Emerging Technologies for 

Reef Fisheries Research 

and Management

J. Christopher Taylor (editor)

NOAA Professional Paper NMFS 5

U.S. Department 

of Commerce

December 2006
U.S. Department

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NOAA Professional Paper NMFS 5

Emerging Technologies for Reef 

Fisheries Research and Management

J. Christopher Taylor (editor)

December 2006

U.S. Department of Commerce

Seattle, Washington
Suggested reference

Full report: Taylor, J. C. 2006. Emerging technologies for reef fisheries research 

and management. NOAA Professional Paper NMFS 5, 116 p. 

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fisheries research and management, J. C. Taylor (ed.), p. [#-#]. NOAA Professional 

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iii

CONTENTS

Emerging technologies for reef fisheries management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

J. Christopher Taylor

A video method for quantifying size distribution, density, and three-dimensional spatial structure 

of reef fish spawning aggregations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Peter S. Rand, J. Christopher Taylor, and David B. Eggleston

Hydroacoustic evaluation of spawning red hind (Epinephelus guttatus) aggregations along 

the coast of Puerto Rico in 2002 and 2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Samuel V. Johnston, José A. Rivera, Aida Rosario, Mark A. Timko, Patrick A. Nealson, and Kevin K. Kumagai

Nassau grouper (Epinephelus striatus) spawning aggregations: 

hydroacoustic surveys and geostatistical analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

J. Christopher Taylor, David B. Eggleston, and Peter S. Rand

Designing marine fishery reserves using passive acoustic telemetry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Robert A. Glazer and Gabriel A. Delgado

Acoustic signatures of the seafloor: tools for predicting grouper habitat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Arthur C. R. Gleason, Anne-Marie Eklund, R. Pamela Reid, and Veronique Koch

Deepwater reef fishes and multibeam bathymetry of the Tortugas South Ecological Reserve, 

Florida Keys National Marine Sanctuary, Florida . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

Douglas C. Weaver, David F. Naar, and Brian T. Donahue

Deep reef fish surveys by submersible on Alderdice, McGrail, and Sonnier Banks 

in the Northwestern Gulf of Mexico . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

Douglas C. Weaver, Emma L. Hickerson, and George P. Schmahl

Detecting fish aggregations from reef habitats mapped with high resolution side scan sonar imagery . . . . . . . . . . 88

José A. Rivera, Martha C. Prada, Jean-Luc Arsenault, Gary Moody, and Nicolas Benoit

Integration of technologies for understanding the functional relationship between reef habitat 

and fish growth and production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

Doran M. Mason, Brian Nagy, Mark Butler, Stephen Larsen, Debra J. Murie, and William J. Lindberg
iv

Participants at Emerging Technologies for Reef Fisheries Research and Management Symposium, held during the 

56th annual meeting of the Gulf and Caribbean Fisheries Institute, Tortola, British Virgin Islands, November 2003.
Introduction

It is now recognized that fisheries 

activity around the world has reached 

and most probably exceeded its maximum sustainable yield (Pauly et al., 

2002; Myers and Worm, 2003). This 

is most certainly the case in coral 

reef systems where significant human impacts such as overexploitation, 

harmful fishing practices, and habitat 

destruction have decimated many reef 

fish populations (Jackson et al., 2001; 

Pandolfi et al., 2003). Reef ecosystems 

support some of the most diverse taxa 

in the aquatic world (Sale, 2002) and 

therefore degradation of habitats and 

extirpation of species through fisheries harvest can have unexpected consequences to the functioning of these 

ecosystems. Artisanal harvest of reef 

species also contributes significantly 

to the local economy of many island 

communities of the Caribbean, Gulf 

of Mexico, and tropical western Atlantic, and so has also suffered from 

the decline in reef ecosystems (Russ, 

1991). 

While we are gaining a better appreciation for the role coral reef habitats and reef fisheries play in coastal 

ecosystems and tropical economies, 

the techniques which fisheries managers have used to collect vital statistics on the status of populations lag 

behind the need for such data. This 

is not surprising as many of these species are solitary as adults or elusive 

as sub-adults and juveniles, using the 

reef structure as refuge from predation, making them difficult to detect 

and enumerate. Traditional means 

of surveying for population estimates 

Emerging technologies for reef fisheries research 

and management

J. Christopher Taylor

Center for Marine Science and Technology

North Carolina State University

303 College Circle

Morehead City, NC 28557

E-mail address: Chris_Taylor@ncsu.edu 

Abstract—This publication of the 

NOAA Professional Paper NMFS Series 

is the product of a special symposium on 

“Emerging Technologies for Reef Fisheries Research and Management” held during the 56th annual Gulf and Caribbean 

Fisheries Institute meeting in Tortola, British Virgin Islands, November 2003. The 

purpose of this collection is to highlight 

the diversity of questions and issues in reef 

fisheries management that are benefiting 

from applications of technology. Topics 

cover a wide variety of questions and issues from the study of individual behavior, 

distribution and abundance of groups and 

populations, and associations between 

habitats and fish and shellfish species.

1

or community structure, such as diver-visual surveys, have proven costly 

and sometimes ineffective in assessing abundance and distribution patterns of many of the most important 

reef fish populations. In addition, the 

limitations on dive bottom times and 

underwater characteristics such as 

light penetration and visibility place a 

necessary limit on the range, areal coverage, and depth of the study site that 

can be surveyed using this traditional 

method. It is critical that we develop 

methods that will provide extensive 

and yet rapid and efficient surveys of 

both reef habitats as well as the fish 

species that inhabit them. Ideally, such 

methods would permit rapid coverage 

of large spatial extents, produce abundance estimates or species distribution 

movement maps in real-time or with 

minimal post-processing, and be noninvasive so they do not interfere with 

the behavior and thus affect the detection of species of interest.

This collection of papers is a product of a special symposium entitled 

“Emerging technologies for reef fisheries management” that was held at 

the 56th annual meeting of the Gulf 

and Caribbean Fisheries Institute in 

Tortola, British Virgin Islands, in November 2003. The papers presented 

in this volume encompass a wide diversity of habitats, species, regions, 

and techniques and also address issues of reef fisheries ecology including the study of individual behavior, 

distribution and abundance of groups 

and populations, and associations between habitats and species. They also 

cover many aspects of fisheries management such as species conservation, 
2 Professional Paper NMFS 5

marine protected area design and implementation, and 

stock assessment and management. These papers are 

meant to serve as examples of ways in which technology 

has been employed and resulted in significant improvements in our understanding of reef fisheries and ecosystem processes. Readers interested in the foundations 

of a particular technology described in this collection 

should consult references in each paper. Moreover, this 

collection does not cover other applications of technologies in coral reef conservation and management, 

particularly remote sensing techniques that are being 

used to map coastal habitats and coral cover (Mumby 

et al., 2004). However, several papers in this series do 

attempt to link these habitat-based objectives with applications that include linking habitats with species of 

commercial or ecological interest. 

This collection of papers is divided into two broad 

categories: 1) population assessments and distribution 

patterns of single species, and 2) organism-habitat 

relationships. In many cases, the technology serves as 

a complimentary tool in each project. Several of the 

authors also identify advantages and limitations of their 

respective application and suggest how the limitations 

may be overcome in the future.

Distribution and abundance

Rand et al. used a diver-operated stereo video system to 

improve measurements of individual Nassau grouper in 

a spawning aggregation. The application also provided 

some of the first measures of small-scale, three-dimensional spatial structure within a reef fish spawning aggregation. 

Johnston et al. used mobile split-beam hydroacoustic 

surveys to assess the spatial distribution and abundance 

of red hind (Epinephelus guttatus) spawning aggregations along the continental shelf of Puerto Rico. Their 

survey covered several hundred kilometers along the 

shelf-break on Puerto Rico and identified regions that 

held large red hind aggregations during the February 

and March spawning season. 

Taylor et al. present results from a high-resolution 

mobile hydroacoustic sampling of a Nassau grouper 

(Epinephelus striatus) aggregation. They used a two-stage 

geostatistical model to estimate the abundance of fish 

and compared these estimates with those made using 

scuba visual census. The geostatistical model provided 

an objective measure of the spatial extent of the aggregation. Measures such as these can be difficult to obtain 

using traditional diver visual surveys.

Habitat-organism relationships

Glazer and Delgado used sonic telemetry coupled with 

Geographic Information Systems (GIS) to determine 

movement and home ranges for queen conch (Strombus gigas) on a back reef in Florida Keys. Overlapping 

home ranges of several individuals during spawning 

and non-spawning seasons were used to make objective 

and informed decisions on establishing boundaries of 

a marine fishery reserve to protect conch populations 

around the Florida Keys.

Gleason et al. used recent advances in bottom habitat 

classification from single-beam hydroacoustics along with 

diver-assisted surveys for grouper to make inferences 

about species-habitat associations. They found a significant relationship between the distribution of grouper 

and sediment type and a newly derived acoustic property 

of the bottom. Their work will lead to habitat-based population assessments for near-shore grouper species and 

improve site selection for fish surveys using divers.

Weaver, Naar, and Donahue and Weaver, Hickerson, 

and Schmahl used high-resolution bathymetric maps 

produced from multibeam sonar surveys to direct submersible surveys for reef habitat features in the Tortugas 

South Ecological Reserve and Northwestern Gulf of 

Mexico, respectively. Unique and rare coral habitats 

and deep reef species were identified at both sites using 

these complimentary surveying techniques.

Rivera et al. used side-scan sonar imaging from habitat 

surveys to identify acoustic targets in the water column; 

acoustic targets not associated with bottom features 

are typically considered as noise in habitat surveys, 

so current software packages do not permit analyses 

of such data. The authors manually extracted water 

column signals and linked them to the habitat maps 

through synoptic geopositioning data. Using this novel 

technique, they are able to begin to make connections 

between fish distribution and habitat types in a shallow, 

near-shore reef environment. 

Mason et al. used several complimentary technologies 

to establish relationships between reef habitat quality 

and fish growth. A combination of active hydroacoustics, passive acoustics, video, and advanced biochemical 

techniques provided insights into the functional linkages between reef attributes (e.g., structural complexity 

and prey availability) and processes that regulate gag 

grouper (Mycteroperca microlepis) performance.

Acknowledgements

I thank the members of the board at the Gulf and Caribbean Fisheries Institute for welcoming this special 

symposium and providing the logistical support during 

the 56th annual GCFI meeting in Tortola, BVI. A special thanks is extended to A. Costa, L. Creswell, and R. 

Glazer. I also thank participants in the symposium for 

valuable discussions during the symposium and their 

patience and persistence during the completion of 
Taylor: Emerging technologies for reef fisheries research and management 3

this collection. This manuscript benefited from discussions with L. Avens and S. Shoffler. Funding during the 

organization of this symposium and while compiling 

these papers was provided by grants from NOAA via 

the University of Miami (P715647 via NA17RJ1226), 

the NOAA Caribbean Marine Research Center (CMRC01-NRDE-04-02A; CMRC-01-NRDE-04-03C; CMRC00-NRDE-03-01C) and US EPA STAR Fellowship 

(Grant No. U-9155885). 

Literature cited

Jackson, J. B. C., M. X. Kirby, W. H. Berger, K. A. Bjorndal, L. W. 

Botsford, B. J. Bourque, R. H. Bradbury, R. Cooke, J. Erlandson, 

J. A. Estes, T. P. Hughes, S. Kidwell, C. B. Lange, H. S. Lenihan, 

J. M. Pandolfi, C. H. Peterson, R. S. Steneck, M. J. Tegner, and 

R. R. Warner. 

2001. Historical overfishing and the recent collapse of coastal 

ecosystems. Science 293(5530): 629–638.

Mumby, P. J., W. Skirving, A. E. Strong, J. T. Hardy, E. F. LeDrew, 

E. J. Hochberg, R. P. Stumpf, and L. T. David. 

2004. Remote sensing of coral reefs and their physical environment. Mar. Poll. Bull. 48(3–4): 219–228.

Myers, R. A., and B. Worm. 

2003. Rapid worldwide depletion of predatory fish communities. 

Nature 423(6937): 280–283.

Pandolfi, J. M., R. H. Bradbury, E. Sala, T. P. Hughes, K. A. Bjorndal, 

R. G. Cooke, D. McArdle, L. McClenachan, M. J. H. Newman, 

G. Paredes, R. R. Warner, and J. B. C. Jackson. 

2003. Global trajectories of the long-term decline of coral reef 

ecosystems. Science 301(5635): 955–958.

Pauly, D., V. Christensen, S. Guenette, T. J. Pitcher, U. R. Sumaila, 

C. J. Walters, R. Watson & D. Zeller. 

2002. Towards sustainability in world fisheries. Nature 418(6898): 

689–695.

Russ, G. 

1991. Coral reef fisheries: effects and yields. In The ecology of 

fishes on coral reefs (P. F. Sale, ed.), p. 601–635. 

Sale, P. F. 

2002. Coral reef fishes dynamics and diversity in a complex ecosystem. Academic Press, New York, 500 p.
4

Introduction

There has been growing interest in 

measuring individual sizes of fishes 

at liberty and capturing three dimensional (3D) attributes of fish schools 

or aggregations in situ (Parrish and 

Hamner, 1997), particularly on exploited populations that have not 

been assessed using fisheries independent methods. There is a relatively 

long history of using ship-mounted 

sonar for quantifying attributes of 

fish schools and, with the recent introduction of split- and multi-beam 

echosounders, we have improved our 

capabilities of resolving bathymetry 

and water column targets in 3D (Simmonds and MacLennan, 1996; MacLennan, 2003). These methods suffer, however, from a lack of certainty 

in identifying targets. SCUBA divers 

have been employed to provide observations for species identification 

and to quantify density and spatial extent of fish schools and aggregations. 

Unless these surveys are designed to 

address observer bias, the approach 

is prone to generating imprecise data 

A video method for quantifying size distribution, 

density, and three-dimensional spatial structure 

of reef fish spawning aggregations

Peter S. Rand

The Wild Salmon Center

The Natural Capital Center

721 NW 9th Avenue, Suite 280

Portland, OR 97209

E-mail address: prand@wildsalmoncenter.org

J. Christopher Taylor

Center for Marine Science and Technology

North Carolina State University

303 College Circle

Morehead City, NC 28557

David B. Eggleston

Department of Marine, Earth, and Atmospheric Sciences

North Carolina State University

Jordan Hall

Raleigh, NC 27695 

Abstract—There is a clear need to 

develop fisheries independent methods 

to quantify individual sizes, density, and 

three dimensional characteristics of reef 

fish spawning aggregations for use in 

population assessments and to provide 

critical baseline data on reproductive 

life history of exploited populations. We 

designed, constructed, calibrated, and 

applied an underwater stereo-video system to estimate individual sizes and three 

dimensional (3D) positions of Nassau 

grouper (Epinephelus striatus) at a spawning aggregation site located on a reef 

promontory on the western edge of Little 

Cayman Island, Cayman Islands, BWI, on 

23 January 2003. The system consists of 

two free-running camcorders mounted 

on a meter-long bar and supported by a 

SCUBA diver. Paired video “stills” were 

captured, and nose and tail of individual fish observed in the field of view of 

both cameras were digitized using image 

analysis software. Conversion of these two 

dimensional screen coordinates to 3D 

coordinates was achieved through a matrix inversion algorithm and calibration 

data. Our estimate of mean total length 

(58.5 cm, n = 29) was in close agreement 

with estimated lengths from a hydroacoustic survey and from direct measures of 

fish size using visual census techniques. 

We discovered a possible bias in length 

measures using the video method, most 

likely arising from some fish orientations 

that were not perpendicular with respect 

to the optical axis of the camera system. 

We observed 40 individuals occupying a 

volume of 33.3 m3, resulting in a concentration of 1.2 individuals m–3

 with a mean 

(SD) nearest neighbor distance of 70.0 

(29.7) cm. We promote the use of roving 

diver stereo-videography as a method 

to assess the size distribution, density, 

and 3D spatial structure of fish spawning 

aggregations. 

and can be difficult to replicate (Harvey et al., 2001). There is a clear need 

to develop methods that can reliably 

measure individual sizes, density, and 

3D structure of known targets in situ 

to overcome the limitations of these 

other methods.

In field situations that permit it, 

the use of stereo photography and 

videography offers a straightforward 

method for generating data on individual sizes, density, and 3D positions 

of fish. In addition to providing species identification in most cases, it 

also allows for estimates of size and 3D 

positions of fish in the water column, 

measures that are often difficult using SCUBA observations. There has 

been a relatively short history of using 

stereo-pairs of underwater cameras 

in situ to quantify individual lengths 

and behaviors of fishes, particularly 

swimming speed. Klimley and Brown 

(1983) were the first to conduct such 

a study, relying on 35 mm still cameras 

to estimate lengths of hammerhead 

sharks (Sphyrna spp.). Since then, 

there have been a number of studies 

in freshwater and marine systems inRand et al.: Video method for quantifying reef fish spawning aggregations 5

volving stereovideography (Boisclair, 1992; 

Hughes and Kelly, 1996; Hinch and Rand, 

2000; Harvey et al., 2001; Cocito et al., 2003; 

Harvey et al., 2003; Standen et al., 2004). All 

of these field efforts have focused on quantifying 3D positions and size of individuals, 

and, in some cases, estimating swimming 

speed and resolving swimming maneuvers. 

While some effort has been devoted to quantifying the structure of fish schools under 

laboratory conditions (Dill et al., 1981), we 

are not aware of any published work on combining 3D positioning and size estimation 

with spatial attributes of fish schools or aggregations in situ. Here we report on results 

of an application of stereo-videography for 

quantifying 3D positions, sizes, and spatial 

properties of a spawning aggregation of Nassau grouper (Epinephelus striatus). 

60 cm

A B

Figure 1

Schematic diagram of a 3D underwater stereo-video system. Two 

housings containing digital video recorders were fastened on either 

end of a 1 m steel bar. The cameras were positioned on the bar 

to achieve a 60 cm optical axis separation. The filled upright bars 

in the figure are the handles used by diver to support the system 

during a survey.

Nodes (n = 36 for each face) were digitized using the 

Manual Tag method in ImagePro. Pixel coordinates 

(360 by 270 image resolution) for each node were saved. 

We used a new program, Mathematica (v. 4.0, Wolfram 

Research, Champaign, IL), adapted from the approach 

of Hughes and Kelly (1996), that uses a transformation matrix to convert pixel screen coordinates to view 

coordinates as a means to estimate 3D positions. This 

approach can significantly reduce errors in determining positions that are not within the calibration area 

(Hughes1).

We synchronized the two free-running cameras in 

the field. Frame synchronization was achieved using 

an underwater laser pointer (Model MBSL, Class IIIA, 

maximum output <5mW, wavelength 635 nm) directed 

onto a light background. The diver operating the camera pointed the camera assembly either at another diver 

or on the reef, and repeatedly illuminated an area with 

the laser pointer such that both cameras would record 

the point simultaneously. To assure that we had proper 

frame synchronization for a particular pair of images 

captured during a dive, we relied on additional unique 

visual or auditory clues during playback (e.g. diver 

movements, taps on housing or sounds from regulator, 

and recognizable distinct fish behaviors captured in the 

field of view). 

We estimated 3D positions of fish within the spawning 

aggregation during a dawn dive on 23 January 2003. For 

all these periods, the anterior- and posterior-most points 

of each fish clearly identified in both paired camera images were digitized. Pixel coordinates were then read 

into the transformation matrix program for conversion 

Methods

We assembled an underwater stereo-video system for use 

in resolving 3D attributes of grouper aggregations. The 

system consisted of two underwater housings (Ikelite 

Model #6035.36) fitted with dome ports and mounted 

on opposite ends of a stainless steel bar (see Fig. 1). The 

weight in air of the bar support was 1.2 kg. This weight 

was sufficient to achieve slight positive buoyancy of the 

entire video assembly at the depths where grouper typically aggregate during the spawning period (ca. 30 m 

depth). The video cameras were SONY Model TRV-11 

(single 1.4 type CCD, 680,000 pixel resolution). The 

cameras were mounted to achieve an optical axis separation of 60 cm (Fig. 1). The zoom lens was fixed at wide 

angle (3.3 mm focal length), and the cameras were set 

on auto-focus. Video records were archived on 60 min 

DV tape format. A single diver operated the camera system and supported the system while underwater using 

handles placed between the two housings (Fig. 1).

The stereo-video system was calibrated with a 50 cm ×

50 cm × 30 cm quadrat consisting of 6.4 mm diameter 

welded aluminum pipe (Fig. 2). The pipe was welded in 

a regular grid pattern on each face of the quadrat such 

that the nodes of adjoining pipe were 10 cm apart. Images of the quadrat were captured in digital format. We 

conducted the full calibration analysis with the quadrat 

placed 2 m from the camera assembly (measured from 

the center point of the steel bar support). The calibration was conducted in approximately 1 m water depth 

in a swimming pool located at the Southern Cross Club 

on Little Cayman (see Fig. 2). Still images of the quadrat 

were captured as JPEG format and analyzed using ImagePro (v. 4.5, Media Cybernetics, Silver Spring, MD). 

1

 Hughes, N. 2004. Personal commun. University of Alaska Fairbanks, 

School of Fisheries and Ocean Sciences, 204 Arctic Health, P.O. 

Box 757220, Fairbanks, AK 99775-7220.
6 Professional Paper NMFS 5

Camera A Camera B 

Figure 2

Paired digital images of the calibration quadrat captured with the stereo video system. The calibration was 

conducted in a swimming pool at the Southern Cross Club, Little Cayman Island, Cayman Islands, BWI. The 

camera was placed 2 m from the near face of the quadrat. Nodes on the near and far face of the quadrat were 

digitized. 

to a view coordinate system oriented relative to the position of the camera. The origin of the view coordinate 

system was positioned at 2 m range from the camera 

system and was located at the point in space occupied by 

the left bottom corner node of the near face of the calibration quadrat at the time the system was calibrated. 

In the view coordinate system, the positive x-axis points 

to the right, the y-axis points away from the camera, and 

the z-axis points upward.

The resulting 3D positions of each individual fish were 

plotted as a 3D scatter plot with dropline in SPlus 2000 

(Insightful, Inc., Seattle, WA). A convex 3D hull was 

computed using qhull (Barber et al., 1996). A convex 

hull of a set of points is the smallest convex set containing the points. The resulting coordinates defining each 

of the polygons that comprised the hull were uploaded 

to Mathematica and plotted using the Graphics3D function. This allowed us to define a volume occupied by 

the fish within the view coordinate system to estimate 

volumetric density.

We computed a matrix of linear distances separating all members of the fish visible in the image pair. 

We computed straight line distance between the 3D 

position of each fish by taking the square root of the 

sum of the squared distances, using the view coordinates determined for the anterior point on each fish. 

We computed the mean and standard deviation of the 

nearest neighbor distances. Finally, we selected a subset 

of fish that were clear in the image and appeared to be 

oriented perpendicular to the optical axis of our cameras. We estimated their length by taking the square 

root of the sum of the squared differences, using the 

view coordinates of the nose and tail of each fish in the 

aggregation. 

Results

We captured a pair of images of the aggregation at approximately 0725 hr on 23 January 2003 (Fig. 3). Fish 

were swimming relatively close to the bottom and exhibited polarized swimming behavior. We digitized the nose 

of 40 individual fish in the field of view and estimated 

the aggregation in the field of view occupied 33.3 m3, 

resulting in a volumetric density of 1.2 grouper m–3

(Fig. 4). Nearest neighbor distances ranged from 22.1 

to 139.3 cm, with a mean of 70 cm and a standard deviation of 29.7 cm (Fig 5A). This equates to a mean interindividual spacing within the aggregation of 1.2 body 

lengths. We digitized the nose and tail of a subset of 

these individuals and computed an average size of 58.5 

cm TL (n = 29), with a range from 32 to 107 cm TL (Fig. 

5B). The aggregation was located between 5 and 10 m 

from the camera system.

Discussion

Our video estimate of fish length is in good agreement 

with the TL ranges reported on the same aggregation 

using diver visual methods (45–75 cm TL) and acoustic 

methods (60–90 cm TL) as reported in Taylor et al. (this 

volume). Size ranges observed for both of these methods were more constricted than estimated using the 
Rand et al.: Video method for quantifying reef fish spawning aggregations 7

Figure 3

Stereo paired images of a Nassau grouper aggregation. Images were captured on digital video at 0725 hr on 23 January 

2003 at Little Cayman Island, Cayman Islands, BWI. 

Camera A Camera B

Figure 4

Results of three dimensional analyses depicted as a 3D scatter plot with drop line of individual fish (A) and a 3D rendering 

of a complex hull surrounding the aggregation (B). A total of 40 individuals were identified in the view of both cameras 

occupying a total volume of 33.3 m3 and a volumetric density of 1.2 grouper m–3. Scales on axes in (A) and (B) are in cm. 

A

Z

0

100

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-200

-100

0

100

200

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800

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1200

Y

X

B

stereovideography method describe herein. Harvey et 

al. (2001) reported greater statistical power in resolving 

changes in mean lengths reported using a stereovideo 

method, compared to visual surveys performed by divers. We do, however, identify some limitations in estimating total length of grouper using the stereo-video 

approach described here. The most important source 

of error likely arises from an alignment of the main axis 

of the body of the fish that is askew relative to the optical axis of the cameras, resulting in estimated lengths 

that may be biased low. This is evident in the shape of 

the distribution of fish lengths generated from images 

captured during our 23 January 2003 dawn dive. We 

made an effort to select fish that appeared to be oriented perpendicular to the optical axis of the cameras, 

but this was relatively difficult to determine, particularly 

if the fish were at a distance away from the cameras. In 

past work we have obtained reasonable estimates of fish 

length by opportunistically targeting small groups of 

individuals at relatively close range, helping to assure 
8 Professional Paper NMFS 5

Figure 5

Nearest neighbor distances separating Nassau grouper (A) and size 

frequency histogram of Nassau grouper (B) measured using stereo-video 

methods. Average nearest neighbor distance was 70.0 (29.7 SD) cm. 

Average total length of individuals was 58.5 cm. Measurements were made 

during 23 January 2003 at a spawning aggregation site off Little Cayman 

Island, Cayman Islands, BWI. Bold line is density.

A

B

0.015

0.010

0.005

0.000

10 30 50 70 90 100 130 150

Nearest Neighbor Distance (cm)

30 40 50 60 70 80 90 100 110

Fish Length (cm)

0.000

0.005

0.010

0.015

0.025

0.020

that the fish are oriented near perpendicular to the 

optical axis of the camera (Rand, unpubl. data). The error in estimated size described here is analogous to that 

introduced from variation in tilt angle exhibited by fish 

measured using vertical incidence sonar (MacLennan 

and Simmonds, 1992). 

Our stereo-video estimate of volumetric density is 

within the range reported by Taylor et al. (this volume) 

on echo-integrated densities. Taylor et al. (this volume) 

reported maximum volumetric densities of 1.05 and 

0.74 grouper m–3 during two surveys of the aggregation 

site, which compare favorably with the estimate of 1.2 

grouper m–3

 reported here. These estimates, however, 

are not directly comparable because the samples were 

not collected at the same time of day (stereovideo sampling was conducted during morning of 23 January, 

and the acoustic sampling was conducted during the 

afternoon). The stereovideo sampling focused at the 

center of the main aggregation determined visually 

during a dive, so it is most appropriate to compare our 

estimate with that of the maximum recorded during 

the acoustic sampling. Our average nearest-neighbor 
Rand et al.: Video method for quantifying reef fish spawning aggregations 9

estimate of 0.7 m is the first reported for Nassau grouper 

within a spawning aggregation. Shapiro et al. (1993) 

reported nearest neighbor distances greater than 3.2 m 

for a spawning aggregation of red hind Epinephelus guttatus. Their visual observations were made from above 

the aggregation and in two dimensions; therefore, they 

may not represent true nearest-neighbor distances (Dill 

et al., 1981).

This effort underscores the need to conduct more 

rigorous cross-calibration involving independent measures of abundance, density, distribution, and individual 

sizes of fish within schools or aggregations. As in most 

cases involving population estimation and biological 

sampling, there are a variety of approaches that can 

be applied. It is clear from our work that there is not 

one clearly superior sampling method to quantify attributes of spawning aggregations of reef fishes; rather, 

we support a sampling approach involving two or more 

methods that will likely provide more rigorous and 

defensible results. Emerging technologies involving 

acoustic and video techniques will undoubtedly assume 

a more prominent role in efforts directed toward assessing status and trends of reef fishes in the tropical 

Atlantic Ocean and Caribbean Sea.

Acknowledgements

We were invited to survey the Little Cayman aggregation 

site by the Cayman Islands Department of the Environment (CIDoE). We thank P. Bush and K. Luke with 

CIDoE and L. Whaylen with Reef Environmental Education Foundation (REEF) for their cooperation and 

collaboration in this effort. Logistical and vessel support 

was provided by H. Fourier and P. Hillenbrand of the 

Southern Cross Club. We also thank the Gulf Caribbean 

Fisheries Institute for hosting this special symposium. 

Funding was provided by grants from NOAA via the 

University of Miami (P715647 via NA17RJ1226), NOAA 

Caribbean Marine Research Center (CMRC-01-NRDE04-02A; CMRC-01-NRDE-04-03C; CMRC-00-NRDE-03-

01C), PADI Project AWARE, and Sigma Xi.

Literature cited

Barber, C. B., D. P. Dobkin, and H. T. Huhdanpaa. 

1996. The Quickhull algorithm for convex hulls, ACM Trans. on 

Mathematical Software 22(4):469–483.

Boisclair, D. 

1992. An evaluation of the stereocinematographic method to 

estimate fish swimming speed. Can. J. Fish. Aquat. Sci. 49:523–

531

Cocito, S., S. Sgorbini, A. Peirano, and M. Valle. 

2003. 3-D reconstruction of biological objects using underwater 

video technique and image processing. J. Exp. Mar. Biol. Ecol. 

297:57–70.

Dill, L. M., R. L. Dunbrack, and P. F. Major. 

1981. A new stereophotographic technique for analyzing the 

three-dimensional structure of fish schools. Environ. Biol. 

Fish 6:7–13.

Harvey, E., D. Fletcher, and M. Shortis. 

2001. Improving the statistical power of length estimates of reef 

fish: a comparison of estimates determined visually by divers 

with estimates produced by a stereo-video system. Fish. Bull. 

99:72–80.

Harvey, E., M. Cappo, M. Shortis, S. Robson, J. Buchanan, and P. 

Speare. 

2003. The accuracy and precision of underwater measurements 

of length and maximum body depth of southern bluefin tuna 

(Thunnus maccoyii) with a stereo-video camera system. Fish. 

Res. 63:315–326.

Hinch, S. G., and P. S. Rand. 

2000. Optimal swimming speeds and forward-assisted propulsion: energy-conserving behaviours of upriver-migrating adult 

salmon. Can. J. Fish. Aquat. Sci. 57:2470–2478.

Hughes, N. F., and L. H. Kelly. 

1996. New techniques for 3-D video tracking of fish swimming 

movements in still or flowing water. Can. J. Fish. Aquat. Sci. 

53:2473–2483.

Klimley, A. P., and S. T. Brown. 

1983. Stereophotography for the field biologist: measurement 

of lengths and three-dimensional positions of free-swimming 

sharks. Mar. Biol. 74:175–185.

MacLennan, D. N. (ed.). 

2003. Acoustics in fisheries and aquatic ecology. Part 1. Proceedings of an ICES Symposium held in Montpellier, France, 10–14 

June 2002. ICES Marine Science Symposia, 218,706 p.

MacLennan, D. N., and E. J. Simmonds. 

1992. Fisheries acoustics. Fish and Fisheries Series 5. Chapman 

and Hall, New York, 325 p.

Parrish, J. K., and W. H. Hamner (eds.). 

1997. Animal groups in three dimensions. Cambridge University 

Press, New York, 378 p.

Shapiro, D. Y., Y. Sadovy, and M. A. McGehee. 

1993. Size, composition, and spatial structure of the annual 

spawning aggregation of the red hind, Epinephelus guttatus. 

Copeia 1993(2):399–406.

Simmonds, E. J., and D. N. MacLennan (eds.). 

1996. Fisheries and plankton acoustics. Proceedings of an ICES 

International Symposium held in Aberdeen, Scotland, 12–16 

June 1995. ICES Marine Science Symposia, 202.

Standen, E. M., S. G. Hinch, and P. S. Rand. 

2004. Influence of river speed on path selection by migrating 

adult sockeye salmon (Oncorhynchus nerka). Can. J. Fish. Aquat. 

Sci. 61:905–912.

Taylor, J. C., D. B. Eggleston, and P. S. Rand. 

2006. Nassau grouper (Epinephelus striatus) spawning aggregations: hydroacoustic surveys and geostatistical analysis. In

Emerging technologies for reef fisheries research and management (J. C. Taylor, ed.), p. 18–25. NOAA Prof. Paper NMFS 5.
10

Introduction

Tropical marine fish species, such 

as groupers, are generally considered to be overfished across most of 

their worldwide distribution (Sadovy, 

1994; Gascoigne1). Over fishing has 

driven many grouper stocks below sustainable levels and eliminated them 

from much of their historic range 

(Sadovy, 1997). Effective management of these fisheries requires innovative approaches, including harvest 

restrictions, community management 

systems, and networks of marine protected areas (MPA’s). Selection of the 

most effective management approach 

requires reliable information as to the 

distribution, abundance, biology, and 

harvest of these populations.

Caribbean groupers include a variety of species of the family Serranidae 

(order Perciformes), belonging to either the genera Epinephelus or Mycteroperca. Common representatives of 

these genera that are found in Puerto 

Rican waters include the speckled 

hind (E. drummondhayi), rock hind 

(E. adscensionis) and red hind (E. 

guttatus). The red hind is the most 

commercially important of the Puerto 

Rican hinds and has historically supported significant regional fisheries 

Hydroacoustic evaluation of spawning red hind 

(Epinephelus guttatus) aggregations along the 

coast of Puerto Rico in 2002 and 2003

Samuel V. Johnston

Hydroacoustic Technology, Inc.

715 NE Northlake Way

Seattle, WA 98105-6429

E-mail: sjohnston@htisonar.com

José A. Rivera

Under contract to: 

Biodiversity and Protected Resources Division

Miami Laboratory

National Marine Fisheries Service, NOAA

Box 1736

Boquerón, PR 00622-9704 

Aida Rosario

Department of Natural and 

Environmental Resources

Commonwealth of Puerto Rico

P.O. Box 9066600

Pta. De Tierra Station

San Juan, PR 00906

Mark A. Timko

Patrick A. Nealson

Kevin K. Kumagai

Hydroacoustic Technology, Inc.

715 NE Northlake Way

Seattle, WA 98105-6429

Abstract—Red hind (Epinephelus guttatus) have been overfished in the Caribbean and were included with seven other 

regional grouper species deemed vulnerable to risk of extinction. The Puerto Rico 

Department of Natural and Environmental Resources desired to map spawning red 

hind aggregations within commonwealth 

waters as part of their resource management program for the species. Mobile 

hydroacoustic surveys were conducted 

over 3-day periods in 2002 and 2003, 

indexed to the full moon phase in February or March when red hind were known 

to aggregate. Four vessels concurrently 

sampled the southwest, south, and southeast coasts of Puerto Rico in 2002. In 2003, 

three vessels conducted complementary surveys of the northwest, north, and 

northeast coasts of the island, completing 

a circuit of the coastal shelf-spawning 

habitat. These surveys indicated that red 

hind spawning aggregations were prevalent along the south and west coasts, and 

sparse along the north coast during the 

survey periods. Highest spawning red hind 

concentrations were observed in three 

areas offshore of the west coast of Puerto 

Rico, around Mona and Desecheo islands 

(20,443 and 10,559 fish/km2, respectively) 

and in the Bajo de Cico seasonal closed 

area (4,544 fish/km2). Following both 

2002 and 2003 surveys, a series of controlled acoustic measurements of known 

local fish species in net pens were conducted to assess the mean target strength 

(acoustic backscatter) of each group. Ten 

species of fish were measured, including 

red hind (E. guttatus), coney (E. fulvus), 

white grunt (Haemulon plumieri), pluma 

(Calamus pennatula), blue tang (Acanthurus coeruleus), squirrel fish (Holocentrus 

spp.), black durgeon (Melichtyhs niger), 

ocean file fish (Canthidermis sufflamen), 

ocean surgeon fish (Acanthurus bahianus), 

and butter grouper (Mycteroperca spp.). In 

general, the mean target strength results 

from the caged fish experiments were in 

agreement with published target strength 

length relationships, with the exception of 

white grunt and pluma.

along the west and south coasts of 

the island (Matos-Caraballo, 2004). 

However, available information shows 

that overall grouper landings in the 

U.S. Caribbean have declined 45% 

between 1977 and 1985, with an even 

greater rate of decrease between 1987 

and 1989 (Appeldoorn et al., 1992; 

Sadovy, 1994).

From fishermen it is known that 

red hind spawn within a specific time 

period surrounding the full moon in 

late winter, typically in January, February, or March (Sadovy et al., 1994). 

They aggregate only at specific locations, where they are present in large 

numbers (Shapiro, 1993; Sadovy et al., 

1994). This stage in the life cycle of 

the red hind is particularly important 

since the entire annual reproduction 

for a region may be concentrated at 

a few particular locations where aggregations occur for only a few days 

each year (Nemeth, 2005). In many 

instances, entire regional stocks have 

1

 Gascoigne, J. 2002. Nassau grouper and 

queen conch in the Bahamas: Status and 

management options. Report by MacAlistair 

Elliot and Partners, Ltd.. (MEP) to the Bahamas Reef Environment Educational Foundation (B.R.E.E.F.). http://www.macalisterelliott.com/media/reports/1651R02C.pdf 

[Accessed 25 Sept. 2006.]
Johnston et al.: Hydroacoustic evaluation of spa wining red hind aggregations 11

been wiped out due to intense fishing pressure on 

spawning aggregations (Bohnsack2).

While the spatial and temporal aggregating behavior 

of spawning grouper makes them subject to high exploitation, it also provides an annual opportunity to assess 

population density and distribution. The objective of 

this paper was to use this localized spawning behavior to 

identify, map, and quantify red hind aggregations along 

the entire Puerto Rican shelf using mobile hydroacoustics. The study quantified school aggregations with the 

characteristic vertical stacking behavior consistent with 

spawning behavior in red hind and similar grouper 

species along the continental shelf surrounding Puerto 

Rico. Due to the limited effort available and the narrow 

time window for the aggregations, the study objectives 

were exploratory in nature. Specific project objectives 

included the following:

1) Employ mobile hydroacoustics to estimate the 

density and map the location and distribution of 

red hind/grouper schools across the surveyed 

coastline.

2) Characterize fish school metrics including school 

location and mean target strength values. 

3) Estimate mean target strength values from caged 

fish of known species and size to aid in interpretation of the field data and accurate scaling of echo 

integration density results. 

4) Compare the distribution of observed fish aggregations with fishing activity indicated by the distribution of fishing vessels. 

Methods

Burczynski (1979) and MacLennan and Simmonds 

(1992) discuss the principles of mobile hydroacoustic 

fisheries assessment. Over 1300 km of transects were 

surveyed during the 2002–2003 Puerto Rico red hind 

surveys, encompassing the entire 500 km coastline of 

the island, with the exception of the area between Culebra and Vieques islands. 

The areas surveyed during the 2002 hydroacoustic 

red hind assessment (Johnston et al.3) were located 

along the edge of the continental shelf surrounding 

2

 Bohnsack, J. A. 1989. Protection of grouper spawning aggregations. 

Coastal Resource Division Contribution CRD-88/89-06. Southeast 

Fisheries Science Center, NMFS, 75 Virginia Beach Dr., Miami, FL 

33149. 8 p.

3

 Johnston, S. V., P. A. Nealson, K. K. Kumagai, M. A. Timko, and 

G. W. Tritt. 2002. Hydroacoustic evaluation of spawning red hind 

(Epinephelus guttatus) aggregations along the coast of Puerto Rico 

in February 2002. Report by Hydroacoustic Technology, Inc. (715 

NE Northlake Way, Seattle, WA 98105-6429) to Puerto Rico Dept. of 

Natural Resources, San Juan, PR.

the south, southwest, and southeast coasts of Puerto 

Rico. Four individual geographic survey areas were designated within the overall region of interest, based on 

the length of transects that could be monitored within 

a 3-day period of time surrounding the full phase of 

the moon in February (Fig. 1). The areas were adjacent 

to one another, providing a coherent sampling record 

along the southern half of the Island of Puerto Rico. 

A total of approximately 957 kilometers (517 nautical 

miles) of transects were surveyed by the four survey vessels between 27 February and 2 March 2002, gathering 

data for approximately 12 h/d. 

1) Area 2002-A: the southern half of the west coast 

centered around Mayaguez between approximately 

Rincon and Cabo Rojo. A total of 92 km of transects 

were surveyed.

2) Area 2002-B: the southern half of the eastern 

Puerto Rican coast between Arroyo and the eastern 

end of the Island of Vieques. A total of 182 km of 

transects were surveyed.

3) Area 2002-C: the southern coast between approximately Cabo Rojo and Ponce, centered around 

La Perguera. A total of 111 km of transects were 

surveyed.

4) Area 2002-D: the south central coast of Puerto Rico 

between approximately Ponce and Arroyo, based 

out of Salinas. A total of 143 km of transects were 

surveyed. 

The areas surveyed during the 2003 hydroacoustic 

red hind assessment were located along the edge of the 

continental shelf surrounding the north, northwest, 

and western offshore islands of Puerto Rico (Nealson 

et al.4

). Three individual geographic survey areas were 

designated within the overall region of interest, based 

on the length of transects that could be monitored 

within a 3-day period of time surrounding the full 

phase of the moon in March (Fig. 1). The areas were 

adjacent to one another, providing a coherent sampling 

record along the northern half of Puerto Rico. A total 

of approximately 395 kilometers (213 nautical miles) 

of trackline were surveyed by the three survey vessels 

between March 18 and March 21, 2003, gathering data 

for approximately 12 h/d.

1) Area 2003-A: the northeast Puerto Rican coast, 

from Culebra Island to San Juan. A total of 128 km 

of transects were surveyed.

4

 Nealson, P. A., G. W. Tritt, M. A. Timko, K. K. Kumagai, and S. V. 

Johnston. 2003. Hydroacoustic evaluation of spawning red hind 

(Epinephelus guttatus) aggregations along the coast of Puerto Rico 

in March 2003. Report by Hydroacoustic Technology, Inc. (715 NE 

Northlake Way, Seattle, WA 98105-6429) to Puerto Rico Dept. of 

Natural Resources, San Juan, PR.
12 Professional Paper NMFS 5

Figure 1

Survey transects and presumed school aggregations for red hind (Epinephelus guttatus). Transects in all four designated 

areas surveyed 27 February to 2 March 2002 are indicated by a dotted line. Transects in all four designated survey areas 

surveyed 27 February–2 March 2002 are indicated by a solid line. 

2) Area 2003-B: the northwest coast of Puerto Rico, 

from San Juan to Aguadilla. A total of 153 km of 

transects were surveyed.

3) Area 2003-C: the western coast from Aguadilla to 

Rincon, Mona, Monita, and Desecheo Islands, and 

Baja de Cico. A total of 114 km of transects were 

surveyed. 

Six-second echo integration sampling intervals were 

used to define the minimum spatial areas of resolution over which to estimate school biomass during 

field data collection. During data analysis, individual 

school events were identified and processed from 

the digital samples (DAT tape) data set, to provide 

finer-scale resolution and editing of these events. GPS 

data were collected simultaneously, permitting individual schools to be precisely located along the survey 

transects.

Fishing boat locations were obtained from aerial 

surveys conducted in 2002 and 2003 by the Puerto Rico 

Department of Natural and Environmental Resources 

(DRNA).

Hydroacoustic equipment

Split-beam hydroacoustic systems (Model 243, Hydroacoustic Technology Inc., Seattle, WA) operating 

at 200 kHz were used for the 2002–2003 Puerto Rico 

red hind population assessments. Data were recorded 

to computer and DAT tape during field collection, and 

later analyzed using EchoScape, an HTI data entry and 

analysis program. 

In 2002, the echo sounders on each survey vessel sampled both 6° and 15° transducers in a fast-multiplexed 

(simultaneous) mode. The 15° beam width transducers 

were incorporated to ensure adequate sampling volumes in shallow water. Based on exploratory transects 

surveyed in shallow waters in 2002, the characteristic 

red hind spawning aggregations were not observed in 

shallow water, and it was determined that a single 6° 

transducer was sufficient for monitoring the relatively 

deeper areas along the shelf breaks where these schools 

were observed. This single 6° configuration was used 

for sampling in 2003. Both transducers were oriented 

straight down in the water column and mounted on a 

1-m long aluminum dead-weight towing vehicle. The 

maximum sample depth for the hydroacoustic systems 

was approximately 200 m for the sizes of red hind encountered during the 2002 and 2003 surveys.

Each survey system collected the hydroacoustic data 

files directly to a laptop computer. All data were also 

concurrently recorded to DAT tape, providing an ultimate data backup of the unfiltered digital samples 

for later reprocessing as digital samples. Differential 

or WAAS-enabled (Wide Area Augmentation System) 

Global Positioning System (GPS) receivers were interfaced with all hydroacoustic systems to provide position 

to within approximately 3 m.

Prior to the survey period, the hydroacoustic systems 

used in this study were calibrated relative to a U.S. 
Johnston et al.: Hydroacoustic evaluation of spa wining red hind aggregations 13

Table 1

Fish species used in the 2002 and 2003 caged fish 

measurements, and the average length of each 

individual. 

Fish name Fish species Length (cm)

2002

 Red hind Epinephelus guttatus 20.2

 Coney Epinephelus fulvus 21.3

 White grunt Haemulon plumieri 16.0

 Pluma Calamus pennatula 17.9

 Blue tang Acanthurus coeruleus 18.0

2003

 Squirrel fish Holocentrus spp. 22.9

 Black durgeon Melichthys niger 27.9

 Ocean file fish Canthidermis sufflamen 45.7

 Ocean surgeon fish Acanthurus bahianus 41.9

 Butter grouper Mycteroperca spp. 25.4

Naval standard transducer of known sensitivity. The 

2002–2003 Puerto Rico red hind surveys employed a 

minimum on-axis target detection threshold of –50 dB. 

