 21 8.8 64 40.1

Scomberomorus maculatus percent of surveys 9 50 2 0

Spanish mackerel mean abundance 5 4.3 22 19.6 0 0.1

mean biomass (g) 1693 1539.4 8036 6912.8 42 41.6

Scorpaena sp. percent of surveys 0 0 0 8

scorpionfish mean abundance 0

mean biomass (g) 6

Serola sp. percent of surveys 17 17 2 17

almaco/amberjack mean abundance 1 0.7 0 0 0 0.3

mean biomass (g) 999 785.5 235 5 62 47.0

Serranculus pumlo percent of surveys 0 0 2 0

pygmy sea bass mean abundance 0

mean biomass (g) 0

Serranus sublgarus percent of surveys 100 100 78 100

belted sandfish mean abundance 20 2.8 22 5.0 7 1.2 23 5.7

mean biomass (g) 35 5.1 37 8.4 15 3.3 39 9.6

Sphyraena barracuda percent of surveys 13 0 4 8

great barracuda mean abundance 0 0.3 0 0

mean biomass (g) 320 246.8 61 370

Sphyraena sp. percent of surveys 0 17 0 0

barracuda mean abundance 1 1.3

mean biomass (g) 1 1.0

Stegastes varabls percent of surveys 17 0 8 8

cocoa variabilis mean abundance 1 0.4 0 0.1 1 1.2

mean biomass (g) 2 1.2 0 0.3 4 3.6

Stenotomus sp. percent of surveys 52 17 98 92

scup/longspine porgy mean abundance 10 4.3 0 32 3.8 31 6.6

mean biomass (g) 2107 1124.7 109 3470 454.4 4218 1465.1

Stephanoleps hspdus percent of surveys 9 17 6 8

planehead filefish mean abundance 0 0 0 0

mean biomass (g) 13 38 13 5

Syngnathdae sp. percent of surveys 0 0 2 0

pipefish mean abundance 0

mean biomass (g) 0

Synodus sp. percent of surveys 0 0 4 0

lizardfish mean abundance 0

mean biomass (g) 17

Urophycs earll percent of surveys 26 17 31 8

Carolina hake mean abundance 1 0.3 0 2 0.6 0

mean biomass (g) 254 123.1 74 383 130.3 19

Xyrchtys novacula percent of surveys 0 0 2 0

pearly razorfish mean abundance 0

mean biomass (g) 0
Acknowledgements

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91

ACKNOWLEDgEMENTS

Chris Caldow, Randy Clark, Kelly Gleason, Jenny Waddell, and Kim Woody assisted in the collection of the 

field data at Gray’s Reef. Thanks to the boat captains Keith Golden, Jay Fripp, and Todd Recicar as well as 

the officers and crew of the NOAA ship Nancy Foster for safely getting us to and from our field sites. We are 

grateful to Captain Judy Helmey, William H. “Bing” Phillips, and John Duren for valuable discussions regarding 

recreational fishing at Gray’s Reef. Thanks to Randy Clark, Charlie Menza, Simon Pittman, Jenny Waddell, 

and Adam Zitello of the Biogeography Team for providing helpful comments and editorial assistance. Sarah 

Davidson-Hile organized and improved the many photos taken during the field missions. Jamie Higgins formatted and organized the figures, tables, images, and text into the work of art before you. Thanks to the GRNMS 

staff, particularly Greg McFall for fielding questions throughout the process and for helpful suggestions to 

improve this document. Funding for this study was provided by GRNMS and the NMSP/NCCOS Long Term 

Agreement.
Characterization of the Benthos, Marine Debris and Bottom Fish at 

Gray’s Reef National Marine Sanctuary - NOAA Technical Memorandum NOS NCCOS 50
United States Department of Commerce

Carlos M. Gutierrez

Secretary

National Oceanic and Atmospheric Administration

Vice Admiral Conrad C. Lautenbacher, Jr. USN (Ret.)

Under Secretary of Commerce for Oceans and Atmopsheres

National Ocean Service

John H. Dunnigan

Assistant Administrator