This threshold corresponded to a minimum fish detection length across the full nominal transducer beam 

width of approximately 55 mm based on Love (1971). 

To verify the laboratory calibration of the hydroacoustic 

system, in situ field measurements were made using a 

38.1 mm tungsten carbide standard target (MacLennan 

and Simmonds, 1992) during each individual survey 

cruise.

Data collection

The 2002 red hind hydroacoustic surveys were conducted between 27 February and 2 March 2002. All school 

density data were successfully collected within 2 days following the full moon (27 February). The 2003 red hind 

hydroacoustic surveys were conducted 18–21 March 

2003 (the full moon occurred on 18 March). Surveys 

consisted of a series of predetermined transects following the continental shelf area and the region just inshore of this break. Survey transects generally zigzagged 

along the edge of the continental shelf approximately 

55 m (180 ft) deep. Transects were traversed at a boat 

speed of approximately 4–5 knots. The overall acoustic 

repetition (ping) rate of the hydroacoustic system was 

2 pings/sec per transducer.

Data analysis

Data were analyzed to provide estimates of fish population and biomass in the survey areas, estimates of fish 

target strength (related to fish length), and spatial 

school metrics. Data visualization and editing was performed using the program EchoScape (Version 2.52, 

Hydroacoustic Technology, Inc., Seattle, WA) to visually 

identify aggregations from the field computer data file 

records and mark all single echoes within identified 

school aggregations as tracked fish. The EchoScape 

program linked all of the field data records such that, 

within the tracked fish table, each aggregation was identified with mean target strength, detection time, and a 

latitude/longitude location.

Raw digital sample files that were collected on the 

DAT were reintegrated at fine integration intervals of 

6 sec in duration and 1 m deep. This provided the ability to isolate the red hind schools from surrounding 

structure and other targets. These reintegrated files 

were then read back into EchoScape for final editing. 

EchoScape was used to manually select the cells that 

contain aggregations for subsequent estimation of 

school densities.

In situ target strengths of ten species of coral reefdwelling fish were measured by using the same splitbeam hydroacoustic system described above (Table 1). 

Measurements were made at La Perguera on 2 March 

2002, and Escollo Negro, near Cabo Rojo, on 21 March 

2003. Individual fish were placed in a cylindrical, steelframed cage that was 3 m in diameter, 0.9 m in height, 

covered with fine mesh monofilament netting. The cage 

was suspended 7 m below the surface, and fish were 

placed in the cage individually by divers. One fish of 

each species was measured at a time. All fish were alive 

when placed in the cage. The squirrel fish was dead 

upon retrieval, but all others were alive. The transducer 

was suspended 5.7 m above the cage, and aimed downward through the fine mesh netting. The ping rate was 

10 pings per second. EchoScape was used to summarize 

the target strength values of red hind and other commonly occurring species measured during the caged 

fish experiments. These mean backscatter (ó) values 

were compared to the in situ measures of observed fish 

target strength collected during the mobile surveys, 

which were used to scale the integrated relative biomass 

values to estimates of absolute red hind spawning fish 

density (fish/m3

).

Results

Geographic distribution 

of spawning aggregations 

The distribution of the red hind spawning aggregations 

during the 2002 and 2003 survey periods are presented 

by regional subarea in Fig. 1. The geographic (latitude/

longitude) locations, time of observation, and estimated 
14 Professional Paper NMFS 5

mean aggregation density (in fish/m2) are presented 

for each designated survey area in Table 2 for 2002 and 

Table 3 for 2003. The acoustic data sets were carefully 

scrutinized during the analysis process and only coherent, vertically-stacked aggregations of fish were included 

in the red hind spawning density estimates (Fig. 2). 

Based on prior knowledge of red hind spawning behavior and the targeted nature of the hydroacoustic surveys, 

the stacked aggregations were presumed to consist 

solely of red hind.

Comparison of estimated overall spawning 

red hind densities in 2002 and 2003

Highest densities were observed associated with either 

islands or reef areas off of the west coast of the island 

(Fig. 3). The area surrounding Mona Island had the 

highest estimated spawning densities (20,443 fish/km2). 

Desecheo, in the same general area, had approximately 

half the spawning density (10,559 fish/km2) of Mona Island. The Bajo de Cico rise area had estimated spawning 

densities of 4,544 fish/km2

. The area between Arroyo 

and Vieques Island had the fourth highest estimated 

spawning densities (1,357 fish/km2). Estimated spawning densities along the north and south coasts of Puerto 

Rico were consistently low, and were lowest off of the 

northeast coast (32 fish/km2

). 

Table 2

Mean observed spawning red hind (Epinephelus guttatus) density (presumed) in 2002 for each designated survey area. 

Sequences are defined as the 1-min field echo integration intervals (approximately 150 m long at 5 knots).

 Sequences Sequences Percent Transect

Area Location Fish/km2 surveyed w/schools w/schools length (km)

A Cabo Rojo and Mayaguez 1,286 777 67 8.62% 91.5

B Arroyo and Vieques 1,357 1144 74 6.47% 110.6

C Cabo Rojo to Cayo Parguera 395 834 85 10.19% 143.1

D Ponce to Arroyo 962 1073 102 9.51% 182.0

Table 3

Mean observed red hind (Epinephelus guttatus) density (presumed) in 2003 for each designated survey area. Sequences are 

defined as the 1-min field echo integration intervals (approximately 150 m long at 5 knots).

 Sequences Sequences Percent Transect

Area Location Fish/km2 surveyed w/schools w/schools length (km)

C Mona 20,443 412 154 37.4% 63.7

C Desecheo 10,559 82 26 31.7% 9.9

C Bajo de Cico 4,544 148 82 55.4% 19.9

C West coast 686 151 3 2.0% 20.3

B Northwest coast 629 897 47 5.2% 153.3

A Northeast coast 32 822 24 2.9% 128.2

Comparison of acoustic spawning estimates 

and fishing vessel distribution in 2002

The location of fishing boats targeting red hind, based 

on Puerto Rico Dept. of Natural and Environmental 

Resources overflights conducted on 14 February 2002 

and 23 January 2003, are shown in Fig. 4, along with the 

distribution of spawning aggregations observed by the 

hydroacoustic surveys. The location of fishing vessels 

was closely associated with the occurrence of schools 

of red hind, as observed by the hydroacoustic surveys 

conducted later.

Caged fish target strength experiments

Table 4 presents the mean dorsal aspect target strength 

and surrounding standard deviation for red hind and 

nine other species measured during the caged fish 

target strength experiments. The 2002 experiments revealed mean target strength values of approximately –37 

dB for red hind (20 cm in length) and for coney (21 cm 

in length). White grunt, although physically smaller (16 

cm) than the red hind, returned higher target strength 

values (–32 dB), perhaps due to differing morphology 

or swim bladder size. The single example of blue tang 

measured was 18 cm in length and returned a target 

strength value of –33 dB, also larger than the mea-
Johnston et al.: Hydroacoustic evaluation of spa wining red hind aggregations 15

Table 4

Summary of the observed acoustic target strength 

values of 10 species of caged fish during 2002 and 

2003.

 Target 

Length strength Standard Number

Fish species (cm) (dB) dev. echoes

2002

 Red hind 20.2 –36.6 2.95 73

 Coney 21.3 –37.1 3.31 440

 White grunt 16.0 –31.9 0.59 48

 Pluma 17.9 –43.2 2.58 124

 Blue tang 18.0 –33.4 1.38 16

2003

 Squirrel fish1 22.9 –35.1 2.07 21

 Black durgeon 27.9 –35.8 4.39 696

 Ocean file fish 45.7 –31.7 4.87 475

 Ocean surgeon 

 fish 41.9 –32.0 2.42 549

 Butter grouper 25.4 –33.2 2.64 225

1 Dead upon retrieval from cage.

Figure 2

Example hydroacoustic echogram showing identified 

aggregations near the bottom of the shelf edge. 

Horizontal scale represents distance (m) along transect 

line. Vertical axis represents depth (m).

sured grouper species. Eight pluma were acoustically 

measured during the caged fish experiments, varying 

in length from 16–24 cm. Pluma returned the smallest 

mean target strength values, averaging –43 dB, indicating that they reflect less energy from the acoustic signal 

than the other evaluated species.

The 2003 experiments produced similar target 

strengths for 23 cm squirrel fish (–35 dB), 28 cm black 

durgeon (–36 dB), 46 cm ocean file fish (–32 dB), 42 cm 

ocean surgeon fish (–32 dB), and 25 cm butter grouper 

(–33 dB).

Discussion

The Puerto Rico 2002–2003 spawning red hind population surveys demonstrated the feasibility of hydroacoustic survey techniques for quantification of schooled 

spawning aggregations along the continental shelf during the brief full moon spawning period in early spring. 

The use of fish behavior, specifically the known site 

fidelity and timing of red hind spawning aggregations, 

to infer a red hind population in a mixed-species environment was an innovative aspect of the study. However, 

this inference was also an aspect of the research worthy 

of additional investigation in future hydroacoustic sampling of red hind and other grouper species exhibiting 

similar aggregating behavior during spawning. The 

tight sampling timetable mandated by the requirement 

to cover large areas of the Puerto Rican coastline within 

a day of the full moon spawning period did not allow 

for interruptions to directly sample school aggregations 

for species identification. The vertically-stacked coherent schools were unique and readily identified from 

the more frequently observed diffuse and horizontally 

spread fish aggregations. However, the density estimates 

obtained may include species other than red hind 

(Whaylen et al., 2004; Luckhurst5

). Species-inference 

of the resulting density estimates based on the vertical schools was solely based on their shape, near-shelf 

distribution, and timing (proximity to the full-moon). 

These characteristics, along with the occurrence of fishing vessels targeting red hind, were assumed to uniquely 

identify spawning red hind schools. 

Recommendations for future quantitative population 

surveys of this resource include incorporation of additional species identification sampling. Options include 

5

 Luckhurst, B. 2001. Policies, legislation and management options. 

Workshop report, Towards sustainable Management of Nassau 

groupers in Belize, Belize City, 30 July 2001. Marine Resources Divison, Bermuda Dep. of Env. Prot., P.O. Box CRS2, Crawl CRBX, 

Bermuda.
16 Professional Paper NMFS 5

Figure 3

Number of spawning red hind (Epinephelus guttatus) per square kilometer by geographic area 

along the Puerto Rican costal shelf, as estimated by 2002 and 2003 hydroacoustic surveys. 

Densities on the bar chart are coded and referenced to general geographic location on the map 

insert.

video, divers, or hook and line sampling. These efforts 

should be supported by an independent vessel in contact 

with the hydroacoustic survey boat, which could identify 

observed school aggregations for investigation. 

It would also be worthwhile to investigate spawning aggregation persistence, that is, whether red hind 

spawn in the same general areas over time. Spawning 

aggregation persistence could be readily measured 

within a designated area by a single vessel conducting 

repetitive survey transects. These longer-term acoustic 

surveys could also be used to investigate the patchiness 

of red hind spawning in time over the months of January-March. The fidelity of spawning relative to the full 

moon phase (number of days over which significant 

spawning activity occurs) should also be investigated by 

repetitive acoustic surveys. If spawning activity occurs 

over a longer time frame than targeted by these acoustic 

surveys, the overall estimates of spawning biomass could 

be affected. 

Adding hydroacoustic survey transects inshore of the 

shelf break areas would aid in defining the overall red 

hind spawning habitat area surrounding Puerto Rico 

(J. Rivera, pers. observ.). Defining spawning habitat 

would allow for better identification and delineation 

of spawning habitats for red hind and potentially other 

aggregating grouper species around Puerto Rico. In 

addition, side scan or multi-beam bottom profiling 

acoustic systems (or geo-referenced data acquired by 

these systems at an earlier time) could be employed to 

correlate benthic habitat types with the observed red 

hind densities. These data may also help to better define 

spawning habitat for the species.

In general, the mean target strength results of the 

caged fish experiments for red hind and nine other 
Johnston et al.: Hydroacoustic evaluation of spa wining red hind aggregations 17

Figure 4

Overview of the entire acoustically-surveyed area in 2002 and 2003 showing the observed school aggregations along the 

sampled route and the positions of fishing vessels observed targeting red hind (Epinephelus guttatus), based on Puerto Rico 

Dept. of Natural and Environmental Resources aerial survey data.

species were in agreement with the expected lengthto-target strength relationship of Love (1971), with the 

exception of white grunt and pluma. These two species 

appeared to have higher backscatter values than would 

be predicted using this relationship. The observed mean 

in situ target strength values for the observed verticallystacked fish aggregations enumerated in 2002–2003 

were consistent with the controlled measurements of 

known red hind, varying between approximately –37 

and –42 dB. It would be advisable to conduct additional 

caged fish target strength experiments on individual red 

hind specimens over the entire expected length distribution of the species (approximately 10–80 cm). Using 

these data, a target strength-to-length relationship for 

the species could be developed. These data would also 

aid in scaling density estimates and permit examination 

if unique acoustic descriptors exist that could aid in 

resolving red hind from other species.

Literature cited

Appledoorn, R., J. Beets, J. Bohnsack, S. Bolden, D. Matos, S. Meyers, 

A. Rosario, Y. Sadovy, and W. Tobias. 

1992. Shallow water reef fish stock assessment for the U.S. Caribbean. NOAA Tech Memo NMFS-SEFSC-304, 70 p.

Burczynski, J. J. 

1979. Introduction to the use of sonar systems for estimating 

fish biomass. Food and Agriculture Organization of the 

United Nations, Rome, FAO Technical Paper No. 191, 89 p.

Kanciruk, P. 

1982. Hydroacoustic biomass estimation techniques. Oak Ridge 

National Lab., Environ. Sci. Div., Publ. No. 2019.

Love, R. H. 

1971. Dorsal-aspect target strength of an individual fish. J. 

Acoust. Soc. Am. 49:816–823.

Matos-Caraballo, D. 

2004. Overview of Puerto Rico’s small-scale fisheries statistics, 

1998–2004. Proceedings of the Gulf and Carib Fish. Inst. 

55:103–118.

MacLennan, D. N., and E. J. Simmonds. 

1992. Fisheries Acoustics. Chapman and Hall, London, 325 p.

Nemeth, R. S.

2005. Population characteristics of a recovering U.S. Virgin Islands red hind spawning aggregation following protection. 

Marine Ecology Prog. Series 286:81–97.

Sadovy, Y. 

1994. Grouper stocks of the western central Atlantic: the need 

for management and management needs. Proceedings of the 

Gulf and Carib. Fish. Inst. 43:43–64.

1997. The case of the disappearing grouper: Epinephelus striatus, 

the Nassau grouper, in the Caribbean and Western Atlantic. 

Proceedings of the Gulf and Carib. Fish. Inst. 45:5–22.

Sadovy, Y., A. Rosario, and A. Roman. 

1994. Spawning dynamics in an aggregating grouper, the red 

hind, Epinephelus guttatus. Environ. Biol. Fish., 41:269–286.

Shapiro, D. Y. 

1993. Size, composition, and spatial structure of the annual 

spawning aggregation of the red hind, Epinephelus guttatus (Pisces: Serranidae). Copeia 1993(2):399–406.

Whaylen, L, C. Pattengill, B. Semmens, P. Bush, and M. Boardman. 

2004. Observations of a Nassau grouper, Epinephelus striatus, 

spawning aggregation site in Little Cayman, Cayman Islands, 

including multi-species spawning information. Environ. Biol. 

Fish 70:305–313.
18

Introduction

Reef fish spawning aggregations have 

gained distinction in terms of their 

importance in species conservation 

and their socio-economic contribution to many Caribbean, tropical 

western Atlantic, and Gulf of Mexico 

fishing communities. With the near 

extinction of many spawning aggregations of large grouper and snapper 

throughout the Caribbean, Gulf, and 

tropical western Atlantic, it is essential 

that we increase our ability to study 

and document the remaining known 

(and unknown) aggregations to provide baselines for their conservation 

(Sadovy, 1994; Sadovy, 1997; Sala et 

al., 2001; Colin et al., 2003). 

The Nassau grouper (Epinephelus 

striatus) is a commercially important 

tropical reef species that forms discrete spawning aggregations, typically 

around full moons from December 

to March (Sadovy and Eklund, 1999). 

Historically, spawning aggregations 

of Nassau grouper have occurred 

throughout the Caribbean, tropical 

Nassau grouper (Epinephelus striatus) 

spawning aggregations: hydroacoustic surveys 

and geostatistical analysis

J. Christopher Taylor

Department of Zoology

Center for Marine Sciences and Technology

North Carolina State University

303 College Circle

Morehead City, NC 28557

E-mail: chris_taylor@ncsu.edu

David B. Eggleston

Department of Marine, Earth, and Atmospheric Sciences

North Carolina State University

Campus Box 8208

Raleigh, NC 27695-8208

Peter S. Rand

The Wild Salmon Center

The Natural Capital Center

721 NW 9th Avenue, Suite 290

Portland, OR 97209

Abstract—With the near extinction 

of many spawning aggregations of large 

grouper and snapper throughout the 

Caribbean, Gulf of Mexico, and tropical 

Atlantic, we need to provide baselines 

for their conservation. Thus, there is a 

critical need to develop techniques for 

rapidly assessing the remaining known 

(and unknown) aggregations. To this 

end we used mobile hydroacoustic surveys to estimate the density, spatial extent, and total abundance of a Nassau 

grouper spawning aggregation at Little 

Cayman Island, Cayman Islands, BWI. 

Hydroacoustic estimates of abundance, 

density, and spatial extent were similar 

on two sampling occasions. The location 

and approximate spatial extent of the 

Nassau grouper spawning aggregation 

near the shelf-break was corroborated by 

diver visual observations. Hydroacoustic 

density estimates were, overall, three-times 

higher than the average density observed 

by divers; however, we note that in some 

instances diver-estimated densities in localized areas were similar to hydroacoustic 

density estimates. The resolution of the 

hydroacoustic transects and geostatistical interpolation may have resulted in 

over-estimates in fish abundance, but still 

provided reasonable estimates of total spatial extent of the aggregation. Limitations 

in bottom time for scuba and visibility 

resulted in poor coverage of the entire 

Nassau grouper aggregation and low estimates of abundance when compared 

to hydroacoustic estimates. Although the 

majority of fish in the aggregation were 

well off bottom, fish that were sometimes 

in close proximity to the seafloor were 

not detected by the hydroacoustic survey. 

We conclude that diver observations of 

fish spawning aggregations are critical to 

interpretations of hydroacoustic surveys, 

and that hydroacoustic surveys provide 

a more accurate estimate of overall fish 

abundance and spatial extent than diver 

observations. Thus, hydroacoustics is an 

emerging technology that, when coupled 

with diver observations, provides a comprehensive survey method for monitoring 

spawning aggregations of fish.

western Atlantic, and Bermuda (Sadovy, 1997; Sadovy and Eklund, 1999). 

Nassau grouper often migrate great 

distances (in some cases documented 

on a scale of 100’s of km) to aggregate on reefs at promontories of islands (Colin et al., 1987; Colin, 1992; 

Bolden, 2000). Long-term monitoring 

and anecdotal evidence from fisheries 

have documented use of the same site 

by some aggregations for as long as 

30 years, suggesting high site fidelity 

by the species (Colin, 1996). Nassau 

grouper have been the most valuable 

finfish in the insular Caribbean and 

the tropical western Atlantic (Sadovy 

and Eklund, 1999; Sala et al., 2001), 

and heavy exploitation primarily during spawning seasons has resulted 

in local extirpation of most aggregations in the Cayman Islands, Bahamas, 

and Belize (Sadovy, 1997; Sala et al., 

2001). 

Hydroacoustics has emerged as a 

valuable tool in fishery population 

assessments throughout the world. 

Hydroacoustics provides a method 

of 1) non-invasively sampling fish 
Taylor et al.: Nassau grouper spawning aggregations 19

communities, 2) collecting spatially continuous data in 

three dimensions along transects providing distribution 

information on sub-meter to kilometer scales, and 3) 

rapidly assessing fish abundance and distribution over 

large areas within marine systems. While the technology provides acoustic size of fish targets, supplemental 

sampling or groundtruthing is still needed to identify 

species composition and verify size distributions. 

Applying fisheries hydroacoustics to assess reef species is rarely done, because it is difficult to verify species 

and resolve fish targets closely associated with bottom 

relief. Hydroacoustics may be useful in situations where 

fish form very large, single species aggregations during 

spawning, as observed in cod (Gadus morhua) (Lawson 

and Rose, 2000) and orange roughy (Hoplostethus atlanticus) (Bull et al., 2001). Formation of very large aggregations (>1000 fish) makes underwater visual censuses 

by scuba divers and assessments of spatial distributions 

difficult due to limitations in bottom time (especially at 

depths exceeding 30 m) and visibility (often reduced 

due to low water clarity and low light conditions). In 

such cases, hydroacoustics may provide a suitable means 

to assess reef fish abundance.

Figure 1

Nassau grouper spawning aggregation survey site at Little Cayman Island, Cayman Islands, BWI, as indicated by star and 

spawning aggregation (SPAG) in inset. Reef Environmental Education Foundation (REEF) marker buoys (north and south 

buoys) indicate location of 100-m dive transect line parallel to shelf-break. Idealized rectangular hydroacoustic transect 

design is overlaid on interpolated bathymetry, which is represented as grayscale shading. The hydroacoustics transect was 

completely surveyed twice in two segments.

The objectives of this work were to 1) test the applicability of mobile hydroacoustics as a repeatable survey 

method to rapidly assess a Nassau grouper spawning 

aggregation, 2) apply geostatistical models to produce 

objective measures of the spatial extent and total abundance of grouper in an aggregation, and 3) compare 

distribution, density, and abundance estimates with 

diver-visual surveys. For this paper, we focus on a survey 

of a single Nassau grouper spawning aggregation at 

Little Cayman Island, Cayman Islands, BWI, in January 

2003.

Methods

Study sites

Hydroacoustic and diver surveys were conducted near 

Little Cayman Island, Cayman Islands, BWI, on 23 January 2003 (Fig. 1). The site is located on a promontory on 

the southwestern end of Little Cayman. The shelf slopes 

from shore out 0.6 km to a depth of 24–33 m at the shelf 

edge (Fig. 1). Bottom relief at the site is as much as 
20 Professional Paper NMFS 5

5 m and made up of hard and soft corals, sponges, and 

large expanses of sand. Scientists have observed annual 

spawning aggregations of Nassau grouper at this site 

since 2001 (Whaylen et al., 2004). 

Underwater visual survey

Scuba divers conducted underwater visual surveys beginning at 1430h on 23 January 2003. Marker buoys 

were deployed by Reef Environmental Education Foundation (REEF) at the end of a 100-m transect line. The 

transect line ran parallel to the shelf-break and served 

as a general marker for the location of the aggregation. 

It was located approximately 20 m inshore of the main 

group of aggregating Nassau grouper (Fig. 1). Three 

divers spent approximately 35 to 50 minutes in the water and either swam on the shoreward side of the 100 

m transect line, swam as far as 100 m to the southeast 

of the line, or maintained position at a point along the 

transect and documented fish behaviors, color patterns, and estimated total abundance. Divers estimated 

their area searched using the 100-m transect line as a 

reference. Divers also used 30-cm measuring poles to 

estimate fish lengths underwater. The total number of 

fish at the aggregation site was subsequently estimated 

by at least one of the divers. Density estimates were calculated by dividing the total counts made by divers by 

the estimated area searched.

Hydroacoustic equipment deployment

The hydroacoustic survey was conducted during the 

afternoon immediately following the dive survey. The 

hydroacoustic survey design consisted of a set of 9 to 

16 parallel transects 0.3 to 0.5 km in length and spaced 

approximately 20- to 30-m apart. Transects ran perpendicular to shore from the 20-m depth contour nearshore 

to >100-m depths offshore (Fig. 1). The complete set of 

transects was covered twice in two survey segments. The 

first segment began at 1550 h, and the second segment 

began at 1640 h.

We used a HTI Model 241 200 kHz split-beam echosounder (Hydroacoustic Technology Incorporated, 

Seattle, WA) coupled with a circular (6° nominal beam 

dimension) transducer. The transducer was mounted to 

a 1.2-m long towbody towed 0.5- 1.5-m below the water 

surface rigged from a 1.5-m boom attached mid-ship on 

the starboard side of a 9-m dive support vessel traveling 

at about 2 m s–1. Rigging of the towbody included a 

shock-dampening system that minimized the oscillations due to pitch and roll of the vessel. Ping rate was 

5 pulses s–1 and the pulse width was 0.18 ms during all 

transect runs. Target resolution was calculated based 

on pulse width and sound velocity and found to be approximately 0.2 m; however due to significant bottom 

relief, fish targets were usually not resolved from reefs 

at distances less than about 1 m. At the beginning of the 

cruise we conducted an in situ system calibration using 

a tungsten-carbide reference sphere of known target 

strength placed greater than 5-m from the transducer 

(MacLennan and Simmonds, 1992). The data were acquired in real-time for split-beam and echo-integration 

data processing (HTI DEP v. 3.54, HTI Seattle, WA) 

and stored as text files on a laptop computer for data 

analyses. 

Hydroacoustic data processing

Hydroacoustic data were post-processed using splitbeam and echo-integration analyses. Split-beam analysis 

was used to determine acoustic size (target strength) of 

individual fish targets in decibels (dB). Algorithms were 

used to accumulate several consecutive echoes from 

individual fish to produce an average acoustic size and 

3-dimensional position within the water column (HTI 

Echoscape v. 2.11, HTI, Seattle, WA). Target strength is 

proportional to fish size (MacLennan and Simmonds, 

1992), and using established equations for reef species 

encountered during previous studies (Ehrhardt and 

Deleveaux1

), target strengths were converted to fish 

size (cm TL) and verified to species during dive surveys. 

Only fish target strengths between –50 and –25 dB were 

used for split-beam analysis, representing the range of 

fish sizes observed by divers. Split-beam analysis was used 

to locate and enumerate large targets that likely represented Nassau grouper. On numerous occasions during 

the surveys of the aggregation site at Little Cayman, fish 

targets were densely packed, making split-beam analysis 

difficult due to overlapping echoes. In these instances, 

individual targets that were on the periphery of the aggregation were used to generate size estimates.

When targets overlapped and individual echoes were 

not discernable, echo-integration was used to estimate 

density of fishes present. Echo-integration (EI) is based 

on the principle that the total sound energy returned 

from an ensonified volume of water is proportional to 

the fish density. When scaled to the average fish size 

observed, volumetric densities (fish m–3) can be estimated. Returning acoustic energy was binned into georeferenced (latitude/longitude) elementary distance 

sampling units (EDSU) having dimensions of 20-m 

along the horizontal axis and 0.25-m on the vertical axis. 

For each EDSU, the average fish size was determined 

through split-beam analysis, either from analyses of fish 

in that cell or mean size of the fish observed along the 

transect when single targets were not discernable within 

1

 Ehrhardt, N. M., and V. Deleveaux. 1999. Report on the 1999 Nassau grouper stock assessment in the Bahamas. University of Miami 

RSMAS/MBF, Miami, FL 33149-1098.
Taylor et al.: Nassau grouper spawning aggregations 21

a cell. The total acoustic energy was then standardized 

by this average fish acoustic size, which allowed for 

estimates of absolute fish density (fish m–3) for each 

EDSU. Two-dimensional densities (fish m–2) were then 

calculated by summing the density estimates for each 

EDSU in the vertical dimension.

Abundance estimates

Mean fish density, spatial extent, and total fish abundance were calculated for each of the two survey segments. An estimate of total survey coverage was also 

calculated using the georeferenced transects in a GIS. 

Total abundance was calculated using arithmetic extrapolation and geostatistical modeling. First, mean 

fish density, calculated from all EDSUs in the entire 

sampling region, was extrapolated over the total survey 

area. Second, we used a two-stage geostatistical modeling procedure to estimate the spatial extent, fish density, 

and total fish abundance within the aggregation. Echo 

integration data from each transect was reclassified as 

a “mark” and scored as a one when fish were present 

within an EDSU; when they were absent, they were not 

a “mark” and scored as a zero. The spatial structure of 

the “marks” was calculated using a classical variogram 

estimator and a spherical variogram model was fitted 

with weighted non-linear least squares (Cressie, 1993). 

The survey area was divided into 20-m square cells and 

indicator kriging was used to predict the probability of 

a “mark” occurring in each of the cells based on the variogram and proximity to the sampled locations (Rossi et 

al., 1992). Cells for which the probability of occurrence 

was greater than 0.5 were designated as “mark.” The 

number of marked cells and total area were calculated 

to determine the spatial extent (in m2) of the aggregation during each segment. The second stage of the modeling procedure used block kriging to determine the average density within the predicted “mark” region (Isaaks 

and Srivastava, 1989; Cressie, 1993). Block-kriged mean 

fish density was extrapolated over the estimated spatial 

extent of the aggregation to produce a global estimate 

of fish abundance for each sampling segment. All spatial 

analyses and visualizations were performed in SPLUS (v. 

6.1, Insightful Corp., Seattle, WA) and ARCVIEW (v. 8.3, 

ESRI Corp., Redlands, CA). 

Results

Dive survey

Divers counted a total of 450 grouper over approximately 5,400 m2

 searched. Fish were observed in three 

separate groups, one group south of the south buoy of 

the 100-m transect (250 fish in approximately 900 m2

), 

another group on the southern end of the 100-m dive 

transect (150 fish in approximately 2000 m2

), and a 

smaller group of fish lying on the bottom to the northeast and closer to shore (50 fish in approximately 2500 

m2). Observations from divers identified and visually 

estimated lengths of 20 Nassau grouper with an average 

of 60 cm TL and a range of 35 to 75 cm TL.

Hydroacoustic survey

Dense aggregations of Nassau grouper were observed 

during both segments of the hydroacoustic survey 

resulting in occasions where individual echoes were 

not discernable due to fish target overlap. Statistics 

on target strengths were limited to tracks that had a 

minimum of four echoes in a sequence and produced 

traces indicative of a single fish passing through the 

acoustic beam. Typically, these targets were located on 

the outer boundaries (either vertically or horizontally) 

of the aggregation. Individual fish sizes ranged from 

–26 – –46 dB. Using established conversion equations, 

these target sizes equate to Nassau grouper of approximately 60–90 cm TL. There was no significant difference 

between fish sizes observed during the two segments 

(Kolmogorov-Smirnov test, P>0.1), though the number 

of targets differed between segments. A total of 135 and 

90 targets were tracked during the first and second segment, respectively. 

The location of the aggregation was restricted to 

the shelf break on the southern portion of the survey 

region (Fig. 2). In most cases, the Nassau grouper 

aggregation was well off the bottom (Fig. 3). Other 

aggregations of relatively large acoustic targets were 

observed outside the region typically observed by divers. Because we could not be sure that these targets 

were Nassau grouper, those marks were not included 

in estimates of aggregation spatial extent or fish abundance. Fish density estimates for the two segments 

ranged from 0 to 1.50 fish m–2 (0 to 1.05 fish m–3) 

and 0 to 1.05 fish m–2 (0 to 0.74 fish–3), respectively. 

Average estimated fish density over the entire survey 

region, ignoring spatial correlation in the data, was 

0.05 and 0.03 fish m–2 for segment 1 and 2, respectively. 

Total spatial coverage of the survey was approximately 

134,266 m2. Extrapolating these average fish density 

estimates over the entire sampling region resulted in 

total fish abundances of 6713 and 4027 fish in segment 

1 and 2, respectively.

Both segments produced similar spatial extent maps 

with fish located from just south of the north buoy to 

nearly 200 m south of the south buoy (Fig. 2). Estimated 

spatial extents for the aggregation during the two segments were 6372 m2

 and 9628 m2, respectively. Average 

density within the aggregation was 0.32 and 0.21 fish m–2

for segments 1 and 2, respectively. Abundance estimates 
22 Professional Paper NMFS 5

Figure 2

Distribution of Nassau grouper densities from two segments of the hydroacoustic survey of the Little Cayman spawning 

aggregation site. Bathymetry and REEF marker buoys are shown as in Fig. 1. White circles indicate zero fish observed, 

positive densities are according to gray scale in legend. Results of indicator kriging of spatial extent of the fish aggregation 

are noted by outlined grayscale-shaded polygons indicating probability of occurrence.

using the two-stage kriging procedure were 2039 fish for 

segment 1 and 2022 fish for segment 2.

Discussion 

We successfully located the Nassau grouper spawning aggregation off Little Cayman, BWI during both segments 

of the hydroacoustic survey. In general, the aggregation 

was observed at or near the shelf-break, although divers 

observed a smaller group of fish shoreward of the shelfbreak that was not observed during the hydroacoustic 

survey. This may be because the acoustic transects did 

not cover this area adequately, the fish were too close 

to the bottom to be detected, or the grouper had 

moved prior to the hydroacoustic transect coverage. In 

some cases, other groups of large acoustic targets were 

located well outside the region observed by the divers. 

Because previous surveys of Nassau grouper showed 

that in certain instances target strengths of other species were sometimes quite similar to Nassau grouper, 

we were reluctant to assign these targets to the Nassau 

grouper abundance estimate (Taylor, Eggleston, and 

Rand, unpubl. data). Unfortunately, it was not feasible 

to use divers to verify the species in these outer groups, 

so their identity remains unknown. Estimates of the 

spatial extent and abundance were restricted to those 

regions that were surveyed by divers. 

Other species are known to use the site at Little Cayman as a spawning aggregation site (Whaylen et al., 

2004). During our study, the Little Cayman aggregation 

site did contain a mix of smaller species in close proximity to the Nassau grouper. When smaller fish were mixed 

in with the Nassau grouper, it was easy to delineate 

larger targets that were likely grouper from the smaller 

targets that represented other smaller species. Larger 

species such as bar jack (Caranz rubber) and horse-eye 

jack (Caranx latus) were also present at the aggregation 

site during our study, but these species were located over 

deeper water or were distant from the main concentration of Nassau grouper. These marks were excluded 

from the acoustic analysis. 

Average density of Nassau grouper over the entire survey region estimated arithmetically from hydroacoustic 

data was similar to the average overall grouper density 

estimated from the dive survey. Grouper densities within 

the aggregation site estimated using hydroacoustics 

were on average three times the average density observed by divers; however, densities as high as 0.27 fish 

m–2 were observed by divers in one of the three separate 

groups. During this survey, it was clear that fish density 

was not uniform over the aggregation site. Other observations using underwater video provide indications 

that volumetric densities varied throughout the aggregation and were even twice as high as those observed 

using hydroacoustics (Rand, unpubl. data). These high 

fish densities were restricted to a very localized region 

within the aggregation site and may have been missed 

by the hydroacoustics or difficult to quantify by divers. 

Patchy or non-uniform distributions of fishes at spawn-
Taylor et al.: Nassau grouper spawning aggregations 23

Figure 3

Echogram example for a Nassau grouper spawning aggregation positioned at the shelf break at the Little 

Cayman site highlighted by hashed oval. Fifty pings along the x-axis represent approximately 20 m linear 

distance.

ing aggregation sites has been observed in other reef 

fish species (Shapiro et al., 1993).

Although estimates of average grouper densities over 

the entire region were similar between divers and hydroacoustics, total abundance estimates were not. The 

large differences in areas searched by each method 

may help to explain the discrepancies between the total 

abundance estimates from the two methods. The divers 

were only able to survey a total of 5400 m2 compared to 

over 134,000 m2

 surveyed using hydroacoustics. Diver 

observations also may have been confounded by low 

water clarity and light level, which limits visibility, especially during dusk. In some cases, especially during 

periods of high grouper densities, it may have been 

difficult for divers to make accurate counts of fish that 

were exhibiting rapid movements. Due to limitations in 

bottom time using scuba, divers were not able to search 

the entire areal extent of the grouper aggregations as 

revealed by the hydroacoustics. 

The extrapolation of the arithmetic hydroacoustic 

density estimates over the entire survey region produced abundance estimates that were two to three 

times higher than the estimates using the geostatistical 

models. The arithmetic estimates were also an order 

of magnitude higher than those made by divers. This 

result comes as no surprise as the presence of spatial 

correlation in the data, particularly the patchy nature 

of spawning aggregations, can result in significant biases 

in global estimates of abundance. Previous efforts to 

estimate Nassau grouper population abundance using 

a simple extrapolation method have been criticized for 

not recognizing such biases (Ehrhardt and Deleveaux1

; 

Gascoigne2). Abundance estimates calculated using the 

two-stage kriging method, on the other hand, implicitly 

incorporate the patchy nature of the distribution pattern and produce a more robust estimate of abundance. 

Still, there may be some limitations to this two-stage 

kriging approach. First, abundance estimates using this 

method are dependent upon accurate estimates of the 

spatial extent of the aggregation. The hydroacoustic 

transects were spaced 30 m apart and kriging probability 

interpolations may have interpolated high densities of 

fishes between transects, when in fact the groups may 

have been separated by as much as 30 m. This was likely 

the case for our estimates of grouper abundance and 

spatial extent on the mid-day survey of 23 January 2003, 

since divers reported a disaggregated pattern of distri2

 Gascoigne, J. 2002. Nassau grouper and queen conch in the Bahamas: State and management options. 44+iv p. http://www.breef.

org/groupersummary.pdf [Accessed 20 December 2004.]
24 Professional Paper NMFS 5

bution. A closer examination of the density distributions 

during segment 1 indicates a possible break in the highdensity marks, implying that the Nassau grouper were in 

separate groups. The region of this separation was not 

sampled during segment 2 (Fig. 2).

Previous applications of hydroacoustics coupled with 

geostatistical approaches have worked well on other 

aggregating species (Rivoirard et al., 2000). For deepwater species such as cod (Gadus morhua) and orange 

roughy (Hoplostethus atlanticus), acoustics provide the 

principal method for fishery independent population estimates during spawning periods (Lawson and 

Rose, 2000; Bull et al., 2001). The orange roughy typically forms a contiguous group over deep sea mounts 

(Doonan et al., 2003). Using a star-transect pattern 

and analyzing the data using a similar two-stage kriging 

model, Doonan et al. (2003) estimated orange roughy 

abundance with good precision. However, they do 

note that accurately positioning the aggregation in the 

transect pattern can affect the precision of the spatial 

extent and abundance estimates (Doonan et al., 2003). 

If fish are not in a contiguous unit, as was the case in 

our study, abundance estimates may not be as reliable 

(Doonan et al., 2003). 

Despite difficulties making accurate estimates of 

abundance under patchy fish distributions, mobile 

hydroacoustic surveys coupled with the geostatistical 

probability mapping still provides an objective, repeatable measure of the spatial extent of the aggregation. 

Estimates such as these are valuable when establishing 

boundaries for reserves or area closures during spawning seasons, and in estimating overall fish abundance 

(Glazer and Delgado, this issue).

A recognized limitation of the acoustic method is 

that the fish need to be greater than 1–2 m from the 

bottom relief to be detected. Large reef species such 

as snapper and grouper are known to be cryptic and 

closely associated with the structure of the bottom 

(Sale and Douglas, 1981). Previous efforts to survey 

hard-bottom habitats have documented difficulties in 

detecting fish targets when they are in close proximity (<1-m) to the bottom (Gledhill et al., 1996). Diver 

observations during our afternoon survey of the Nassau grouper spawning aggregation at Little Cayman 

found numerous fish on or very close to the bottom. 

Observations made by divers during surveys of this 

Nassau grouper spawning aggregation at others times 

of the day found that most fish were well off bottom 

during dusk and evening surveys, presumably when 

fish were exhibiting more spawning behavior. When 

abundance estimates are desired for Nassau grouper, 

hydroacoustic surveys may be best suited for dusk or 

night periods when the fish are well into the water 

column (Whaylen et al., 2004). In addition to verifying species, observations by divers can provide critical 

data on the diel behaviors of the species and provide 

valuable insight into the best approach for conducting 

mobile hydroacoustic surveys of a known spawning 

aggregation.

Conclusions

The mobile hydroacoustic method provided a valuable 

sampling method for surveying Nassau grouper spawning aggregations. Our technique provided a means to 

rapidly cover large (>100,000 m2 in less than 1 h) areas 

when compared to underwater visual surveys using divers (<5500 m2

 in 0.5 h). Analyzing the data using the 

geostatistical probability mapping provided an objective measure of the spatial extent of the aggregation. 

In our study, the presence and location of the Nassau 

grouper aggregation at Little Cayman was well-known. 

In many cases, however, the location of an aggregation is unknown, or may have moved several hundreds 

of meters on a promontory (Colin, 1992; Sala et al., 

2001). In these cases, hydroacoustics can provide a 

method to initially survey a relatively large area and 

locate large targets before using divers for more finescale observations of fish distribution and abundance 

patterns, and species identification. Alternatively, 

coarse transects could be used to locate patches or aggregations of grouper followed by a finer-scale transect 

design to characterize the smaller-scale spatial structure of the group. This adaptive sampling approach 

can have significant advantages over a simple random 

stratified approach for rare, patchy or aggregating 

populations (Everson et al., 1996; Hanselman et al., 

2003). Other advances in technology such as remotely 

operated vehicles (Adams et al., 1995; Johnson et al., 

2003) may provide additional means to identify large 

targets prior to using divers to make visual observations. Thus, hydroacoustics is an emerging technology 

that, when coupled with diver observations, provides a 

comprehensive survey method for monitoring spawning aggregations of reef fishes.

Acknowledgements

We were invited to survey the Little Cayman aggregation 

site by the Cayman Islands Department of the Environment (CIDoE). We thank P. Bush and K. Luke with 

CIDoE and L. Whaylen with Reef Environmental Education Foundation (REEF) for their cooperation and 

collaboration in this effort. Logistical and vessel support 

was provided by H. Fourier and P. Hillenbrand of the 

Southern Cross Club. This manuscript benefited from 

discussions with L. Avens, S. Searcy, N. Reyns, K. Luke, P. 

Bush, and L. Whaylen. Funding was provided by grants 
Taylor et al.: Nassau grouper spawning aggregations 25

from NOAA via the University of Miami (P715647 via 

NA17RJ1226), the NOAA Caribbean Marine Research 

Center (CMRC-01-NRDE-04-02A; CMRC-01-NRDE-04-

03C; CMRC-00-NRDE-03-01C), PADI Project AWARE, 

and Sigma Xi.

Literature cited

Adams, P. B., J. L. Butler, C. H. Baxter, T. E. Laidig, K. A. Dahlin, and 

W. W. Wakefield. 

1995. Population estimates of Pacific coast groundfishes from 

video transects and swept-area trawls. Fish. Bull. 93:446–455.

Bolden, S. K. 

2000. Long-distance movement of a Nassau grouper (Epinephelus 

striatus) to a spawning aggregation in the Central Bahamas. 

Fish. Bull. 98:642–645.

Bull, B., I. Doonan, D. Tracey, and A. Hart. 

2001. Diel variation in spawning orange roughy (Hoplostethus atlanticus, Trachichthyidae) abundance over a seamount feature 

on the north-west Chatham Rise. N. Z. J. Mar. Freshwat. Res. 

35:435–444.

Colin, P. L. 

1992. Reproduction of the Nassau grouper, Epinephelus striatus

(Pisces: Serranidae) and its relationship to environmental 

conditions. Environ. Biol. Fish. 34:357–377.

1996. Longevity of some coral reef fish spawning aggregations. 

Copeia 1996:189–192.

Colin, P. L., Y. Sadovy, and M. L. Domeier.

2003. Manual for the study and conservation of reef fish spawning aggregations. Society for the Conservation of Reef Fish 

Aggregations. Special Publication No. 1, 98 p. http://www.

scrfa.org. [Accessed 10 November 2004.]

Colin, P. L., D. Y. Shapiro, and D. Weiler. 

1987. Aspects of the reproduction of two groupers, Epinephelus guttatus and E. striatus, in the West Indies. Bull. Mar. Sci. 

40:220–230.

Cressie, N. 

1993. Statistics for spatial data. Wiley Interscience, New York, 

928 p.

Doonan, I. J., B. Bull, and R. F. Coombs. 

2003. Star acoustic surveys of localized fish aggregations. J. Mar. 

Sci. 60:132–46.

Everson, I., M. Bravington, and C. Goss. 

1996. A combined acoustic and trawl survey for efficiently estimating fish abundance. Fish. Res. 26:75–91.

Gledhill, C. T., J. Lyczkowski-Shultz, K. Rademacher, E. Kargard, 

G. Crist, and M. Grace. 

1996. Evaluation of video and acoustic index methods for assessing reef-fish populations. ICES J. Mar. Sci. 53:483–485.

Hanselman, D. H., T. J. Quinn, C. Lunsford, J. Heifetz, and 

D. Clausen. 

2003. Applications in adaptive cluster sampling of Gulf of Alaska 

rockfish. Fish. Bull. 101:501–513.

Isaaks, E. H., and R. M. Srivastava. 

1989. Applied geostatistics. Oxford University Press, New York, 

572 p.

Johnson, S. W., M. L. Murphy, and D. J. Csepp. 

2003. Distribution, habitat, and behaviour of rockfishes, Sebastes

spp., in nearshore waters of southeastern Alaska: observations from a remotely operated vehicle. Environ. Biol. Fish. 

66:259–270.

Lawson, G. L., and G. A. Rose. 

2000. Small-scale spatial and temporal patterns in spawning of 

Atlantic cod (Gadus morhua) in coastal Newfoundland waters. 

Can. J. Fish. Aq. Sci. 5:1011–1024.

MacLennan, D. N., and E. J. Simmonds. 

1992. Fisheries acoustics. Chapman and Hall, New York, 352 p.

Rivoirard, J., J. Simmonds, K. G. Foote, P. Fernandes, and N. Bez. 

2000. Geostatistics for estimating fish abundance. Blackwell Science, Oxford; Malden, MA, 216 p.

Rossi, R. E., D. J. Mulla, A. G. Journel, and E. H. Franz. 

1992. Geostatistical tools for modeling and interpreting ecological spatial dependence. Ecol. Monogr. 62:277–314.

Sadovy, Y. 

1994. Grouper stocks fo the western central Atlantic: the need 

for management and management needs. Proc. Gulf Car. 

Fish. Inst. 43:43–64.

1997. The case of the disappearing grouper: Epinephelus striatus, the Nassau grouper, in the Caribbean and Western Atlantic. Proc. Gulf Car. Fish. Inst. 45:5–22.

Sadovy, Y., and A. M. Eklund. 

1999. Synopsis of biological data on the Nassau grouper, Epinephelus striatus (Bloch, 1792), and the jewfish, E. itajara (Lichtenstein, 1822). NOAA Tech. Rep. NMFS 146, 65 p.

Sala, E., E. Ballesteros, and R. M. Starr. 

2001. Rapid decline of Nassau grouper spawning aggregations in 

Belize: Fishery management and conservation needs. Fisheries 26:23–29.

Sale, P. F., and W. A. Douglas. 

1981. Precision and accuracy of visual census technique for fish 

assemblages on coral patch reefs. Environ. Biol. Fish. 6:333–

339.

Shapiro, D. Y., Y. Sadovy, and M. A. McGehee. 

1993. Size, composition, and spatial structure of the annual 

spawning aggregation of the red hind, Epinephelus guttatus (Pisces: Serranidae). Copeia 1993:399–406.

Whaylen, L., C. V. Pattengill-Semmens, B. X. Semmens, and P. G. 

Bush. 

2004. Observations of a Nassau grouper, Epinephelus striatus, 

spawning aggregation site in Little Cayman, Cayman Islands, 

including multi-species spawning information. Environ. Biol. 

Fish. 70:305–313.
26

Introduction

Passive acoustic telemetry has been 

used for many years by fisheries and 

wildlife biologists to elucidate a number of life-history parameters and to 

examine the ecological requirements 

of targeted populations (White and 

Garrott, 1990). The technology has 

evolved so that tags are now smaller 

and battery lives are longer, thus increasing the amount of data acquired 

and available to the manager or researcher. Coincident with the evolving 

technology has been the emergence 

of computer programs using geospatially referenced data (i.e., Geographic Information Systems or GIS). Taken 

together, these tools provide opportunities for understanding how populations behave and function within the 

resources available to them.

At the same time, the toolbox of 

strategies to conserve and manage fish 

stocks has expanded from traditional 

management schemes (e.g., limitedentry fisheries, quotas) to include 

the consideration of no-take marine 

fishery reserves (MFRs) (Bohnsack, 

1994). A growing body of empirical 

evidence now suggests that in a variety of ways, the no-take approach 

may enhance commercial and recreational fishers’ opportunities to 

target a population (Lubchenko et 

al., 2003). In particular, studies have 

suggested that more and larger individuals may become available to adjacent fisheries by “spillover” from the 

reserve via emigration (Roberts et al., 

Designing marine fishery reserves using 

passive acoustic telemetry

Robert A. Glazer and Gabriel A. Delgado

Florida Fish and Wildlife Conservation Commission

Fish and Wildlife Research Institute

2796 Overseas Highway, Suite 119

Marathon, FL 33050

e-mail: bob.glazer@myfwc.com 

Abstract—Marine Fishery Reserves 

(MFRs) are being adopted, in part, as a 

strategy to replenish depleted fish stocks 

and serve as a source for recruits to adjacent fisheries. By necessity, their design 

must consider the biological parameters 

of the species under consideration to 

ensure that the spawning stock is conserved while simultaneously providing 

propagules for dispersal. We describe how 

acoustic telemetry can be employed to 

design effective MFRs by elucidating important life-history parameters of the species under consideration, including home 

range, and ecological preferences, including habitat utilization. We then designed 

a reserve based on these parameters using 

data from two acoustic telemetry studies 

that examined two closely-linked subpopulations of queen conch (Strombus 

gigas) at Conch Reef in the Florida Keys. 

The union of the home ranges of the individual conch (aggregation home range: 

AgHR) within each subpopulation was 

used to construct a shape delineating the 

area within which a conch would be located with a high probability. Together with 

habitat utilization information acquired 

during both the spawning and non-spawning seasons, as well as landscape features 

(i.e., corridors), we designed a 66.5 ha 

MFR to conserve the conch population. 

Consideration was also given for further 

expansion of the population into suitable 

habitats.

2001), the reproductive output of the 

population may be enhanced by the 

increased spawning biomass inside 

the reserve (Côté et al., 2001), and 

there may be a concomitant enhanced 

supply of propagules to downstream 

populations (Kramer and Chapman, 

1999; Martel et al., 2000; Meyer et al., 

2000; Lowe et al., 2003). However, 

other, less obvious benefits may also 

occur including increasing encounters between individuals due to the 

reduction of Allee effects (Stoner and 

Ray-Culp, 2000), increased fecundity 

(Bertelsen and Matthews, 2001), and 

enhanced or restored biodiversity via 

cascading trophic effects (Babcock et 

al., 1999). Furthermore, MFRs may 

provide opportunities to examine the 

effects of various fishing strategies in 

a controlled environment (Bohnsack, 

1998). Despite these purported benefits, there are still many unanswered 

questions related to the optimal design (Boersma and Parrish, 1999), 

siting (Crowder et al., 2000), and 

connectivity within MFR networks 

(Warner and Cowen, 2002).

To design an effective MFR, knowledge of a variety of life-history parameters and ecological requirements of 

the population are required (Dugan 

and Davis, 1993). Because the size 

of an effective reserve will depend 

in part on the daily, seasonal, and 

ontogenetic movements of that species (Polacheck, 1990; Lowe et al., 

2003), the evaluation of the home 

range (Kramer and Chapman, 1999), 

site fidelity (Lembo et al., 1999), and 
Glazer and Delgado: Designing MFRs using passive acoustic telemetry 27

movements and migrations (Zeller et al., 2003) of individuals in the population is critical.

Habitat requirements must also be incorporated into 

an effective MFR design (Recksiek et al., 2001; Rodwell 

et al., 2003). Ideally, the high-quality habitats most 

preferred for forage, reproduction, and refuge will 

be conserved and protected not only for the existing 

individuals in the population, but also for providing additional resources as the population expands (Allison et 

al., 1998; Fogarty, 1999; Murray et al., 1999; Glazer and 

Kidney, 2004). Ecologically critical landscape features 

(e.g., corridors) must also be incorporated into the design to ensure that the reserve will protect the targeted 

population (Recksiek and Appeldoorn, 1998; Recksiek 

et al., 2001; Glazer and Kidney, 2004).

Despite the widespread recognition of these critical 

variables, the literature is depauparate with methods 

that provide guidance on how to incorporate a species’ 

life-history and habitat requirements into an effective 

MFR design a priori (Recksiek and Appeldoorn, 1998). 

Rather, there are numerous examples of the evaluation 

of an MFR’s function after it has been established (e.g., 

Meyer et al., 2000). Additionally, there are comprehensive papers that describe the theoretical considerations 

in marine reserve design, but few that provide practical 

guidelines (Botsford et al., 2003).

We present here an approach that links life-history 

parameters with habitat utilization of the targeted species to define the spatial extent of an optimally designed 

MFR. The design process incorporates data obtained 

from acoustic telemetry studies to delineate the area 

encompassed, the available habitat, and the habitat used 

by a population targeted for protection. Socioeconomic 

factors, as well as larval dispersal are beyond the scope 

of this paper and are therefore not considered in the 

design criteria. The method is applied to construct an 

MFR designed to protect a population of queen conch 

(Strombus gigas) in the Florida Keys.

Methods

Designing an MFR 

We propose an MFR design process based on the distribution and ecological requirements of the population 

that is targeted for protection. The data on which the design will be based are acquired using acoustic telemetry.

Initially, two important considerations must be addressed. First, the manager must conclude that an MFR 

is an appropriate management tool to meet a specific 

objective based on a variety of social, economic, political, and scientific considerations. Upon embracing this 

strategy, the proportion of the population targeted for 

protection must be identified using many of these same 

criteria. In many cases, the entire population is targeted 

for conservation; in other cases, the population may be 

so dispersed that conservation of the entire population 

is impractical.

After these determinations are made, the design process is fairly straightforward (Fig. 1). First, the distribution of the population is identified in order to preliminarily determine the approximate location of the MFR. 

In heavily exploited or otherwise affected populations, 

historical information may be required. In any case, 

the high-quality habitats associated with reproduction, 

refuge, and forage should be considered.

In the next step, both the home ranges and the 

habitats used by individuals in the population are determined. Passive acoustic telemetry presents a versatile 

and relatively inexpensive method to locate tagged individuals, an important consideration in habitat-utilizations studies. Additionally, with a global positioning system receiver, the geospatial positions of each individual 

can be easily obtained for home range estimations.

Recognizing that the goal of these studies is to extend the results to a population that includes untagged 

individuals, an appropriate sample size is required. Ideally, this determination would be made with a power 

test; however that may be beyond the resources of 

the researcher or manager. Additionally, an adequate 

number of individuals must be resighted to ensure 

sufficient statistical power. However, the number of 

individuals tagged will also depend upon a variety of 

factors, including budgetary constraints. In any case, 

the acoustic tagging study should be conducted at a 

sufficient temporal scale to ensure that short-term (i.e., 

diurnal) and/or long-term (i.e., seasonal) movements 

are captured (Glazer et al., 2003). In our previous work, 

we determined that conch needed to be tracked for a 

minimum of eight months in order to ensure that seasonal differences in movements and habitat usage did 

not influence site fidelity and home range estimations 

(Glazer et al., 2003). These values may change depending on the species under examination.

The geospatial data acquired from the acoustic telemetry are used to examine how individuals behave in their 

environment. Computerized methods are making these 

analyses increasingly accessible within GIS (Hooge et 

al., 2001). In our process, the location data are used 

in two ways. First, site fidelity of the tagged individuals 

is examined. This is a critical step because our process 

for MFR design requires examination of home ranges, 

and meaningful home-range estimations require a high 

degree of site fidelity (Hooge et al., 2001). After site 

fidelity is validated, the second way the geospatial data 

are used is to estimate the home range of each tagged 

individual. We suggest using a probabilistic model (i.e., 

kernel method) because of its accuracy and robustness 

(Worton, 1989; Hooge et al., 2001).
28 Professional Paper NMFS 5

Create or Obtain

Habitat Map

Determine Current or Historical

Distribution of Population

Tag Representative

Number of Individuals

Evaluate Site

Fidelity

Resighting Sampling

Determine

Habitat

Availability

Determine

Home Ranges

of Individuals

Construct AgHR

Evaluate Utilized,

Preferred, and Avoided

Habitats

MFR Design

Recommendation

Map

Overlay

YES

Based also on expansion of the

population, the proportion of the

population to be protected,

metapopulation dynamics and social,

political, and economic considerations

Determine Critical

Landscape Features

Linking Subpopulations

TA IT BAH

LAVE

U

TA ION

HOME RANGE

FIELD

SAMPLING

Other

Method NO

ACOUSTIC

TELEMETRY

Evaluate MFR vs Alternatives

Management Strategies

YES

Other

Management

Strategy

NO

Figure 1

The process for designing a marine fishery reserve. Data acquired from acoustic telemetry studies are used to estimate 

home ranges of individuals and habitat utilization of the population. 
Glazer and Delgado: Designing MFRs using passive acoustic telemetry 29

The home range estimation can then be extended to 

the entire population. In 2003, we introduced a concept 

we called the aggregation home range (AgHR) to describe an area within which there is a 95% probability 

that the entire population of tagged individuals will be 

found (Glazer et al., 2003). The AgHR is represented as 

an irregular shape that describes the union of the polygons associated with the kernel home ranges of each 

tagged individual at the 95% probability level (Fig. 2). 

Given sufficient sample size, this shape approximates, 

with a high probability, the area occupied by the entire 

population.

Habitat-use studies provide additional data needed 

for designing an effective reserve. Because the size and 

shape of a home range is often dependant on habitat 

(Kramer and Chapman, 1999), the integration of these 

parameters into reserve design provides a powerful basis 

to define the spatial extent of the MFR. Data for these 

analyses are composed of two components: habitat availability and the habitats occupied by individuals in the 

population. These analyses then provide statistical representations of the habitats that are utilized, preferred, 

and avoided by the population (Neu et al., 1974).

Estimating the available habitat requires a map with 

a suitable spatial scale and appropriate habitat classifications for the targeted species. In many cases, habitat 

maps may already be available. Alternatively, they can 

be constructed, and a variety of methods exist to do so 

(White and Garrot, 1990).

Examining the habitats occupied by individuals 

requires an efficient method of locating individuals 

in what may be complex environments, and acoustic 

telemetry is, in many cases, ideal for this application 

(for a review, see Eristhee and Oxernford, 2001). The 

number of observations within each habitat is compared 

with the percent coverage of each habitat to provide a 

statistical representation of habitat utilization. Given a 

habitat map of the area with sufficient scale and resolution, habitats that should be conserved or which the 

population may colonize may be easily discerned.

Other habitat features should also be considered 

when designing an MFR. For example, corridors function to permit migration between patches of critical 

habitat—areas that are essential for reproduction and 

survival (White and Garrott, 1990). Corridors may also 

facilitate dispersal between habitat patches in fragmented landscapes (Berggren et al., 2002). If corridors are 

not protected, individuals may be susceptible to harvest 

when in transit or they may be unable to migrate into 

critical habitats (Simpson and Mapleston, 2002). Acoustic telemetry provides an efficient method to locate 

individuals that are in transit between habitat features 

and to identify those areas that must be conserved.

Further consideration must be given to habitats in 

cases where expansion of the population after protection is anticipated and desired. A population expands 

in one of two ways. First, the population may expand 

beyond its current boundaries as a result of densitydependent processes. In some cases, animals may even 

begin to use less desirable habitats (Rowley, 1994). 

Second, a corridor may be permeable and individuals 

may immigrate into previously unused adjacent habitats 

(Acosta, 1999). Both processes reinforce the need to 

identify corridors.

Once the AgHR and the important habitat features 

are identified, an overlay is constructed to visually 

interpret the results and to assist in the design of the 

MFR. This is easily accomplished within a GIS, where 

boundaries can be identified and manipulated.

An example

To construct an MFR, we used data from two studies of 

queen conch conducted in 1997 on a Florida Keys back 

reef (Glazer et al., 2003; Glazer and Kidney, 2004). In 

those studies, we tagged adult queen conch with acoustic transmitters (Sonotronics, Inc. Tuscon Arixona, 

USA; Fig. 3) at Conch Reef in the upper Florida Keys. 

Conch Reef is a shallow-water reef escarpment with two 

areas delineated for protection: a limited-use Sanctuary 

Figure 2

The aggregation home range (AgHR) describing the 

union of the 95% probability contours of the individual 

animals (HR). Data are from 23 queen conch (Strombus 

gigas) at French Reef in the Florida Keys (Glazer et al., 

2003).

AgHR

HR
30 Professional Paper NMFS 5

Preservation Area (SPA) approximately 23.3 ha in area 

and a restricted access Research Reserve (RR) encompassing approximately 47.5 ha. The conch were located 

within two spatially distinct sub-populations (Fig. 4), but 

interchange was possible between them across a rubble 

field. We estimated that the total number of adult conch 

in both aggregations was approximately 345 individuals; therefore, about 11% of the population was tagged 

with acoustic transmitters. We conducted resighting 

sampling using hydro-acoustic receivers from a research 

vessel.

Using acoustic telemetry data (i.e., latitudes and longitudes), we determined the 95% probability contours 

for the home ranges for 21 conch (Glazer et al., 2003). 

These probabilities were graphically represented in a 

GIS.

We also examined the habitat requirements of the 

conch using acoustic telemetry data (Glazer and Kidney, 2004). These data were used in two ways. First we 

determined the available habitat to the population 

(Table 1) by constructing the minimum convex polygon 

of all observations of the 39 conch tagged with acoustic 

Figure 3

Passive acoustic transmitter (AT) attached to a queen conch (Strombus gigas) used in acoustic telemetry studies. A second 

tag, which was used to identify the conch if the acoustic tag was lost, is shown attached to the spire (for a further discussion, 

see Glazer et al., 2003).

AT

transmitters. Then we examined the habitat utilized and 

preferred by the conch by comparing the available habitat with the habitats occupied by the conch (Glazer and 

Kidney, 2004). Based on these analyses, we determined 

that the conch preferred coarse sand (Cs) and rubble/

coarse sand (RbCs) substrates, especially during reproductive season (Table 1). At one site (C2), conch avoided rubble (Rb) relative to its availability; however, the 

rubble habitat served an important function. Two conch 

(5% of the tagged population) were observed moving 

from C1 to C2 across the shallow rubble ridge separating the two sites (Fig. 5). Additionally, observations of 

untagged conch on this rubble ridge suggest that this 

habitat serves as an important transit corridor.

These results were used to design an MFR for conch 

at this site. To design the reserve, we first constructed 

an AgHR at each subpopulation by drawing a smooth 

shape around the union of all the individual home 

range polygons (Fig. 5). We then used the answers from 

the following questions to further refine the design: 1) is 

there exchange between the two subpopulations (i.e., is 

there a migration corridor), 2) what habitats are critical 
Glazer and Delgado: Designing MFRs using passive acoustic telemetry 31

ATLANTIC

OCEAN

GULF

of

MEXICO

FLORIDA

2 0 2 4 KM

N

W E

S

C1

C2

Reef Tract

TK

SPA

RR

25 02 o

′

25 00 o ′

24 58 o′

24 56 o ′

80 26 o80 28 ′ o80 30 ′ o80 34 ′ o ′

Figure 4

Sample sites for the queen conch (Strombus gigas) acoustic telemetry studies in the Florida Keys. All surveys were conducted 

at Conch Reef; C1 and C2 are subpopulations of queen conch. Two existing marine reserves are shown: a limited-use 

sanctuary preservation area (SPA) and a restricted-entry research reserve (RR). TK is Tavernier Key.

to conserve, 3) if expansion of the population were to 

occur in the future, what areas should be conserved relative to the preferred habitats, and 4) what is the simplest 

design for practical purposes (e.g., enforcement). Furthermore, we decided that this reserve should protect 

the entire population (i.e., both subpopulations).

The design we chose addressed each of these criteria. We selected a simple shape (i.e., a rectangle) that 

encompassed the following features: 1) the AgHRs of 

the two sub-aggregations, 2) the migration corridor 

between the two sub-aggregations, 3) habitats that were 

not avoided (except in the case of C2 where the rubble 

habitat represented the migration corridor), and 4) 

areas of adjacent habitats that were suitable (i.e., coarse 

sand, rubble/coarse sand, sparse seagrass/coarse sand, 

and rubble) but unoccupied and would allow for future 

expansion. For example, the majority of a deeper area 

of unoccupied course sand to the southeast of C1 was 

included in the MFR design (Fig. 5).

One other observation was considered when we 

designed our reserve. We observed two conch outside 

the boundaries of the AgHR at C2. We concluded that 

these animals represented “spillover,” in a management 

context, from our proposed reserve design; therefore, 

an effective reserve would not need to encompass this 

location. The proposed reserve measures approximately 475 m x 1400 m and encompasses approximately 

66.5 ha (Fig. 5).
32 Professional Paper NMFS 5

Table 1

Habitat classification and utilization by queen conch tagged with acoustic transmitters at Conch Reef in the Florida Keys. 

The habitats were defined from high altitude photographs. Asterisks indicate habitats that were occupied by conch during 

the study. Those habitats that were preferred are indicated with a “P” and those that are avoided are indicated with an “A.” 

N/A is not applicable; “—” indicates those habitats for which there was neither preference nor avoidance. Adapted from 

Glazer and Kidney (2004).

Preferred Spawning season—

(P, subsite) Preferred (P, subsite)

or Avoided or Avoided 

Code Habitat (A, subsite) (A, subsite)

Rf Reef—continuous barren carbonate substrate N/A N/A

Sd Sand—particles pass through 2-mm sieve, but are retained on 0.5-mm sieve N/A N/A

Cs* Coarse Sand—particles pass through 12-mm sieve, but are retained on 2-mm sieve P,C2 P,C1

Rb* Rubble—particles are retained on 12-mm sieve A,C2 A,C2

RbCs* Rubble/Coarse Sand—homogenous mix of Rb and Cs P,C2 P,C2

SgsSd* Mixed seagrass community (Thalassia testudinum and Syringodium filiforme), — —

sparse with Sd substrate; seagrass blade density < 1200 • m–2 and canopy height 

< 15 cm

SgsCs Mixed seagrass community (T. testudinum and S. filiforme), sparse with Cs substrate; N/A N/A

seagrass blade density < 1200 • m–2 and canopy height < 15 cm

SgdSd* Mixed seagrass community (T. testudinum and S. filiforme), dense with Sd substrate; — —

seagrass blade density > 1200 • m–2 and canopy height > 15 cm

SgdCs* Mixed seagrass community (T. testudinum and S. filiforme), dense with Cs substrate; — —

seagrass blade density > 1200 • m–2 and canopy height > 15 cm

Discussion

Passive acoustic telemetry is a method commonly employed in a wide variety of aquatic studies (Zeller, 1998); 

as of 2003, there were over 320 articles referenced in the 

literature. In coral reef environments, it is becoming 

increasingly popular (Zeller, 1997; Eristhee et al., 2001; 

Beets et al., 2003). Perhaps one of the greatest strengths 

of this method is that it allows researchers to examine 

the habits of organisms under conditions that make other sampling strategies difficult. For example, occupied 

habitats may be examined during times when sampling 

is problematical (e.g., night: Meyer et al., 2000; Beets et 

al., 2003; Cartamil et al., 2003) and at sampling frequencies not practical using most other methods (Eristhee 

and Oxenford, 2001). Additionally, when hydrophones 

are deployed in situ, passive data collection becomes 

much simpler than on-site sampling (Lindholm and 

Auster, 2003) and provides a method to obtain large 

amounts of highly accurate data (Bolden, 2001). Furthermore, acoustic telemetry circumvents many of the 

problems associated with traditional mark-recapture 

technologies including sample size issues (Appeldoorn, 

1997). For these reasons, acoustic telemetry has become 

a popular tool for conducting home range (Zeller, 1997; 

Eristhee and Oxenford, 2001; Parsons et al., 2003) and 

habitat preference (Lowe et al., 2003) studies.

In our case, the use of acoustic telemetry allowed us 

to increase our sample sizes by reducing the amount 

of labor needed to locate tagged individuals. Had we 

used traditional tag-recovery methods, it is likely that we 

would have had a significant reduction in recaptures. As 

a result, the home range estimations of each individual 

would likely have been much larger due to the increased 

variance associated with a reduction in sample size. Likewise, the habitat utilization estimations would probably 

have suffered from Type 2 errors from the increased 

variance associated with sample size reduction.

An additional benefit of passive acoustic telemetry 

is that it is fairly inexpensive. Nevertheless, there are 

trade-offs. In our studies, we used a single hydrophone 

deployed from a vessel. This required that at least one 

researcher was on the vessel for sampling. Using this approach, the up-front investment is fairly modest (approximately $1,500 U.S. for the receiver and hydrophone and 

$150 for each tag); however, a fairly significant investment in manpower and vessel use was required. On the 

other hand, labor can be reduced with a significantly 

greater investment in capital equipment, as in the case 

where the hydrophones are deployed in an array and the 

data are acquired passively (Bolden, 2001).

Despite the benefits attributed to acoustic telemetry 

for the examination of animal movements, the literature 

is devoid of papers that provide guidance for designing 
Glazer and Delgado: Designing MFRs using passive acoustic telemetry 33

a reserve using data acquired from this method. In fact, 

we were only able to locate one paper that used empirical data of the movement of a population to design a 

reserve (Griffiths and Wilke, 2002) and, even in that 

study, movements were determined using traditional 

mark-recapture surveys. They described a method for 

estimating the minimum reserve size for temperate reef 

fishes in South Africa based on the radius of movements 

observed for a population of Sparids. They attached that 

radius to locations progressively further from the core 

of the population. In this way, they could predict the 

proportion of the population that would be protected 

and concluded that a minimum of three radii and a 

maximum of five radii would be sufficient to conserve 

the population.

In contrast to their method, we present a model 

based not on linear movements, but on the area occupied by individuals with probabilities assigned to that 

estimation. The use of spatially-explicit, probabilistic 

home-range models adds a level of security to the 

determination of what constitutes the area likely to 

be occupied by an existing or expanding population, 

especially given that a reserve may not function effectively if the size of the home range is underestimated 

(Stoner, 1997; Kramer and Chapman, 1999). Our 

approach has an added advantage in that it includes 

behavioral and ecological data in the design criteria. 

For example, by incorporating habitat utilization into 

the equation, one can make an informed estimation of 

what areas are likely to be colonized by an expanding 

population during their foraging and reproductive 

activities. This approach requires both an objective 

evaluation of the data and a subjective interpretation 

of the results.

Figure 5

Habitat map derived from high-altitude photogrammetry. The proposed fishery reserve is indicated by MFR. The white 

triangles represent observations of queen conch (Strombus gigas) that are occupying a corridor (Cor) between C1 and C2 

or “spillover” conch (SO) that have emigrated from the area defining the proposed reserve. The perimeters of the AgHRs 

of each sub-population are shown in red. Preferred, utilized, and avoided habitats are described in Table 1. Bathymetric 

contours (m) are shown for reference.

N

W E

S

500 0 500 Meters

C1

C2

Rf

Uk

Sd

Cs

Rb

RbCs

SgsSd

SgdSd

SgdCs

SgsCs

Habitats

AgHR

MFR

Cor

SO

20

30

10

6 2

4

4 6

10

10 20

6

4

20

2
34 Professional Paper NMFS 5

In our study, we used a species that, in general, exhibits a high degree of site fidelity. However, not all 

marine animals exhibit this characteristic (i.e., they 

may be nomadic or highly migratory) and thus may 

lack fixed home ranges (Colton and Alevizon, 1983; 

White and Garrott, 1990). Our method may have limited applicability in these cases, especially as it relates 

to the incorporation of home range into the design of 

an MFR.

Additionally, queen conch move little and there is 

a relatively high probability of recapturing tagged individuals relative to more mobile species. For highly 

mobile species, recapture sitings may be limited due to 

emigration from the study site. However, probabilistic 

home range models account for this limitation in the 

estimation of the home ranges by assigning larger home 

ranges to individuals with fewer recaptures.

Acoustic telemetry studies may also provide other 

essential information for effective MFR design. Acosta 

(2002) studied carrying capacity within reserves in Belize using queen conch and spiny lobsters (Panuluris 

argus) tagged with acoustic transmitters. He predicted 

the maximum density for spiny lobsters and queen 

conch for a refuge in Belize by employing a recruitment/diffusion model and, at the end of his study, 

confirmed that densities of individuals had increased. 

Because an oft-cited benefit of reserve implementation 

is the predicted increase in the biomass of the targeted 

species as a reserve matures (Lubchenko et al., 2003), 

estimating carrying capacity may provide guidance on 

expected reproductive output from that reserve as well 

as functional changes within the ecosystem.

As population densities increase, other changes may 

occur. Habitat quality, a well-recognized variable that 

must be addressed for effective marine reserve design 

(Rowley, 1994; Appeldoorn, 1997; Acosta, 1999; Fogarty, 

1999), may be affected. As expansion of the population 

occurs, less desirable habitats may become occupied in 

greater frequencies due to depensatory mechanisms 

(Fretwell and Lucas, 1970). We observed that an unoccupied habitat adjacent to an existing spawning 

aggregation at Looe Key in the Florida Keys was soon 

colonized after adult conch were transplanted into the 

existing aggregation. Additionally, this habitat was positively affected before long as spawning soon began to occur there (Glazer, personal observ.). For these reasons, 

what are seemingly poor habitats adjacent to an existing 

population should be carefully considered for inclusion 

in a reserve before being discounted.

Acoustic telemetry often provides an efficient mechanism to study the complexities associated with examining the movements of individuals and populations in 

fragmented landscapes (Beets et al., 2003). Because 

examining these movements is necessary for the conservation of critical habitats (Berggren et al., 2002), these 

data have now begun to be incorporated into designs 

of marine reserves (Christensen et al., 2003). However, 

the holistic approach that uses empirical observation to 

combine these features with areas occupied by populations is absent from the literature. The conservative 

approach is to include all habitats connecting distinct 

patches. This, of course, oversimplifies the question 

and may result in overprotection if isolated populations 

are not connected via corridors and are functioning as 

isolated metapopulations (Dethier et al., 2003). This 

may then have adverse economic, social, or enforcement consequences. For these reasons, it is essential to 

determine landscape features that must be conserved 

even though they are used infrequently. In our proposed reserve, we justified the inclusion of the rubble 

ridge between C1 and C2 because this feature served 

an important function as a migration corridor and, if 

not conserved, the two subpopulations may become 

isolated. It is unlikely that this area would have been 

identified as a critical feature without the use of acoustic 

telemetry given the rapid transit over the rubble ridge 

by the tagged individuals and the infrequent observations of untagged conch in the area.

When using our criteria to design an MFR, decisions 

must be founded on information obtained from appropriate temporal scales (Starr et al., 2002). Ontogeny and reproduction may require drastically different 

resources, and it is critical to design studies or use data 

that capture habitat requirements during different life 

stages. Shifts in habitat use during reproduction must be 

examined (Kramer and Chapman, 1999; Glazer et al., 

2003). Because many fish species congregate to spawn, 

fishermen often target spawning aggregations. If a goal 

of the reserve is to enhance reproductive output, these 

aggregations should be identified and their conservation should be considered a high priority (Claro and 

Lindeman, 2003).

Despite the fairly straightforward approach we described, a good deal of subjectivity will ultimately be required when considering the dimensions of an effective 

MFR. In our example, we included areas outside of the 

existing AgHRs in our proposed MFR design to allow for 

future expansion of the population. We placed added 

emphasis on including course sand and rubble/coarse 

sand habitats because conch preferred them during 

both the reproductive and non-reproductive seasons. 

Areas of these habitats that were in close proximity to 

the subpopulations and to the corridor connecting 

them were included in our design to support increased 

reproduction in an expanding population. We also 

included a substantial amount of sparse seagrass/sand 

habitat that was not currently occupied, despite the fact 

that there was no preference or avoidance of that habitat (Glazer and Kidney, 2004), because we felt that these 

were areas that could be rapidly colonized.
Glazer and Delgado: Designing MFRs using passive acoustic telemetry 35

It is critical that managers predefine the goals of a 

prospective MFR. Our approach may be perfectly suitable for enhancing biomass in the reserve and in adjacent fisheries via spillover, if the MFR is well designed. 

However, guaranteeing that the population is protected 

and that areas are available for future expansion are not 

enough to ensure that the reserve functions to meet all 

the predefined goals. For example, one goal of an MFR 

may be to provide propagules for dispersal to targeted 

locations. Therefore, consideration of oceanographic 

features is critical to ensure proper MFR function (Chiappone and Sullivan Sealey, 2000; Crowder et al., 2000), 

and the design criteria defined in this study may do little 

to ensure that this goal is achieved.

Managers must also evaluate the biological, logistical, 

and/or human resource constraints that may limit the 

scope of MFR design studies. For example, if the population in the targeted area is too small for telemetry studies, the project may need to be conducted in a different 

location with similar features. In these instances, home 

range and habitat utilization information may have to 

be obtained from the literature, and the researchers 

and/or managers should recognize that critical sitespecific habitat features (e.g., corridors) would not be 

identified. In other cases, fiscal realities may limit the 

capacity to effectively evaluate MFR design parameters. 

In addition to scientific considerations, social, political, 

and economic factors must be evaluated when designing reserves. Thus, the spatially defined boundaries 

determined from home range and habitat preference 

studies are only a few of a suite of variables that must 

be considered to ensure that an MFR is appropriately 

designed.

Acknowledgments

This project was supported by a grant from the Florida 

Fish and Wildlife Conservation Commission’s NonGame grants program (NG97-005). Alejandro Acosta, 

Rod Bertelsen, Jim Quinn, John Hunt, and three 

anonymous reviewers provided valuable comments on 

the manuscript. Judy Leiby and Lynn French assisted in 

the editorial process. Waz prepared the illustration of 

the conch. Jim Kidney conducted the field sampling, 

in some cases with assistance, in others independently. 

The Nature Conservancy provided volunteer support 

for assistance with the field sampling.

Literature cited

Acosta, C. A.

1999. Benthic dispersal of Caribbean spiny lobsters among insular habitats: implications for the conservation of exploited 

marine species. Conserv. Biol. 13:603–612.

2002. Spatially explicit dispersal dynamics and equilibrium 

population sizes in marine harvest refuges. ICES J. Mar. Sci. 

59:458–468.

Allison, G. W., J. Lubchenko, and M. Carr.

1998. Marine reserves are necessary but not sufficient for conservation. Ecol. Appl. 8:79–92.

Appeldoorn, R. S.

1997. Dispersal rates of commercially important coral reef-fishes: what do tagging studies tell us about potential emigration 

from marine reserves? Proc. Gulf Caribb. Fish Inst. 49:54–63.

Babcock, R. C., S. Kelly, N. T. Shears, J. W. Walker, and T. J. Willis.

1999. Changes in community structure in temperate marine 

reserves. Mar. Ecol. Prog. Ser. 189:125–134.

Beets, J., L. Muehlstein, K. Haught, and H. Schmitges.

2003. Habitat connectivity in coastal environments: patterns and 

movements of Caribbean coral reef fishes with emphasis on 

bluestripe grunt, Heamulon sciurus. In Caribbean marine protected areas: practical approaches to achieve economic and 

conservation goals (R. S. Appeldoorn, ed.), p. 29–42. Gulf 

Caribb. Res. 14(2).

Berggren, Å, B. Birath, and O. Kindvall.

2002. Effect of corridors and habitat edges on dispersal behavior, 

movement rates, and movement angles of Roesel’s bush-cricket (Metrioptera roeseli). Conserv. Biol. 16:1562–1569.

Bertelsen, R. D., and T. Matthews.

2001. Fecundity dynamics of female spiny lobster (Panulirus argus) in a south Florida fishery and Dry Tortugas National Park 

lobster sanctuary. Mar. Freshwater Res. 52:1559–1565.

Bohnsack, J. A.

1994. How marine fishery reserves can improve reef fisheries. 

Proc. Gulf Caribb. Fish. Inst. 43:217–241.

1998. Application of marine reserves to reef fisheries management. Aust. J. Ecol. 23:298–304.

Bolden, S. K.

2001. Nassau grouper (Epinephalus striatus, Pisces: Serranidae) 

movement in the Bahamas, as determined by ultrasonic telemetry. Ph.D. dissert., University of Miami, 172 p.

Boersma, P. D., and J. K. Parrish.

1999. Limiting abuse: marine protected areas, a limited solution. 

Ecol. Econ. 31:287–304.

Botsford, L. W., F. Micheli, and A. Hastings.

2003. Principles for the design of marine reserves. Ecol. Appl.13:

S25–S31.

Cartamil, D. P., J. J. Vaudo, C. G. Lowe, B. M. Wetherbee, and K.N. 

Holland.

2003. Diel movement patterns of the Hawaiian stingray, Dasyatis 

lata: implications for ecological interactions between sympatric elasmobranch species. Mar. Biol. 142:841–847.

Chiappone, M., and K. M. Sullivan Sealey.

2000. Marine reserve design criteria and measures of success: 

lessons learned from the Exuma Cays Land and Sea Park. Bull. 

Mar. Sci. 66:691–705.

Christensen, J. D., C. F. G. Jeffrey, C. Caldow, M. E. Monaco, M. S. 

Kendall, and R. S. Appeldoorn.

2003. Cross-shelf habitat utilization patterns of reef fishes in 

southwestern Puerto Rico. In Caribbean marine protected areas: practical approaches to achieve economic and conservation goals (R. S. Appeldoorn, ed.), p. 9–27. Gulf Caribb. Res. 

14(2).

Claro, R., and K. C. Lindeman.

2003. Spawning aggregation sites of snapper and grouper species (Lutjandae and Serranidae) on the insular shelf of Cuba. 

In Caribbean marine protected areas: practical approaches to 

achieve economic and conservation goals (R. S. Appeldoorn, 

ed.), p. 91–106. Gulf Caribb. Res. 14(2).
36 Professional Paper NMFS 5

Colton, D. E., and W. S. Alevizon.

1983. Movement patterns of bonefish, Albula vulpes, in Bahamian waters. Fish. Bull. 81:148–154.

Côté, I. M., I. Mosqueira, and J. D. Reynolds.

2001. Effects of marine reserve characteristics on the protection 

of fish populations: a meta analysis. J. Fish Biol. 59:178–189.

Crowder, L. B., S. J. Lyman, W. F. Figueira, and J. Priddy.

2000. Source-sink population dynamics and the problem of siting marine reserves. Bull. Mar. Sci. 66:799–820.

Dethier, M. N., K. McDonald, and R. R. Strathmann.

2003. Colonization and connectivity of habitat patches for coastal marine species distant from source populations. Conserv. 

Biol. 17:1024–1035.

Dugan, J. E., and G. E. Davis.

1993. Applications of marine refugia to coastal fisheries management. Can. J. Fish. Aquat. Sci. 50:2029–2042.

Eristhee, N., and H. Oxenford.

2001. Home range size and use of space by Bermuda chub Kyphosus sectatrix (L.) in two marine reserves in the Soufriere 

Marine Mangement Area, St. Lucia, West Indies. J. Fish Biol. 

59:129–151.

Eristhee, N., I. Popple, H. Oxenford, and W. Hunte.

2001. Methods and lessons learnt in the application of acoustic 

telemetry to coral reef fish movement studies. Proc. Gulf Caribb. Fish. Inst. 52:145–159.

Fogarty, M. J.

1999. Essential habitat, marine reserves and fishery management. Trends Ecol. Evol. 14:133–134.

Fretwell, S., and H. Lucas.

1970. On the territorial behavior and other factors influencing 

habitat distribution in birds. Acta Biotheoretica 19:16–36.

Glazer, R. A., and J. A. Kidney.

2004. Habitat associations of adult queen conch in an unfished 

Florida Keys back-reef: applications to essential fish habitat. 

Bull. Mar. Sci. 75:205–224.

Glazer, R. A., G. A. Delgado, and J. A. Kidney.

2003. Estimating queen conch (Strombus gigas) home ranges using acoustic telemetry: implications for the design of marine 

fishery reserves. In Caribbean marine protected areas: practical approaches to achieve economic and conservation goals 

(R. S. Appeldoorn, ed.), p. 79–89. Gulf Caribb. Res. 14(2).

Griffiths, M. H., and C. G. Wilke.

2002. Long-term movement patterns of five temperate-reef fishes (Pisces: Sparidae): implications for marine reserves. Mar. 

Freshwater Res. 53:233–244.

Hooge, P. N., W. M. Eichenlaub, and E. K. Solomon.

2001. Using GIS to analyze animal movements in the marine 

environment. In Spatial processes and management of marine populations (G.H. Kruse, N. Bez, A. Booth, M.W. Dorn, 

S. Hills, R.N. Lipcius, D. Pelletier, C. Roy, S.J. Smith, and D. 

Witherell, eds.), p. 37–51. University of Alaska Sea Grant, AKSG-01-02, Fairbanks, Alaska.

Kramer, D. L., and M. R. Chapman.

1999. Implications of fish home range size and relocation for 

marine reserve function. Environ Biol. Fishes 55:65–79.

Lembo, G. I. A. Fleming, F. Økland, P. Carbonara, and M. T. 

Spedicato.

1999. Site fidelity of the dusky grouper Epinephelus marginatus

(Lowe, 1834) studied by acoustic telemetry. Mar. Life 9:37–

43.

Lindholm, J., and P. Auster.

2003. Site utilization by Atlantic cod (Gadus morhua) in offshore 

gravel habitat as determined by acoustic telemetry: implications for the design of marine protected areas. Mar. Sci. Tech. 

J. 37:27–34.

Lowe, C. G., D. T. Topping, D. P. Cartamil, and Y. P. Papastamatiou.

2003. Movement patterns, home range, and habitat utilization 

of adult kelp bass Paralabrax clathratus in a temperate no-take 

reserve. Mar. Ecol. Prog. Ser. 256:205–216.

Lubchenko, J., S. R. Palumbi, S. D. Gaines, and S. Andelman.

2003. Plugging a hole in the ocean: the emerging science of marine reserves. Ecol. Appl. 13:S3–S7.

Martell, S. J. D., C. J. Walters, and S. S. Wallace.

2000. The use of marine protected areas for conservation of lingcod (Ophiodon elongatus). Bull. Mar. Sci. 66:729–743.

Meyer, C. G., K. N. Holland, B. M. Wetherbee, and C. G. Love.

2000. Movement patterns, habitat utilization, home range size 

and site fidelity of whitesaddle goatfish, Parupeneus porphyreus, 

in a marine reserve. Environ. Biol. Fishes 59:235–242.

Murray, S. N., R. F. Ambrose, J. A. Bohnsack, L. W. Botsford, M. 

H. Carr, G. E. Davis, P. K. Dayton, D. Gotshall, M. A. Hixon, J. 

Lubchenko, M. Mangel, A. MacCall, D. A. McArdle, J. C. Ogden, J. 

Roughgarden, R. M. Starr, M. J. Tegner, and M. M. Yoklavich.

1999. No-take reserve networks: sustaining fishery populations 

and marine ecosystems. Fisheries 24:11–24.

Neu, C. W., C. R. Byers, J. M. Peek, and V. Boy.

1974. A technique for analysis of utilization availability data. J. 

Wildl. Manage. 38:541–545.

Parsons, D. M., R. C. Babcock, R. K. S. Hankin, T. J. Willis, J. P. Aitken, 

R. K. O’Dor, and G. D. Jackson.

2003. Snapper Pagrus auratus (Sparidae) home range dynamics: 

acoustic tagging studies in a marine reserve. Mar. Ecol. Prog. 

Ser. 262:253–265.

Polacheck, T.

1990. Year around closed areas as a management tool. Nat. Res. 

Modeling 4:327–354.

Recksiek, C. W., and R. A. Appeldoorn.

1998. In pursuit of design criteria for marine fishery reserves. 

Proc. Gulf and Caribb. Fish Inst. 50:372–384.

Recksiek, C. W., B. R. Murphey, R. S. Appeldoorn, and K. C. 

Lindeman.

2001. Integrating fish fauna and habitat assessments: a fundamental step in fishery reserve design criteria. Proc. Gulf and 

Caribb. Fish Inst. 52:654–666.

Roberts, C. M., J. A. Bonsack, F. Gell, J. P. Hawkins, and R. 

Goodridge.

2001. Effects of marine reserves on adjacent fisheries. Science 

294:1920–1923.

Rodwell, L. D., E. B. Barbier, C. M. Roberts, and T. R. McClanahan.

2003. The importance of habitat quality for marine reserve—

fishery linkages. Can. J. Fish. Aquat. Sci. 60:171–181.

Rowley, R. J.

1994. Marine reserves in fisheries management. Aquatic Conserv.: Mar. Freshw. Ecosyst. 4:233–254.

Simpson, R. R., and A. J. Mapleston.

2002. Movements and habitat use of the endangered Australian 

freshwater Mary River cod, Maccullochella peelii mariensis. Environ. Biol. Fishes 65:401–410.

Starr, R. M., J. N. Heine, J. M. Felton, and G. M. Cailliet.

2002. Movement of the bocaccio (Sebastes paucispinis) and 

greenspotted rockfishes in a Montery submarine canyon: 

implications for the design of marine reserves. Fish. Bull. 

100:324–337.

Stoner, A.W.

1997. The status of queen conch, Strombus gigas, research in the 

Caribbean. Mar. Fish. Rev. 59:14–22.

Stoner, A.W., and M. Ray-Culp.

2000. Evidence for Allee effects in an over-harvested marine gastropod: density-dependent mating and egg production. Mar. 

Ecol. Prog. Ser. 202:297–302.
Glazer and Delgado: Designing MFRs using passive acoustic telemetry 37

Warner, R. R., and R. K. Cowen.

2002. Local retention of production in marine populations: 

evidence, mechanisms, and consequences. Bull. Mar. Sci. 

70:245–249.

White, G. C., and R. A. Garrott.

1990. Analysis of wildlife radio-tracking data. Academic Press, 

San Diego, 383 p.

Worton, B. J.

1989. Kernel methods for estimating the utilization distribution 

in home range studies. Ecology 70:164–168.

Zeller, D. C.

1997. Home range and activity patterns of the coral trout Plectropomus leopardus (Serranidae). Mar. Ecol. Prog. Ser. 154:65–77. 

1998. Ultrasonic telemetry: its application for coral reef fisheries 

research. Fish. Bull. 97:1058–1065.

Zeller, D., S. L. Stoute, and G. R. Russ.

2003. Movements of reef fishes across marine reserve boundaries: effects of manipulating a density gradient. Mar. Ecol. Prog. 

Ser. 254:269–280.
38

Introduction

Several species of groupers (family: 

Serranidae) are important components of recreational and commercial 

fisheries. These fish also contribute to 

healthy coral reef ecosystems and are 

often a focus of recreational diving 

and photography. As such, preserving healthy populations of groupers 

is desirable for economic, ecological, 

and aesthetic reasons.

The life history and behavior of groupers make them especially susceptible 

to overexploitation (Coleman et al., 

1999). Groupers are top predators in 

the coral reef ecosystem, with long life 

spans and a low natural mortality rate. 

When predation by man decreases 

their abundance, however, groupers 

are slow to recover because they do 

not begin to reproduce until late ages 

(Polovina and Ralston, 1987; Sadovy, 

1994). Many species of grouper, such 

as goliath grouper (Epinephelus itajara), Nassau grouper (E. striatus), and 

red grouper (E. morio), are unwary of 

divers and are easily caught in traps 

or by angling. Furthermore, many 

groupers form predictable, seasonal, 

and site specific aggregations, which 

are easy to eradicate once located 

by fishermen (Polovina and Ralston, 

1987; Sadovy, 1994; Coleman et al., 

1999; Sadovy and Eklund, 1999). For 

Acoustic signatures of the seafloor: 

tools for predicting grouper habitat 

Arthur C. R. Gleason

Division of Marine Geology and Geophysics

Rosenstiel School of Marine and 

Atmospheric Science

University of Miami

4600 Rickenbacker Cswy.

Miami, FL 33149

E-mail address: art.gleason@miami.edu

Anne-Marie Eklund

Southeast Fisheries Science Center

National Marine Fisheries Service, NOAA

75 Virginia Beach Dr.

Miami, FL 33149

R. Pamela Reid

Division of Marine Geology and Geophysics

Rosenstiel School of Marine and 

Atmospheric Science

University of Miami

4600 Rickenbacker Cswy.

Miami, FL 33149

Veronique Koch

Southeast Fisheries Science Center

National Marine Fisheries Service, NOAA

75 Virginia Beach Dr.

Miami, FL 33149

Abstract—Groupers are important components of commercial and recreational 

fisheries. Current methods of diver-based 

grouper census surveys could potentially 

benefit from development of remotely 

sensed methods of seabed classification. 

The goal of the present study was to determine if areas of high grouper abundance 

have characteristic acoustic signatures.

 A commercial acoustic seabed mapping 

system, QTC View Series V, was used to 

survey an area near Carysfort Reef, Florida 

Keys. Acoustic data were clustered using 

QTC IMPACT software, resulting in three 

main acoustic classes covering 94% of the 

area surveyed. Diver-based data indicate 

that one of the acoustic classes corresponded to hard substrate and the other 

two represented sediment. A new measurement of seabed heterogeneity, designated 

acoustic variability, was also computed 

from the acoustic survey data in order to 

more fully characterize the acoustic response (i.e., the signature) of the seafloor. 

 When compared with diver-based grouper census data, both acoustic classification and acoustic variability were significantly different at sites with and without 

groupers. Sites with groupers were characterized by hard bottom substrate and high 

acoustic variability. Thus, the acoustic 

signature of a site, as measured by acoustic 

classification or acoustic variability, is a 

potentially useful tool for stratifying diver 

sampling effort for grouper census.

these reasons, groupers are a family 

of fishes that are likely to benefit from 

marine protected areas (MPAs; areas 

of no take).

For MPAs to be useful in grouper 

conservation, they must incorporate 

appropriate habitat. Currently, however, essential grouper habitat is poorly 

defined. Like most reef fishes, groupers prefer hard bottom (e.g., coral 

reef) to unconsolidated substrate 

(e.g., seagrass or bare sediment). Beyond this, knowledge of grouper habitat is largely anecdotal. The National 

Oceanic and Atmospheric Administration (NOAA) National Marine 

Fisheries Service (NMFS) Southeast 

Fisheries Science Center (SEFSC) has 

been monitoring grouper density near 

Carysfort Reef since 1994 (Eklund et 

al., 2000) and more recently at other 

reefs of the Florida Keys. Through 

experience, the NOAA divers have 

developed a qualitative “feel” for good 

grouper habitat, which often includes 

features such as high relief and the 

presence of caves or crevices, especially on steeply sloping surfaces.

Maps showing the distribution of 

potential grouper habitat are limited. 

In the Florida Keys, for example, an 

aggregation of 70–100 black groupers 

(Mycteroperca bonaci) was observed just 

100 m outside the protected area at 

Carysfort Reef less than a year after 
Gleason et al.: Acoustic signatures of the seafloor 39

the preserve opened (Eklund et al., 2000). Discovery of 

the first known aggregation of any grouper species in 

the Florida Keys (Eklund et al., 2000) just outside the 

largest MPA in the Keys is ironic. Information on the 

distribution of fish habitat is highly relevant to MPA 

design, yet often such critical information is unavailable. 

The experience at Carysfort underscores the need for 

efficient methods of 1) seabed mapping and 2) prioritizing limited dive time for fish census. 

Diver-based grouper census surveys could potentially 

benefit from improved methods of remotely sensed 

seabed classification. Optical mapping products, such as 

the Benthic Habitats of the Florida Keys (FMRI, 1998), 

are useful in some applications; however, much important grouper habitat, including the area of the large 

aggregation observed by Eklund et al. (2000) outside 

the Carysfort MPA, is located in deeper water where 

optical mapping techniques are not useful. Acoustic 

mapping systems are a promising technology for mapping areas where the bottom cannot be detected by 

optical methods.

Acoustic methods have been successfully used to 

discriminate substrate classes in many areas around the 

world (e.g., Hamilton et al., 1999; Morrison et al., 2001; 

Anderson et al., 2002; Ellingsen et al., 2002; Freitas et 

al., 2003a). To date, however, applications of this methodology in carbonate reefal environments are limited. 

The overall goal of the present study was to evaluate 

the potential of a commercial acoustic mapping system, 

QTC View Series V (QTC-V; Quester Tangent Corporation, Sidney, BC, Canada, 2001), to identify potential 

grouper habitat and prioritize sites for diver surveys.

Specifically, this project addressed the question: Do 

areas of high grouper abundance have characteristic 

acoustic signatures? Results demonstrate two effective 

predictors of grouper presence or absence: 1) simple 

acoustic seabed classification, which distinguishes hard 

bottom from sediment substrate, and 2) a newly developed index of acoustic variability.

Methods

The study focused on Carysfort Reef, Florida Keys 

(Fig. 1). An acoustic survey was performed and the 

resulting data processed in two ways. First, clusters 

of acoustically distinct echoes were segmented using 

commercially available software. Second, a new index 

of acoustic variability was developed. This index was designed to measure seabed heterogeneity by quantifying 

the degree to which the echo at a particular location is 

similar to other nearby echoes.

The acoustic survey was complemented with diver 

surveys, which collected “ground truth” data on bottom 

type and grouper abundance. Correlations between 

acoustic and diver surveys were conducted to test the value of using acoustic signatures for identifying potential 

sites for grouper habitat and prioritizing sites for diver 

surveys. Details of the methods are presented below. 

Acoustic survey

Data collection and seabed classification The acoustic survey at Carysfort Reef (Fig. 1) was conducted using 

a QTC-V acoustic mapping system. Acoustic data were 

recorded using a Suzuki 50 kHz echo sounder (model 

2025). A wide area augmentation system (WAAS) enabled global positioning system (GPS), mounted with 

its antenna directly over the acoustic transducer, provided vessel positioning. The survey, conducted on 14 

March, 28 March, and 4 April 2002, consisted of transects spaced 100 m apart running perpendicular to the 

reef crest from an inshore depth of 3 m to a maximum 

offshore depth of 42 m.

Data processing for seabed classification involved 

four steps (Fig. 2). Processing was done using IMPACT 

(version 3.4, QTC, Sidney, BC, 2004), the processing 

software provided with QTC-V. During the first step, the 

data acquisition phase, the signal generated by an echo 

sounder is passed to a head amplifier that applies both 

time-varying gain, to compensate for beam spreading 

and water depth, and auto gain control, to compensate 

for variable bottom reflectance. Individual echoes are 

then digitized using a 5 MHz analog to digital card and 

recorded by a computer.

In the second step, the data reduction phase, the 

raw bi-polar waveforms are converted to echo “envelopes” (essentially echo amplitude only). The echo 

envelopes are stacked (averaged) in groups of five to 

reduce ping-to-ping variability. The stacked echoes are 

characterized by a number of algorithms that respond 

to features of the echo shape. The ensemble of features 

is reduced using principal components analysis (PCA) 

to the first three principal components. The end result 

is that each stacked echo is represented by a single 

point in three-dimensions (“Q-space”; QTC, 2004). The 

shape of the stacked echo determines the coordinates 

of this point.

In the third step, the clustering phase, the “cloud” 

of points in Q-space is partitioned into clusters using a 

simulated annealing clustering procedure. The statistical descriptions (mean, covariance) of these clusters 

comprise a “catalog” (QTC, 2004). 

Finally, in the classification stage, a catalog is used to 

assign a class to all points in a dataset. The catalog can 

be applied to the original data used to create the catalog 

(via clustering) or it can be applied to another data set 

acquired with the same hardware configuration.

The four steps of acquisition, reduction, clustering, 

and classification are fundamental to the IMPACT 
40 Professional Paper NMFS 5

Florida

26 ̊N

25 ̊N

81 ̊W 80 ̊W

500 m 45 km

N

Figure 1

Track lines from the acoustic survey superimposed on an IKONOS satellite image of Carysfort Reef 

and surroundings. The Carysfort lighthouse (star) and protected area (bold rectangular box) are 

also shown. The arrow in the inset shows the location of the IKONOS image in the wider context 

of South Florida (solid black), the Florida Keys National Marine Sanctuary (dashed line), and the 

area that has been mapped by FMRI (1998) from aerial photography (grey). The track lines extend 

from near the reef crest to deeper water where the bottom is no longer visible.

processing procedure. The overview above is similar 

to previous descriptions of data processing using QTC 

View Series IV and older versions of the IMPACT software (e.g. Hamilton et al., 1999; Morrison et al., 2001; 

Anderson et al., 2002; Ellingsen et al., 2002; Freitas et 

al., 2003a). A series of conference papers (e.g. Preston1) 

provide more detailed descriptions of each of the four 

steps.

Acoustic variability index Standard QTC analysis, as 

described above, characterizes the acoustic response of 

a position on the seafloor relative to all others in the 

survey area based solely on echo shape. The geographic 

location of the echo is irrelevant in the clustering 

process; location is used only to plot the classification 

results. An additional way to characterize acoustic 

response at a point is to quantify the degree to which 

a particular echo is similar in shape to its geographic 

neighbors (as opposed to its neighbors in Q-space). 

Such a measure, designated acoustic variability, was developed as part of this study (Fig. 3). The computation 

of acoustic variability, described below, complements 

the standard QTC classification to more fully characterize the acoustic signature of any given location on 

the seafloor.

1

 Preston, J. M., A. C. Christney, L. S. Beran, and W. T. Collins. 2004. 

Statistical Seabed Segmentation - From Images and Echoes to 

Objective Clustering. 7th European Conference on Underwater 

Acoustics, Delft, Netherlands, 5–8 July 2004. 6 p. is available, along 

with other papers describing the details of processing QTC View 

data, from the QTC Web site: http://www.questertangent.com [Accessed on 21 Dec 2004]
Gleason et al.: Acoustic signatures of the seafloor 41

Figure 3

Computation of acoustic variability. A moving window sliding over the geographic dataset (left plot) selects neighboring 

points. Variability is the sum of the lengths of the principal axes of the cloud formed by the selected points in Q-space. 

If these points have low acoustic variability, they will cluster close together in Q-space (upper center plot). If they have 

high acoustic variability, they will form a scattered cloud in Q-space (lower center plot). The spatial pattern of variability is 

apparent when displayed using the geographic location of each echo (right plot).

Variability

High

Low

Acoustic variability was computed point-by-point 

across the data set by considering a small moving window applied around each echo in the survey (Fig. 3). For 

each point, all the echoes within 40 m of that point were 

identified. In Q-space this subset of the data produces 

a small cloud of points (typically between 10–20 points, 

depending on vessel speed). Variability was defined 

as the sum of the standard deviations along the three 

principal axes of this cloud of points and was computed 

by taking the square root of the trace of the covariance 

matrix computed for each subset of data (Davis, 1986). 

A window of data that includes echoes that are all very 

similar will have points very close to one another in 

Q-space and will therefore have low acoustic variability. 

Figure 2

Overview of acoustic processing. A) The full waveform for each point is acquired, georeferenced, and recorded. 

B) Each echo is converted to three coordinates in “Q-space” based on the shape of the echo “envelope” (echo amplitude). 

C) Q-space is partitioned into distinct groups defined by their means and covariances. D) Each point is assigned a class 

based on its location in Q-space.

A) Acquisition B) Reduction C) Clustering D) Classification

[Q1, Q2, Q3]

Q-space Catalog
42 Professional Paper NMFS 5

Conversely, a window of data containing echoes that are 

all very dissimilar will have points spread across Q-space 

and will have high acoustic variability.

Diver survey

Twenty-two dives were conducted near Carysfort during August of 2002 and October of 2003 to acquire 

“ground truth” for the acoustic measurements. The 

locations of the dives were chosen based on the maps 

of seafloor classification and acoustic variability. Since 

only a limited number of dives were possible, the sites 

were chosen to ensure that multiple dives were placed 

in 1) homogenous areas of each acoustic class, and 2) 

areas of high and low acoustic variability.

Diver surveys followed NOAA/SEFSC procedures 

for conducting fish census (Bohnsack and Bannerot, 

1986) and benthic habitat assessment (Franklin et al., 

2003), as described by McClellan and Miller (2003). At 

every site, two divers each surveyed non-overlapping, 

7.5 m radius cylinders; results from the two divers were 

averaged to produce a single set of values for each dive 

site. Diver collected data that were compared with the 

acoustics were: 1) the number of groupers (Epinephelus

and Mycteroperca spp.) observed in a five minute interval, 

and 2) estimated percent cover of three substrate classes 

(sediment, hard bottom, and rubble).

Comparison of acoustic and diver surveys

Results from acoustic and diver surveys were compared 

to 1) assess the accuracy of the acoustic classification, 

and 2) correlate grouper abundance with acoustic classification and variability. The general strategy in both 

cases was to compare a diver-estimated parameter with 

the closest acoustically derived values.

Acoustic classification accuracy assessment Assessing 

the accuracy of acoustic classification involved two steps. 

First, diver-estimated bottom cover was overlain on the 

acoustic classification map to visually determine which 

acoustic classes corresponded with which bottom types. 

Second, the accuracy of the classification was assessed 

using an error matrix.

The error matrix is a common method of quantifying 

the accuracy of a thematic map by comparing “ground 

truth” for a sample of points on the map with the predictions made by the map (Congalton and Green, 1999). 

Ground truth is often acquired by visiting sites and visually determining what is there (e.g., by divers). A matrix 

can then be constructed with one column per ground 

truth class, one row per map class, and entries in the appropriate row and column for each ground truth point 

visited. The sum of all the elements in the matrix equals 

the total number of ground truth points, and the sum of 

the elements in the matrix for which the ground truth 

class is the same as the map class is the total number of 

“correct” points visited on the map. The overall accuracy 

is the latter divided by the former. This technique was 

used here with one modification. Usually each point 

visited is assigned one ground truth class and one map 

class. In this study, however, each ground truth site (a 

single dive) was assigned a mixture of three classes 

(sediment, hard bottom, and rubble), but an echo had 

only one acoustic class. To accommodate mixed bottom 

types, the entry in the error matrix for the closest echo 

to a particular dive site was divided among the columns 

of the matrix in proportion to the diver-estimated bottom cover for that site.

Grouper abundance vs. acoustic classification and 

variability Acoustic classes and variability were compared with grouper abundance using analysis of variance (ANOVA) and multiple comparison tests to determine the significance of any correlation. Dive sites were 

grouped into treatments by the number of groupers 

at the site, and two ANOVA procedures were run. The 

first tested the null hypothesis that the mean percent 

of a given acoustic class was the same for sites with different number of groupers. The second tested the null 

hypothesis that mean variability was the same for sites 

with different numbers of groupers.

The MATLAB statistics toolbox (Version 4.0:R13; 

The MathWorks, Inc., Natick, MA 2002) was used to 

perform the tests. First, the null hypothesis that the 

variables being compared followed a normal distribution was evaluated using a Lilliefors test (“lillietest” 

command; see also Conover, 1980). Based on the output of the Lilliefors test, the parametric (“anova1”) or 

non-parametric (“kruskalwallis”) MATLAB implementations of ANOVA were used to test the significance of 

differences between the group means. Finally, if the 

null hypothesis that all group means were equal was 

rejected by the ANOVA, the “multcompare” function 

(based on procedures from Hochberg and Tamhane, 

1987) was used to determine which pairs of means 

were significantly different from one another. A 95% 

confidence interval (P< 0.05) was used for all statistical tests.

Results

Acoustic survey

Clustering the acoustic survey data discriminated seven 

acoustic classes. The three major classes, which comprised 94% of the echoes in the survey area, are plotted in Figure 4. The four minor classes were dispersed 

widely across the study area. Due to limited dive time, 
Gleason et al.: Acoustic signatures of the seafloor 43

the sites chosen for ground truth focused on the three 

largest classes.

Diver survey

Diver estimates of substrate at the twenty-two sites selected to ground truth the three major acoustic classes 

are shown in Figure 4. All sites were dominated by sand 

or hard bottom substrate. Seven sites had small amounts 

of rubble substrate, with only one site having >10% 

rubble (Fig. 4).

Grouper abundance at each of the dive sites is shown 

in Figure 5. At ten sites, no groupers were observed. 

Groupers were observed at 12 sites, with higher numbers corresponding to decreasing frequency of sites. 

The maximum number of groupers observed at any site 

was six (n=1).

Acoustic classification accuracy assessment

Bottom types were assigned to acoustic classes based on 

visual observation of diver survey results overlain on the 

acoustic classification map (Fig. 4). Visually, Acoustic 

Class 1 corresponds with the dive sites dominated by 

hard bottom and Acoustic Classes 2 and 3 both correspond with the dive sites dominated by sediment 

(Fig. 4). 

The overall accuracy of the acoustic classification 

considering only hard bottom and sand classes was 86% 

(Table 1), which is comparable to the accuracy of optical 

Figure 4

The three main acoustic classes, which comprise 94% of echoes in the survey, and 

diver-estimated substrate at 22 sites. Visual observation of this figure suggests that 

Acoustic Class 1 corresponds to hard bottom and Acoustic Classes 2 and 3 both 

correspond to sediment.

Acoustic Classes

 1 

 2 

 3 

Diver-Estimated Substrate

Points of Reference

 MPA Boundary 

 Lighthouse 

Hard bottom Sediment

Rubble
44 Professional Paper NMFS 5

sensors for mapping coarse bottom types (Mumby and 

Edwards, 2002).

Grouper abundance vs. acoustic classification 

and variability

Visual inspection of the grouper abundance data in 

Figure 5 suggests that sites with high grouper abundance were associated with hard bottom and had higher 

acoustic variability than sites with fewer groupers. The 

differences between group means were not, however, 

statistically significant when the data were tested with 

ANOVA using seven categories (one each for sites with 

number of groupers from zero to six); this negative 

result may be due to the small number of sites in most 

categories. The ANOVA analysis was repeated with sites 

grouped into only two categories based on the presence 

(n=12) or absence (n=10) of groupers. Sites with groupers had both a significantly higher percentage of hard 

bottom relative to sand (P = 0.006) and significantly 

higher acoustic variability (P < 0.001) than sites without 

groupers (Fig. 6).

Discussion

Results from this study demonstrate that acoustic signatures consisting of a simple substrate classification 

and an index of local heterogeneity were different for 

dive sites with and without groupers at Carysfort Reef. 

In general, sites where groupers were present had 

hard substrate with high local heterogeneity, and sites 

without groupers had sediment substrate with low local 

heterogeneity.

QTC systems have previously distinguished outcropping rock from sediment (e.g. Anderson et al., 2002); 

most of these studies, however, have focused on siliciclastic environments. Moreover, previous work with an 

older QTC-IV system suggested that rough terrain could 

adversely affect system accuracy (Hamilton et al., 1999). 

It is therefore noteworthy that results from this study 

show that acoustics can be used to distinguish hard bottom and sediment with high accuracy in a high relief, 

carbonate reef environment.

The reason that sediment in the Carysfort Reef area 

maps as two distinct acoustic classes (Fig 4) is uncertain, 

Figure 5

Grouper (Epinephelus and Mycteroperca spp.) abundance at each of the dive sites relative to (A) acoustic classification and 

(B) acoustic variability. Groupers were found primarily in areas with hard bottom substrate and high acoustic variability. 

Note that the area of high variability is much smaller than the area of hard bottom. Transect A-Aʹ is plotted in Figure 7.

A B

Points of Reference

 MPA Boundary 

 Lighthouse 

Grouper Abundance

(groupers / site)

 3-6

 1-2

 0 

A

A'

A

A'

Acoustic Variability

 > 2 std above mean

 1-2 std above mean

 0-1 std above mean

 < mean

Acoustic Classes

 1 

 2 

 3 

Hard bottom

Sediment
Gleason et al.: Acoustic signatures of the seafloor 45

Figure 6

Acoustic classification and acoustic variability computed from the echoes closest to each dive site and grouped by the 

presence/absence of groupers (Epinephelus and Mycteroperca spp.). A) Bars indicate the percent of dive sites for which the 

closest echo classified as hard bottom (Acoustic Class 1) or sediment (Acoustic Classes 2 or 3). B) Box plots of acoustic 

variability near dive sites. Horizontal lines mark the lower quartile, median, and upper quartile values. The lines extending 

from each end of the box show the range of all data. Note that grouper presence correlates with hard bottom and high 

acoustic variability.

Table 1

Error matrix comparing acoustic classification with 

diver-based “ground truth.” Acoustic Class 1 was 

interpreted as a hard bottom class, and Acoustic Classes 

2 and 3 were combined to form a single sediment 

substrate class. Fractional values are possible because 

the entry for each point was divided proportionally by 

the diver-estimated substrate at that site. The sum of 

all entries is 18, indicating that the closest echoes to 18 

of the 22 dive sites were classified as Acoustic Classes 

1, 2, or 3.

Diver-estimated substrate 

Hard Overall

Acoustic classes bottom Sediment Rubble accuracy

Class 1 (hard bottom) 8.2 1.1 0.7 0.86

Class 2+3 (sediment) 0.7 7.3 0.0

but is likely related to differences in physical properties, 

such as sediment grain size. Clustering of sediment with 

different grain sizes as distinct acoustic classes would 

be consistent with previous QTC-derived classification 

schemes (Anderson et al., 2002; Ellingsen et al., 2002; 

Freitas et al., 2003a; Freitas et al., 2003b).

The acoustic classification results at Carysfort Reef 

demonstrate that the location of a point in Q-space is 

related to physical characteristics of the bottom. A set 

of points that are spread out in Q-space are therefore 

more likely to represent different bottom types than 

a set of points that are tightly clustered in Q-space. 

Areas where different bottom types are located close 

together, such as patchy environments or along edges, 

have high acoustic variability, and areas where the 

bottom does not change rapidly have low acoustic 

variability (Fig. 7). Like the Berger-Parker index 

(Morrison et al., 2001), acoustic variability highlights 

transitions between classes (edges) and heterogeneous 

areas with mixed classes. Acoustic variability, however, 

is computed directly from the reduced acoustic echo 

features (Q-space) as opposed to operating on classified data. Operating directly on the Q-values may be 

advantageous because differences between echoes are 

measured continuously, rather than in discrete classes, 

and because the results are not dependent on the classification scheme used.

Observations by NMFS divers that groupers are often 

found over “complex” bottoms (caves, crevices, ledges) 

led to the idea of testing acoustic variability. It should 

be noted, however, that topographic complexity as observed by divers is not the same as acoustic variability as 

defined in this study. Topographic complexity occurs on 

0 (Absent) 1-6 (Present)

0

10

20

30

40

50

60

70

80

90

Hard bottom

Sediment seti s e

vi df ot necr e

P

Number of groupers at site

0.2

0.3

0.4

0.5

0.6

0.7

ytili bair aVcit suoc A

A B

0 (Absent) 1-6 (Present)

Number of groupers at site
46 Professional Paper NMFS 5

Figure 7

Depth, acoustic class, and acoustic variability along transect A-Aʹ, shown in Figure 5. Acoustic variability within large areas 

of a single class ranges from high to low, but the highest variability is found at the transitions between classes. Patchy areas, 

such as the deep outcropping hard bottom, can form extended regions of high acoustic variability.

the scale of meters and might be thought of as a rough 

or steep bottom. Acoustic variability, on the other hand, 

is measured on the scale of tens of meters and reflects 

the proximity of acoustically distinct bottom types.

The observation that sites with groupers had higher 

acoustic variability than sites without groupers does 

not mean that acoustic variability is a measure of essential grouper habitat. Acoustic variability does not 

measure what a diver might perceive as important 

variables for grouper habitat. Acoustic variability 

could, however, help prioritize diving effort for grouper population surveys. Acoustic variability might 

also contribute to a better understanding of grouper 

habitat. For example, it is not clear why aggregations 

are so site-specific. From a diver’s point of view, the 

bottom at the site of an aggregation can appear very 

similar to the bottom just a few hundred meters away. 

Measurements of acoustic variability may help to interpret diver observations by providing context on a 

larger spatial scale.

The distinct differences in acoustic signatures of sites 

with and without groupers (Fig. 6) suggest that acoustic 

classification and acoustic variability are potentially useful tools for stratifying diver sampling effort for grouper 

census. A simple map distinguishing hard bottom from 

sediment, which can be easily produced with acoustics, 

is a substantial improvement over a lack of any bottom 

type information in optically deep water. A map of 

acoustic variability may further refine the location of 

potential grouper habitat, thereby increasing the efficiency of divers to conduct fish census surveys.

Conclusions

The results of this study showed:

1. A commercial acoustic seafloor classification system 

(QTC View V) was successfully used to discriminate 

hard bottom from sediment in a carbonate reef 

environment. 

2. A simple map of hard bottom versus sediment was a 

useful first step in discriminating potential grouper 

habitat.

3. An index of acoustic variability, which measures 

heterogeneity of bottom types, complemented the 

simple bottom classification map to further target 

areas of potential grouper habitat.

Therefore, the acoustic signature of the seafloor, as 

measured with acoustic classification and acoustic 

diversity, is a useful tool for stratifying sampling effort 

for diver-based grouper census surveys. Both acoustic 

classification and acoustic variability can be rapidly and 
Gleason et al.: Acoustic signatures of the seafloor 47

inexpensively acquired when needed by fisheries and 

park managers around the world because they are easily 

measured with a single beam echo sounder.

Acknowledgments

Funding for this project was provided by a grant from 

the NOAA-NMFS Coral Reef Initiative (A.M.E.) and 

ONR grant N000140110671 (R.P.R.). Small boat and 

SCUBA diving operations were conducted with the invaluable help of D. McClellan, D. Harper, G.M. Miller, 

J. Contillo, H. Balchowsky, J. Schull, S. Cimilluca, J. Javech, and N. Baertlein. The authors appreciate insight 

provided by W. Collins and J. Preston, Quester Tangent 

Corporation, during the analysis of the acoustic data. 

Two anonymous reviewers provided detailed and helpful comments on the manuscript.

Literature cited

Anderson, J. T., R. S. Gregory, and W. T. Collins. 

2002. Acoustic classification of marine habitats in coastal Newfoundland. ICES J. Mar. Sci. 59:156–167.

Bohnsack, J. A., and S. P. Bannerot. 

1986. A stationary visual census technique for quantitatively assessing community structure of coral reef fishes. NOAA Tech. 

Rep. NMFS 41, 15 p.

Coleman, F. C., C. C. Koenig, A.-M. Eklund, and C. B. Grimes. 

1999. Management and conservation of temperate reef fish in 

the southeastern United States. In Life in the slow lane— ecology and conservation of long-lived marine animals (J. A. Musick, ed.), p. 233–242. American Fisheries Society Bethesda, 

Maryland.

Congalton, R. G., and K. Green. 

1999. Assessing the accuracy of remotely sensed data: Principles 

and practices. Lewis Publishers, Boca Raton, 137 p.

Conover, W. J. 

1980. Practical nonparametric statistics. Wiley, New York, 493 p.

Davis, J. C. 

1986. Statistics and data analysis in geology. John Wiley & Sons, 

New York, 646 p.

Eklund, A.-M., D. B. McClellan, and D. E. Harper. 

2000. Black grouper aggregations in relation to protected areas 

within the Florida Keys National Marine Sanctuary. Bull. Mar. 

Sci. 66(3):721–728.

Ellingsen, K. E., J. S. Gray, and E. Bjornbom. 

2002. Acoustic classification of seabed habitats using the QTC 

VIEW system. ICES J. Mar. Sci.(59):825–835.

Florida Marine Research Institute (FMRI). 

1998. Benthic habitats of the Florida Keys. FMRI Technical Report TR-4, 53 p.

Franklin, E. C., J. S. Ault, S. G. Smith, J. Luo, G. A. Meester, G. A. Diaz, 

M. Chiappone, D. W. Swanson, S. L. Miller, and J. A. Bohnsack.

2003. Benthic habitat mapping in the Tortugas Region, Florida. 

Mar. Geod. 26:19–34.

Freitas, R., A. M. Rodrigues, and V. Quintino. 

2003a. Benthic biotopes remote sensing using acoustics. J. Exp. 

Mar. Biol. Ecol. 285–286:339–353.

Freitas, R., S. Silva, V. Quintino, A. M. Rodrigues, K. Rhynas, and W. 

T. Collins. 

2003b. Acoustic seabed classification of marine habitats: studies 

in the western coastal-shelf area of Portugal. ICES J. Mar. Sci. 

60:599–608.

Hamilton, L. J., P. J. Mulhearn, and R. Poeckert.

1999. Comparison of RoxAnn and QTC-View acoustic bottom 

classification system performance for the Cairns area, Great 

Barrier Reef, Australia. Cont. Shelf. Res. 19:1577–1597.

Hochberg, Y., and A. C. Tamhane. 

1987. Multiple comparison procedures, Wiley, 450 p.

McClellan, D. B., and G. M. Miller. 

2003. Reef fish abundance, biomass, species composition, and 

habitat characterization of Navassa Island. In Status of reef 

resources of Navassa Island: Nov. 2002 (M. W. Miller, ed.), p. 

24–42. NOAA Tech. Memo. NMFS-SEFSC-501. 

Morrison, M. A., S. F. Thrush, and R. Budd.

2001. Detection of acoustic class boundaries in soft sediment systems using the seafloor acoustic discrimination system QTC 

VIEW. J. Sea Res. 46:233–243.

Mumby, P. J., and A. J. Edwards.

2002. Mapping marine environments with IKONOS imagery: 

enhanced spatial resolution can deliver greater thematic accuracy. Remote Sens. Environ. 82:248–257.

Polovina, J. J., and S. Ralston. 

1987. Tropical snappers and groupers: biology and fisheries 

management. Westview Press, Boulder, CO, 659 p.

Quester Tangent Corporation (QTC). 

2004. QTC IMPACT User Guide Version 3.40, Sidney, BC, Canada, 126 p.

Sadovy, Y. 

1994. Grouper stocks of the western central Atlantic: the need 

for management and management needs. Proc. Gulf Carib. 

Fish. Inst. 43:43–64.

Sadovy, Y., and A.-M. Eklund. 

1999. Synopsis of biological data on the Nassau grouper, Epinephelus striatus (Bloch, 1792), and the jewfish E. itajara (Lichtenstein, 1822). NOAA Tech. Rep. NMFS 146, 65 p.
48

Introduction

The Tortugas Ecological Reserve is 

located 225 km west of Key West, 

Florida, at the western terminus of 

the Florida Keys island chain (Fig. 1). 

The reserve was implemented in July 

2001, and consists of two sections: Tortugas North (312 km2

) and Tortugas 

South (206 km2), with a total area of 

518 km2 (Cowie-Haskell and Delaney, 

2003). Tortugas North protects relatively deep (30–50 m) coral reef banks 

known as Tortugas Bank, while Tortugas South protects Riley’s Hump, 

a known spawning aggregation site 

for many species of snappers (family 

Lutjanidae), and a range of deepwater habitats (50–600 m) (Dahlgren et 

al., 2001; Cowie-Haskell and Delaney, 

2003). The Tortugas South Preserve 

is part of the largest fully protected 

marine reserve in the United States 

(Cowie-Haskell and Delaney, 2003). 

Despite the widespread distribution 

of deep reef ecosystems in the southeastern United States (Avent et al., 

1977; Barans and Henry, 1984), our 

knowledge of deep reef fish diversity, 

distribution, and ecology is extremely 

limited. Numerous large predators, 

including commercially important 

scamp, gag, red snapper, snowy grouper, Warsaw grouper, and wreckfish, 

reach abundant population numbers 

Deepwater reef fishes and multibeam bathymetry 

of the Tortugas South Ecological Reserve, 

Florida Keys National Marine Sanctuary, Florida

Douglas C. Weaver

Flower Garden Banks National Marine Sanctuary, NOAA

4700 Avenue U, Building 216

Galveston, TX 77551

Email: doug.weaver@noaa.gov

David F. Naar

Brian T. Donahue

College of Marine Science

University of South Florida

140 7th Avenue S.

St. Petersberg, FL 33701

Abstract—The Tortugas South Ecological Reserve, located along the margin of the southwest Florida carbonate 

platform, is part of the largest no-take 

marine reserve in the U.S. Established in 

July 2001, the reserve is approximately 

206 km2 in area, and ranges in depths 

from 30 m at Riley’s Hump to over 600 m 

at the southern edge of the reserve. Geological and biological information for 

the Tortugas South Reserve is lacking, 

and critical for management of the area. 

Bathymetric surveys were conducted with 

a Simrad EM 3000 multibeam echosounder at Riley’s Hump and Miller’s Ledge, 

located in the northern and central part 

of the reserve. Resulting data were used 

to produce basemaps to obtain geological ground truth and visual surveys of 

biological communities, including reef 

fishes. Visual surveys were conducted using SCUBA and the Phantom S2 Remotely 

Operated Vehicle (ROV) at Riley’s Hump. 

Visual surveys were conducted using the 

ROV and the Deepworker 2000 research 

submersible along Miller’s Ledge, within 

and outside of the reserve. A total of 108 

fishes were recorded during SCUBA, ROV, 

and submersible observations. Replicate 

survey transects resulted in over 50 fishes 

documented at Miller’s Ledge, and eight 

of the top ten most abundant species were 

planktivores. Many species of groupers, 

including scamp (Mycteroperca phenax), 

red grouper (Epinephelus morio), snowy 

grouper (E. niveatus), speckled hind (E. 

drummondhayi), and Warsaw grouper (E. 

nigritus), are present in the sanctuary. 

Numerous aggregations of scamp and 

a bicolor phase of the Warsaw grouper 

were observed, indicating the importance 

of Miller’s Ledge as a potential spawning 

location for both commercially important 

and rare deep reef species, and as a potential source of larval recruits for the Florida 

Keys and other deep reef ecosystems of 

Florida. 

at water depths between 100 and 500 

m along the southeastern U.S. and 

Gulf of Mexico. Smaller species of 

western Atlantic deep reef fishes are 

taxonomically and ecologically diverse, and may reach great abundances in these deepwater ecosystems. 

Hard bottom reef communities at 

the shelf-slope break, formed by the 

consolidation of post-glacial paleoshorelines and formation of relict 

reef communities following sea level 

lowstands some 18,000 years ago, provide extensive shelf edge fish habitat 

worldwide (Mallinson et al., 2003). 

Holocene reefs of the Dry Tortugas 

and Riley’s Hump actively developed 

on Pleistocene reef rock between 6 

and 10 kbp, but coral growth rates diminished at Riley’s Hump at approximately 4 kbp due to rising sea levels, 

resulting in a “give up” condition 

of low reef growth (Mallinson et al., 

2003). To the south of Riley’s Hump, 

hard bottom habitats are formed from 

Pleistocene paleoshoreline rock and 

erosional features, forming a dramatic 

scarp known as “Miller’s Ledge.”

Water depths in TSER range from 

approximately 30 m at Riley’s Hump 

to over 600 m at the southern edge. 

However, previous research has focused on the coral reef and reef fish 

assemblage of Riley’s Hump, and few 

studies have been conducted below 
Weaver et al.: Reef fish surveys and multibeam bathymetry at the Tortugas South Ecological Reserve 49

Figure 1

Location of Tortugas South Ecological Reserve, at the western terminus of the Florida Keys tract.

50 m in the reserve (Dahlgren et al., 2001, Franklin 

et al., 2003). To assist with biological characterization, 

geological characterization, and habitat mapping within 

the reserve, we provide bathymetry and information on 

surface geology between 40 and 150 m within the reserve. To provide comparative ecological data for other 

deep reef communities and assist with management decisions concerning the TSER, we provide a preliminary 

list of fishes occurring at the reserve as a representative 

shelf-edge reef community of the Gulf of Mexico and 

Caribbean.

Materials and methods

Two additional research cruises were conducted to 

document reef fish communities within the reserve 

using SCUBA, remotely operated vehicle (ROV), and 

manned submersibles. Multibeam bathymetric surveys 

were conducted using a pole mounted Kongsberg Simrad EM3000 multibeam echo sounder aboard the RV 

Suncoaster (operated by the Florida Institute of Oceanography). During data collection, survey speeds reached 

a maximum of 9 knots. Sound velocity casts were limited 

to twice a day because of the relative stability of sound 

velocity in this area. Sound velocity data were collected 

using a SBE-19 Seabird CTD, from which the speed of 

sound was calculated using the Chen-Millero equation. 

The largest change in sound velocity normally occurs 

at sea level, which was monitored using an AML sound 

velocity smart sensor sending data directly to the acquisition computer once per second. The data were 

post-processed using Neptune software (Kongsberg 

Maritime, Kongsberg, Norway). Navigation data were 

cleaned by flagging out positions where the distance between points was greater than 5 m and survey speed was 

above 9 knots. For data cleaning, the cell size was set to 

4 m. Points were rejected if they exceeded a noise limit 

that was two times the standard deviation of the average 

mean value of the cell. Final processing was the correction for tidal variation, using a tidal estimating program 

developed by Ruoying He and Robert Weisberg at the 

University of South Florida (Ruoying He, pers. commun.). The data were then exported in an ASCII x,y,z 

, , 
50 Professional Paper NMFS 5

Figure 2

The Phantom S2 Remotely Operated Vehicle (ROV), provided by the National Undersea 

Research Center at the University of North Carolina, Wilmington. Surveys were conducted 

to characterize benthic habitat and reef fish communities of Tortugas South Ecological 

Reserve. 

format and imported into Fledermaus software (Interactive Visualization Systems, Fredericton, NB, Canada). 

The Fledermaus software provided the base maps in 

this article and a guide to the subsequent fish surveys. 

Attitude and position of the sonar head were calculated 

using the Applanix PosMV 320 (previously called TSS 

POS/MV), which uses dual GPS antennas and an Inertial Motion Unit, which provided Roll and Pitch within 

0.2 degrees accuracy and heading within 0.3 degrees 

accuracy. Positions accuracy is 1 m or better. 

Reef fish surveys were conducted using a Phantom 

S2 ROV provided by the National Undersea Research 

Center at the University of North Carolina, Wilmington 

(NURC-UNCW) to document geological formations and 

associated reef fish community structure throughout 

the reserve (Fig. 2). Digital video cameras in underwater 

housings were attached to the ROV to improve resolution of the digital videotapes for taxonomic identification of reef fishes. Fishes were identified to the lowest 

possible taxon following Humann and DeLoach (2002), 

based on the taxonomic nomenclature of Robins et al. 

(1991). Trophic categories were assigned based on dietary information from Randall (1967), Smith-Vaniz et 

al. (1999), or Bullock and Smith (1991) for individual 

species or closely related taxa.

Multiple transects were conducted along Miller’s 

Ledge, both within the no-take reserve, and the unprotected areas to the west of the sanctuary. The third 

research cruise was conducted as part of the Sustainable 

Seas Expeditions (SSE) using the Deepworker 2000 research submersible, equipped with digital videocamera 

and sampling arm (Fig. 3). A single submersible transect, 

5 km in length, was conducted on 19 July 2002 within 

the reserve to survey geological features identified in 

the multibeam basemap and further document reef fish 

community structure. The first author conducted an additional visual survey on 20 July 2002, using SCUBA and 

a housed video camera at Riley’s Hump to document 

representative reef fishes occurring on the feature.

Results

Multibeam bathymetry surveys of Riley’s Hump reveal 

a relatively flat crest with an elevated rim along the 

southwestern and SSE margins (Fig. 4, Fig. 5). A shallow depression is present in the center of the feature, 

with numerous regions of variable topography along 

the eastern-central half of the feature. The southern 

margin of the bank is characterized by a steep (~20 m 
Weaver et al.: Reef fish surveys and multibeam bathymetry at the Tortugas South Ecological Reserve 51

in relief) escarpment, ranging in depth from 30 m at 

the top of the bank to 50+ m at the base, where a welldeveloped trough surrounds the southern margin and 

extends eastward. Reported aggregation sites of the 

mutton snapper (Lutjanus analis) are associated with 

this steep southern scarp (Fig. 5). The slope is less 

steep to the east of the bank, suggesting a debris field 

generated by the prominent easterly flow of the Gulf 

Loop/Florida Current in this region. A detailed review 

of geology of Riley’s Hump is presented in Mallinson 

et al. (2003).

Hard bottom communities observed at Riley’s Hump 

are dominated by scleractinian coral assemblages, gorgonians, assorted sponges, and leafy algae (Fig. 6). ROV 

surveys revealed isolated reef communities interspersed 

with sand channels and carbonate debris. Dominant 

scleractinians were Montastraea cavernosa, M. annularis, 

and Siderastrea siderea. Individual colonies of Montastrea

sp. were typically small, ranging from 10 to 50 cm in 

diameter. Occasional large colonies were observed to 

2 m in height. Massive sponges, including vase sponges 

(Callyspongia sp.) and giant barrel sponges (Xestospongia 

muta), were abundant on the reef surface. Sea plumes 

(Pseudopterogorgia sp.) and assorted octocorals were 

abundant on patch reef communities, and extended 

out onto the carbonate debris fields surrounding the 

reef platform. At deeper water depths, the benthic 

community was dominated by rope sponges (Aplysina 

cauliformis) and calcareous algae including Halimeda

and Penicillus sp. Dense algal mats of Dictyota sp. and Lobophora were observed during SCUBA and ROV surveys 

on the shallower patch reef communities.

Multibeam bathymetry surveys at Miller’s Ledge provide a detailed view of the ~40 m (in relief) escarpment 

occurring through the center of the TSER (Figs 7–9). 

Water depths at Miller’s Ledge range from approximately 84 m at the crest to 124 m at the base of the feature, 

where a distinct trough and moat has formed (Fig. 7). 

Figure 3

The first author in the Deepworker 2000 Research submersible. This one-person submarine is 

rated to a depth of 2000 fsw (610 m) and equipped with a digital video camera, manipulator 

arm, and sampling basket. The submersible was used to survey Miller’s Ledge at Tortugas 

South Ecological Reserve. Photograph provided by Kip Evans, National Geographic Society, in 

association with the Sustainable Seas Expeditions, 2002 (Sylvia Earle, Chief Scientist). 
52 Professional Paper NMFS 5

Figure 4

Plan view of color-shaded bathymetry of Riley’s Hump. Vertical exaggeration=4X. Depth in meters.

The escarpment is steep, with an estimated rise of approximately 20–30 degrees. There are numerous areas 

of rock outcrops at the base of the feature (Fig 8A). The 

escarpment begins along the eastern boundary of TSER 

and extends 11 km to the western edge of the survey 

area. Bathymetric charts indicate that it may extend an 

additional 40 km to the southern extent of Pulley Ridge 

along the SW corner of the shelf. The western half of the 

study area is characterized by abrupt topography and 

many isolated mounds and peaks (Fig. 8A). In contrast, 

the eastern half of the survey area is characterized by a 

gradual decrease in the height of the scarp, a reduced 

number of solitary mounds at the base of the feature, 

and its eventual burial under shelf sediments (Fig 8C). 

North of the escarpment, distinct linear ridges are present parallel to the crest of the feature. These ridges most 

likely represent consolidated paleoshorelines formed 

during sea level lowstands, and were observed as linear 

belts of low profile rock outcrops during ROV and submersible dives.

In contrast to the deep coral reef communities of 

Riley’s Hump, benthic assemblages of Miller’s Ledge 

are dominated by small sponges, bryozoans, small 

solitary corals, and the corkscrew sea whip, Cirrhipathes 

sp. Rocky outcrops associated with paleoshorelines 

and high profile outcrops at the crest of the ledge had 

relatively low levels of encrusting invertebrate growth. 

Consolidated rock cobbles, boulders, and larger blocks 

were scattered along the face of Miller’s Ledge, and 

colonized by bryozoans, hydrozoans, and solitary corals. 

This encrusting assemblage was observed on all hard 

surfaces below 90 m. During submersible dive A20-
Weaver et al.: Reef fish surveys and multibeam bathymetry at the Tortugas South Ecological Reserve 53

Figure 5

Oblique view of color-shaded bathymetry of Riley’s Hump, looking east. Vertical exaggeration=4X. Depth in meters. 

National Marine Fisheries Service study locations depicted by blue stars, mutton snapper aggregation sites represented by 

yellow stars (locations courtesy M. Burton, NMFS Beaufort). See Figure 4 for scale.

164, extensive expanses of coarse sediments and fields 

of carbonate debris were observed along the eastern 

extent of the feature. This area appears to be buried 

by coarse sediments and carbonate material carried to 

the southeast from the shallower regions of the shelf, 

including Riley’s Hump (Fig. 8B). Few hard bottom 

communities were observed in this area, with occasional 

low profile rock outcrops and scattered rock fragments. 

At three separate locations along the submersible track, 

large aggregations of pencil urchins, Eucidaris sp., were 

observed evenly distributed across the coarse sediment 

fields. These urchin aggregations were not observed 

during ROV operations.

Also during the submersible dive, an abandoned set 

of traps was observed to form an artificial reef (Figs. 

10A–C). Seven individual traps were tightly bundled 

together by a length of trapline (Fig 10A). The surface 

of the trap mesh was covered with colonial oysters, 

forming a high profile oyster reef. This artificial reef 

was densely colonized by small reef fishes, primarily 

the red barbier (Hemanthias vivanus), and attracted 

numerous species of grouper and snapper, including 

a pair of speckled hind (Epinephelus drummondhayi) 

and a large aggregation of scamp (Mycteroperca phenax) 

(Fig 10B). A large hogfish (Lachnolaimus maximus) was 

observed foraging on the surface of the artificial reef 

(Fig 10C).

During ROV surveys at Miller’s Ledge, we observed a 

large Warsaw grouper (Epinephelus nigritus) in a distinct 

bicolor phase, with dark brown grey dorsum, head and 

caudal fin coloration clearly demarcated from a bright 

white lateral surface. The black anal fin spot was clearly 

visible in this individual (Fig. 11). 

During ROV surveys at the central and western portion 

of the survey area, extensive areas of deep rocky reefs 

were observed at the base of the feature (Fig. 12). The 

rectilinear fracture pattern observed on these blocks 

suggest transport of a large section from the rocky layers 

forming the crest of the escarpment. At other areas of 

the base of the scarp and trough, extensive regions of 
54 Professional Paper NMFS 5

Figure 6

Biological communities of Riley’s Hump. A) French angelfish (Pomacanthus paru) above scattered 

small hard coral heads and sea plumes. B) Larger colonies of Montastrea cavernosa, sea plumes, and 

sponges.

A

B
Weaver et al.: Reef fish surveys and multibeam bathymetry at the Tortugas South Ecological Reserve 55

Figure 7

Top) Plan view of color-shaded bathymetry for Miller’s Ledge of the Tortugas South Ecological Reserve. Bottom) Crosssection (A–B) of Miller’s Ledge revealing steep escarpment. Depth range from 80 m (red) to 150 m (purple). This feature 

provides extensive habitat for a variety of groupers, including scamp, speckled hind, Warsaw, and snowy grouper.

white clay substratum and scattered rock cobbles and 

boulders were observed (Fig. 13). This formation was 

termed “cookie dough” reef due to the rough surface 

of the rock structures and the presence of small solitary 

coral colonies scattered about the surface. Dredge 

samples taken along the west-central region of the study 

area reveal these reef structures to be biogenic, formed 

by sessile molluscs, bryozoans, tube worms, and solitary 

corals (Fig 13, bottom). These small reef structures attracted numerous species of reef fishes, and a variety of 

groupers were observed on the larger rocky outcrops at 

the base of the feature.

One hundred and six (106) species of reef fishes 

and a total of 13,766 individuals were observed at both 

Riley’s Hump and Miller’s Ledge via all survey methods 

(Appendix A). Comparisons of reef fish assemblages at 

Riley’s Hump and Miller’s Ledge reveals distinct differences in the reef fish communities (Table 1). Reef fish 

surveys conducted at Riley’s Hump revealed a diverse 

shallow water coral reef assemblage, dominated by 

labrids, pomacentrids, and scarids, making up over 

70% of the fish population by number. The bluehead 

(Thallasoma bifasciatum), bicolor damsel (Stegastes 

partitus), yellowhead wrasse (Halichoeres garnoti), and 

greenblotch parrotfish (Sparisoma atomarium) were 

the dominant reef fish taxa observed by ROV surveys. 

In addition to these taxa, the masked goby (Coryphopterus personatus) and striped grunts (Haemulon striatum) 

were abundant during SCUBA surveys. Other families 

of fishes represented were serranids, tetraodontids, 

acanthurids, and chaetodontids. In contrast, fishes 

of Miller’s Ledge are overwhelmingly dominated by 

schooling serranids, including the roughtongue bass 

(Pronotogrammus martinicensis), red barbier (Hemanthias vivanus), and creole-fish (Paranthias furcifer). 

Other serranids, including tattler (Serranus phoebe) and 

scamp (Mycteroperca phenax), are abundant along the 

ledge and associated habitats. Other dominant taxa 

at Miller’s Ledge include the yellowtail reeffish (Chromis enchrysura) and striped grunts. Of the top 15 fish 
56 Professional Paper NMFS 5

Figure 8

A) Oblique view of color-shaded bathymetry of Miller’s Ledge, looking west. B) Oblique view of color-shaded 

bathymetry of Miller’s Ledge, looking east. Vertical exaggeration=4X. Depth in meters. See Fig. 7 for scale.

A

B
Weaver et al.: Reef fish surveys and multibeam bathymetry at the Tortugas South Ecological Reserve 57

Figure 9

Oblique view of color-shaded bathymetry of Miller’s Ledge, looking north. Vertical exaggeration=4X. Depth in meters. 

See Fig. 7 for scale.

taxa observed at each location, only sharpnose puffer 

(Canthigaster rostrata) and yellowtail reef fish were represented in the 15 most abundant fish taxa observed at 

both locations (Table 1). 

Reef fishes documented at Riley’s Hump were ecologically diverse, and dominated by planktivores (three 

of the top four most abundant species—Table 1). Herbivores, benthic carnivores, and epibenthic browsers 

were also common in the deep coral reef community. 

In contrast, the fish community at Miller’s Ledge was 

overwhelmingly dominated by planktivores, with over 

95% of the individuals observed by ROV and submersible belonging to this feeding guild. The top five most 

abundant species observed by ROV, and top four observed by submersible, were planktivores. Piscivores, 

represented primarily by scamp, and generalized 

carnivores, represented by tattler, were the next most 

abundant groups on the submersible transect and 

ROV transects, respectively. Other groups observed 

included benthic carnivores (greenband wrasse, Halichoeres bathyphilus and red hogfish, Decodon puellaris). 

Epibenthic browsers were represented by the reef butterflyfish (Chaetodon sedentarius) and the goldface toby 

(Canthigaster jamestyleri).

Discussion

Reef fish communities observed at the TSER are distinctly divided between the deep coral reef fish assemblage of Riley’s Hump, and the outer shelf/upper slope 

deep reef assemblage associated with drowned/fossil 

reef formations at Miller’s Ledge. Common reef fish 

taxa of the drowned reef assemblages also dominate 

hard bottom reefs and banks of the northwestern Gulf 

of Mexico (Rezak et al., 1985, 1990; Dennis and Bright, 

1988). Reef fish assemblages follow similar patterns to 

benthic invertebrate assemblages, where distance from 

shore, water turbidity, seasonal temperatures, and water 

depth determines benthic assemblages and the associated reef fish assemblages (Dennis and Bright, 1988). 

While the northwestern Gulf of Mexico is characterized by a diversity of deep reef habitats, including 

coralgal reefs (partly drowned reefs) and algal nodulesponge communities, these communities, and many of 

their associated reef fishes, are absent at the crest of 

Miller’s Ledge. Elevated turbidity levels and swift currents associated with the persistent flow of the Loop 

Current/Florida Current likely increases suspended 

sediment levels in this region, and lead to the burial of 
58 Professional Paper NMFS 5

A

B

Figure 10

A) Seven ghost fish traps forming an artificial reef, colonized by oysters, at the eastern 

terminus of Miller’s Ledge. A speckled hind (Epinephelus drummondhayi) swims above the 

reef. B) A large group of scamp (Mycteroperca phenax) aggregating around the artificial reef. 

Natural rock outcrops of Miller’s Ledge visible in background. C) A large male hogfish 

(Lachnolaimus maximus) foraging on the reef surface. Stills taken from video footage courtesy 

the Sustainable Seas Expedition.
Weaver et al.: Reef fish surveys and multibeam bathymetry at the Tortugas South Ecological Reserve 59

Figure 10 (continued)

C

the scarp at the eastern boundary of TSER. The formation of the steep vertical scarp of Miller’s Ledge along its 

central and western portion, the associated trough and 

moat at the base of the formation, and the sweeping of 

sediments from Riley’s Hump and the surrounding shelf 

eastward all appear to be linked to the prominent flow 

of the Florida Current. Turbulent water flow and upwelling was observed at the surface during our research 

cruises, and appear to be caused by the impact of this 

major current on this steep scarp.

The dominance of planktivores in deep reef fish 

assemblages has been observed in other studies of 

community structure in the Gulf of Mexico (PattengillSemmens et al., 1997) and the Pacific (Hamner et al., 

1988; Thresher and Colin, 1986). Pettengill Semmens 

et al. (1997) and Hamner et al. (1988) consistently observed high numbers in the reef fish communities of the 

northwestern Gulf of Mexico (Flower Garden Banks and 

Stetson Banks) and the Great Barrier Reef, Australia, 

respectively. Results of the Riley’s Hump reef fish surveys 

show parallel trends at assemblages, with over 50% of 

the individuals represented by planktivores. Plantivores 

and piscivores also appear to dominate deeper reef communities worldwide and form the primary trophic pathways between 90 and 300 m (Thresher and Colin, 1986). 

Roughtongue bass and red barbier were the dominant 

species observed at Miller’s Ledge, and are the most 

abundant reef fish by number on most drowned reef 

areas throughout the southeastern U.S. and Gulf of 

Mexico. Roughtongue bass, red barbier, and threadnose 

bass form dense schools over high relief rocky structures 

throughout the Gulf of Mexico, and provide a forage 

base for many deep reef predators. The high profile 

rock face of Miller’s Ledge provides feeding grounds for 

large groupers and snappers, and their prey. Numerous 

scamp were observed along the ledge, and the bicolor 

phase of the Warsaw grouper indicates a dominant male 

of the species and potential for spawning activity, as has 

been observed for other groupers (Gilmore and Jones, 

1992).

While small prey fishes often reach great abundances 

at shelf edge reef structures, upper and middle slope regions are often food poor (Weaver and Sedberry, 2001). 

With greater depths (150–250 m), the main trophic 

pathways for dominant predators include benthic invertebrates (snowy grouper, Epinephelus niveatus, and tilefish, Caulolatilus spp.) and plankton for large schools of 

yellowtail bass (Anthias nicholsi) associated with isolated 

rock outcrops (Weaver and Sedberry, 2001). While few 

reef fishes were observed along the eastern (leeward) 

extent of Miller’s Ledge, the persistent currents and 

abundant hard bottom structures observed along the 

scarp and base present high profile structures for reef 

fishes to aggregate. This abrupt change in topography 
60 Professional Paper NMFS 5

Figure 11

A large Warsaw grouper (Epinephelus nigritus) occurring at Tortugas South Ecological Reserve. This 

individual displays a “bicolor” phase typical of dominant males in spawning condition, and indicates 

a potential spawning area for this species. The Warsaw grouper is a rare species that is of management 

concern. Digital still frame provided by NURC-UNCW.
Weaver et al.: Reef fish surveys and multibeam bathymetry at the Tortugas South Ecological Reserve 61 Table 1 Top 15 fishes observed at the Tortugas South Ecological Reserve via SCUBA, ROV, and submersible. Trophic categories follow Randall (1967) and Bullock and Smith (1991). TSER0801 SSE2002 TSER0801 SSE2002-164 Riley’s ROV Riley’s SCUBA Miller’s ROV Miller’s submersible Species Common name Diet1 Rank No. Percent Rank No. Percent Rank No. Percent Rank No. Percent Thallasoma bifasciatum bluehead PL 1 424 25.48 3 127 11.27 — — — — — — Stegastes partitus bicolor damselfish PL 2 270 16.23 4 102 9.05 — — — — — — Halichoeres garnoti yellowhead wrasse BC 3 233 14.00 7 35 3.11 — — — — — — Chromis scotti purple reeffish PL 4 173 10.40 1 306 27.15 — — — — — — Sparisoma atomarium greenblotch parrotfish H 5 104 6.25 18 9 0.80 — — — — — — Inermia vittata boga PL 6 100 6.01 — — — — — — — — — Clepticus parrae creole wrasse PL 7 44 2.64 15 13 1.15 — — — — — — Sparisoma aurofrenatum redband parrotfish H 8 26 1.56 14 14 1.24 — — — — — — Serranus tortugarum chalk bass PL 9 24 1.44 — — — — — — — — — Canthigaster rostrata sharpnose puffer EB 10 21 1.26 21 4 0.35 25 3 0.04 — — — Chromis cyanea blue chromis PL 11 19 1.14 6 45 3.99 — — — Acanthurus coeruleus blue tang H 12 17 1.02 17 11 0.98 — — — — — — Scarus iserti striped parrotfish H 13 16 0.96 8 25 2.22 — — — — — — Chromis enchrysura yellowtail reeffish PL 14 14 0.84 — — — 4 69 0.96 3 104 2.67 Coryphopterus personatus masked goby PL 15 12 0.72 2 201 17.83 — — — — — — Acanthurus chirurgus doctorfish H 18 11 0.66 9 22 1.95 — — — — — — Pseudopeneus maculatus spotted goatfish BC 20 9 0.54 12 16 1.42 45 1 0.01 — — — Chaetodon sedentarius reef butterflyfish EB 21 8 0.48 13 14 1.24 9 36 0.50 11 12 0.31 Chromis insolata sunshinefish PL 23 7 0.42 11 16 1.42 — — — — — — Chaetodon capistrata foureye butterflyfish EB 28 4 0.24 10 19 1.69 — — — Priacanthus arenatus bigeye PL 30 4 0.24 — — — 15 16 0.22 28 1 0.03 Mycteroperca phenax scamp PI 62 1 0.06 — — — 7 44 0.61 5 33 0.85 Pronotogrammus martinicensis roughtongue bass PL — — — — — — 1 5663 79.03 2 804 20.66 Hemanthias vivanus red barbier PL — — — — — — 2 602 8.40 1 2709 69.62 Paranthias furcifer creole-fish PL — — — — — — 5 61 0.85 8 19 0.49 Serranus phoebe tattler GC — — — — — — 6 60 0.84 6 26 0.67 Halichoeres bathyphilus greenband wrasse BC — — — — — — 8 43 0.60 9 17 0.44 Sargocentron bullisi deepwater squirrelfish PL — — — — — — 10 24 0.33 12 7 0.18 Decodon puellaris red hogfish BC — — — — — — 11 23 0.32 14 4 0.10 Chaetodon aya bank butterflyfish PL — — — — — — 12 21 0.29 7 19 0.49 Seriola rivoliana almaco jack PI — — — — — — 13 20 0.28 — — — Canthigaster jamestyleri goldface toby EB — — — — — — 14 19 0.27 13 6 0.15 Bodianus pulchellus spotfin hogfish GC — — — — — — 19 7 0.10 10 12 0.31 Haemulon striatum striped grunts PL — — — 5 76 6.74 3 361 5.01 4 104 2.67 Epinephelus drummondhayi speckled hind GC — — — — — — 36 1 0.01 15 4 0.10 1 BC=benthic carnivores (benthic crustaceans and infauna), EB=epibenthic browsers (sessile invertebrates), GC=general carnivore (mobile invertebrates and fishes), H=herbivore, PI=piscivore, PL=planktivore.
62 Professional Paper NMFS 5

Figure 12

Large natural reef blocks located at the base of Miller’s Ledge. Extensive areas of reef at the base of the ledge offer shelter 

for speckled hind, snowy grouper, scamp, and Warsaw grouper.

associated with rocky outcrops has been shown to support high numbers of fishes in other oceans (Yoklavich 

et al., 2000). Elevated structures provide shelter and 

access to plankton brought by impinging water currents 

(Hamner et al., 1988; Weaver and Sedberry, 2001). In 

addition to increases in local productivity and prey availability, reef fishes also appear to select areas of elevated 

topography based on behavioral preferences (Bohnsack, 1989). Further submersible and ROV operations 

within the reserve should target hard bottom communities in deeper waters to identify additional shifts in fish 

assemblages.

While the deep coral reef assemblage and underlying geology at Riley’s Hump has been well documented 

(Franklin et al., 2003; Mallinson et al., 2003), the underlying processes leading to the formation of Miller’s 

Ledge remain unresolved. The benthic assemblage 

leading to the formation of biogenic hard bottom reef 

structures, and the geologic processes that form rocky 

outcrops along the northern margin and crest of the 

scarp, are unknown. Further studies should be conducted to determine the origin and structure of the feature, 

the interaction between the Loop/Florida Current and 

the abrupt change in reef topography, and the transport 

and suspension of sediments from Riley’s Hump and 

the surrounding shelf, so that impacts on the benthic 

invertebrate assemblages and associated reef fishes can 

be determined.

Acknowledgements

The Sustainable Seas Expedition (SSE) was a five-year 

collaborative effort between NOAA and the National 
Weaver et al.: Reef fish surveys and multibeam bathymetry at the Tortugas South Ecological Reserve 63

Figure 13

Top) “Cookie dough” reef cobbles and boulders located on a clay substratum along the base of Miller’s Ledge. Bottom) 

These bioherms are built from oysters, bryozoans, and solitary corals, and form extensive hard bottom habitat for small reef 

fishes along the base of Miller’s Ledge.
64 Professional Paper NMFS 5

Geographic Society (NGS) to explore important marine ecosystems with special emphasis on the nation’s 

National Marine Sanctuaries. The first author thanks 

Sylvia Earle for the opportunity to be part of the SSE 

missions, train as a pilot, and for the use of the DW2000 

submersible during the research cruise to the TSER. 

We also thank the NGS assistants and Nuytco Research 

Ltd. personnel for logistical and technical support during cruises in the Dry Tortugas. We thank the captain 

and crew of the R/V Suncoaster of the Florida Institute 

of Oceanography for hospitality and support during 

two research cruises. Lance Horn and Tom Potts provided expert operation of the NURC-UNCW Phantom 

ROV during transect surveys. Funding for ship time 

and ROV surveys were provided by the National Fish 

and Wildlife Foundation and the Walt Disney Wildlife 

Conservation Fund, and NURC-Wilmington Grant No. 

9911. Multibeam surveys were supported by USGS, USF, 

ONR, and NOAA. Generous technical and equipment 

assistance was provided by the Naval Oceanographic 

Office (NAVO). The authors thank Barbara Reed and 

others in NAVO for their rapid response, assistance, 

and data sharing.

Literature cited

Avent, R. M., M. E. King, and R. H. Gore. 

1977. Topographic and faunal studies of shelf-edge prominences off the central eastern Florida Coast. In. Rev. Hydrobiol. 

62(2):185–208.

Barans, C. A., and V. J. Henry Jr. 

1984. A description of the shelf edge groundfish habitat along 

the southeastern United States. NE Gulf Sci. 7(1):77–96.

Bohnsack, J. A. 

1989. Are the high densities of fishes at artificial reefs the result 

of habitat limitation or behavioral preference? Bull. Mar. Sci. 

44:631–645. 

Bullock, L. H., and G. B. Smith. 

1991. Seabasses (Pisces:Serranidae). Memoirs of the Hourglass

cruise. Florida Marine Research Institute. Dept. Nat. Res., St. 

Petersburg, FL, Vol. 8, 243 p.

Cowie-Haskell, B. D., and J. M. Delaney. 

2003. Integrating science into the design of the Tortugas Ecological Reserve. Mar.Techn. Soc. J. 37(1): 68–79.

Dahlgren, C. P., J. A. Sobel, and D. E. Harper. 

2001. Assessment of the reef fish community, habitat, and 

potential for larval dispersal from the proposed Tortugas 

South Ecological Reserve. Proc. Gulf Caribb. Fish. Inst. 

52:700–712. 

Dennis, G. D., and T. J. Bright. 

1988a. Reef fish assemblages on hard banks in the northwestern 

Gulf of Mexico. Bull. Mar. Sci. 43(2):280–307.

Franklin, E. C., J. S. Ault, S. G. Smith, J. Luo, G. A. Meester, G. A. Diaz, 

M. Chiappone, D. W. Swanson, S. L. Miller, and J. A. Bohnsack. 

2003. Benthic habitat mapping in the Tortugas Region, Florida. 

Mar. Geod. 26:19–34.

Gilmore, R. G., and R. S. Jones. 

1992. Color variation and associated behavior in the epinepheline groupers, Mycteroperca microlepis (Goode and Bean) and 

M. phenax Jordan and Swain. Bull. Mar. Sci. 51(1):83–103.

Hamner, W. M., M. S. Jones, J. H. Carleton, I. R. Hauri, and D. McB. 

Williams. 

1988. Zooplankton, piscivorous fish, and water currents on a 

windward reef face: Great Barrier Reef, Australia. Bull. Mar. 

Sci. 42(3):459–479.

Humann, P., and N. DeLoach. 

2002. Reef fish identification (3rd ed.). New World Publications, 

Jacksonville, FL, 481 p.

Mallinson, D., A. Hine, P. Hallock, S. Locker, E. Shinn, D. Naar, B. 

Donahue, and D. Weaver.

2003. Development of small carbonate banks on the south Florida platform margin: response to sea level and climate change. 

Mar. Geol. 3333: 1–19.

Pattengill-Semmens, B. X. Semmens, and S. R. Gittings. 

1997. Reef fish trophic structure at the Flower Gardens and Stetson Bank, NW Gulf of Mexico. Proc. 8th Int. Coral Reef Symp. 

1:1023–1028.

Randall, J. E. 

1967. Food habits of reef fishes of the West Indies. Stud. Trop. 

Ocean. 5:665–847.

Rezak, R., T. J. Bright, and D. W. McGrail. 

1985. Reefs and banks of the Northwestern Gulf of Mexico: Their 

geological, biological, and physical dynamics. John Wiley and 

Sons, New York, 259 p.

Rezak, R., S. R. Gittings, and T. R. Bright. 

1990. Biotic assemblages and ecological controls on reefs and 

banks of the northwest Gulf of Mexico. Amer. Zool. 30:23–35.

Robins, C. R., R. M. Bailey, C. E. Bond, J. R. Brooker, E. A. Lachner, 

R. N. Lea, and W. B. Scott. 

1991. Common and scientific names of fishes from the United 

States and Canada (5th ed.). Am. Fish. Soc. Spec. Publ. 20, 

183 p.

Smith-Vaniz, W. F., B. B. Collette, and B. E. Luckhurst. 

1999. The Fishes of Bermuda. Amer. Soc. Ichthy. Herpet. Spec. 

Publ. 4, 424 p.

Thresher, R. E., and P. L. Colin. 

1986. Trophic structure, diversity and abundance of fishes of 

the deep reef (30–300 m) at Enewetak, Marshall Islands. Bull. 

Mar. Sci. 38:253–272.

Weaver, D. C., and G. R. Sedberry. 

2001. Trophic subsidies in the twilight zone: food web structure 

of reef fishes on the continental slope of the southeastern 

United States. In Islands in the Stream: oceanography and 

fisheries of the Charleston Bump (G. R. Sedberry, ed.), p. 

137–152. American Fisheries Society, Symposium 25, Bethesda, Maryland.

Yoklavich, M. M., H. G. Greene, G. M. Caillet, D. E. Sullivan, R. N. 

Lea, and M. S. Love.

2000. Habitat association of deep-water rockfishes in a submarine canyon: an example of a natural refuge. Fish. Bull. 

98:625–641.
Weaver et al.: Reef fish surveys and multibeam bathymetry at the Tortugas South Ecological Reserve 65 Appendix A Fishes observed at the Tortugas South Ecological Reserve via SCUBA, ROV, and submersible. TSER0801 SSE2002 TSER0801 SSE2002-164 Riley’s ROV Riley’s SCUBA Miller’s ROV Miller’s submersible Family Species Common name Diet1 Rank No. Percent Rank No. Percent Rank No. Percent Rank No. Percent Acanthuridae Acanthurus bahianus ocean surgeonfish H 19 10 0.60 — — — — — — — — — Acanthurus chirurgus doctorfish H 18 11 0.66 9 22 1.95 — — — — — — Acanthurus coeruleus blue tang H 12 17 1.02 17 11 0.98 — — — — — — Apogonidae Apogon pseudomaculatus twospot cardinalfish PL 44 1 0.06 — — — — — — — — — Blenniidae Parablennius marmoreus seaweed blenny H — — — — — — — — — 19 3 0.08 Carangidae Caranx ruber bar jack PI 46 1 0.06 — — — — — — — — — Seriola dumerili greater amberjack PI 17 12 0.72 — — — — — — 24 2 0.05 Seriola rivoliana almaco jack PI — — — — — — 13 20 0.28 — — — Carcharhinidae Carcharhinus sp. PI 47 1 0.06 — — — — — — — — — Negaprion brevirostis lemon shark PI 63 1 0.06 — — — — — — — — — Chaetodontidae Chaetodon aya bank butterflyfish PL 12 21 0.29 7 19 0.49 — — — — — — Chaetodon capistrata foureye butterflyfish EB 28 4 0.24 10 19 1.69 — — — — — — Chaetodon ocellatus spotfin butterflyfish EB 27 5 0.30 22 4 0.35 23 4 0.06 — — — Chaetodon sedentarius reef butterflyfish EB 21 8 0.48 13 14 1.24 9 36 0.50 11 12 0.31 Chaetodon striata banded butterflyfish EB 51 1 0.06 27 2 0.18 — — — Cirrhitidae Ambylocirrhus pinos redspotted hawkfish GC — — — 34 1 0.09 — — — — — — Diodontidae Diodon holocanthus balloonfish GC 52 1 0.06 — — — — — — — — — Echeneidae Echineus naucrates sharksucker GC 53 1 0.06 28 2 0.18 35 1 0.01 — — — Gobiidae Coryphopterus personatus masked goby PL 15 12 0.72 2 201 17.83 — — — — — — Gnatholepis thompsoni goldspot goby EB — — — 41 1 0.09 32 2 0.03 — — — Ptereleotris calliurus blue goby PL 34 3 0.18 — — — — — — — — — Haemulidae Haemulon plumieri white grunt BC 57 1 0.06 23 4 0.35 — — — — — — Haemulon striatum striped grunt PL — — — 5 76 6.74 33 361 5.01 4 104 2.67 Holocentridae Corniger spinosus spinycheek soldierfish GC — — — — — — 17 12 0.17 — — — Holocentrus adscensionus squirrelfish PL 24 9 0.54 — — — 21 5 0.07 — — — Holocentrus rufus longspine squirrelfish P 16 12 0.72 16 12 1.06 — — — — — — Sargocentron bullisi deepwater squirrelfish PL — — — — — — 10 24 0.33 12 7 0.18 continued
66 Professional Paper NMFS 5 Appendix A (continued) TSER0801 SSE2002 TSER0801 SSE2002-164 Riley’s ROV Riley’s SCUBA Miller’s ROV Miller’s submersible Family Species Common name Diet1 Rank No. Percent Rank No. Percent Rank No. Percent Rank No. Percent Inermiidae Inermia vittata boga PL 6 100 6.01 — — — — — — — — — Labridae Bodianus pulchellus spotfin hogfish GC — — — 19 7 0.10 10 12 0.31 — — — Clepticus parrae creole wrasse PL 7 44 2.64 15 13 1.15 — — — — — — Decodon puellaris red hogfish BC — — — — — — 11 23 0.32 14 4 0.10 Halichoeres bathyphilus greenband wrasse BC — — — — — — 8 43 0.60 — — — Halichoeres bivittatum slippery dick BC 32 3 0.18 29 2 0.18 — — — 9 17 0.44 Halichoeres cyanocephalus yellowcheek wrasse BC 58 1 0.06 — — — — — — — — — Halichoeres garnoti yellowhead wrasse BC 3 233 14.00 7 35 3.11 — — — — — — Halichoeres maculipinna clown wrasse BC 25 6 0.36 19 7 0.62 — — — — — — Halichoeres radiatus puddingwife BC — — — 42 1 0.09 — — — — — — Lachnolaimus maximus hogfish BC 39 2 0.12 24 4 0.35 — — — 26 1 0.03 Thallasoma bifasciatum bluehead PL 1 424 25.48 3 127 11.27 — — — — — — Lutjanidae Lutjanus analis mutton snapper GC 40 2 0.12 — — — 21 2 0.05 — — — Lutjanus campechanus red snapper GC 61 1 0.06 — — — 40 1 0.01 27 1 0.03 Lutjanus griseus gray snapper GC 33 3 0.18 — — — — — — — — — Lutjanus synagris lane snapper GC — — — — — — — — — 17 4 0.10 Ocyurus chrysurus yellowtail snapper PL 64 1 0.06 — — — — — — — — — Monacanthidae Cantherhines macroceros whitespotted filefish EB 45 1 0.06 — — — — — — — — — Canthidermis sufflamen ocean triggerfish PL 22 7 0.42 36 1 0.09 — — — — — — Mullidae Psuedopeneus maculatus spotted goatfish BC 20 9 0.54 12 16 1.42 45 1 0.01 — — — Muraenidae Gymnothorax moringa spotted moray GC — — — — — — 38 1 0.01 — — — Gymnothorax sp. GC — — — — — — 39 1 0.01 — — — Opistognathidae Opstognathus aurifrons yellowhead jawfish PL 41 2 0.12 — — — — — — — — — Ostraciidae Acanthostacion polygonia honeycomb cowfish EB 35 2 0.12 — — — — — — — — — Lactophrys triqueter smooth trunkfish EB 60 1 0.06 — — — — — — — — — Pomacanthidae Centropyge argi cherubfish EB 48 1 0.06 — — — — — — — — — Holacanthus bermudensis blue angelfish EB 29 4 0.24 20 7 0.62 27 3 0.04 — — — Holacanthus ciliaris queen angelfish EB — — — 43 1 0.09 — — — — — — Holacanthus tricolor rock beauty EB 36 2 0.12 44 1 0.09 28 3 0.04 — — — Pomacanthus paru French angelfish EB 65 1 0.06 45 1 0.09 — — — — — — Chromis cyanea blue chromis PL 11 19 1.14 6 45 3.99 — — — — — — continued
Weaver et al.: Reef fish surveys and multibeam bathymetry at the Tortugas South Ecological Reserve 67 Appendix A (continued) TSER0801 SSE2002 TSER0801 SSE2002-164 Riley’s ROV Riley’s SCUBA Miller’s ROV Miller’s submersible Family Species Common name Diet1 Rank No. Percent Rank No. Percent Rank No. Percent Rank No. Percent Chromis enchrysura yellowtail reeffish PL 14 14 0.84 — — — 4 69 0.96 3 104 2.67 Chromis insolata sunshinefish PL 23 7 0.42 11 16 1.42 — — — — — — Chromis multilineata brown chromis PL — — — 26 3 0.27 — — — — — — Chromis scotti purple reeffish PL 4 173 10.40 1 306 27.15 — — — — — — Stegastes partitus bicolor damselfish PL 2 270 16.23 4 102 9.05 — — — — — — Stegastes variabilis cocoa damselfish H 68 1 0.06 33 2 0.18 — — — — — — Priacanthus arenatus bigeye PL 30 4 0.24 — — — 15 16 0.22 28 1 0.03 Pristigenys alta short bigeye PL — — — — — — 44 1 0.01 — — — Rhincodontidae Ginglyostoma cirratum nurse shark GC 56 1 0.06 40 1 0.09 — — — — — — Scaridae Scarus iserti striped parrotfish H 13 16 0.96 8 25 2.22 — — — — — — Scarus taeniopterus princess parrotfish H 66 1 0.06 — — — — — — — — — Sparisoma atomarium greenblotch parrotfish H 5 104 6.25 18 9 0.80 — — — — — — Sparisoma aurofrenatum redband parrotfish H 8 26 1.56 14 14 1.24 — — — — — — Sparisoma viride stoplight parrotfish H 43 2 0.12 46 1 0.09 — — — — — — Sciaenidae Equetus lanceolatus high-hat BC — — — — — — — — — 20 2 0.05 Pareques iwamotoi blackbar drum BC — — — — — — 16 15 0.21 — — — Scorpaenidae Scorpaena dispar hunchback scorpionfish GC — — — — — — 46 1 0.01 — — — Scorpaena sp. GC — — — — — — 20 12 0.17 — — — Serranidae Centropristis fuscula twospot seabass GC 34 1 0.01 — — — — — — — — — Cephalopholis cruentata graysby GC 49 1 0.06 37 1 0.09 22 4 0.06 25 1 0.03 Cephalopholis fulva coney GC 50 1 0.06 — — — — — — — — — Epinephelus drummondhayi speckled hind GC — — — — — — 36 1 0.01 15 4 0.10 Epinephelus guttatus red hind GC 54 1 0.06 — — — — — — — — — Epinephelus morio red grouper GC 55 1 0.06 39 1 0.09 30 2 0.03 18 3 0.08 Epinephelus nigritus Warsaw grouper GC — — — — — — 31 2 0.03 — — — Epinephelus niveatus snowy grouper GC — — — — — — 37 1 0.01 — — — Gonioplectrus hispanus Spanish flag GC — — — — — — 26 3 0.04 — — — Hemanthias vivanus red barbier PL — — — — — — 2 602 8.40 1 2709 69.62 Hypoplectrus gemma blue hamlet GC 59 1 0.06 30 2 0.18 — — — — — — Hypoplectrus unicolor butter hamlet GC 38 2 0.12 31 2 0.18 — — — — — — Liopropoma eukrines wrasse bass GC — — — — — — 18 12 0.17 16 4 0.10 Mycteroperca bonaci black grouper PI — — — — — — 41 1 0.01 22 2 0.05 Mycteroperca phenax scamp PI 62 1 0.06 — — — 7 44 0.61 5 33 0.85 Paranthias furcifer creole-fish PL — — — — — — 5 61 0.85 8 19 0.49 Plectranthias garrupellus apricot bass GC — — — — — — 43 1 0.01 — — —continued
68 Professional Paper NMFS 5 Appendix A (continued) TSER0801 SSE2002 TSER0801 SSE2002-164 Riley’s ROV Riley’s SCUBA Miller’s ROV Miller’s submersible Family Species Common name Diet1 Rank No. Percent Rank No. Percent Rank No. Percent Rank No. Percent Pronotogrammus martinicensis roughtongue bass PL — — — — — — 1 5663 79.03 2 804 20.66 Serranus annularis orangeback bass GC 42 2 0.12 — — — — — — — — — Serranus phoebe tattler GC — — — — — — 6 60 0.84 6 26 0.67 Serranus tabacarius tabaccofish GC — — — 25 4 0.35 — — — — — — Serranus tigrinus harlequin bass GC 31 4 0.24 32 2 0.18 — — — — — — Serranus tortugarum chalk bass PL 9 24 1.44 — — — — — — — — — Sparidae Calamus nodosus knobbed porgy BC 26 5 0.30 35 1 0.09 — — — — — — Pagrus pagrus red porgy BC — — — — — — 42 1 0.01 23 2 0.05 Sphyraenidae Sphyraena barracuda great barracuda PI 67 1 0.06 — — — — — — — — — Synodontidae Synodus intermedius sand diver PI — — — — — — 24 4 0.06 — — — Synodus synodus red lizardfish PI — — — — — — 47 1 0.01 — — — Tetraodontidae Canthigaster rostrata sharpnose puffer EB 10 21 1.26 21 4 0.35 25 3 0.04 — — — Canthigaster jamestyleri goldface toby EB 14 19 0.27 13 6 0.15 — — — — — — Total 1664 1145 7169 3888 1 BC=benthic carnivores (benthic crustaceans and infauna), EB=epibenthic browsers (sessile invertebrates), GC=general carnivore (mobile invertebrates and fishes), H=herbivore, PI=piscivore, PL=planktivore.
69

Introduction

The northwestern Gulf of Mexico 

(NWGOM) reefs and banks are associated with the surface expression of 

salt domes, and have been identified 

as unique biological features warranting protection by Minerals Management Service (MMS) from oil and gas 

activities. The most recent investigations of reef fishes on the shelf-edge 

features in the northwestern Gulf 

(with the exceptions of East and West 

Flower Garden, Stetson and Sonnier 

Banks) were conducted during the 

1970s and 1980s (Rezak et al., 1985; 

Dennis and Bright, 1988a, 1988b), 

without the use of high-resolution 

multibeam bathymetry or high-resolution camera systems.

The Sustainable Seas Expedition 

(SSE) was a five-year collaborative 

effort between NOAA and the National Geographic Society (NGS) to 

explore important marine ecosystems 

with special emphasis on the nation’s 

National Marine Sanctuaries. Led by 

Sylvia Earle, Explorer-in-Residence of 

the NGS, the SSE program utilized 

both manned and unmanned submersible technologies to explore the 

NWGOM in 1999, 2001, and 2002. 

The use of the submersibles allowed 

the Flower Garden Banks National 

Marine Sanctuary (FGBNMS) team 

to visit several of the reefs and banks 

of the NWGOM and conduct exploratory dives to investigate reef fish and 

benthic communities in July 2002. 

Deep reef fish surveys by submersible on 

Alderdice, McGrail, and Sonnier Banks in 

the Northwestern Gulf of Mexico

Douglas C. Weaver

Emma L. Hickerson

George P. Schmahl

Flower Garden Banks National Marine Sanctuary, NOAA

4700 Avenue U, Building 216

Galveston, TX 77551

Email (for Weaver): doug.weaver@noaa.gov

Abstract—Submersible surveys at numerous reefs and banks in the northwestern Gulf of Mexico (NWGOM) were 

conducted as part of the Sustainable Seas 

Expedition (SSE) during July/August 

2002 to identify reef fish communities, 

characterize benthic habitats, and identify deep coral reef ecosystems. To identify the spatial extent of hard bottom reef 

communities, the Flower Garden Banks 

National Marine Sanctuary (FGBNMS) 

and the U.S. Geological Survey (USGS) 

mapped approximately 2000 km2 of the 

Northwestern Gulf of Mexico (NWGOM) 

continental shelf during June 2002 with 

high-resolution multibeam bathymetry. 

Previous investigations conducted on the 

features of interest (with the exceptions 

of East and West Flower Garden and Sonnier Banks, accessible by SCUBA) had 

not been conducted since the 1970s and 

1980s, and did not have the use of highresolution maps to target survey sites. The 

base maps were instrumental in navigating 

submersibles to specific features at each 

study site during the Sustainable Seas 

Expedition (SSE)—a submersible effort 

culminating from a partnership between 

the National Atmospheric and Oceanic 

Administration (NOAA) and the National 

Geographic Society (NGS). We report the 

initial findings of our submersible surveys, 

including habitat and reef fish diversity at 

McGrail, Alderdice, and Sonnier Banks. 

A total of 120 species and 40,724 individuals were identified from video surveys 

at the three banks. Planktivorous fishes 

constituted over 87% by number for the 

three banks, ranging from 81.4% at Sonnier Banks to 94.3% at Alderdice Bank, 

indicating a direct link to pelagic prey 

communities, particularly in the deep 

reef zones. High numbers of groupers, 

snappers, jacks, and other fishery species were observed on all three features. 

These sites were nominated as Habitat 

Areas of Particular Concern (HAPC) by 

the Gulf of Mexico Fishery Council in 

March 2004. Data obtained during this 

project will contribute to benthic habitat 

characterization and assessment of the associated fish communities through future 

SCUBA, ROV, and submersible missions, 

and allow comparisons to other deep reef 

ecosystems found throughout the Gulf of 

Mexico and western Atlantic Ocean.

The reefs and banks of the northwestern Gulf have been afforded some 

protection since the early 1970s. The 

MMS recognized early on that these 

sensitive areas should not be subjected 

to the direct impacts of offshore oil and 

gas development, and defined them as 

“no activity” zones (Fig. 1). However, 

these features are subject to a variety 

of other potential impacts unrelated to offshore development, which 

may not be regulated sufficiently. 

These impacts include those of commercial shipping (predominantly anchoring), fishing, cultural resource 

recovery and recreational activities. 

There is recent emphasis on the increased use of marine protected areas 

(MPA’s) in many ocean regions as a 

method to consolidate management 

under a coordinated mechanism. Accurately identifying the resources in 

the northwestern Gulf will contribute 

greatly to this effort.

Background

The Department of Oceanography 

at Texas A&M University (TAMU) 

conducted studies on “topographic 

features”—Outer Continental Shelf 

(OCS) reefs and banks—during the 

1970s and 1980s. Funding was provided by the U.S. Department of the 

Interior (DOI), Bureau of Land Management (BLM), out of which MMS 

later emerged through reorganization. The purpose of the TAMU study 
70 Professional Paper NMFS 5

Figure 1

Location of outer shelf banks in the northwestern Gulf of Mexico.

was to provide the MMS with data on the geologic, 

biologic, hydrologic, and chemical characteristics of the 

reefs and banks, to be used as a basis for management 

decisions on tracts nominated for oil and gas leasing 

(Rezak et al., 1985). 

As a comparison, the technology used for the historic surveys conducted by Rezak et al. (1985) included 

bathymetric mapping using a LORAN-C positioning system for navigation, a 3.5kHz subbottom profiler, EG&G 

Uniboom seismic system for subbottom information, 

and EG&G side-scan sonar systems for bottom characterization. Direct observations of the sea floor were 

made using the Texas A&M submersible DRV Diaphus 

(Rezak and Tieh, 1984; Dennis and Bright, 1988a). 

Each of the survey sites has a distinct biological zonation associated with base depths and water turbidity, 

and the following zones (when present), described during the early TAMU/MMS studies (Rezak et al., 1985; 

1990). The Stephanocoenia-Millepora Zone is inhabited 

by a low-diversity coral assemblage of 12 hermatypic 

coral species and can be found at the FGBNMS, McGrail Bank, and Bright Bank to depths of 50 m. On 

midshelf banks, such as Stetson and Sonnier Banks, the 

Millepora-Sponge Zone occupies depths less than 36 m. 

Crusts of the hydrozoan coral, Millepora, sponges, and 

other epifauna occupy the tops of siltstone, claystone, or 

sandstone outcrops on the crests of these features. The 

Algal-Sponge Zone covers the largest area among the 

reef-building zones on shelf-edge banks. The dominant 

organisms of the zone are the coralline algae, which 

are the most important carbonate producers through 

algal nodules and algal reefs (Minnery et al., 1985). The 

nodules range from 1 to 20 cm in size, cover up to 80 

percent of the bottom, and generally occur between 55 

and 85 m. Partly Drowned Reefs are also a major biotope 

occurring at similar depths to the Algal-Sponge Zone, 

and are defined as massive reefal structures covered with 

living crusts of coralline algae. With increased water 

depth, the assemblages of the zone become less diverse, 

characterized by antipatharians, comatulid crinoids, diminished leafy or coralline algae, and limited fish. High 

turbidity, sedimentation, and re-suspension occur in the 

Nepheloid Zone, where exposed rocks lack encrusting 

coralline algal crusts. Drowned Reefs occurring in this 

zone are often covered with a thin veneer of sediment, 

and epifauna are scarce. This zone occurs on all banks, 

but its depth differs at each bank and extends to the 

surrounding soft bottom.

The Sonnier Bank complex (historically referred 

to as Three Hickey Rock and Candy Mountain) is a 

mid-shelf bank, defined by Rezak et al. (1985, 1990) as 

salt dome structures rising from depths of 80 m or less 
Weaver et al: Reef fish surveys by submersible in the Northwestern Gulf of Mexico 71

and having a relief of about 4 to 50 m. The banks are 

located 135 km south of the Louisiana border, at position 28°20ʹN/92°27ʹW. As noted by Rezak et al. (1985) 

the Sonnier Bank complex consists of eight separate 

banks or peaks associated with a single salt dome. The 

peaks are nearly conical features with a maximum relief of about 30 m. Observations made by SCUBA and 

submersible have characterized the reef habitat on the 

peaks as entirely encrusted with fire coral (Millepora 

sp.) and sponges (primarily Neofibularia nolitangere and 

Ircinia sp.) (Rezak et al., 1985). This is the basis for the 

zonation name of Millepora-Sponge Zone. Rezak et al. 

(1985) reported three species of coral from the crests 

at Sonnier Banks: Stephanocoenia sp., Millepora sp., and 

Agaricia sp. In 1997, SCUBA surveys conducted by MMS 

and FGBNMS representatives compiled coral observations on the two shallowest peaks (24 m and 18 m) of 

Sonnier Banks. Other hermatypic coral species added 

by MMS diver surveys include Madracis decactis, M. 

mirabilis, M. pharensis, Siderastrea radians, Montastraea 

cavernosa, and Agaricia fragilis. 

McGrail Bank (formerly referred to as 18 Fathom 

Bank) is located 180 km south of the coast of Louisiana, at 27°58ʹN/92°36ʹW. As described by Rezak et al. 

(1985), the McGrail Bank is an arcuate pair of northeast-southwest trending ridges separated by a valley. McGrail Bank is one of the few banks in the northwestern 

Gulf of Mexico that has extensive growth of reef-building corals, in addition to East and West Flower Garden 

Banks. The original BLM OCS surveys documented 

four species of coral: the blushing star coral (Stephanocoenia intersepta; formerly S. michilini), fire coral (Millepora sp.), the great star coral (Montastraea cavernosa), 

and Agaricia sp.

Alderdice Bank is unique among the offshore banks 

in that it bears outcrops of basalt associated with the 

underlying salt dome (Rezak and Tieh, 1984). Alderdice 

Bank is located about 160 km south of Marsh Island, LA 

at 92°00ʹW/28°05ʹN. The bank is an oval, elongate in an 

east-west direction, and covers an area of approximately 

9.8 km2. Rezak and Tieh (1984) states that the bank 

is a classic example of the surface expression of a salt 

dome. A single basalt outcropping, described by Rezak 

and Tieh (1984), was discovered during a submersible 

dive during the BLM OCS studies. Potassium-argon age 

determination anaysis indicated an age of 76.8 ± 3.3 ×

106 years (late Cretaceous-Campanian). 

As a result of these investigations, MMS initiated regulatory zones around the majority of the hard bottom 

habitat in the NWGOM, regulating oil and gas activity. 

Regulations placed on the oil and gas industry included 

the mandatory shunting of discharges to within 10 m of 

the sea floor.

During 8–28 June 2002, a multibeam mapping cruise 

was conducted to survey approximately 2000 km2 of the 

northwestern Gulf of Mexico continental shelf1

. Multibeam bathymetry data sets were used as base maps to 

guide submersible surveys of select topographic features 

during the 2002 Sustainable Seas Expedition to the 

northern Gulf of Mexico, conducted by the FGBNMS 

in conjunction with the NGS.

The SSE missions initiated an ongoing multidisciplinary project led by the FGBNMS to revisit the reefs 

and banks of the NWGOM and build on the description and characterization effort conducted during past 

research programs.

During the SSE missions, submersible dives were conducted on the East Flower Garden (EFGB), West Flower 

Garden (WFGB), Jakkula, Sidner, Alderdice, Bright, 

McGrail, and Sonnier Banks. Results from the SSE 

submersible surveys at Alderdice, McGrail, and Sonnier 

Banks are reported herein, due to the more extensive 

surveys conducted at these features. 

Materials and methods

The SSE dives were conducted using Nuytco Research 

Ltd’s Deepworker 2000 (DW2000) and Deep Rover 

(DR) submersibles (Fig. 2). Both systems are singleperson submersibles—the DW2000 rated to 610 m and 

DR rated to 1000 m. One of the benefits of these two 

submersibles is the nearly 360° observation capability 

afforded to the pilot. Other technology that enhanced 

the capabilities of the submersibles during the SSE 

missions included accurate underwater tracking using 

the ORE International 4410C Track Point II with the 

Winfrog integrated navigation package, underwater 

communication, and high resolution digital video and 

still camera. 

Submersible surveys were conducted along selected 

waypoints based on reef topography to survey as much 

hard bottom habitat as possible during each dive. Reef 

fishes and macroinvertebrates were only counted during segments where the field of view was illuminated 

and the submersible maintained close contact to the 

bottom. Numbers of individuals for schooling species 

were estimated based on paused still frames and extrapolated to the entire school. Fishes were identified 

to the lowest possible taxon following Humann and DeLoach (2002), based on the taxonomic nomenclature of 

Robins et al. (1991). Trophic categories were assigned 

based on dietary information from Randall (1967), 

Smith-Vaniz et al. (1999), or Bullock and Smith (1991) 

for individual species or closely related taxa.

1 USGS (United States Geological Survey). 2002. Multibeam Bathymetry Mapping of the Northwestern Gulf of Mexico. http://

walrus.wr.usgs.gov/pacmaps/wg-index.html. [Accessed 14 August 

2005].
72 Professional Paper NMFS 5

Figure 2

Submersibles used during SSE2002. A) Deepworker 2000, piloted by G.P. Schmahl during SSE Dive A20-185 at McGrail 

Bank. B) Deep Rover, piloted by Kip Evans, during SSE Dive A20-186 at McGrail Bank. Photograph and still image by Kip 

Evans, NGS, and G. P. Schmahl, FGBNMS.

A

B
Weaver et al: Reef fish surveys by submersible in the Northwestern Gulf of Mexico 73

The submersible dives were facilitated by the availability of detailed multibeam bathymetry collected 

during June 2002. A Konsberg Simrad EM1000 high 

resolution multibeam echosounder was used to map 

the seafloor at specific features, following hydrographic 

standards.1 The multibeam surveys were conducted and 

data analyzed by James Gardner (Univ. of New Hampshire, formerly of USGS, Menlo Park). Resulting data 

were gridded at 5 m resolution and used to produce 

georeferenced images and 3D visualizations. Discussion 

of the earlier bathymetric surveys conducted within the 

FGBNMS is presented in Gardner et al. (1998), and data 

from those surveys are also available1.

Base maps were used to guide submersible operations as part of the SSE mission to the northern Gulf 

of Mexico during 24 July–6 August 2002, co-sponsored 

by NGS and NOAA. Submersible dives were conducted 

using the Deepworker 2000 and Deep Rover submersibles aboard the Oceaneering International, Inc. M/V 

OCEAN PROJECT. 

Bathymetry maps were geo-referenced in ArcView 

GIS (Version 3.2, Environmental Research Systems 

Institute, Redlands, CA), and used to plot waypoints 

for submersible transects. Submersible position was 

continuously logged during dives, and dive tracks were 

later superimposed on bathymetry to identify location 

of video surveys.

Results

Six submersible dives were conducted at the three study 

sites, focusing on hard bottom communities and high 

profile reef structures. Three submersible dives were 

made on the basalt spire features and large mound of 

Alderdice Bank (Table 1). Two dives were conducted 

Table 1

Submersible dives conducted at Sonnier, McGrail, and Alderdice Banks.

Depth Survey Track 

SSE dive no. Bank Submersible Pilot range (m) duration length (km) Habitats observed

A20-179 Alderdice Deep Worker D. Weaver 62–73 5.6 hours 1.1 basalt spires, drowned 

 reef zone

A20-180 Alderdice Deep Worker G. Schmahl 62–90 4.2 hours 0.9 basalt spires, algal 

 nodule-sponge zone

A20-181 Alderdice Deep Worker E. Hickerson 68–85 4.3 hours 1.3 Algal nodule sponge 

 zone

A20-184 McGrail Deep Worker G. Schmahl 45–78 5.2 hours 1.7 Coral cap, algal 

 nodule-sponge zone

A20-185 McGrail Deep Rover K. Evans 45–85 5.5 hours 2.5 Coral cap, algal 

 nodule-sponge zone

A20-186 Sonnier Deep Worker D. Weaver 19–60 3.2 hours 2.1 Millepora-sponge zone, 

 drowned reef zone

around the coral cap region of McGrail Bank, while a 

single submersible transect was conducted at Sonnier 

Bank (Fig. 3). Depths surveyed at Sonnier Banks ranged 

from 19 to 60 m, McGrail Bank 45–85 m, and Alderdice 

Bank 62–90 m. Sonnier Banks were characterized by 

Millepora-sponge and drowned reef habitats, while McGrail and Alderdice transects were dominated by algal 

nodule-sponge communities (Table 1). 

One hundred and twenty (120) fishes were observed 

in association with the deep reef communities of the 

three banks (Appendix A). McGrail Bank had the highest observed species richness (78 species), followed by 

Sonnier Banks (77), and Alderdice Bank (68). A total of 

40,724 fishes were counted from submersible videotape 

from submersible dives (Table 2). 

The top 25 species observed on each bank are presented in Table 3. Planktivorous fishes dominated the 

reef communities of all three banks, but different taxa 

were numerically abundant at each location. Sonnier 

Bank reef fish populations are dominated by the yellowtail reeffish (Chromis enchrysura), creole-fish (Paranthias furcifer), and brown chromis (Chromis mulilineata). 

McGrail Bank reef fish populations are dominated by 

P. furcifer, the threadnose bass (Anthias tenuis), and the 

yellow goatfish (Mulloidichthys martinicis). In contrast,

Alderdice Bank is dominated by the roughtongue bass 

(Pronotogrammus martinicensis), C. enchrysura, and P. 

furcifer.

Planktivorous fishes constituted over 81.4% by number at each bank, ranging from 81.6% at Sonnier Banks 

to 94.3% at Alderdice Bank. The remaining categories 

comprised less than 10%, with the exception of benthic carnivores at Sonnier Banks and McGrail Bank, 

represented by large populations of tomtate (Haemulon 

aurolineatum) and yellow goatfish, respectively. Benthic 

carnivores represented the second most abundant tro-
74 Professional Paper NMFS 5

Table 2

Reef fishes by trophic category at Sonnier, McGrail, and Alderdice Banks. Numbers of individuals observed during video 

surveys are given. 

Sonnier Banks McGrail Bank Alderdice Bank Totals

No. Percent No. Percent No. Percent No. Percent

Planktivores 6284 81.4 12,465 81.6 16,717 94.3 35,466 87.1

Piscivores 68 0.9 249 1.6 335 1.9 652 1.6

General carnivores 351 4.5 218 1.4 226 1.3 795 2.0

Herbivores 49 0.6 39 0.3 11 0.1 99 0.2

Epibenthic browsers 350 4.5 237 1.6 97 0.5 783 1.9

Benthic carnivores 615 8.0 2067 13.5 346 2.0 3028 7.4

Totals 7717 100.0 15,275 100.0 17,732 100.0 40,724 100.0

Figure 3

A) Multibeam bathymetry map of Sonnier Banks, oblique view (bathymetry data courtesy James Gardner, University of 

New Hampshire, Center for Coastal and Ocean Mapping, 24 Colovos Road, Durham, NH 03824). Submersible surveys 

were conducted on Millepora-sponge zones and drowned reef areas as part of the Sustainable Seas Expeditions during 

July–August 2002. B) Plan view (ve=vertical exaggeration) of Sonnier Banks (bathymetry data courtesy James Gardner). 

C) A still video frame of the Millepora-Sponge cap at Sonnier Banks, with a queen angelfish, Holacanthus ciliaris. Assorted 

sponges and Millepora (tan in color) are present.

A

phic guild on each bank. Epibenthic browsers, including invertebrate browsers such as pomacanthids and 

chaetodontids, declined in number from Sonnier Banks 

(4.5%) to McGrail (1.6%) to Alderdice (0.5%). Herbivores also decreased in this order, and herbivores such 

as scarids and acanthurids made up 0.6% at Sonnier, 

0.3% at McGrail, and 0.1% at Alderdice. Generalized 

carnivores also followed this general pattern. In contrast, piscivores exhibited a slight increase from 0.9% 

at Sonnier Banks to 1.6% at McGrail Bank to 1.9% at 

Alderdice Bank.

Sonnier Banks

High-resolution multibeam bathymetry of the Sonnier 

Banks reveals up to at least a dozen additional lower 
Weaver et al: Reef fish surveys by submersible in the Northwestern Gulf of Mexico 75

B

Figure 3 (continued)

C
76 Professional Paper NMFS 5

Table 3

Top 25 fish species observed during submersible surveys of Sonnier, McGrail, and Alderdice Banks during SSE2002. 

Dominance rank is based on numerical abundance from videotaped ROV surveys. Trophic categories are based on dietary 

studies of Randall (1967) or Bullock and Smith (1991).

Sonnier Banks McGrail Bank Alderdice Bank

Species ID Common name Trophic guild1 Rank Percent Rank Percent Rank Percent

Chromis enchrysura yellowtail reeffish PL 1 23.1 5 6.19 2 24.61

Paranthias furcifer creole-fish PL 2 17.7 1 43.3 3 18.49

Chromis multilineatus brown chromis PL 3 13.6 12 0.58 — —

Chromis insolata sunshine fish PL 4 6.36 4 7.64 5 5.67

Thallasoma bifasciatum bluehead PL 5 5.73 18 0.22 24 0.07

Haemulon aurolineatum tomtate GC 6 5.55 — — — —

Chromis scotti purple reeffish PL 7 5.34 10 0.69 8 0.97

Stegastes variabilis cocoa damselfish H 8 2.80 — — — —

Clepticus parrae creole wrasse PL 9 2.34 15 0.48 14 0.19

Stegastes partitus bicolor damselfish FL 10 2.03 7 1.04 — —

Kyphosus sp. Bermuda/yellow chub PL 11 1.80 — — — —

Rhomboplites aurorubens vermilion snapper PL 12 1.42 — — 6 3.29

Balistes capriscus gray triggerfish BC 13 1.03 — — — —

Lutjanus griseus gray snapper GC 14 1.03 9 0.73 9 0.52

Lutjanus campechanus red snapper GC 15 0.90 — — — —

Bodianus pulchellus spotfin hogfish GC 16 0.66 21 0.18 10 0.42

Bodianus rufus Spanish hogfish GC 17 0.58 24 0.14 — —

Lutjanus buccanella blackfin snapper GC 18 0.55 — — — —

Abudefduf saxatalis sergeant major PL 19 0.53 — — — —

Myripristis jacobus blackbar soldierfish PL 20 0.40 — — — —

Chaetodon sedentarius reef butterflyfish EB 21 0.32 13 0.54 13 0.28

Ptereleotris calliurus blue goby PL 22 0.28 — — — —

Epinephelus adscensionis rock hind GC 23 0.27 — — — —

Canthigaster rostrata sharpnose puffer EB 24 0.25 — — — —

Holacanthus tricolor rock beauty EB 25 0.25 — — 22 0.08

Mulloidichthys martinicus yellow goatfish BC 26 0.25 3 13.0 7 1.60

Halichoeres bathyphilus greenband wrasse BC 28 0.24 — — 25 0.06

Holacanthus bermudensis blue angelfish EB 29 0.23 — — 23 0.07

Mycteroperca interstitialis yellowmouth grouper PI 33 0.18 20 0.19 15 0.17

Caranx latus horse-eye jack PI 34 0.16 14 0.53 — —

Pareques umbrosus cubbyu BC 36 0.16 — — 18 0.10

Chromis cyanea blue chromis PL 38 0.15 8 0.93 — —

Pseudupeneus maculates spotted goatfish BC 39 0.15 — — 20 0.09

Sparisoma atomarium greenblotch parrotfish H 42 0.14 23 0.15 — —

Seriola dumerili greater amberjack PI 44 0.12 — — 11 0.30

Holocentrus rufus longfin squirrelfish PL 45 0.11 22 0.18 — —

Anthias tenuis threadnose bass PL — — 2 17.9 4 13.13

Apogon affinis bigtooth cardinalfish PL — — — — 12 0.29

Aulostomus maculates trumpetfish PI — — — — 16 0.14

Caranx bartholomaei yellow jack PI — — 17 0.24 — —

Caranx hippos crevalle jack PI — — 19 0.19 — —

Centropyge argi pygmy angelfish EB — — 11 0.62 — —

Chaetodon aya bank butterflyfish PL — — — — 17 0.10

Gonioplectrus hispanus Spanish flag PI — — — — 19 0.09

Pronotogrammus martinicensis roughtongue bass PL — — 16 0.48 1 28.45

Schultzea beta school bass PL — — 6 1.43 — —

Seriola rivoliana almaco jack PI — — — — 21 0.09

1

 BC-Benthic carnivores (benthic crustaceans and infauma), EB-Epibenthic browsers (sessile invertebrates), GC-General carnivore (mobile invertebrates and 

fishes), H-Herbivore, PL-Planktivore, PI-Piscivore (fishes and cephalopods).
Weaver et al: Reef fish surveys by submersible in the Northwestern Gulf of Mexico 77

relief peaks associated with the feature, ranging in 

depth from 18 to 55 m (Fig. 3). Deeper peaks surveyed 

during the SSE mission had reduced coverage of fire 

coral and a more extensive and diverse community of 

encrusting and massive sponges.

The 2002 SSE submersible dive covered three of the 

major peaks and two of the lower relief peaks. No additional species of hermatypic coral were observed during 

these surveys. Astropyga magnifica, a deep water echinoid, 

was observed in crevices along the rocky reef slopes in 

2002. Submersible investigations revealed a diverse assemblage of fishes and invertebrates, from the speciesrich reef fish assemblages on the Millepora sponge zones 

associated with the two shallowest banks, to speciesdepauperate drowned reef assemblages on the smaller 

features within the region (Fig. 3). Results of our surveys 

indicate unique biological assemblages associated with 

each bank within the region, related to the depth of the 

bank crest and the extent of the turbid Nepheloid Zone, 

which was encountered over surrounding soft bottom 

areas at each peak during submersible surveys.

Reef fishes observed at Sonnier were numerically 

dominated by planktivores, representing the top five 

species, eight of the top ten numerically abundant 

species. In addition to C. enchrysura, P. furcifer, and C. 

multilineatus, the sunshinefish (Chromis insolata) and 

bluehead (Thallasoma bifasciata) were next most abundant. Dense schools of planktivores, including P. furcifer, 

C. multilineata, T. bifasciata, and creole wrasse (Clepticus 

parrae), characterize the shallow crest areas of the larger 

peaks. Deeper reef communities were characterized 

by an assemblage of H. aurolineatum, red snapper (L. 

campechanus), greater amberjack (Seriola dumerili), and 

gray triggerfish (Balistes capriscus). Soft bottom regions 

surrounding the main peaks had few fishes, with the exception of the blue goby (Ptereleotris calliurus), living in 

burrows in the circum-reef talus zones, and tattler (Serranus phoebe), associated with talus zones and carbonate 

debris surrounding hard bottom features.

McGrail Bank

Submersible surveys conducted during SSE on McGrail 

Bank revealed extensive growth of a hard coral community dominated by S. intersepta, large brain corals 

(Diploria strigosa), M. cavernosa, and a species of Agaricia. 

Estimated coral coverage reached 30% in some areas, at 

a depth range of 45 m–60 m, while the base of the bank 

is approximately 85 m (Fig. 4A). Two SSE 2002 submersible dives targeted the crest of the southernmost ridge 

to identify the extent of the deep coral reef community. 

In addition to these four previously documented species of hermatypic corals, a 2 m tall colony of Diploria 

strigosa was noted, as was a more extensive S. intersepta

reef than previously described. One reef site (45–60 m 

depth range)—a crest approximately 0.07 km2

 (0.40 km 

× 0.28 km), was documented to be populated by up to 

30% coverage of S. intersepta coral heads, averaging 1m 

width × 0.75 m tall (Fig. 4B). Numerous heads reached 

upwards of 2 m in height. In addition to the S. intersepta 

colonies, numerous 1–2 m tall M. cavernosa colonies 

were encountered. 

Reef fishes observed at McGrail Bank were overwhelmingly dominated by P. furcifer, which made up 

almost half of the individuals observed (43%). Threadnose bass comprised 18% of fishes observed, followed by 

the yellow goatfish. The occurrence of a large school of 

goatfish has not been previously reported in the literature. While this species was observed on all three banks, 

they were in much lower abundance at Alderdice and 

Sonnier Banks. Sunshinefish (C. insolata), school bass 

(Schultzea beta), bicolor damselfish (Stegastes partitus), 

and the blue chromis (Chromis cyanea), were the next 

most dominant reef fish taxa, and all are planktivorous. 

McGrail Bank lacked many of the species associated with 

the turbid drowned reef areas of Sonnier Bank, including H. aurolineatum and L. campechanus.

A notable observation made during the submersible 

surveys is that up to five aggregations of longspine 

urchins (Astropyga magnifica) were encountered in the 

sandy valley between the ridges of McGrail Bank. These 

aggregations were made up of at least 100 individuals. 

Associated with each aggregation were upwards of six 

juvenile marbled grouper (Dermatolepis inermis) individuals to each A. magnifica aggregation (Fig. 4A). 

Alderdice Bank

While previous bathymetric surveys of the area identified a single “spire” at Alderdice Bank, the multibeam 

data set resolved two distinct spires with associated talus 

fields (Fig. 5). The second basalt spire is located approximately 220 m NNE of the outcropping described 

by Rezak and Tieh (1984). Both spires provide high 

profile structure that attracts large schools of creolefish, vermilion snapper (Rhomboplites aurorubens), and 

several species of grouper, snapper, and jacks. In 2002, 

the original basalt feature was surveyed and characterized by a similar biota as originally described by Rezak et 

al. (1985). Through direction from a topside navigation 

technician, the submersible pilot was able to navigate 

both of the spires, confirming that the second feature 

was indeed an additional basalt outcrop (Figs. 5A, B). 

Reef fishes observed at Alderdice Bank were overwhelmingly dominated by roughtongue bass, with 

yellowtail reeffish and creole-fish also dominating the 

reef fish community. These three species made up over 

71% of the individual fishes observed. A notable observation during the SSE mission is the occurrence of 

the rare marbled grouper (Dermatolepis inermis), both at 
78 Professional Paper NMFS 5

A

Figure 4

A) Top: Multibeam bathymetry map of McGrail Banks, oblique view (bathymetry data courtesy James Gardner; see Fig. 3 

for contact info.). Bottom: Marble grouper, Dermatolepis inermis, resting at the base of sea urchin aggregation. B) Extensive 

growth of hard corals, including Stephanocoenia, Montastraea, and Diploria spp., on the summits of the bank were documented 

during submersible surveys during the Sustainable Seas Expedition during summer 2002. Video still courtesy SSE.
Weaver et al: Reef fish surveys by submersible in the Northwestern Gulf of Mexico 79

Figure 4 (continued)

B

the basalt outcroppings and the eastern algal/sponge 

mound. 

Discussion

Reef fishes of the northwestern Gulf of Mexico, as previous authors have noted, are represented by a tropical 

assemblage on mid to outer shelf banks where suitable 

habitat occurs (Rezak et al., 1985, 1990; Dennis and 

Bright, 1988a). Results of this survey provide the first 

detailed look at reef fish communities associated with 

the chosen features using modern submersibles, and 

identify differences among the fish communities associated with their unique benthic assemblages. The 

number of reef-associated species at each bank was 

very similar, ranging from 68 at Sonnier Banks to 78 at 

McGrail Bank. While total species richness was similar, 

the numerically dominant taxa vary between sites and 

reflect the distribution of habitat at each of the chosen 

study sites. Sonnier Banks are comprised of steep sided, 

relatively flat-topped features that have limited talus 

aprons and shallow peaks. The predominance of Millepora-sponge zones on the shallow peaks of the main 

features is reflected in the abundance of creole-fish, 

creole wrasse, brown chromis, and other planktivores 

that typically associate with the shallow crests of other 

reefs and banks in the Gulf of Mexico, such at East 

Flower Garden Bank, West Flower Garden Bank, and 

Stetson Bank (Rezak et al., 1985; Pattengill-Semmens 

et al., 1997). Sonnier Banks also have limited deep reef 

habitat, due to the dominance of a nepheloid layer at 

the deeper areas surrounding the main peaks (Rezak et 

al., 1990). This results in extensive soft bottom communities around the banks, a lack of algal nodule-sponge 

zones, and limited distribution of partly drowned and 

drowned reef structures and associated reef fish communities. 

In addition to general comments on the reef fish 

community at McGrail Bank, Rezak et al. (1985) noted 

significant populations of common black sea urchins 

(Diadema sp.) and spiny lobsters (Panulirus sp.) occurring on the ledges and along the slopes of many of the 

shelf edge banks. These observations are inconsistent 

with the 2002 observations. This can be accounted in 

part by the well-documented Caribbean-wide mass die-
80 Professional Paper NMFS 5

A

Figure 5

A) Multibeam bathymetry map of Alderdice Bank, plan view. B) Multibeam bathymetry, oblique view (ve=vertical 

exaggeration) of basalt spires and isolated mound (bathymetry data courtesy James Gardner, see Fig. 3 for contact info.). 

C) A school of roughtongue bass, Pronotogrammus martinicensis, the most abundant species observed at Alderdice Bank. This 

small, planktivorous sea bass is one of the most common members of the deep reef fish community, and a common prey 

species of groupers and snappers. Photograph courtesy FGBNMS/NURC-UNCW.

off of D. antillarum in the early 1980s (Lessios et al., 

1984). This die-off was reported at the Flower Garden 

Banks NMS in 1984 (Gittings and Hickerson, 1998). 

Spiny lobster observations were rare in 2002, with a total 

of three individuals observed on all three banks during 

submersible dives. 

Observations of biological communities at Alderdice 

Bank were very similar to those reported in the earlier 

studies. Both basalt spires appear abruptly out of the 

soft bottom sediments, and the rock surfaces are clearly 

visible. Rezak and Tieh (1984) speculated that the uplifting of the basalt is a geologically recent occurrence, 

based on the lack of growth on the basalt blocks. The 

2002 observations of the basalt spires gave no indication 

that growth on the blocks had increased significantly in 

the 25 years since the last observations. Additional dives 

on the eastern algal/sponge mound revealed comparable biological assemblages as reported by Rezak et 

al. (1985). Rezak et al. (1985) report encountering an 

exceptionally large number of yellowtail reeffish (Chromis enchrysura) on the basalt peak—this is consistent 

with observations made on the eastern algal/sponge 

mound in 2002

The domination of planktivores in the deep reef fish 

community is both a regional and worldwide pattern 

(Thresher and Colin, 1986; Hamner et al., 1988). Pattengill-Semmens et al. (1997) reported planktivores to 

be the dominant trophic guild on Stetson Bank and 

the East and West Flower Garden Banks. Percentages 

of planktivores were lower in their study (36 to 73%), 

reflecting the shallower depths surveyed by SCUBA, limited to the shallow crests of each bank. Deep-water reef 

fishes at Enewetak, Marshall Islands (Thresher and Colin, 

1986), became dominated by planktivores and piscivores 
Weaver et al: Reef fish surveys by submersible in the Northwestern Gulf of Mexico 81

with increasing depths between 90 and 120 m, and were 

numerically dominated by anthiine serranids (streamer 

basses), and the pomacentrids (Chromis spp.). The results 

of our study directly parallel this pattern, as planktivores 

became increasingly abundant on the deeper reef site 

(Alderdice), and were also numerically dominated by 

anthiine serranids (A. tenuis and P. martinicensis) and 

planktivorous damselfishes (Chromis spp.). Planktivores 

abundant on the shallower peaks of Sonnier Bank, T. 

bifasciata, C. multilineata, and S. partitus, decreased in 

abundance at Alderdice Bank, and were replaced by P. 

martinicensis and A. tenuis, two species that dominate other deepwater areas of the Flower Garden Banks National 

Marine Sanctuary (Dennis and Bright, 1988a).

Figure 5 (continued)

B

C
82 Professional Paper NMFS 5

Dennis and Bright (1988a) reported three distinct 

reef fish assemblages based on cluster analysis, a Millepora-sponge community associated with the crest of midshelf banks, an algal nodule-sponge fish assemblage, 

and a drowned reef fish assemblage. These categories 

are the dominant habitats characterizing Sonnier, McGrail, and Alderdice Banks, respectively, and results of 

this study parallel those observed by these authors on 

previous submersible dives. While Dennis and Bright 

(1988a) did not provide an extensive species list for 

each of the banks surveyed, their observations of reef 

fish/habitat associations remain valid. Increased resolution of video cameras, including zoom capabilities and 

clarity of digital videotape, allows for identification of 

a larger component of the reef fish fauna, including 

smaller cryptic species such as juvenile anthiines, blennies, and gobies. Many of the species observed here, 

including the roughtongue bass (P. martinicensis), have 

only recently been reported to occur in the NWGOM, 

and the threadnose bass (A. tenuis) has not previously 

been reported to occur in our area (Dennis and Bright, 

1988a, b). Recent deepwater surveys at the Flower Garden Banks NMS have indicated that it is one of the most 

abundant species in the deep reef community between 

50 and 100 m.

Rezak et al. (1990) comment on the similarity of geology and biological communities of Sonnier and Stetson 

Banks. Submersible observations conducted during 

2002 SSE surveys support this observation, and provides 

data on habitat distribution and fish community structure to compare to ongoing surveys being conducted 

by FGBNMS staff at Stetson Bank. Dennis and Bright 

(1988a) reported the unique reef fish assemblage associated with the bank crests of the Millepora sponge 

zone. On the Pacific Coast, Yoklavich et al. (2000) found 

higher numbers of rockfishes associated with abrupt, 

steep sided rock features surrounded by mud and sand 

habitats. Both Sonnier Banks and Alderdice Bank are 

characterized by steep sided rocky features surrounded 

by relatively flat soft bottom communities. Bohnsack 

(1989) commented on the behavioral attraction that 

many fishes have for high profile rocky structure, and 

that as a result they aggregate in large numbers. High 

numbers of reef fishes, both small and large species, 

were observed at the crests of all three banks.

In general, the observations made between the historical surveys and the 2002 SSE mission are similar. 

The fish assemblages in 2002 appear to be similar 

to those observed in earlier surveys, although large 

schools of yellow goatfish that were documented 

in the 2002 surveys were not listed as representative assemblages in the earlier surveys at any of the 

shelf edge features. Variations are noted, but do not 

indicate deterioration of habitat or major shift in 

associated biological communities. The three banks 

under discussion were identified as areas warranting 

regulatory action through MMS, in regards to oil and 

gas development. These areas are potential ecological 

and biological significance, harboring juvenile populations of grouper, recovering populations of the longspine urchins, and hermatypic deep coral reefs. These 

reefs and banks have also been adopted by the Gulf 

of Mexico Fisheries Management Council as Habitat 

Areas of Particular Concern (HAPC). No regulations 

are currently in place to protect Sonnier and Alderice 

from over-fishing, anchoring, or excavation. Designation of McGrail Bank as a Coral HAPC zone prohibits 

trawling, bottom longlines, deployment of fish traps, 

and bottom anchoring.

The availability of accurate, high-resolution bathymetric base maps has allowed the development of 

comprehensive datasets that can be integrated via a 

GIS database. The FGBNMS multidisciplinary effort to 

characterize the reefs and banks of the NWGOM continues to benefit through partnerships such as SSE, and 

through the advancement of the available technology. 

While our studies were exploratory in nature, the basic 

patterns of habitat distribution and reef fish assemblages will allow more rigorous, quantitative surveys of 

reef fish densities and habitat association during future 

studies. Information on habitat distribution and associated biological assemblages is also being used to revise 

the current benthic habitat scheme for the reefs and 

banks of the NWGOM, including the Flower Garden 

Banks National Marine Sanctuary. The data acquired 

will be used to make well-informed management decisions regarding the diverse biological resources of the 

Northwestern Gulf of Mexico.

Acknowledgments

The authors would like to particularly thank Sylvia Earle 

for the opportunity to be part of the SSE missions, to 

train as pilots, and the use of submersible during the 

research cruises. The SSE program was funded by grants 

from the Richard and Rhoda Goldman Foundation and 

NOAA. We also thank the NGS assistants and Nuytco 

Research Ltd. personnel for logistical and technical 

support during cruises in the Northwestern Gulf of 

Mexico. We thank the captain and crew of the M/V 

OCEAN PROJECT for hospitality and support during 

the extended research cruise. Multibeam surveys were 

funded by NOAA’s Office of Ocean Exploration and the 

Minerals Management Service (MMS). We acknowledge 

the dedication and cooperation of James Gardner of 

UNH, in his efforts to expediate the extensive multibeam surveys prior to our submersible mission and to 

accelerate the processing of the data for use during our 

research cruises.
Weaver et al: Reef fish surveys by submersible in the Northwestern Gulf of Mexico 83

Literature cited 

Bohnsack, J. A. 

1989. Are the high densities of fishes at artificial reefs the result 

of habitat limitation or behavioral preference? Bull. Mar. Sci. 

44:631–645. 

Bullock, L. H., and G. B. Smith. 

1991. Seabasses (Pisces:Serranidae). Memoirs of the Hourglass 

cruise. Florida Marine Research Institute. Dept. Nat. Res., St. 

Petersburg, FL. Vol. 8, 243 p.

Dennis, G. D., and T. J. Bright.

1988a. Reef fish assemblages on hard banks in the northwestern 

Gulf of Mexico. Bull. Mar. Sci. 43(2): 280–307.

1988b. New records of fishes in the northwestern Gulf of Mexico, with notes on some rare species. Northeast Gulf Sci. 

10(1):1–18.

Gardner, J. V., L. A. Mayer, J. E. Hughes Clarke, and A. Kleiner. 

1998. High-resolution multibeam bathymetry of East and West 

Flower Gardens and Stetson Banks, Gulf of Mexico. Gulf of 

Mexico Sci. 16(2):131–143.

Gittings, S. R., and E. L. Hickerson. 

1998. Introduction, Flower Garden Banks National Marine Sanctuary dedicated issue. Gulf of Mexico Sci. 16(2):128–130.

Hamner, W. M., M. S. Jones, J. H. Carleton, I. R. Hauri, and D. McB. 

Williams. 

1988. Zooplankton, piscivorous fish, and water currents on a 

windward reef face: Great Barrier Reef, Australia. Bull. Mar. 

Sci. 42(3):459–479.

Humann, P., and N. DeLoach. 

2002. Reef fish identification (3rd ed.). New World Publications, 

Jacksonville, FL. 481 p.

Lessios, H. A., D. R. Robertson, and J. D. Cubit. 

1984. Spread of Diadema mass mortality through the Caribbean. 

Science 226:335–337.

Minnery, G. A., R. Rezak, and T. J. Bright. 

1985. Depth zonation and growth form of crustose coralline 

algae: Flower Garden Banks, Northwestern Gulf of Mexico. 

In Paleoalgology: Contemporary Research and Applications 

(D.F. Toomey and M. H. Nitecki, eds.), p. 237–246. SpringerVerlag, Berlin.

Pattengill-Semmens, B. X. Semmens, and S. R. Gittings. 

1997. Reef fish trophic structure at the Flower Gardens and Stetson Bank, NW Gulf of Mexico. Proc. 8th Int. Coral Reef Symp. 

1:1023–1028.

Randall, J. E. 

1967. Food habits of reef fishes of the West Indies. Stud. Trop. 

Ocean. 5:665–847.

Rezak, R., and T. T. Tieh. 

1984. Basalt from Louisiana Continental Shelf. Geo-Marine Letters 4:69–76.

Rezak, R., T. J. Bright, and D.W. McGrail. 

1985. Reefs and banks of the Northwestern Gulf of Mexico: Their 

geological, biological, and physical dynamics. John Wiley and 

Sons, New York, 259 p.

Rezak, R., S. R. Gittings, and T. R. Bright. 

1990. Biotic assemblages and ecological controls on reefs and 

banks of the northwest Gulf of Mexico. Amer. Zool. 30:23–35.

Robins, C. R., R. M. Bailey, C. E. Bond, J. R. Brooker, E. A. Lachner, 

R. N. Lea, and W. B. Scott. 

1991. Common and scientific names of fishes from the United 

States and Canada (5th ed.). Am. Fish. Soc. Spec. Publ. 20, 

183 p.

Smith-Vaniz, W. F., B. B. Collette, and B. E. Luckhurst. 

1999. The Fishes of Bermuda. Amer. Soc. Ichthy. Herpet. Spec. 

Publ. 4, 424 p.

Thresher, R. E., and P. L. Colin. 

1986. Trophic structure, diversity and abundance of fishes of the 

deep reef (30–300m) at Enewetak, Marshall Islands. Bull. Mar. 

Sci. 38:253–272.

Yoklavich, M. M., H. G. Greene, G. M. Caillet, D. E. Sullivan, R. N. 

Lea, and M. S. Love.

2000. Habitat association of deep-water rockfishes in a submarine canyon: an example of a natural refuge. Fish. Bull. 

98:625–641.
84 Professional Paper NMFS 5 Appendix A Fishes observed during SSE2002 Submersible Dives at Sonnier, McGrail, and Alderdice Banks. Sonnier Banks McGrail Bank Alderdice Bank Family Trophic Species Common name guild1 Rank No. Percent Rank No. Percent Rank No. Percent Acanthuridae Acanthurus chirurgus doctorfish EB 27 19 0.25 36 9 0.06 31 8 0.05 Acanthurus coeruleus blue tang EB 52 5 0.06 — 2 — — — — Apogonidae Apogon affinis bigtooth cardinalfish PL — — — — — — 13 50 0.29 Apogon maculatus flamefish PL 57 3 0.04 54 3 0.02 — — — Apogon psuedomaculatus twospot cardinalfish PL 60 2 0.03 — — — — — — Aulostomidae Aulostomus maculatus trumpetfish PI — — — — — — 17 25 0.14 Balistidae Balistes capriscus gray triggerfish BC 13 80 1.04 27 19 0.12 28 10 0.06 Balistes vetula queen triggerfish BC — — — 44 6 0.04 Cantherhines macrocerus whitespotted filefish EB — — — 38 8 0.05 56 1 0.01 Cantherhines pullus orangespotted filefish EB — — — — — — 55 1 0.01 Canthidermis sufflamen ocean triggerfish PL — — — 29 18 0.12 — — — Xanthichtys ringens sargassum triggerfish PL 42 7 0.05 — — — — — — Blenniidae Parablennius marmoreus seaweed blenny EB 59 3 0.04 — — — — — — Carangidae Alectis ciliaris African pompano PI 53 5 0.06 — — — — — — Caranx bartholomaei yellow jack PI — — — 17 38 0.25 39 4 0.02 Caranx chrysos blue runner PL 54 4 0.05 — — — — — — Caranx hippos crevalle jack PI — — — 19 30 0.20 — — — Caranx latus horse-eye jack PI 34 13 0.17 14 81 0.53 — — — Caranx lugubris black jack PI — — — 39 8 0.05 — — — Caranx ruber bar jack PI 40 11 0.14 30 17 0.11 — — — Selene vomer lookdown GC 56 4 0.05 — — — — — — Seriola dumerili greater amberjack PI 44 10 0.13 35 10 0.07 12 53 0.30 Seriola rivoliana almaco jack PI — — — 34 11 0.07 22 16 0.09 Carcharhinidae Carcharhinus plumbeus sandbar shark PI 50 6 0.08 59 2 0.01 58 1 0.01 Carcharhinus sp. PI — — — 64 1 0.01 — — — Chaetodontidae Chaetodon aculeatus longsnout butterflyfish EB — — — 45 6 0.04 59 1 0.10 Chaetodon aya bank butterflyfish PL — — — — — — 18 18 0.10 Chaetodon ocellatus spotfin butterflyfish EB 61 2 0.03 66 1 0.01 60 1 0.01 Chaetodon sedentarius reef butterflyfish EB 21 25 0.32 13 84 0.55 14 49 0.28 Clinidae Starksia sp. unidentified blenny BC 77 1 0.01 — — — — — — continued
Weaver et al: Reef fish surveys by submersible in the Northwestern Gulf of Mexico 85 Appendix A (continued) Sonnier Banks McGrail Bank Alderdice Bank Family Trophic Species Common name guild1 Rank No. Percent Rank No. Percent Rank No. Percent Congridae Conger oceanicus conger eel GC — — — — — — 61 1 0.01 Dasyatidae Dasyatis americana southern stingray GC — — — — — — 62 1 0.01 Echeneidae Echeneis naucrates sharksucker GC 69 1 0.01 — — — — — — Gobiidae Gnatholepis thompsoni goldspot goby BC 63 2 0.03 49 5 0.03 — — — Gobiidae unidentified gobies BC 32 16 0.21 — — — — — — Gobiosoma xanthiprora prownose goby BC 47 7 0.09 68 1 0.01 — — — Ptereleotris calliurus blue goby PL 22 22 0.29 — — — — — — Haemulidae Haemulon aurolineatum tomtate GC 6 428 5.55 — — — — — — Holocentridae Corniger spinosus spinycheek soldierfish GC — — — — — — 44 2 0.01 Holocentrus adscensionis squirrelfish PL 43 10 0.13 46 6 0.04 49 2 0.01 Holocentrus rufus longfin squirrelfish PL 45 9 0.12 22 28 0.18 65 1 0.01 Myripristis jacobus blackbar soldierfish PL 20 31 0.40 28 19 0.12 33 8 0.05 Sargocentron bullisi deepwater squirrelfish PL 66 2 0.03 75 1 0.01 38 5 0.03 Kyphosidae Kyphosus sp. Bermuda/yellow chub PL 11 139 1.80 — — — — — — Labridae Bodianus pulchellus spotfin hogfish GC 16 51 0.66 21 28 0.18 11 73 0.42 Bodianus rufus Spanish hogfish GC 17 45 0.58 24 22 0.14 54 1 0.01 Clepticus parrae creole wrasse PL 9 181 2.35 15 74 0.48 15 34 0.19 Decodon puellaris red hogfish BC — — — — — — 45 2 0.01 Halichoeres bathyphilus greenband wrasse BC 28 19 0.25 — — — 26 11 0.06 Halichoeres bivitattus slippery dick BC 41 11 0.14 — — — — — — Halichoeres garnoti yellowhead wrasse BC — — — 37 9 0.06 47 2 0.01 Halichoeres maculipinna clown wrasse BC — — — 56 3 0.02 — — — Thallasoma bifasciatum bluehead PL 5 442 5.73 18 35 0.23 25 12 0.07 Lutjanidae Lutjanus buccanella blackfin snapper GC 18 43 0.56 — — — 41 3 0.02 Lutjanus campechanus red snapper GC 15 70 0.91 — — — 29 10 0.06 Lutjanus cyanopterus cubera snapper PI — — — 71 1 0.01 — — — Lutjanus griseus gray snapper GC 14 80 1.04 9 112 0.73 10 91 0.52 Lutjanus jocu dog snapper GC 48 7 0.09 26 20 0.13 30 10 0.06 Lutjanus synagris lane snapper GC 55 4 0.05 — — — — — — Ocyurus chrysurus yellowtail snapper PL 72 1 0.01 73 1 0.01 — — — Rhomboplites aurorubens vermilion snapper PL 12 110 1.43 — — — 6 576 3.29 continued
86 Professional Paper NMFS 5 Appendix A (continued) Sonnier Banks McGrail Bank Alderdice Bank Family Trophic Species Common name guild1 Rank No. Percent Rank No. Percent Rank No. Percent Malacanthidae Malacanthus plumieri sand tilefish BC — — — 57 3 0.02 66 1 0.01 Mullidae Mulloidichthys martinicus yellow goatfish BC 26 20 0.26 3 2000 13.09 7 280 1.60 Pseudupeneus maculatus spotted goatfish BC 39 12 0.16 31 15 0.10 21 16 0.09 Muraenidae Gymnothorax moringa spotted moray GC 64 2 0.03 69 1 0.01 — — — Ophichthidae Myrichthys breviceps sharptail eel GC — — — — 1 — — — — Opistognathidae Opistognathus aurifrons yellowhead jawfish PL — — — 62 2 0.01 — — — Ostraciidae Lactophrys triqueter smooth trunkfish BC 58 3 0.04 70 1 0.01 — — — Pomacanthidae Centropyge argi pygmy angelfish EB — — — 11 96 0.63 34 6 0.03 Holacanthus bermudensis blue angelfish EB 29 18 0.23 40 8 0.05 24 13 0.07 Holacanthus ciliaris queen angelfish EB 35 13 0.17 52 4 0.03 48 2 0.01 Holacanthus tricolor rock beauty EB 25 20 0.26 25 22 0.14 23 14 0.08 Pomacanthus paru French angelfish EB 30 18 0.23 53 4 0.03 — — — Pomacentridae Abudefduf saxatalis sargeant major PL 19 41 0.53 — — — — — — Chromis cyanea blue chromis PL 38 12 0.16 8 143 0.94 40 3 0.02 Chromis enchrysura yellowtail reeffish PL 1 1782 23.11 5 947 6.20 2 4311 24.61 Chromis insolata sunshinefish PL 4 491 6.37 4 1168 7.64 5 993 5.67 Chromis multilineatus brown chromis PL 3 1055 13.68 12 90 0.59 — — — Chromis scotti purple reeffish PL 7 412 5.34 10 106 0.69 9 170 0.97 Stegastes partitus bicolor damselfish PL 10 157 2.04 7 159 1.04 — — — Stegastes planifrons threespot damselfish EB 31 18 0.23 — — — — — — Stegastes variabilis cocoa damselfish EB 8 216 2.80 62 2 0.01 — — — Priacanthidae Priacanthus arenatus bigeye PL 74 1 0.01 — — — 27 11 0.06 Rachycentridae Rachycentron canadum cobia GC 65 2 0.03 — — — — — — Rhincodontidae Ginglymostoma cirratum nurse shark GC 70 1 0.01 — — — 64 1 0.01 Scaridae Cryptotomus roseus bluelip parrotfish EB 68 1 0.01 — — — — — — Scaridae unidentified parrotfish EB 75 1 0.01 — — — — — — Scarus taeniopterus princess parrotfish EB 67 2 0.03 — — — — — — Sparisoma atomarium greenblotch parrotfish EB 42 11 0.14 23 23 0.15 42 3 0.02 Sparisoma aurofrenatum redband parrotfish EB 49 7 0.09 51 5 0.03 — — — continued
Weaver et al: Reef fish surveys by submersible in the Northwestern Gulf of Mexico 87 Appendix A (continued) Sonnier Banks McGrail Bank Alderdice Bank Family Trophic Species Common name guild1 Rank No. Percent Rank No. Percent Rank No. Percent Sciaenidae Equetus punctatus spotted drum BC 62 2 0.03 — — — — — — Pareques iwamotoi blackbar drum BC — — — — — — 37 5 0.03 Pareques umbrosus cubbyu BC 36 13 0.17 — — — 19 17 0.10 Scombridae Scomberomorus cavalla king mackerel PI — — — — — — 8 200 1.14 Serranidae Anthias tenuis threadnose bass PL — — — 2 2745 17.96 4 2300 13.13 Cephalopholis cruentata graysby GC 37 12 0.16 48 5 0.03 — — — Cephalopholis fulva coney GC — — — 65 1 0.01 — — — Dermatolepis inermis Marble grouper GC — — — 41 7 0.05 46 2 0.01 Epinephelus adscensionis rock hind GC 23 21 0.27 67 1 0.01 — 1 0.01 Epinephelus guttatus red hind GC — — — 55 3 0.02 — — — Gonioplectrus hispanus Spanish flag GC — — — — — — 20 16 0.09 Liopropoma eukrines wrasse bass GC 71 1 0.01 — — — 36 5 0.03 Mycteroperca bonaci black grouper PI — — — 33 11 0.07 50 2 0.01 Mycteroperca interstitialis yellowmouth grouper PI 33 14 0.18 20 30 0.20 16 29 0.17 Mycteroperca microlepis gag PI — — — — — — 51 2 0.01 Mycteroperca phenax scamp PI 46 9 0.12 61 2 0.01 52 2 0.01 Mycteroperca venenosa yellowfin grouper PI — — — 50 5 0.03 — — — Paranthias furcifer creole-fish PL 2 1370 17.77 1 6625 43.35 3 3239 18.49 Pronotogrammus martinicensis roughtongue bass PL — — — 16 74 0.48 1 4984 28.45 Schultzea beta school bass PL — — — 6 220 1.44 — — — Serranus annularis orangeback bass GC — — — 32 15 0.10 34 6 0.03 Serranus notospilus saddle bass GC 76 1 0.01 76 1 0.01 68 1 0.01 Serranus phoebe tattler GC 51 6 0.08 77 1 0.01 53 2 0.01 Sparidae Calamus sp. BC — — — 47 5 0.03 43 2 0.01 Pagrus pagrus red porgy BC 73 1 0.01 — — — — — — Sphyraenidae Sphyraena barracuda great barracuda PI — — — 78 1 0.01 — — — Synodontidae Synodus intermedius sand diver PI — — — — — — 69 1 0.01 Synodus synodus red lizardfish PI — — — 79 1 0.01 — — — Tetraodontidae Canthigaster jamestyleri goldface toby EB — — — — — — 57 1 0.01 Canthigaster rostrata sharpnose puffer EB 24 20 0.26 59 2 0.01 32 8 0.05 No. Sp. Total N No. Sp. Total N No. Sp. Total N 77 7,717 79 15,288 69 17,519

1 PL-Planktivore, PI-Piscivore, GC-General carnivore (mobile invertebrates and fishes), H-Herbivore, EB-Epibenthic browsers (sessile invertebrates), BC-Benthic carnivores (benthic crustaceans and infauna).
88

Introduction

Ideally, fishery biologists dream of a 

sensor that, when placed in a water 

body, provides all density and meristic 

information of the fishes present in 

the water body. Even better, the sensor 

would also identify the fish species. In 

addition, all this information would 

be available synoptically for a large water area. In reality, such a sensor exists 

taking the shape of a single beam fish 

finder with a very narrow search cone 

extending from the surface to the 

bottom of the water body. Availability 

of acoustic sensors such as side scan 

sonar (SSS) and multibeam bottom 

sounders incorporating very fast processor capabilities are beginning to 

actualize the fishery biologist’s dream 

(Fish and Carr, 1990).

Remote sensing techniques offer 

a viable option for mapping marine 

habitats, determining not only the 

location and amount of distinct benthic habitats, but also how these habitats are distributed and their degree 

of connectivity. In tropical regions, 

specifically in coastal waters, the use 

of traditional passive sensors (Ikonos 

Detecting fish aggregations from reef habitats 

mapped with high resolution side scan sonar imagery

José A. Rivera

Under contract to:

Miami Laboratory-Biodiversity and Protected Resources Division

NOAA, NMFS

HC-01, Box 1736

Cabo Rojo, PR 00622-9704

Email: jarivera@msn.com

Martha C. Prada

P.O. Box 2048

San Andres Isla, Colombia

Jean-Luc Arsenault

Gary Moody

Nicolas Benoit

Geophysique GPR International, Inc.

2545 Rue Delorimier

Longueuil (Quebec), Canada J4K 3P7

Abstract—As part of a multibeam and 

side scan sonar (SSS) benthic survey of 

the Marine Conservation District (MCD) 

south of St. Thomas, USVI and the seasonal closed areas in St. Croix—Lang Bank 

(LB) for red hind (Epinephelus guttatus) 

and the Mutton Snapper (MS) (Lutjanus 

analis) area—we extracted signals from 

water column targets that represent individual and aggregated fish over various 

benthic habitats encountered in the SSS 

imagery. The survey covered a total of 18 

km2 throughout the federal jurisdiction 

fishery management areas. The complementary set of 28 habitat classification 

digital maps covered a total of 5,462.3 ha; 

MCDW (West) accounted for 45% of that 

area, and MCDE (East) 26%, LB 17%, and 

MS the remaining 13%. With the exception of MS, corals and gorgonians on consolidated habitats were significantly more 

abundant than submerged aquatic vegetation (SAV) on unconsolidated sediments 

or unconsolidated sediments. Continuous coral habitat was the most abundant 

consolidated habitat for both MCDW 

and MCDE (41% and 43% respectively). 

Consolidated habitats in LB and MS predominantly consisted of gorgonian plain 

habitat with 95% and 83% respectively. 

Coral limestone habitat was more abundant than coral patch habitat; it was found 

near the shelf break in MS, MCDW, and 

MCDE. Coral limestone and coral patch 

habitats only covered LB minimally. The 

high spatial resolution (0.15 m) of the 

acquired imagery allowed the detection of 

differing fish aggregation (FA) types. The 

largest FA densities were located at MCDW 

and MCDE over coral communities that 

occupy up to 70% of the bottom cover. 

Counts of unidentified swimming objects 

(USOs), likely representing individual 

fish, were similar among locations and occurred primarily over sand and shelf edge 

areas. Fish aggregation school sizes were 

significantly smaller at MS than the other 

three locations (MCDW, MCDE, and LB). 

This study shows the advantages of utilizing SSS in determining fish distributions 

and density. 

or Landsat 7 satellites, aerial photographic camera) is restricted because 

light is exponentially absorbed by 

the water column, phytoplankton 

minimize remote benthic reflectance, 

and colored dissolved organic matter 

alters measured wavelengths (Roesler 

and Perry, 1989; Gordon and Wang, 

1994; Lee et al., 1994; Arono and 

Gould, 1998). Even in clear water, the 

bottom signature can be inconclusive 

because it is the result of mixed spectra, which becomes more complicated 

with depth (Mumby et al., 1998). Existing passive sensors are also limited 

by low spatial resolution and lack of 

information from deep or turbid areas with high cloud cover. Additional 

problems result from back scattering 

from inorganic suspended particles, 

which add noise and may modify the 

bottom signal reaching the sensor. 

To avoid confusion between water 

and bottom signatures, bathymetry 

measurements and knowledge of water attenuation is required. Elevation 

models for reef areas, when available, 

are often inaccurate because they do 

not include a complete coverage of 

depth and they lack precise positionRivera et al.: Detecting fish aggregations from reef habitats mapped with high resolution SSS imagery 89

ing. Lizenga (1978, 1981) developed the depth-invariant 

bottom index, which is widely applied when mapping 

benthic habitats regardless of algorithm limitations. Despite the above limitations, passive sensors are currently 

the preferred mapping tool. 

An alternative to passive sensors is the use of active remote sensors. Active sensors emit a signal and detect the 

intensity of the signal reflected from an object. Active 

remote sensing in the oceanic environment is feasible 

using acoustic or optical techniques. An optical sensor 

(using a laser) can have excellent spatial resolution (cm 

or mm, depending on the altitude of the sensor), but 

accuracy falls off rapidly with increasing range; therefore its use is limited to a swath of 3–5 m (Klepsvik et 

al., 1994). At this time, laser sensors are too expensive 

and time-consuming to be an option for mapping large 

areas (10s km2 or larger). 

The remaining alternative, the use of acoustic technology such as SSS, is a promising approach for mapping coral reefs. By selecting an appropriate SSS system 

it is possible to obtain images of features as large as 

seamounts or as small as sand ripples. SSS images can be 

combined to generate mosaics over large areas, showing 

all structures and their position in a planimetric manner. To solve this limitation, SSS data can be combined 

with high resolution multibeam echo sounder, which 

adds depth data as a third dimension to the SSS images. 

The merging of these technologies has rapidly become 

the preferred mapping tool for accurate positioning 

and navigation. 

SSS technology was used for the first time in 1963 in 

England and has been routinely used by hydrographers 

to help determine the characteristics of the ocean bottom in the making of nautical charts. SSS is especially 

valuable in identifying bottom objects in turbid waters. 

Historically, SSS has been used to map seabed configuration and predominant bottom targets for petroleum 

industry applications, dredging, and mine hunting. 

Geologists and geophysicists see fish and other water 

column signals as interference that affects the interpretation of the ocean bottom features. Such signals 

are eliminated from the sonar record during either 

acquisition or post processing. The utilization of SSS 

for environmental and fisheries applications is more 

recent (Siljestrom et al., 1996; Friedlander et al., 1999; 

Karl et al., 1994). 

The advantage of using SSS over conventional single 

beam fish finders to locate fish echoes is the added volume sampled per transducer signal or ping. Typically, 

SSS can sample out to a range of 10–100 m on each 

side of a transducer, depending on the range setting 

and transducer frequency. The single beam transducer 

sampling area depends on depth and transducer beam 

geometry. The shallower the depth, the smaller the area 

sampled. All SSS systems marketed today are designed 

for working in relatively deep water (>10 m depth). This 

limitation is primarily due to the mechanical design for 

transducer deployment and recovery and the undesirable interaction of the transmitted sound wave with the 

water surface and the propeller wash. In deeper water, 

this problem is avoided by submerging the SSS transducer to the lower 30% of the water column. 

In October 1996, Congress re-authorized the Magnuson-Stevens Fishery Conservation and Management 

Act (Public Law 94-265) with amendments (Kantor et 

al., 1996). One amendment in Title III, section 305 

mandates Fisheries Management Councils to revise all 

Fisheries Management Plans (FMP) to include delineation of “essential fish habitat” (EFH) by October 1998. 

Although some FMPs will benefit from existing information portrayed in map format, other FMPs will not have 

such information. Information for these FMPs will be 

collected and relevant maps produced. The main objective of our work was to contribute to the formulation 

of EFH maps for the Caribbean Fishery Management 

Council (CFMC) in the United States Virgin Islands 

Exclusive Economic Zone (EEZ). The aim of the marine surveys was to provide high resolution bathymetry 

and SSS maps of the seabed and detailed maps of the 

benthic habitat at three designated conservation areas. 

The survey areas were designated as follows: Marine 

Conservation District (MCD, south off St. Thomas), and 

Lang Bank and Mutton Snapper (LB and MS, east and 

south west off St. Croix respectively). The work reported 

concentrates on the fish signals encountered in the SSS 

mapping of the seabed.

Materials and methods

Survey region and equipment deployment

Surveys were conducted on St. Thomas, USVI in the 

recently developed Marine Conservation District East 

(MCDE) and West (MCDW, Fig. 1). The data acquisition was carried out from a chartered 17 m Grand 

Banks wood cruiser, with dual inboard motors, Onan 

service generator, Raytheon autopilot, and GPS navigation system. Several remote sensing technologies were 

deployed from this single vessel for navigation and 

hydroacoustic surveying (Fig. 2). The vessel cruising 

speed is 8 knots. 

Positioning

The survey extended from 9 April to 31 May 2003. Vessel 

positioning was achieved using a Trimble Series 5700 

Real-time Kinematic Global Positioning System (RTK 

GPS). The transmission of positional corrections from a 

base station transmitter on shore allowed horizontal and 
90 Professional Paper NMFS 5

vertical accuracy of the order of 5 cm to be achieved. 

A Pacific Crest PDL radio modem on the survey vessel 

received the positional corrections. 

A laptop computer ran the Hypack Max (version 2.12, 

Coastal Oceanographics, Inc., Middletown, CT) navigation software aboard the survey vessel. Latitude and 

longitude measured by the RTK GPS system were stored 

at the rate of five readings per second on the laptop’s 

hard drive. These were converted into Cartesian (X, Y) 

coordinates of the local grid used for surveying. The 

Universal Transverse Mercator (U.T.M.) projection system (Zone 20-N), with the WGS-84 datum was used for 

horizontal positioning. Current vessel position relative 

to the local grid was displayed on a screen dedicated 

for use by the vessel captain. The Sonarwiz (version 

1.65, Chesapeake Technology, Inc., Mountain View, 

CA) SSS data acquisition software system was linked to 

Hypack Max via multiple RS232 interfaces. During data 

acquisition, the SSS output data were attached to the 

(X, Y) grid position and stored on hard disk. The various instrument offsets relative to the GPS antenna were 

entered into the data acquisition systems, allowing realtime positional corrections to be carried out. The vessel 

was also equipped with a Meridian Surveyor gyrocompass from S. B. Brown Ltd., which was used to provide 

precise heading information during surveying.

Swath bathymetry

We use a Geoacoustics, Ltd., Geoswath 250 kHz phase 

comparison system to conduct the swath bathymetry. 

Complete equipment specifications can be found in 

Appendix A. The transducer V-plate was installed in a 

position directly off the port side of the vessel, 0.91 m 

from the stern. The V-plate mounting contains a motion 

reference unit (MRU) and the port and starboard transFigure 1

Location of three mapped areas.

ST. CROIX

TORTOLA

PUERTO RICO

60 km

MARINE CONSERVATION

DISTRICT

LANG BANK

MUTTON SNAPPER

200 m ISOBATH 

MARINE CONSERVATION DISTRICT

1. 18o 13.2í N 65o 06.9í W

2. 18o 13.2í N 64o 59.0í W

3. 18o 11.8í N 64o 59.0í W

4. 18o 10.7í N 65o 06.9í W

LANG BANK

1. 17o 49.3í N 64o 27.9í W

2. 17o 49.3í N 64o 26.8í W

3. 17o 48.3í N 64o 25.9í W

4. 17o 48.3í N 64o 26.8í W

5. 17o 47.3í N 64o 28.0í W

6. 17o 47.3í N 64o 26.8í W

MUTTON SNAPPER

1. 17o 37.8í N 64o 53.0í W

2. 17o 39.0í N 64o 53.0í W

3. 17o 39.0í N 64o 50.5í W

4. 17o 38.1í N 64o 50.5í W

5. 17o 37.8í N 64o 52.5í W

ST. THOMAS

ST. CROIX

TORTOLA

PUERTO RICO

60 km

MARINE CONSERVATION

DISTRICT

LANG BANK

MUTTON SNAPPER

200 m ISOBATH 

MARINE CONSERVATION DISTRICT

1. 18o 13.2í N 65o 06.9í W

2. 18o 13.2í N 64o 59.0í W

3. 18o 11.8í N 64o 59.0í W

4. 18o 10.7í N 65o 06.9í W

LANG BANK

1. 17o 49.3í N 64o 27.9í W

2. 17o 49.3í N 64o 26.8í W

3. 17o 48.3í N 64o 25.9í W

4. 17o 48.3í N 64o 26.8í W

5. 17o 47.3í N 64o 28.0í W

6. 17o 47.3í N 64o 26.8í W

MUTTON SNAPPER

1. 17o 37.8í N 64o 53.0í W

2. 17o 39.0í N 64o 53.0í W

3. 17o 39.0í N 64o 50.5í W

4. 17o 38.1í N 64o 50.5í W

5. 17o 37.8í N 64o 52.5í W

ST. THOMAS

TORTOLA

PUERTO RICO

60 km

MARINE CONSERVATION

DISTRICT

LANG BANK

MUTTON SNAPPER

200 m ISOBATH 

MARINE CONSERVATION DISTRICT

1. 18o 13.2í N 65o 06.9í W

2. 18o 13.2í N 64o 59.0í W

3. 18o 11.8í N 64o 59.0í W

4. 18o 10.7í N 65o 06.9í W

LANG BANK

1. 17o 49.3í N 64o 27.9í W

2. 17o 49.3í N 64o 26.8í W

3. 17o 48.3í N 64o 25.9í W

4. 17o 48.3í N 64o 26.8í W

5. 17o 47.3í N 64o 28.0í W

6. 17o 47.3í N 64o 26.8í W

MUTTON SNAPPER

1. 17o 37.8í N 64o 53.0í W

2. 17o 39.0í N 64o 53.0í W

3. 17o 39.0í N 64o 50.5í W

4. 17o 38.1í N 64o 50.5í W

5. 17o 37.8í N 64o 52.5í W

ST. THOMAS
Rivera et al.: Detecting fish aggregations from reef habitats mapped with high resolution SSS imagery 91

TOWFISH 

WINCH

GEOSWATH 

TRANSDUCERS 

KLEIN 595

GEOSWATH 

PROCESSOR

GYROCOMPASS

RTK GPS RECEIVER 

 MAIN CABIN 

PROCESSING / 

BACKUP PC 

 WORK 

TABLE

NAVIGATION PC

 HELM DISPLAY

HELM

Figure 2

Survey vessel equipment layout diagram.

ducers. The V-plate assembly was fixed at approximately 

1.1 m beneath the waterline, and was secured using an 

anodized aluminum mounting pole attached to a swivel 

over the side mount. The vertical pole was locked at the 

top end, and steel guy wires were run from the V-plate 

to the amidships and the stern of the vessel to ensure 

the V-plate was stable. Signals from the transducers and 

MRU were sent to the Geoswath processing unit, located 

in the main cabin of the vessel. This PC-based system ran 

proprietary software (version 2.07s, Swath32, Geoacous-
92 Professional Paper NMFS 5

tics, Ltd., Norfolk, UK) on the Microsoft Windows® 

98/Me operating system. 

Correction for survey vessel heave, pitch, and roll 

was performed through the use of a dynamic motion 

sensor (DMS) from TSS Limited, located in the V-plate 

assembly between the transducers. The DMS from TSS 

Limited is the MRU. The DMS was connected to the 

Geoswath system via an RS-232 connection, enabling 

correction of the bathymetric data for heave, roll, and 

pitch in real time.

Sound velocity profiles 

For the Geoswath system to accurately survey the seabed, precise information on the water column sound 

speed profile is required. This information was acquired 

by lowering a Valeport DigiBar sound velocity probe 

through the water column at approximately 1 m intervals. The sound speed profile was entered into the 

Geoswath software to accurately convert the raw data 

into depths. 

Side scan sonar 

The side scan sonar system utilized was a Klein Model 

595. Data was captured using a Klein 590 Digital Graphic Recorder unit, and subsequently transferred to a PC 

running Sonarwiz data acquisition software. Although 

the tow fish was a dual frequency fish, capable of acquiring data at 100 kHz and 500 kHz, only the 500 kHz data 

was analyzed. The instrument range was set at 50 m on 

each side of the sonar transducer. During data acquisition, the vessel speed was maintained between 2 and 5 

knots, while the tow fish was kept between 5 and 10 m 

off the seabed. The length of cable out was controlled 

by an electric winch. This length was entered into the 

SonarWiz program, which allowed a layback calculation 

to be applied to the sonar image navigation coordinates. Navigation was supplied to the SonarWiz system 

through serial communication of the NMEA GLL data 

string generated by Hypack Max at the rate of two readings per second. The navigation string was corrected for 

the offset between the vessel reference position and the 

winch tow-block position.

Side scan sonar data processing

The raw side scan sonar data consisted of a digital file 

in standard XTF format. SonarWeb software (version 

3.15G, Chesapeake Technology, Inc., Mountain View, 

CA) was used to process the XTF file into mosaic form. 

Before utilization of SonarWeb, some pre-processing of 

the side scan data was necessary. This was accomplished 

using GPR’s proprietary dedicated software to smooth 

the navigation data and correct for the slowly varying 

amount of cable out. Once the navigation data was 

corrected, the XTF files were partitioned into separate 

blocks of 1.86 km2

. This was necessary due to the scale 

requirements for the maps, and due to file size limitations in creating the mosaics using SonarWeb. Once 

the XTF files were correctly partitioned, they could be 

imported into SonarWeb. Each 1.86 km2 area was processed as a separate project. 

The underlying purpose of each project was to produce finished sonar mosaics at high resolution for display and analysis. The most critical and time-consuming 

task was tracking the return from the seabed (bottomtrack) for all the SSS data (Fig. 3). Bottom-tracking had 

to be carried out by manually digitizing the return since 

the automated tracker provided in the software was not 

sufficiently accurate. Bottom-tracking allowed the water 

column to be accurately measured and its influence 

removed, so as to provide slant range corrected data. 

Once all the files in a project were accurately bottomtracked, the mosaic was generated using a set resolution 

and color scheme. 

No beam angle corrections were made to the mosaic 

data in order to obtain maximum signal contrast. The 

mosaics were exported in geotiff format, and then 

imported into a separate software package for visualization/map preparation. These mosaics were subsequently used to perform habitat mapping.

Delineation of habitat from SSS mosaics

A total of 28-1.86 km2 mosaics in geotiff format from 

SSS imagery processed at 0.2 m resolution were used 

to generate detailed benthic habitat maps through 

visual interpretation and delineation. Interpretation 

was aided by the availability of geotiff mosaics from 

multibeam bathymetry processed at 1 m resolution, 

which provided a three dimensional perspective when 

assigning a habitat class to an area. Habitat maps were 

created using ArcView (version 3.2, Environmental 

Systems Research Institute, Inc., Redlands, CA) and 

projected in Universal Transverse Mercator (UTM) 

coordinates for UTM-83 Zone 20. Digitization utilized 

the ArcView extension Habitat.avx v 1.2 developed by 

the NOAA/NOS/Biogeography Team1

.

A modified version of the hierarchical habitat classification scheme developed by Prada (2002) was used 

to run the Habitat extension, which contained a total 

of 23 different habitat types (one more than originally defined (Table 1)). The hierarchical classification 

scheme was developed after qualitative observations 

were conducted at 107 sites in fifteen 1.86 km2

 areas 

1

 NOAA/NOS Biogeography Team. 2002. Benthic habitats of Puerto 

Rico and the U.S. Virgin Islands. CD-ROM, Silver Spring, Md. Available from http://ccmaserver.nos.noaa.gov/products/biogeography/benthic/order.shtml
Rivera et al.: Detecting fish aggregations from reef habitats mapped with high resolution SSS imagery 93

corresponding to their respective SSS mosaics. Each 

habitat type was visited at least three times within a SSS 

depicted habitat. Habitat types were based on detailed 

observations of the SSS mosaics performed at a series of 

spatial scales (1:500, 1:1000, 1:2500, and 1:5000). The 

qualitative observations were obtained by videotaping 

through an underwater drop video camera an average 

of three minutes per location from a small boat while 

slowly transiting. Quantitative measurements were 

conducted at an additional 59 sites. The quantitative 

data were taken in 5–6 replicates within each sampled 

habitat type by a team of 4–5 divers. At each location, 

four quadrants of 1m2

 were randomly placed along a 48 

m metric tape. Within each quadrant, divers estimated 

the percent cover of sand, rubble, live coral, dead coral, 

gorgonians, sponges, macro algae, sea grass, and Cyanophyta. Number and size (length, height, and width) 

of each live colony of hard corals, gorgonians, and 

sponges within the quadrant were also recorded. Additionally, twelve rugosity estimates were taken at each 

location using a 3 m (2 cm link) chain laid along the 

metric tape. Percent cover and presence or absence data 

were then analyzed utilizing the Ochiai Index (Ochiai, 

1957) and detrended correspondence analysis (DCA) to 

ordinate the habitat types by levels of complexity. The 

scheme was organized into four levels of complexity. 

The first level grouped habitats in three meta-communities designated: corals and gorgonians on consolidated 

sediments, submerged aquatic vegetation (SAV) on 

unconsolidated sediments (algae and sea grasses), and 

unconsolidated sediments (Fig. 3).

Map colors were similar to the palette defined in 

Prada (2002) to easily identify habitat types. The new 

category was included within variations of the color 

green. Most habitat maps overlap 100–200 m in every 

direction, with the exception of the MS area that had 

no overlap. Small areas (hundreds of meters) in the 

MS area, and a larger (thousands of meters) area at LB 

had no SSS information, which consequently resulted 

in gaps in the habitat maps.

Quantitative determination of habitat classification 

map accuracy at the MCDW and MCDE areas by using existing ground truth video was not performed 

because available video lacked precise frame specific 

time or geo-positioning. However, a general idea of 

habitat classification map accuracy was obtained by 

comparing underwater benthic pictures obtained from 

an Autonomous Underwater Vehicle (AUV)2 and drift 

transects from a digital video underwater camera with 

2

 Autonomous Underwater Vehicle Expedition. 2003. Caribbean 

Fisheries Management Council AUV Expedition to the MCD south 

of St. Thomas, USVI. Collection of digital photo transects. Caribbean Fisheries Management Council, 268 Muñoz Rivera Ave., Suite 

1108, San Juan, PR 00918-2577.

Figure 3

Example side scan sonar record with water column (before slant range correction).
94 Professional Paper NMFS 5

Table 1

Hierarchical classification scheme of benthic habitats used to generate detailed habitat maps (Modified from Prada, 2002).

Meta-community Community Sub-community Habitat types Habitat Codes 

Coral and gorgonians corals coral high relief Continuous Corals COCO

on consolidated sediments coral patch Coral Patch COPA

coral low relief Coral Limestone COLI

gorgonian patch Gorgonian Patch GOPA

gorgonians Plains Gorgonian Plains GOPL

elevated plains Elevated Gorgonians ELGO

Submerged Aquatic Vegetation (SAV) seagrass Seagrass Dense Grass DEGR

on unconsolidated sediments Sparse Grass SPGR

Grass-Invertebrates GRIN

Grass Halo & Coral Patch HALO

macro-algae algae on sand Dense Algae DEAL

Sparse Algae SPAL

Algae & Invertebrates ALIN

algae on silt Shallow Algae SHAL

Deep Algae DEEP

Bare or mixed invertebrates sand coarse sand Sand Invertebrates SAIN

on unconsolidated sediments Sand no Ripple SANR

Sand Ripple SARI

fine sand Fine Sand FIMU

silt mud Mud-Invertebrates MUIN

Mud Bare MUBA

Mud Reef MURE

habitat classification maps at presumed AUV transect 

lines. Estimation of habitat classification accuracy for 

maps for the MS and LB areas could not be conducted, 

even at the qualitative level, due to lack of ground truth; 

consequently these maps may need correction.

Habitat polygons were visually delineated from a 

Viscon FV170, 40.8 cm LCD monitor with high contrast 

ratio (350:1). Habitat polygon classification is based 

on the texture, brightness, and shape of the benthic 

habitat features on the high resolution SSS imagery. 

0

10

20

30

40

50

60

70

80

90

100

Consolidated Sav Unconsolidated

Habitat groups

na

e M r ep t nec a

a

er

MCDW

MCDE

LB

MS

Figure 4

Mean percent area for the three habitat groups in the mapped area. Error bars 

indicate one standard error.
Rivera et al.: Detecting fish aggregations from reef habitats mapped with high resolution SSS imagery 95

A. SSS imagery navigation correction

.XTF File format Smooth

Navigation

Data

Correct

Cable out

Data

Partition .XTF file into

1.86 km2 sections

Corrected .XTF File Remove water column

from each image

(slant range correct)

Generate 1.86 km2

mosaics at .25m

resolution as

geotiff files

B. Mosaic creation with SonarWeb

C. Habitat delineation with ArcView

ArcView GIS vs 3.2

Image Analyst v 1

Habitat.avx

Geotiff file 

added as a

theme

Habitat.avx sets:

-MMU of 4m2

-Scale 1:1000

-Classification scheme

-Classification type

accuracy check

Selectoverlappingpolygon.ave sets:

-Detects overlapping polygons

Calcarea.ave sets:

-individual polygon area

and perimeter

Create table of areas

by habitat type for

each mosaic

Assign color palette to

habitat type polygons

for display

Figure 5

Flow diagram of SSS data processing procedures. A) SSS imagery navigation correction. B) Mosaic creation with SonarWeb. 

C) Habitat delineation with ArcView.

The interpreter’s accumulated experience and ground 

truth information were also taken into account. Ground 

truth information consisted of a 1.10 hour digital video 

available for drift transects for MCDW (mosaics 2, 3, 4, 

5, 6) and MCDE (mosaics 2, 3) which covered a total 

length of approximately 2.3 km. In addition, high resolution underwater pictures (foot print of 2 × 2 m) taken 

by an AUV were provided by the CFMC for four 1 km 

transect lines at MCDW (mosaics 1, 2, 10) and MCDE 

(mosaics 6, 7).

Data processing of the SSS habitat survey data involved 

a series of events in order to differentiate habitat types 

according to prescribed classifications and to isolate 

water column signals that would represent individual 

fish or aggregations of fish (Fig. 5). Prior to initiating 

the digitizing process, within the Habitat extension a 

Minimum Mapping Unit (MMU) of 4 m and an image 

scale of 1:1000 was defined. At the end of digitization 

of each image mosaic, resulting small polygons were 

joined if they shared a boundary and had the same classification type. Habitat type classification accuracy was 

checked utilizing the Habitat extension routine. Overlapping polygons (generated after polygon junction) 

were detected by running the ArcView script SelectOverlappingPolygons2.ave. Once detected, overlapping 

polygons were separated to ensure the correct habitat 

type classification. The use of the Calcacre.ave ArcView 

script allowed inclusion of individual polygon area and 

perimeter into the map’s attribute table. 

Processing of fish signals

As the SSS data was being displayed and acquired by 

SonarWiz, two viewers made a visual inspection of all 

water column echo returns. Every time an echo suggesting fish was detected in the water column, the signal 

was saved as a target image in “jpg” format. In addition 

to the raster image, an ancillary text file was also saved 

simultaneously containing image position, name and 

saved location information (e.g., Fig. 6). 
96 Professional Paper NMFS 5

At the end of each data collection day, fish target 

image files were cataloged by date and survey line 

number. After cataloging, each target image was visually inspected to verify that the signal saved was a possible fish signal return. The cataloged ancillary target 

image text files were then transferred into an Excel 

spreadsheet in preparation for spatial analysis. All geographic positions were converted into UTM 83 Zone 20 

northing and easting grid values for plotting in the GIS 

using the geodetic conversion software Tralaine (version 5.17, Mentor Software, Inc., Golden, Colorado). 

The coordinates for each target file were then plotted 

as a point overlay on available habitat maps. Using the 

Geoprocessing tool in ArcView, information on habitat 

was introduced into the point attribute table. This allowed estimation of the total number of USOs and fish 

aggregations (FAs) by habitat type. Some points had 

no benthic habitat classification because their position 

placed them on the shelf edge; these were classified as 

shelf edge (Table 1). Some target files contained more 

than one FA; therefore number of locations may differ 

from the number of FAs.

Individual FAs were digitized from georeferenced 

target files using ArcView. These target files raster images were transformed from .jpg format to .bmp format 

in order to permit ArcView to process them. A pixel 

Figure 6

Example of unidentified swimming objects (USO) and fishing aggregations (FA) target images and 

ancillary text file with position and reference information. All images are from MCDW and MCDE. 

A) Ancillary text file with position and reference information. B) Port and starboard transducer SSS image 

record separated by center black line. USO’s are represented by black dots. Image is from MCDW at shelf 

drop off. C) USO target image from right SSS transducer only to provide more detail. D) FA signal patterns 

pointed out by arrows. Benthic habitat is primarily flatten coral plate (Montastrea spp.). E) Undulating fish 

signals indicate swimming movement relative to SSS transducer. F) Bell shaped FA pointed out by arrow. 

Target marker line is correlated with target data file for locating position.
Rivera et al.: Detecting fish aggregations from reef habitats mapped with high resolution SSS imagery 97

size of 0.15 m was specified for all target files into the 

final text file containing the georeference. The whole 

dataset of FAs was then divided into four subsets to 

match their respective locations: MCDW, MCDE, LB, 

and MS. Bathymetry and habitat maps were also parsed 

by location. 

Digitizing of each FA signal area was performed at 

a scale of 1:1000 (same as that generated for habitat 

maps). It was not possible to estimate FA density based 

on fish counts within a FA image mainly because resolution was lost in raster transformation and in some cases 

the fish density was too high to permit discrimination of 

individual fish signals. Calculation of FA signal area was 

then obtained by running an ArcView Script (Calcacre.

ave) on the delineated and digitized polygon file. The 

attribute tables of all the polygon files created for the 

four studied areas were copied into an Excel worksheet 

for statistical analysis. Georeferencing of the FAs may 

not be as precise as bathymetry or SSS data, since a target file position is referenced to the first raster line of 

the image; however, the FA position will be within 10 m 

of the target file reference position. 

Results

Location of mapped areas

In total, more than 800 km (average of 30 km of line per 

day) of line survey were run to collect the bathymetry 

and side-scan sonar data. The total area covered by the 

bathymetric survey was approximately 18 km2. Slightly 

less coverage was obtained with the SSS due to the entanglement of SSS transducer with surface buoys from 

fish traps, affecting collection of imagery for partial 

areas of LB and MS. 

Bathymetry

All three mapped areas were split into a series of 1.86 

km2 sections. This resulted in the generation of 19 sections for the MCD, 6 sections for LB, and 3 sections for 

MS. The final processed bathymetry data consisted of 

depth grids at 1 m resolution which, in our opinion, 

represented the optimal resolution obtainable from 

the data. The final maps displayed the one-meter grids 

using a color depth coding scheme and sun-illuminated 

overlay. Complete details about paper map products can 

be obtained from a report by Geophysics GPR International, Inc3

. The color table used is based on a histogram 

3

 Geophysics GPR, International, Inc. 2003, Marine Habitat Mapping 

Offshore St. Thomas & St. Croix, USVI EEZ. M-03704. Report to 

the Caribbean Fisheries Management Council, 30 p., maps plus appendixes, 2 volumes. Caribbean Fisheries Management Council, 

268 Muñoz Rivera Ave., Suite 1108, San Juan, PR 00918-2577.

equalization scheme, and therefore was not linear, but 

the smallest color interval was 0.2 meters. Each of the 

color maps has a printed color scale bar for interpretation purposes. These maps were produced at a scale of 

1:10,000 (Figs. 7 and 8).

Side scan sonar mosaics

The final processed SSS mosaics were produced utilizing the software SonarWeb. Each SSS mosaic represented a 1.86 km2 section of the seabed which matched 

the equivalent bathymetric section. The mosaics were 

exported as geo-referenced geotiff files, and imported 

into Oasis Montaj (version 5.1.7, Geosoft, Inc., Toronto, 

Canada) where they could be placed on the background 

coordinate grid and printed as a final product. The 

mosaics were re-processed at 1 m resolution for printing. The mosaics were produced using the SonarWeb 

color scheme “Brown” with a 5% contrast setting. In this 

scheme, black represents low backscatter strength, and 

light brown represents high backscatter strength.

Habitat classification

Eleven of the 23 habitat types of the classification 

scheme used by Prada (2002) were found at the mapped 

areas. See Table 1 for details of the classification scheme 

and Table 2 for acreage summary by habitat type. The 

complementary set of 28 habitat classification digital 

maps covered a total of 5462.3 ha, with the MCDW accounting for 45% of that area, MCDE for 26%, LB for 

17%, and MS the remaining 13%. 

With the exception of MS, corals and gorgonians on 

consolidated habitats were significantly more abundant 

than submerged aquatic vegetation (SAV) on unconsolidated sediments or unconsolidated sediments alone, as 

shown in Table 1. Continuous coral habitat was the most 

abundant consolidated habitat for both MCDW and 

MCDE (41% and 43% respectively). The LB and MS areas had consolidated habitats predominantly consisting 

of gorgonian plain habitat (95% and 83% respectively). 

Coral limestone habitat was more abundant than coral 

patch habitat and was found near the shelf break in MS, 

MCDW, and MCDE. At LB, there was minimal coverage 

for either of those habitat types. 

SAV habitats were the second most abundant habitat 

group in MCDW and MCDE, the most abundant in MS, 

and almost non existent in LB. Unconsolidated habitats 

were present in all areas but not dominant anywhere 

(Table 2, Fig. 9). 

Position of fish signals

A total of 671 fish signal target files were saved for the 

entire mapped areas (Fig. 7 and 8). Within each target 
98 Professional Paper NMFS 5

Figure 7

Bathymetry, habitat classification and fish signal positions for the MCDW and MCDE south of St. Thomas. 

A) MCDW, bathymetry color scale denotes depth in meters. B) MCDE. C) MCDW, habitat types; pavement denotes 

coral communities and gorgonians. Fish signals classified as fish aggregations (FA) are represented by triangles, 

black circles represent unidentified swimming objects (USO). D) MCDE. Scale is 1:40,000.

file, fish signals were classified into two categories. If 

the fish signal was large and isolated, it was identified 

as a unidentified swimming object (USO). If the fish 

signal was a pattern of echoes forming an aggregation 

or school of fish it was identified as a fish aggregation 

(FA). Most of the identified target files were found in 

the water column portion of the SSS signal return, usually within the first 20 m of the 50 m SSS channel range. 

Fish aggregations were either vertically or horizontally 

oriented. We made no distinction between these patterns of aggregation in this study.

The majority of the FA’s positions were located over 

continuous coral, gorgonian, and sand habitat types 

(Fig. 9). FAs were also common over the shelf edge 

or drop off. The majority of USOs were located over 

sand and shelf edge habitat types, although they were 

also common over continuous coral and gorgonian 

habitat types. The highest density of FAs were found in 

the MCDW and MCDE locations; LB and MS densities 

were about five times smaller (Fig. 10). USO’s densities 

were similar at all the locations (Fig. 10). The mean FA 

signal areas were similar between MCDW, MCDE, and 

LB. However, the mean FA signal area for MS was significantly smaller than the other three locations (Fig. 11). 

Discussion

Most single vertical beam sounders used to interpret 

fish signals have beamwidths of 6–30° and at times undersample the ensonified sea bottom compared to an 

SSS (Misund et al., 1996). The SSS has a narrow fore-
Rivera et al.: Detecting fish aggregations from reef habitats mapped with high resolution SSS imagery 99

Figure 8

Bathymetry, habitat classification, and fish signal positions for the LB and MS southwest and east of St. Croix 

respectively. A) LB, bathymetry color scale denotes depth in meters. B) LB, habitat types; pavement denotes 

coral communities and gorgonians. Fish signals classified as fish aggregations (FA) are represented by 

triangles, circles represent unidentified swimming objects (USO). C) MS, bathymetry. D) MS, habitat types. 

White backgrounds within habitat map boundary denote lack of SSS imagery. Scale is 1:40,000.

aft beamwidth of 2° and a wide vertical beam of 20–30° 

extending to each side of the transducer. These characteristics enable it to resolve short horizontal wavelengths 

even at long ranges. The backscatter obtained from an 

SSS facilitates seabed characterization. To effectively 

use SSS systems, slower sampling vessel speeds (in the 

order of 3.5 to 5 knots) usually are required to ensure 

adequate resolution and sampling. Newer models are 

able to maintain resolution at higher speeds (ca. 5 to 8 

knots). The larger sweep area of the SSS also enhances 

its efficiency in locating water column fish aggregations 

compared to single vertical beam systems.

A disadvantage of the SSS is that one usually cannot obtain fish species identification from the return 

acoustic signal, although sometimes one can identify 

the characteristic shapes of species if the range and 

resolution of the imagery are appropriate. This is due 

primarily to the lack of transducer calibrated backscattering strength data (Hammerstad4

). Also, one normally 

needs either a vessel platform or an AUV on which to 

stage a SSS system. Sonar imagery cannot be collected 

independent of these platforms very easily. Processing 

SSS imagery requires a minimal amount of expertise 

and specialized software. Unlike low frequency single 

beam sounders, high frequency SSS signals cannot penetrate to great ranges in the water column. However, in 

deep water, one can usually lower the SSS transducer to 

the required depth by paying out wire from the cable 

winch aboard the vessel or by programming an AUV for 

the desired depths. Future research needs to address a 

more automated quantitative method of correctly classifying SSS acoustic signals to infer habitat designations. 

4

 Hammerstad, E. 2000. EM technical note: backscattering and seabed image reflectivity. Kongsberg maritime products, hydroacoustics, underwater vehicles and systems, echosounders, multibeam 

related links at http://www.km.kongsberg.com.
100 Professional Paper NMFS 5

Table 2

Total area (ha) of benthic habitats for MCD, LB and MS. Habitat codes as presented in Table 1.

SITE COCO COPA COLI GOPA GOPL ALIN DEAL SPAL SAIN SANR SARI Total

MCD1W 47.7 3.9 3.1 49.4 60.3 1.1 48.9 28.0 2.3 244.9

MCD2W 37.1 13.0 2.3 125.5 15.4 15.2 39.4 5.9 253.8

MCD3W 37.1 13.0 2.3 125.5 15.4 15.2 39.4 5.9 253.8

MCD4W 47.3 6.1 5.8 95.6 25.0 0.1 44.1 30.1 7.3 261.3

MCD5W 34.4 3.1 6.4 67.9 0.4 6.9 62.2 4.5 185.8

MCD6W 106.3 2.9 8.4 141.2 0.7 2.4 85.2 1.6 348.7

MCD7W 169.4 6.6 0.8 107.7 0.1 26.0 4.7 315.3

MCD8W 82.7 26.7 4.5 36.9 0.0 0.5 18.8 13.8 183.8

MCD9W 60.3 2.4 2.0 41.3 0.0 0.0 21.7 3.8 131.5

MCD10W 84.0 2.6 0.0 103.9 0.1 0.6 25.1 6.4 222.7

MCD11W 21.1 2.6 0.0 18.7 0.0 0.0 14.6 6.8 63.9

 Total MCDW 727.5 83.0 35.6 913.4 117.3 1.2 134.0 390.4 63.0 2465.5

MCD1E 47.7 3.9 3.1 49.4 60.3 1.1 48.9 28.0 2.3 244.9

MCD2E 5.2 3.2 45.6 76.0 1.0 103.8 10.8 0.2 245.8

MCD3E 15.9 2.3 29.2 73.9 2.3 112.1 8.8 0.5 245.2

MCD4E 3.2 0.8 8.7 12.5 17.3 2.8 0.1 45.4

MCD5E 35.4 2.0 0.1 58.4 0.2 2.1 20.1 19.6 137.9

MCD6E 84.6 2.1 0.1 52.5 7.0 0.2 11.7 28.4 24.5 211.0

MCD7E 20.6 8.2 7.4 45.5 0.2 28.8 46.7 69.3 226.8

MCD8E 2.2 3.5 4.5 10.5 10.8 13.9 45.5

 Total MCDE 214.8 26.1 3.3 251.2 279.9 4.9 335.2 156.4 130.4 1402.4

LB1 0.3 0.6 105.2 0.7 32.7 11.9 151.4

LB2 3.4 0.4 11.9 3.1 18.8

LB3 0.5 0.1 0.6 285.2 49.2 1.3 336.9

LB4 9.5 0.9 10.1 49.4 36.2 24.9 131.0

LB5 0.0 0.3 0.3 163.2 29.0 5.7 198.6

LB6 3.2 0.6 0.3 0.5 22.0 28.7 9.9 65.1

 Total LB 12.7 2.7 11.1 1.4 628.4 1.1 187.8 56.8 901.9

MS1 3.3 10.0 35.5 149.8 2.0 3.8 74.3 17.6 0.0 296.3

MS2 1.1 3.8 46.1 144.8 0.7 7.9 66.3 18.8 0.1 289.5

MS3 0.3 1.9 21.2 46.7 0.1 2.0 22.3 11.8 0.3 106.6

 Total MS 4.7 15.6 102.9 341.3 2.8 13.6 162.9 48.2 0.4 692.4

This would expedite data processing time, providing 

an economic incentive to collect SSS data by resource 

managers that need this type of information.

Development of methods for the identification of fish 

species needs to be encouraged. Species target strength 

determinations need be made for as many reef inhabiting species as possible, to provide the acoustic signal 

strength criteria for unambiguous identification. The 

use of multiple or broadband frequency sonars have 

the potential to aid in resolving species identification 

problems (Fleisher5). The integration of video imagery 

or sonar imagery of near video quality collection synoptically with either SSS or calibrated fish finders when 

performing surveys can provide dynamic visual information essential to fish species identification not available with static acoustic means alone. More research 

on fish species daily water column movement patterns 

can help sort out species identification conflicts by incorporating information about species preferred depth 

strata behaviors. Tagging known species with acoustic 

tags could also be used as method of identifying the 

tracked location of the known species in reference to 

the fish that surround it. This can help identify similar 

acoustic or echo signal shapes as same species. Incorporating hydrophone techniques to collocate fish emitted 

sounds with acoustic tag tracking can also help improve 

species identification for more cryptic inhabiting species (Berk, 1998; Evans and Norris, 1993) by helping 

to correlate emitted sound position with tagged fish 

position.

Future research will focus on elucidating the species 

identification of USOs and FAs through acoustic target 

strength characterization with in situ verification by 

5

 Fleischer, G. W., 2005. Personal commun. NOAA, NMFS, 2725 

Montlake Blvd. East, Seattle, WA 98112 
Rivera et al.: Detecting fish aggregations from reef habitats mapped with high resolution SSS imagery 101

0

50

100

150

200

250

300

s uouit no C

sl ar oc

hct aPl ar o C

l ar o C

enotse mil

s nai nogr o G

&eagl A

set ar betr evni

eagl a esr apS

d

nas esr ao C

egde fl ehS

Habitat types

Ff or eb m

u

N

A

MCDW

MCDE

LB

MS

0

50

100

150

200

250

300

s uouit no C

sl ar oc

hct aPl ar o C

l ar o C

enotse mil

s nai nogr o G

&eagl A

set ar betr evni

eagl a esr apS

d

nas esr ao C

egde fl ehS

Habitat types

' OS Uf or eb

m

u

N s

MCDW

MCDE

LB

MS

Figure 9

Top: fish aggregations frequency distribution by habitat type for all study areas. 

Bottom: unidentified swimming object (USO) frequency distribution by habitat type 

for all study area sites. Only 60% of LB was mapped with SSS.

video observations. We currently suspect that USOs 

may be echoes from turtles since the sampling period 

coincides with their nesting season and the intensity of 

the acoustic signal appears roughly consistent with a 

turtle body size (Rivera and Arsenault6). However, the 

echoes could also be from Cubera snappers (Lutjanus 

cyanopterus) which have been reported for the MCDW 

and MCDE locations of the study area by Beets and 

Friedlander7. This same species has been also reported 

to aggregate for reproduction at the Grammanic Bank 

just east of the MCDE sampling area at the same sam6

 Rivera, J. A., and J. Arsenault, 2003. Unpublished data. See author 

address for data access.

7

 Beets, J., and A. Friedlander, 1997. Evaluation of the Spawning Aggregation for Red Hind (Epinephelus Guttatus), St. Thomas, US Virgin Islands. Report to the Caribbean Fisheries Management Council, 268 Munoz Rivera Avenue, Suite 1108, San Juan, Puerto Rico, 

00918-2577, 26 p.

8

 Whitman, E., 2004. Personal commun. Center for Marine and Environmental Studies, University of the Virgin Islands, St. Thomas, 

USVI 00802-9990.
102 Professional Paper NMFS 5

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

MCDW MCDE LB MS

Location

) ah/r eb mu (N ytis ned OSU- A

F

USO 

FA

0.00

0.05

0.10

0.15

0.20

0.25

MCDW MCDE LB MS

Location

)ah/sl oohcs( ytisned AF

Bathy area

SSS area

Figure 10

Top: FA’s density by location total mapped area. Bottom: unidentified 

swimming object (USO) density estimates by location. Only 60% of LB was 

mapped with SSS. Area in hectares (ha).

pling period as this study (Whitman8). Cubera snapper 

size (1–1.5 m) also fit the derived length from the echo 

signal shape. 

While producing a benthic habitat map of three 

federal jurisdiction fishery management areas with 

SSS technology, we were able to obtain relative fish 

density indices by habitat. At little incremental cost, 

these indices provide fishery managers with resource 

insights not previously available. Specifically, for our 

survey, the largest FA densities were located at MCDW 

and MCDE over coral communities that occupied up 

to 70% of the benthic habitat. USO’s densities were 

similar for the differing locations with highest densities 

primarily over sand and shelf edge areas. FA’s school 

size was significantly smaller at MS than the other three 

locations (MCDW, MCDE, and LB). 

Acknowledgements

We thank NOAA Fisheries through 

their Coral Reef Initiative for grant 

number NA17FC2204 to the Caribbean Fishery Management Council, 

which funded the habitat mapping 

study providing data for this paper. 

This research would not have been 

possible without the collaboration 

and able help of Mike and Cindi 

Clemens, USVI Enforcement Officer 

Oliver Christian, U.S. Coast Guard 

Chief Petty Officer Joseph Restle, 

sonar winch troubleshooter Harry 

Jacobs, and base station tender Hap 

Starr. William Tobias from the USVI 

Department of Planning and Environmental Resources, Division of 

Fish and Wildlife, provided much appreciated logistics support with winch 

parts in St. Croix. The St. Croix Yacht 

Club allowed us use of pier space to 

help support our logistics. Hanumant 

Singh, Roy Armstrong, and Graciela 

Garcia made available the AUV imagery which assisted with ground 

truthing the SSS imagery. We thank 

Christopher Taylor for his initiative, 

patience, and effort in chairing the 

special session in acoustics during the 

56th Annual Meeting of the Gulf and 

Caribbean Fisheries Institute which 

resulted in this publication. 

Literature cited

Arono, R., and R. Gould. 

1998. Coastal monitoring using ocean 

color, inverting ocean reflectance into 

water properties, coastal interactions; 

satellite sensor advances stimulated 

algorithm, data management development. Sea Technology, September, p. 

18–27.
Rivera et al.: Detecting fish aggregations from reef habitats mapped with high resolution SSS imagery 103

Figure 11

Mean Fish Aggregation (FA) signal area (m2

) for each 

location. Error bars indicate standard error.

0

10

20

30

40

50

60

70

80

MCDW MCDE LB MS

m( aer al angis A

F

nae M 2

)

Location

Kantor, M., D. J. Baker, and R. A. Schmitten. 

1996. Magnuson-Stevens Fishery Conservation and Management 

Act. NOAA Tech. Memo. NMFS F/SPO-23, 121 p.

Karl, H. A., W. C. Schwab, A. St. C. Wright, D. E. Drake, J. L. Chin, 

W. W. Danforth, and E. Ueber. 

1994. Acoustic Mapping as an Enviromental Management Tool: 

I. Detection of Barrels of Low-Level Radioactive Waste, Gulf of 

the Farallones National Marine Sanctuary, California. Ocean 

and Coastal Management 22:201–227.

Klepsvik, J, M. Bjarnar, P. Broarstad, D. Braend, and H. Westrum. 

1994. A novel laser radar system for sub-sea inspection and mapping. Oceans 2:700–704.

Lee, Z., K. Carder, S. Hawes, R. Steward, T. Peacock, and C. Davis. 

1994. Model for the interpretation of hyperspectral remote-sensing reflectance. Applied Optics 33:5721–5732.

Lizenga, D. 

1978. Passive remote sensing techniques for mapping water 

depth and bottom features. Applied Optics 17:379–383.

1981. Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and Landsat 

data. Int. J. Remote Sensing. 2:71–82.

Misund, O. A., A. Aglen, J. Hamre, E. Ona, I. Rottingen, D. Skagen, 

and J. W. Valdemarsen. 

1996. Improved mapping of schooling fish near the surface: 

comparison of abundance estimates obtained by sonar and 

echo integration. ICES J. Mar. Sci. 53:383–388.

Mumby, P., C. Clark, E. Green, and A. Edwards. 

1998. Benefits of water column correction and contextual editing for mapping coral reef. Int. J. Remote Sensing. 19(1):203–

210.

Ochiai, A.

1957. Zoogeographical studies on the soleoid fishes found in 

Japan and its neighboring region. Bull. Japan Soc. Sci. Fish. 

22:526–530.

Prada, M. C.

2002. Mapping Benthic Habitats on the South West of Puerto 

Rico as determined by Side Scan Sonar. PhD dissert. University of Puerto Rico at Mayaguez, Department of Marine Sciences. 175 p., 5 app., 4 CD-ROMs with map data.

Roesler, C., and M. Perry. 

1989. Modeling in situ phytoplankton absorption from total 

absortion spectra in productive inland marine waters. Limm. 

Oceanogr. 34:1510–1523.

Siljestrom, P. A., J. Rey, and A. Moreno.

1996. Characterization of phanerogram communities (Posidonia 

oceanica and Cymodocea nodosa) using side scan-sonar images. J. 

Photogramm. Remote Sensing 51:308–315.

Berk, I. M.

1998. Sound production by white shrimp (Penaeus setiferus), analysis of another crustacean-like sound from the Gulf of Mexico, 

and applications for passive sonar in the shrimping industry. 

J. Shellfish Res., 17(5):1497–5000.

Evans, W. E., and J. C. Norris. 

1993. The potential role of passive sonar in fisheries resource 

evaluation. In Recent Advances in Marine Science and Technology, 92, N. Saxena (ed.), p. 423–432. PACON International, Honolulu, HI.

Fish, J. P., and H. A. Carr. 

1990. Sound Underwater Images. Lower Cape Publishing, Orleans, MA, 189 pages.

Friedlander, A. M., G. W. Boehlert, M. E. Field, J. E. Mason, J. V. 

Gardner, and P. Dartnell. 

1999. Sidescan-sonar mapping of benthic trawl marks on the shelf 

and slope off Eureka, California. Fish. Bull. 97(4):786–801.

Gordon, H., and M. Wang. 

1994. Retrieval of water-leaving radiance and aerosol optical 

thickness over the oceans with SeaWIFS: a preliminary algorithm. Applied Optics 33:443–452.
104 Professional Paper NMFS 5

Appendix A

Parameter Report

System Parameter Value

Geoswath system Roll offset (positive starboard horizon) 0.98°

Pitch offset (positive aft horizon) –0.05°

Yaw (heading) offset relative to gyrocompass 2.93°

Time latency (s) < 0.01

Transducer draft from static waterline 1.14 m

System frequency 250 kHz

Swath width (average) 60 m

Pings per second (average) 9

RTK GPS system Main antenna offset (starboard positive)) 0.91 m

Main antenna offset (forward positive) 0.98 m

Main antenna offset (height above SWL) 5.51 m

Time latency (s) 0.02

Position update rate 5 Hz

RTK Radio Modem Effective baud rate 4800

Side-scan sonar system Frequency 100/500 kHz

Horizontal beam width 1°/0.2°

Slant range 50 m

Gains: Auto CPU return & offset, Att. STBD & PORT 7,9,A,A

Sonarwiz DAQ Digitizing rate 66 kHz

Resolution 16 bit

Survey Equipment List

No. Item Manufacturer Model 

1 Swath echo-sounder system Geoacoustics Ltd. (UK) Geoswath 

2 Side-scan sonar system, 500kHz Klein Sonar Inc. (USA) 595

3 Sonarwiz data acquisition system Chesapeake Technology (USA) N/A

4 Sound velocity probe Valeport Ltd. (UK) Soundbar

5 Motion reference unit TSS Ltd. (UK) DMS-05

6 Gyrocompass TSS Ltd. (UK) Meridian surveyor

7 Electric winch for sonar Sea Mac Inc. (USA) EM-302

8 RTK GPS system Trimble Inc. (USA) 5700

9 Radio modem system for RTK GPS Pacific Crest Corp. (USA) RFM96W

10 Navigation software Coastal Oceanographics (USA) Hypack Max (v02.12)

Processing Software List

No. Item Manufacturer Name & Version 

1 Side-scan sonar data processing Chesapeake Technology (USA) SonarWeb Pro (v3.15G) 

2 Bathymetry data processing Geoacoustics Ltd. (UK) Swath32 (v2.17s)

3 Data presentation software Geosoft Inc.(Canada) Oasis Montaj (v5.1.7)
105

Introduction

The Sustainable Fisheries Act of 1996 

and the amended Magnuson-Stevens 

Fishery Conservation and Management Act elevated habitat and conservation as priorities in federal fisheries 

management. In particular, the Essential Fish Habitat (EFH) amendment 

to the Magnuson-Stevens Fishery 

Conservation and Management Act 

establishes guidelines to assist fishery 

managers in the description and identification of EFH. Essential fish habitat 

is defined as “. . . those waters and substrate necessary to fish for spawning, 

breeding, feeding, or growth to maturity. . . . waters include aquatic areas 

and their associated physical, chemical, and biological properties that are 

used by fish, and may include areas 

historically used by fish where appropriate; ‘substrate’ includes sediment, 

hard bottom, structures underlying 

the waters, and associated biological 

communities; ‘necessary’ means the 

habitat required to support a sustainable fishery and a healthy ecosystem; 

. . .” Moreover, National Marine FishIntegration of technologies for understanding 

the functional relationship between reef habitat 

and fish growth and production

Doran M. Mason

NOAA Great Lakes Environmental Research Laboratory

2205 Commonwealth Blvd.

Ann Arbor, MI 48105

email: Doran.Mason@noaa.gov

Brian Nagy

Mark Butler

Stephen Larsen

Debra J. Murie

William J. Lindberg

Department of Fisheries and Aquatic Sciences

University of Florida

7922 NW 71st Street

Gainesville, FL 32653-3071

Abstract—Functional linkage between 

reef habitat quality and fish growth and 

production has remained elusive. Most 

current research is focused on correlative 

relationships between a general habitat 

type and presence/absence of a species, 

an index of species abundance, or species diversity. Such descriptive information largely ignores how reef attributes 

regulate reef fish abundance (densitydependent habitat selection), trophic interactions, and physiological performance 

(growth and condition). To determine the 

functional relationship between habitat 

quality, fish abundance, trophic interactions, and physiological performance, we 

are using an experimental reef system in 

the northeastern Gulf of Mexico where we 

apply advanced sensor and biochemical 

technologies. Our study site controls for 

reef attributes (size, cavity space, and reef 

mosaics) and focuses on the processes that 

regulate gag grouper (Mycteroperca microlepis) abundance, behavior and performance (growth and condition), and the 

availability of their pelagic prey. We combine mobile and fixed-active (fisheries) 

acoustics, passive acoustics, video cameras, 

and advanced biochemical techniques. 

Fisheries acoustics quantifies the abundance of pelagic prey fishes associated 

with the reefs and their behavior. Passive 

acoustics and video allow direct observation of gag and prey fish behavior and 

the acoustic environment, and provide a 

direct visual for the interpretation of fixed 

fisheries acoustics measurements. New application of biochemical techniques, such 

as Electron Transport System (ETS) assay, 

allow the in situ measurement of metabolic expenditure of gag and relates this 

back to reef attributes, gag behavior, and 

prey fish availability. Here, we provide an 

overview of our integrated technological 

approach for understanding and quantifying the functional relationship between 

reef habitat quality and one element of 

production – gag grouper growth on shallow coastal reefs.

eries Service (NMFS) guidelines call 

for analysis of EFH at four levels of detail: Level 1 – the presence/absence 

of distributional data for some or all 

portions of the geographic range of a 

species; Level 2 – habitat-related densities of a species; Level 3 – growth, 

reproduction, or survival rates within 

habitats; and Level 4 – production 

rates by habitat (see technical guidelines at: http://www.nmfs.noaa.gov/

habitat/efh/). That is, the amendment and guidelines highlight a process-oriented framework and provide 

for using ecosystem concepts in the 

management of fisheries and aquatic 

habitats.

Much of the current research is 

focused on correlative relationships 

between general categories of habitat (e.g., sand, coral, hard live bottom, temperature, salinity) and the 

presence or absence of a species, a 

general index of species abundance, 

or species diversity (e.g., Minello, 

1999; Packard and Hoff, 1999). Such 

descriptive information is consistent 

with NMFS EFH Levels 1 and 2, but 

largely ignores how reef attributes 
106 Professional Paper NMFS 5

(complexity, size, cavity space) regulate reef fish abundance (e.g., density-dependent habitat selection), 

trophic interactions, and physiological performance 

(growth and condition) (Lindberg et al. 2006, Lindberg et al.1

), that is, NMFS EFH Level 3. Statistical models have been developed using descriptive information 

as a baseline for managing habitat and fisheries (e.g., 

Rubec et al., 1999). However, such statistical models 

lack mechanistic understanding and a theoretical 

foundation, and as such, may fail unexpectedly and for 

unknown reasons. 

Developing the statistical relationships between 

general habitat categories and fish presence, relative 

abundance, and diversity is an excellent start, but lacks 

the process-based understanding that comes from 

Levels 3 and 4 that ultimately allows us to fully develop 

predictive capabilities. Our long-term program goal 

is to develop this process-based understanding and to 

develop the capacity to predict fish production from 

reef habitat attributes. Such a goal is riddled with complexities, but the availability of emerging technologies 

may help to alleviate the otherwise intractability of 

those complexities.

Herein, we describe the suite of technologies that 

we are currently using to develop our quantitative understanding towards achieving our longer-term goal 

of prediction. In this paper, we focus on growth; other 

aspects of production (abundance, mortality, emigration, immigration) are also being addressed in our 

program (Lindberg et al, 2006) but are not the topic of 

this paper. Some of the techniques (i.e., mobile fisheries acoustics) we use are a part of fisheries assessment 

programs around the world. Other technologies (e.g., 

biochemical) have yet to be implemented for quantifying fish performance in response to reef attributes. 

Moreover, it is the integration of these technologies for 

understanding the fundamental relationship between 

reef habitat attributes and fish growth and production 

that is novel and innovative. Thus, the overall objective of this paper is to describe the technologies (with 

examples) and the integration of these technologies 

for gaining NMFS EFH Levels 3 and 4 understanding 

of reef habitat. First we provide background on our 

experimental system, and then we describe the suite of 

technologies being used to understand the habitat-fish 

production system.

1

 Lindberg, W. J., D. Mason, and D. Murie. 2002. Habitat-mediated 

predator-prey interactions: implications for sustainable production 

of gag grouper. Final Project Report (grant no. R/LR-B-49). Florida 

Sea Grant College Program. 60 p. http://www.glerl.noaa.gov/res/

Task_rpts/Resources/edymason09-3projrpt.pdf [Accessed 1 August 

2006.]

Methods

Approach

Ours is an in situ experimental approach, with a focus 

on reef architecture and gag grouper (Mycteroperca microlepis). We define reef architecture as those physical 

attributes that characterize habitat quality; for gag these 

include vertical relief, cavity volume (volume of interstitial spaces), and aerial extent, with a potential secondary 

characteristic that includes proximity to neighboring 

reefs (i.e., reef mosaics across the landscape). We focus 

on gag grouper because of its economic and ecological 

importance, its dominance in our study region, and 

because characteristics of its life history, behavior, and 

ecology simplify testing key habitat relationships that 

may be of general management consequence for reef 

fisheries (Lindberg et al., 2006).

Our study site is the Suwannee Regional Reef System 

(SRRS), located in the northeastern Gulf of Mexico, 

which controls for reef attributes to evaluate limiting 

factors and habitat constraints to reef fish production 

(Lindberg et al., 2006). The SRRS is a unique large-scale 

experimentally manipulated reef system, consisting of 

22 reef sites, with reef sites located about 24–29 km 

offshore and spaced about 2 km apart along the 12 m 

depth contour. Each reef site is made up of six patch 

reefs made of concrete. Patch reef complexity and 

composition is controlled, while patch reef spacing 

and size is manipulated (Fig. 1). All reefs within the 

SRRS have the same representation of environmental 

characteristics, especially with regard to temperature 

and salinity regimes. In addition, gag grouper on these 

reefs are within the same relative range of body size; 

all are juvenile-to-young-adult females (Lindberg et al., 

2006) and show a strong site/reef fidelity (Kiel, 2004; 

Lindberg et al., 2006). Such an in situ experimental system provides a unique opportunity to quantify the role 

of habitat architecture in mediating fish performance 

(condition, physiology) and predator-prey interactions 

(gag and their pelagic fish prey), and thus gag growth 

and production.

Our past research has demonstrated an empirical 

relationship between reef size and total cavity space 

and the abundance, growth, and condition of gag on 

these reefs. As the size of the reef and total cavity space 

(volume) increases, gag numbers increase, but growth 

and condition declines (Lindberg et al., 2006). Given 

that growth and condition are bioenergetic processes, 

we structure our research about those processes that 

regulate consumption (trophic dynamics) and metabolic expenditures (activity). For growth and condition 

to decline on the larger reefs, either consumption has 

to decrease or metabolic expenditures must increase, 

or some weighted combination of both. This leads to a 
Mason et al.: Technologies for understanding the functional relationship between reef habitat and fish growth and production 107

89 89 cm cm

4-cube

arra

4-cube y

16-cube

array 16-cube

Figure 1

The Suwannee Regional Reef System is comprised of 22 experimental reef 

sites offshore from Cedar Key, Florida, along Florida’s north-central Gulf 

coast. Hexagonal arrays of patch reefs are located at each reef site. Equalsized patch reefs at each site are comprised of either 4 or 16 prefabricated 

concrete cubes, and the spacing between patches is 25 m, 75 m, or 225 m.

series of inter-related hypotheses that we are exploring 

in our research: 

1) Reef architecture determines the abundance of 

gag and pelagic schooling planktivorous fishes, the 

principle prey of gag.

2) Abundance of gag and pelagic prey determines 

prey availability to gag and foraging efficiency of 

gag, and thus gag daily ration.

3) Abundance of gag and pelagic prey determines the 

metabolic expenditure of gag, where increased gag 

numbers may:

a) Decrease the number of prey per gag, which 

directly decreases daily ration.

b) Increase social interaction, which may increase 

energetic expenditures.

c) Decrease foraging efficiency through interference causing an increased number of attacks, 

which may be energetically expensive.

These hypotheses follow a logical progression from 

how habitat regulates the abundance of gag and availability of pelagic prey fishes, the implications of predator and prey abundances on the bioenergetic efficiencies of foraging and metabolism for gag, and ultimately 

to growth and production. To address these specific 

hypotheses, we are simultaneously using several technologies including acoustics, video, and a biochemical 

technique. In this paper, we focus entirely on the technologies and the integration of these technologies to 

address the above hypotheses.

Technologies

Our integrated technological approach directly measures abundance, behavior, and physiological performance of gag in relation to reef attributes. We combine 

mobile and fixed active (fisheries) acoustics, passive 

acoustics, video cameras, and advanced biochemical 

techniques. Mobile and fixed fisheries acoustics quantifies the abundance of pelagic forage fishes with respect 

to reef size, as well as the behavior of pelagic prey fish 

and gag. Passive acoustics and video allow direct observation of gag and prey fish behavior relative to one 

another and to pelagic predators, and to the acoustic 

environment, and provide direct visual observations for 

the interpretation of fixed fisheries acoustics measurements. New application of biochemical techniques, i.e., 

Electron Transport System (ETS) assay, allow the direct 

in situ measurement of total metabolic expenditure of 

gag and relates this back to reef attributes, gag abundance and behavior, and prey fish availability. Below 

we provide details of each of the technologies and examples of their use.

Active fisheries acoustics We use mobile hydroacoustic surveys to estimate pelagic planktivorous (prey) 

fish abundance as a function of patch reef size (4 vs. 16 

cube patch reefs) and to quantify inter-annual variability 

in pelagic prey fish densities. In this section, we provide 

an example of the application of this technology to address one of the above hypotheses, i.e., reef architecture 

determines the abundance of pelagic schooling prey 
108 Professional Paper NMFS 5

fish. We used a 120 kHz split-beam echosounder (Simrad EY500, beam width = 7.2°, power setting = 63 W, 

pulse duration = 0.3 ms, ping rate = 3 pings s–1), which 

consisted of a deck unit (echosounder), laptop for data 

acquisition, power source (12V DC battery), cable, and 

transducer. The acoustic transducer is mounted on a 

stable, 1.2 m towbody and towed alongside the research 

vessel at a depth of about 1 m and at speeds of 2.5–

3.5 m s–1. Acoustic transects traversed each patch reef at 

least five times from different directions to ensure full 

ensonification of any schools present (Fig. 2). Often 

times greater than five passes at a single patch reef is 

required to insure full ensonification above the reef for 

a minimum of five transects. Equipment performance 

is monitored in the field using the acquisition software, 

Figure 2

A reef array showing an acoustic transect (dotted line) and the 50-m radius buffers (large circles) over top of individual 

patch reefs (star) used for data collection and data selection.

and raw digitized acoustic signals are time-marked and 

geocoded using a Global Positioning System (GPS; 

model: Garmin GPS 48) and saved for later processing. 

Calibrations are performed either before or after every 

cruise using a 33 mm diameter tungsten carbide reference sphere (Foote et al., 1987; Foote, 1990).

For our example here, acoustic data were processed 

using the Digital Echo Visualization and Information 

System (DEVIS) (Jech and Luo, 2000). DEVIS performs 

echo-squared integration (Powell and Stanton, 1983; 

Thorne, 1983) and split-beam analyses (Ehrenberg, 

1983) to estimate absolute fish density. Echo-squared 

integration (vertically integrated from surface to bottom with Sv

 threshold of –70 dB) provided a quantitative relative measure of fish density that was scaled to 
Mason et al.: Technologies for understanding the functional relationship between reef habitat and fish growth and production 109

absolute fish density with system parameters obtained 

from equipment calibration and measures of the mean 

backscattering cross-section of the fish obtained from 

split-beam analyses. Split-beam analysis was used to 

determine the depth distribution of fish backscattering 

coefficients (σbs) and fish target strengths (TS), i.e., 

acoustic size. To identify single targets, we used a minimum TS detection threshold of –70 dB, minimum echo 

length of 0.8, maximum echo length of 1.6, maximum 

gain composition of 3.0 dB, and maximum phase deviation of 3.0°. Fish density (number m–2

) was determined 

by dividing the corrected sums of squared voltages from 

the echo-squared integration by σbs. Acoustic data were 

inspected for noise and bottom contamination before 

applying the mean backscattering cross-section to the 

echo-squared integration. Obvious grouper targets, if 

present, were removed from the analysis.

Acoustic data in a 50-m radius of any given patch reef 

were selected for the analyses (Fig. 2). This ensured that 

only fish associated with the patch reef were included 

in the analysis. Once the appropriate acoustic data were 

selected for each patch reef, we estimated fish density 

for each pass over the reef; the mean of these passes 

was used as the estimate of pelagic fish density. This 

provided a density estimate for each patch reef within 

a given reef array. Density estimates were not normally 

distributed, so all density values were log10 transformed. 

We used density estimates to test the hypothesis that pelagic forage fish density was similar between patch reefs 

of different sizes (4-cube vs. 16-cube reefs).

An example of day transects traversing all six patch 

reefs at a reef site is displayed in Fig. 3. Note the strong 

affinity of the fish schools to each patch reef and that for 

each patch reef there is a pelagic fish school. This pattern is common and consistent across all of our reef sites 

such that 99% of the patch reef sampled had pelagic 

schools of forage fishes associated with the reef.

Pelagic fish density was similar between patch reefs 

of different sizes (Fig. 4A,B). Random direct sampling 

and direct visual observations suggested that the pelagic 

prey fish were young of the year sardines (Harengula 

jaguana and Sardinella aurita).

Figure 3

Three-dimensional rendering of a reef site showing acoustic backscatter of pelagic prey fish schools (mostly scaled sardines, 

Harengula jaguana) associated with each patch reef. Color represents intensity of backscatter with red being highest intensity 

(greatest biomass) and blue being lowest intensity (lowest biomass but not zero). Green line above the acoustic backscatter 

demarks the actual acoustic transect. Distance from the green line to acoustic backscatter on bottom (grayish) is range, 

where the distance from surface to bottom is 11 m. Purple line denotes movement between two patch reefs when the 

echosounder was not collecting data.
110 Professional Paper NMFS 5

Fixed fisheries acoustics Fixed-array acoustics are 

being used to measure pelagic prey fish schooling 

behavior and dynamics at patch reefs and to measure 

movement of gag at the reefs. The fixed array acoustic 

system consists of two transducers and two frequencies, 

200 kHz (beam widths = 8°×14°, source level = 218 dB 

re μPa @ 1 m, pulse duration = 0.3 ms) and 420 kHz 

(beam widths = 6.7°×15°, source level = 215 dB re μPa 

@ 1 m, pulse duration = 0.3 ms) (BioSonics DE6000). 

Transducers are attached to two tripods, each equipped 

with a remote control rotator (Remote Ocean Systems 

PT-10) for fine scale aiming in both the horizontal 

and vertical dimension, and placed on the sea floor 

approximately 30 m from a patch and about 0.5 m off 

the bottom. Given the beam widths for each transducer, 

this provides an aerial coverage directly over the reef 

patch of 27 m2 for the 420 kHz transducers and 30 m2

for the 200 kHz transducers. This allows almost complete coverage of the water column above the patch 

reef. All transducers are cabled to a deck unit aboard 

a vessel anchored approximately 100 m from the reef 

patch. Echosounder is calibrated using 36-mm tungsten 

carbide sphere for the 200 kHz and a 21-mm tungsten 

carbide sphere for the 420 kHz. Data are collected for a 

24-hour period, focusing on dawn, day, and dusk so as 

to capture the morning re-aggregation process over the 

reefs, the daylight behavior of fish around the reefs, and 

the nightly dispersion off the reefs.

From these data we are quantifying how pelagic fish 

schools use the reefs. Such information includes the 

strength of the affinity of fish schools to the reefs, the 

residency time of fish schools at the reefs (all day or transient), and the response of the schools to periods in the 

tidal cycle. In addition, we hope to capture the dynamics 

of gag and prey fish interactions, such as corroborating 

the time of day when foraging occurs, determining if 

there are cues prompting a foraging event, and measuring the attack strategy (attack angle and swimming 

velocity) and the response of the school to an attack.

Video and passive acoustics Video and passive acoustics 

are being used to study the daylight behavior of gag and 

pelagic fish schools in response to one another, potential 

gag predators, and periods in the tidal cycle. Visual monitoring of the artificial reefs using ambient light is achieved 

through deployment of a two-camera system (Sony Handycam CCD-TR910 in a Hypertech underwater housing, and 

the Ocean Systems, Inc. self-contained Splash-cam—Deep 

Blue Pro Color). Cameras are powered using 12 V marine 

batteries, and video is recorded on VHS or Hi-8 tapes. The 

two cameras are set on the same side of the reef, about 2 

m from the reef, and 1 m above the bottom. This deployment provides a compromise between invasiveness of the 

camera array and near total reef coverage. There is only 

one blind spot with this configuration, the area just behind 

4 16

0.0

0.2

0.4

0.6

0.8

2000

0.0

0.2

0.4

0.6

0.8

1.0

2001

P = 0.826

P = 0.750

(15) (30)

(82)

(77)

goL 01 ) 1 + ytisne D(

Reef size

4 16

A

B

Figure 4

Box and whisker plot (median, 25% quartiles, 75% 

quartiles, range, and hashed area is the ±95% CL) 

of the log transformed fish density (fish m–2

), LOG10

[density+1] estimated for data collected from July–

October in (A) 2000 and (B) 2001. No significant 

difference was detected between 4 and 16-cube reefs 

for either year (ANOVA, P=0.826 in 2000, P=0.750 in 

2001).

the reef. Both cameras provide a live feed to the surface 

for real-time monitoring.

In addition, we are artificially exposing gag to potential predator threats by manipulating and monitoring 

the acoustic environment to determine how gag use reef 

habitat in response to a potential predator. To accomplish this, we are using a submersible speaker (30 watt 

underwater speaker, University Sound UW-30, source 

level 120 dB re 1 μPa) to broadcast bottlenose dolphin 

vocalizations (predator) and a combined video and hydrophone (High Tech Inc. model HTI-96-MIN) system 

to watch and listen for the gag behavioral response. 

The speaker is positioned 2 m from the reef and centered between the two cameras, with the hydrophone 

positioned at the center of the reef. The audio feed 

is digitally recorded as a *.wav file (digital audio file 

format developed by Microsoft) at a sample rate of 44.1 

kHz. Both the visually observed and vocal response of 

gag is recorded on a video feed and recorded to VHS 

cassette. An example of the combined video and audio 

(listening and transmitting) is shown in Fig. 5. In Fig. 

5, the hydrophones are not visible; frames C and D are 

frames extracted from the video showing schooling fish 
Mason et al.: Technologies for understanding the functional relationship between reef habitat and fish growth and production 111

Figure 5

Fixed, side-facing acoustics (A,B) and the time matched frames from the video recording (C,D). For A and B, the 

transducer is located at the top of the image, vertical dimension is distance from transducer (m), and horizontal 

dimension is time. The first (upper most) red horizontal line is the leading edge of the reef and the bottom red 

horizontal line is the far end of the reef. Each of the black horizontal lines denotes 20 m with the entire range 

displayed at 50 m; the leading edge of the reef is 28 m from the transducer. Circled areas for A–C and B–D 

match the acoustics data to the video output. A and C show school of fish on the reef. B and D show that the fish 

school moved off the reef (just out of view of the camera) and gag milling around on the backside of the reef.

A B

C D

over the reef (Fig. 5C) and the response of gag to dolphin vocalizations (Fig. 5D).

Electron transport system assay Electron transport 

system (ETS) enzyme assay technique provides in situ 

estimates of fish total metabolic rates (Butler et al.2). 

ETS enzyme assay is a method to estimate the time-averaged potential respiratory capacity (potential oxygen 

2

 Butler, M. W., D. M. Mason, W. J. Lindberg, D. J. Murie, and D. C. 

Parkyn. In prep. Non-lethal application of the electron transport 

system assay for in-situ estimation of relative metabolic rates of large 

marine fishes.
112 Professional Paper NMFS 5

Figure 6

Cubes represent the two associated treatments (4 vs 16) on the Suwannee 

Regional Reef System. There is an associated increase in the number of gag 

and decrease in gag size at age with increasing reef size (Lindberg et al., 

2006). We hypothesize that total metabolism as determined by ETS assay will 

follow the same trend.

ETS Activity 

(Total Metabolism)

ETS Activity 

(Total Metabolism)

consumption) of an organism by measuring the enzymatic activity of the rate-limiting step in oxygen use, 

namely adenosine triphosphate (ATP) production. For 

ETS, this step is the oxidation of the coenzyme UQ-cytochrome b complex (Broberg, 1985). The advantages 

in using ETS enzyme assay to measure metabolic rates 

include: 1) ability to measure metabolism in situ; 2) 

ETS responds slower to environmental change than 

respiration, thereby providing an integrated measure of 

metabolism for about a one week duration (Bämstedt, 

1980; Ikeda, 1996), effectively eliminating short-term 

fluctuations in respiration (noise) and the stress associated with specimen collection; 3) it is a simple and 

extremely sensitive technique; 4) samples from animals 

can be collected, quickly frozen until analysis, then later 

thawed and measured for ETS activity, thereby allowing 

measurements on a large number of samples.

ETS enzyme assay technique has been used successfully to measure oxygen uptake potential for bacteria 

(Tan and Ruger, 1989), marine and freshwater plankton 

(Packard, 1971; King and Packard, 1975; Owens and 

King, 1975; Devol, 1979; del Giorgio, 1992), benthic 

polychaetes and amphipods (Cammen et al., 1990), 

and zebra mussels (Dreissena polymorpha) (Madon et al., 

1998; Fanslow et al., 2001). ETS has also been used to 

successfully estimate the metabolic rates of freshwater 

and marine larval and juvenile fishes including hybrid 

striped bass (Morone saxatilis), yellow perch (Perca flavescens), walleye (Stizostedion vitreum), largemouth bass 

(Micropterus salmoides), walleye pollock (Theragra chalcogramma), and leptocephali (Yamashita and Bailey, 1990; 

Pfeiler and Govoni, 1993; Gopalan et al., 1996), and 

small-bodied adult fishes, such as myctophid, gobies, 

and pomacentrids (Ikeda, 1989), where whole bodies 

are used. However, ETS assays have not been applied to 

large fish. Therefore, our current emphasis is on adapting this technique for non-lethal application to larger 

fish species (Butler et al.2). This requires knowledge 

of where and how to collect a small quantity of muscle 

tissue from a fish, as well as evaluating the entire analytical procedure for maximizing the signal (enzymatic 

activity) and minimizing the error in tissue collection 

and preparation.

We have recently developed the ETS assay protocol 

for non-lethal application to gag ranging in size from 25 

to 90 cm (Butler et al.2

). Our next step is to apply this 

technique to our experimental reef system to determine 

the in situ metabolism of grouper and compare it across 

differing habitats. With this information, we expect to 

measure the metabolic demands potentially mediated 

by reef architecture through changes in gag densities, 

foraging efficiencies, and social interactions (Fig. 6). 

This will allow us to make predictions about condition 

based on habitat structure and quality.

Integration across technologies Each of the discussed 

technologies are being used to address specific questions 

related to the above stated hypotheses. However, the integration of these technologies extends the application 

and data. For example, the combined fixed side-look-
Mason et al.: Technologies for understanding the functional relationship between reef habitat and fish growth and production 113

Figure 7

Equipment arrangement for visual observation (cameras), passive acoustics 

(hydrophone), active acoustics (not pictured), and playback experiments 

(speaker). Transducers for stationary active acoustics are located about 30 m 

from the reef in the direction of the boat. 

Hydrophone

Flow Vane

Camera

2m

Speaker

Camera

To Boat 

Direction of view from

stationary active acoustics 

ing acoustical deployment, video, and audio playback 

(Fig. 7), and listening (all of which were synced based 

on time) provides information not available if each were 

used independently. Direct benefits for the application 

and interpretation of the fixed side-looking acoustics 

occurs by having direct species identification from the 

video, as well as the ability to visually track fish schools 

when they move out of the acoustical beam. Also, changes 

in current direction (period in the tidal cycle) are captured in the video by virtue of having a visual reference on 

a flow meter (General Oceanics model 2030) suspended 

in the center of the patch. All provide additional information to link changes in the observed spatial distribution 

and dynamics of fish schools relative to the position of the 

reef. Lastly, the video camera can detect the presence of 

pelagic predators when present at the patch reef, again 

providing additional information on the dynamics of the 

schooling prey fish relative to predator threat.

Data from the fixed side-looking acoustics also provides information necessary for interpreting the behavior of gag observed in the video. For example, information from the fixed active acoustics system can provide 

distance measures (e.g., distance gag are from the reef 

and distance moved), swimming speed estimates, and 

detailed spatial tracking of gag; information that may be 

difficult to measure directly and accurately from video 

(although see Taylor and Rand, 2006). Such information can be used to parameterize movement and home 

range models, as well as to detect subtle changes of 

gag in response to predation threats. For example, the 

meandering gag observed in Fig. 4D have a mean swimming velocity of 0.56 m s–1

 (range: 0.17 to 1.34 m s–1), 

average about 8.4 m distance from the reef (range: 3.8 

to 15.6 m), and have a mean target strength of –25.6 

dB (range: –34.1 to –22.4 dB). Moreover, we detected a 

subtle response of gag to the presence of a diver (disturbance) on the reefs. Prior to a disturbance from a diver, 

gag averaged a distance of 8.2 m (±0.5 m 95% CI) from 

the reef and had an average swimming velocity of 0.57 

m s–1; upon the diver entering the water and approaching the reef, gag moved to a distance of 10.8 m (±2.2 m 

95% CI) from the reef and had an average swimming 

velocity of 0.54 m s–1. The net change in distance of 2.8 

m from the reef was significant (T-test for unequal variances, P=0.027), but the change in swimming velocity 

was not (T-test for equal variances, P=0.617). Swimming 

speeds were estimated using the target tracking module 

available in Echoview© software (SonarData Pty Ltd., 

Hobart, Tasmania, Australia). Environmental conditions 

at this time included a well-mixed water column, salinity 

of 32 ppt, and a water temperature of 20°C. Lastly, observations from the video and hydroacoustics may provide 
114 Professional Paper NMFS 5

the necessary information to explain any observed differences in metabolic expenditure from the ETS assay 

through the direct analysis of activity (swimming velocity 

and frequency of activities) and behavior.

Discussion

We have demonstrated how the integration of various 

technologies will help in our ability to achieve NMFS 

EFH Level 3 for understanding the functional relationship between reef architecture and individual somatic 

growth. Growth is a bioenergetics process that incorporates density-dependence in foraging and energy expenditure, which we are quantifying using the technologies described herein (acoustics, optics, and ETS). To 

achieve the longer-term goal of obtaining NMFS EFH 

Level 4 (production rates by habitat), we are combining 

our integrated technological approach with more traditional techniques. Traditional techniques (visual census 

of reef fish populations using SCUBA, direct biological 

sampling to collect aging structures, diet composition, 

daily ration, and telemetry) provide the other necessary data to complete the bioenergetics mass balance 

of growth, and to quantify emigration, immigration, 

mortality, and maximum number of fish sustainable 

by a reef as a function of reef architecture (Kiel, 2004; 

Lindberg et al., 2006). Ultimately, it is the combination 

of technologies and traditional approaches that will 

provide us with the quantitative understanding of reef 

architecture and reef fish production to develop our 

predictive capabilities.

The technologies highlighted here are not necessarily new, having already had applications in science 

and management. Mobile fisheries acoustics is used in 

freshwater (Burczynski et al., 1987; Brandt et al., 1991; 

Mason et al., 2001), estuarine (Lou and Brandt, 1993), 

and marine (Cushing, 1968; Baily and Simmonds, 1990; 

MacLennan and Simmonds, 1992) ecosystems throughout the world to estimate abundance of pelagic fishes, 

as well as for spatial studies of fish distributions for 

population estimation and for spatial studies of pelagic 

systems (Brandt and Mason, 1994; Mason et al., 1995). 

Passive acoustics are used for the detection and evaluation of aggregations of sound-producing fish (Mann 

and Lobel, 1995; Luczkovich et.al., 1999), monitoring 

of migrations by sound-producing species (Moore et.al., 

1989), as well as general investigations of sound production by fish species (Mann and Lobel, 1998). However, 

the most novel applications of these technologies come 

from their integration, to address a series of inter-related hypotheses and competing hypotheses.

Each of the discussed technologies are being used 

to address specific questions in our process-oriented 

approach. In brief, some of our objectives include 

determining the quantitative and qualitative relationships among gag grouper, pelagic prey fish, and reef 

architecture, and the in situ determination of metabolic 

expenditure. The mobile fisheries acoustics is providing 

the quantitative data for determining if pelagic forage 

fish density may change as a function of contrasting 

reef architectures. Combined video, audio, and fixed 

side-looking acoustics are providing the detailed data 

to explore how reef architecture mediates growth and 

production of gag through behavioral processes (e.g., 

the availability of prey fish and the efficiency and timing of feeding by gag). Another example is how gag 

use reef structure (cavity volume) and proximity to 

the reef under conditions of predation risk. And lastly, 

how reef structure may mediate intensity of behavioral 

interactions and the expenditure of energy. These technologies combined, in the context of our experimental 

design, are a promising approach for understanding 

how reef architecture mediates numerical, behavioral, 

and physiological processes of reef fishes.

Acknowledgements

We thank two anonymous reviewers for helpful comments on the manuscript; Doug Marcinek, Eddie Leonard, Elizabeth Berens, Jackie Debicella, and Rick Kline 

for help in the field; and David Mann for advice and 

equipment for the production and recording of underwater sounds. The Suwannee Regional Reef System was 

constructed with funds from the Florida Artificial Reef 

Program, Florida Fish and Wildlife Conservation Commission (formerly in the Department of Environmental 

Protection), under contract numbers C-6051, C-7062 

and C-7551, with direct cooperation of the Levy County 

Board of County Commissioners. We thank Ginnie 

Vale, Jon Dodrill, and the late State Senator George G. 

Kirkpatrick Jr. for facilitating development of this study 

system. This project was supported by the Florida Sea 

Grant College Program (Grant No. R/LR-B-49 and L/

LR-B-53), the D.M. Smith Fellowship Fund, and NOAA 

Great Lakes Environmental Research Laboratory. This 

is contribution number 1327 of the NOAA Great Lakes 

Environmental Research Laboratory.

Literature cited

Baily, R. S., and E. J. Simmonds. 

1990. The use of acoustics surveys in the assessment of the North 

Sea herring stock and a comparison with other methods. 

Rapp. P-v. Réun. Cons. perm. int. Explor. Mer 189:9–17.

Bämstedt, U. 

1980. ETS activity as an estimator of respiratory rate of zooplankton populations: The significance of variations in environmental factors. J. Exp. Mar. Biol. Ecol. 42:267–283.
Mason et al.: Technologies for understanding the functional relationship between reef habitat and fish growth and production 115

Brandt, S. B., and D. M. Mason. 

1994. Landscape approaches for assessing spatial patterns in fish 

foraging and growth. In Theory and applications in fish feeding ecology (D.J. Stouder, K. L. Fresh, and R. J. Feller, eds.), p. 

211–238. University of South Carolina Press, Columbia, SC.

Brandt, S. B., D. M. Mason, E. V. Patrick, R. L. Argyle, L. Wells, P. A. 

Unger, and D. J. Stewart. 

1991. Acoustic measures of abundance and size of pelagic planktivores in Lake Michigan. Can. J. Fish. Aquat. Sci. 48:894–908.

Broberg, A. 

1985. A modified method for studies of electron transport system activity in freshwater sediments. Hydrobiol. 120:181–187.

Burczynski, J. J., P. H. Michaletz, and G. M. Marrone. 

1987. Hydroacoustic assessment of the abundance and distribution of rainbow smelt in Lake Oahe. N. Amer. J. Fish. Manag. 

7:106–116.

Cammen, L. M., S. Corwin, and J. P. Christensen. 

1990. Electron transport system (ETS) activity as a measure 

of benthic macrofaunal metabolism. Mar. Ecol. Prog. Ser. 

65:171–182.

Cushing, D. H. 

1968. Direct estimation of fish populations acoustically. J. Fish. 

Res. Bd. Can. 25:2349–2364.

del Giorgio, P. A. 

1992. The relationship between ETS (electron transport system) 

activity and oxygen consumption in lake plankton: a cross system calibration. J. Plank. Res. 14:1723–1741.

Devol, A. H. 

1979. Zooplankton respirations and its relationship to plankton 

dynamics in two lakes of contrasting trophic state. Limno. 

Oceanog. 24:893–905.

Ehrenberg, J. E. 

1983. A review of in situ target strength estimation techniques. 

In Symposium on fisheries acoustics (O. Nakken and S. C. 

Venema eds.), p. 85–90. FAO Fisheries Report 300, Bergen, 

Norway.

Fanslow, D. L., T. F. Nalepa, and T. H. Johengen. 

2001. Seasonal changes in the respiratory electron transport system (ETS) and respiration of the zebra mussel, Dreissena polymorpha in Saginaw Bay, Lake Huron. Hydrobiol. 448:61–70.

Foote, K. G. 

1990. Spheres for calibrating an eleven frequency acoustic measurement system. Rapp. P-v. Réun. Cons. perm. int. Explor. 

Mer 46:284–286.

Foote, K. G., H. P. Knudsen, G. Vestnes, D. N. MacLennan, and E. J. 

Simmonds. 

1987. Calibration of acoustic instruments for fish density estimation: a practical guide. International Council for Exploration 

of the Sea Cooperative Research Report no. 144, 57 p.

Gopalan, G., S. P. Madon, D. A. Culver, and P. W. Pappas. 

1996. Measurement of metabolism in free ranging juvenile fishes using electron transport system (ETS) enzyme asssays. In

High Performance Fish II Symposium Proceedings, International Congress on the Biology of Fishes (D. MacKinlay and J. 

Nelson, eds.), p. 21–30. San Francisco State University.

Ikeda, T. 

1989. Estimated respiration rate of Myctophid fish from the enzyme activity of the electron transport system. J Oceanogr. Soc. 

Japan 45:167–173.

1996. Metabolism, body composition, and energy budget of the 

mesopelagic fish Mauriculus muelleri in the Sea of Japan. Fish. 

Bull. 94:49–58.

Jech, J. M., and J. Luo. 

2000. Digital echo visualization and information system (DEVIS) 

for fisheries acoustics data. J. Fish. Res. 47:115–124.

Kiel, B.L. 

2004. Homing and spatial use of gag grouper, Mycteroperca microlepis. M.S. Thesis, University of Florida, Gainesville, 89 p. 

King, F. D., and T. T. Packard. 

1975. Respiration and the activity of the electron transport system in marine zooplankton. Limno. Oceanogr. 20:849–854.

Lindberg, W. J. T. K. Frazer, K. P. Portier, F. Vose, J. Loftin, D. Murie, 

D. M. Mason, B. Nagy, and M. Hart.

2006. Density-dependent habitat selection and performance by a 

large mobile reef fish. Ecol. Appl. 16:731–746.

Lou, J., and S. B. Brandt. 

1993. Bay anchovy production and consumption in mid-Chesapeake Bay based on a bioenergetics model and acoustics measures of fish abundances. Mar. Ecol. Prog. Ser. 98:223–236.

Luczkovich, J. J., M. W. Sprague, S. E. Johnson, and R. C. Pullinger. 

1999. Delimiting spawning areas of weakfish, Cynoscion regalis, 

(Family Sciaenidae), in Pamlico Sound, North Carolina using 

passive hydroacoustic surveys. Bioacoustics, 10:143–160.

MacLennan, D. N., and E. J. Simmonds. 

1992. Fisheries Acoustics, 336 p. Chapman and Hall, London.

Madon, S. P., D. W. Schneider, and J. A. Stoeckel. 

1998. In situ estimation of zebra mussel metabolic rates using 

the electron transport system (ETS) assay. J. Shellfish Res. 

17:195–203.

Mann, D. A., and P. S. Lobel. 

1995. Passive acoustic detection of fish sound production associated with courtship and spawning. Bull. Mar. Sci. 57:705–706.

1998. Acoustic behavior of the damselfish Dascyllus albisella: 

behavioral and geographic variation. Environ. Biol. Fish. 

51:421–428.

Mason, D. M., A. Goyke, and S. B. Brandt. 

1995. A spatially-explicit bioenergetics measure of environmental quality for salmonines: a comparison between Lakes 

Michigan and Ontario. Can. J. Fish. Aquat. Sci. 52:1572–

1583.

Mason, D. M., A. P. Goyke, S. B. Brandt, and J. M. Jech. 

2001. Acoustic fish stock assessment in the Laurentian Great 

Lakes. In The Great Lakes of the World (GLOW): Food web, 

health and integrity (M. Munawar and R. E. Hecky, eds), p. 

317–339. Ecovision World Monograph Series. Backhuys, 

Leiden, The Netherlands.

Minello, T. J. 

1999. Nekton densities in shallow estuarine habitats of Texas 

and Louisiana and the identification of essential fish habitat. 

In Fish habitat: essential fish habitat and rehabilitation (L. 

Benaka, ed.), p. 43–75. Am. Fish. Soc. Symp. 22, Bethesda, 

Maryland.

Moore, S., J. Bennett, and D. Ljungblad. 

1989. Use of passive acoustics in conjunction with aerial surveys 

to monitor the fall bowhead whale (Balaena mysticetus) migration. Rep. Int. Whaling Comm. 39:291–295.

Owens, G., and F. D. King. 

1975. The measurement of respiratory electron-transport system 

activity in marine zooplankton. Mar. Biol. 30:27–36.

Packard, D. B., and T. Hoff. 

1999. Life history, habitat parameters, and essential habitat of 

mid-Atlantic summer flounder. In Fish habitat: essential fish 

habitat and rehabilitation (L. Benaka, ed.), p. 76–92. Am. 

Fish. Soc. Symp. 22, Bethesda, Maryland.

Packard, T. T. 

1971. The measurement of electron transport activity of microplankton. J. Mar. Res. 29:235–244.

Pfeiler, E., and J. J. Govoni. 

1993. Metabolic rates in early life history stages of elopomorph 

fishes. Biol. Bull. 185:277–283.
116 Professional Paper NMFS 5

Powell, L. A. and T. K. Stanton. 

1983. A programmable microcomputer-based sonar echo processor for real-time processing. IEEE J. Oceanics Eng. 8:280–

287.

Rubec, P. J., J. C. W. Bexley, J. Noris, M. S. Coyne, M. E. Monaco, S. 

G. Smith, and J. S. Ault. 

1999. Suitability modeling to delineate habitat essential to sustainable fisheries. In Fish habitat: essential fish habitat and rehabilitation (L. Benaka, ed.), p. 108–133. Am. Fish. Soc. Symp. 

22, Bethesda, Maryland.

Tan, T. L., and H. J. Ruger. 

1989. Benthic studies of the Northwest African upwelling region: 

bacteria standing stock and ETS-activity, ATP-biomass and adenylate energy charge. Mar. Ecol. Prog. Ser. 9:171–179.

Taylor, J. C., and P. S. Rand. 

2006. A roving diver video method for quantifying size distribution, density and three dimensional spatial structure of Nassau grouper aggregations. In Emerging technologies for reef 

fisheries research and management (J. C. Taylor, ed.), p. 4–9. 

NOAA Prof. Paper NMFS 5.

Thorne, R.E. 

1983. Assessment of population abundance by hydroacoustics. 

Biol. Oceanogr. 2:253–262.

Yamashita, T. and K. M. Bailey. 

1990. Electron transport system (ETS) activity as a possible index of respiration for larval walleye pollock (Theragra chalcogramma). Nippon Suisan Gakkaishi (Bull. Jap. Soc. Sci. Fish.) 

556:1059–1062.
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