OCS Study

BOEM 2013-0123

U.S. Department of the Interior

Bureau of Ocean Energy Management

Gulf of Mexico OCS Region

Short-term Movement, Home Range, and

Behavior of Red Snapper around

Petroleum Platforms in the Northern Gulf

of Mexico, as Determined by High

Resolution Acoustic Telemetry
OCS Study 

BOEM 2013-0123 

Coastal Marine Institute 

Short-term Movement, Home Range, and 

Behavior of Red Snapper around 

Petroleum Platforms in the Northern 

Gulf of Mexico, as Determined by High 

Resolution Acoustic Telemetry 

Authors 

Mike McDonough 

James Cowan, Jr. 

October 2013 

Prepared under MMS Contract 

1435-01-99-CA-30951-73515 

by 

Louisiana State University 

Department of Oceanography and Coastal Sciences 

School of the Coast and Environment 

Baton Rouge, LA 70803 

U.S. Department of the Interior 

Bureau of Ocean Energy Management 

Gulf of Mexico OCS Region

Cooperative Agreement 

Coastal Marine Institute 

Louisiana State University
iii 

DISCLAIMER 

This report was prepared under contract between the Bureau of Ocean Energy Management 

(BOEM) and Louisiana State University. This report has been technically reviewed by BOEM, 

and it has been approved for publication. Approval does not necessarily signify that the contents 

reflect the views and policies of BOEM, nor does mention of trade names or commercial 

products constitute endorsement or recommendation for use. 

REPORT AVAILABILITY 

To download a PDF file of this Gulf of Mexico OCS Region report, go to the U.S. 

Department of the Interior, Bureau of Ocean Energy Management, Environmental Studies 

Program Information System website and search on OCS Study BOEM 2013-0123. 

This report can be viewed at select Federal Depository Libraries. It can also be obtained 

from the National Technical Information Service; the contact information is below. 

U.S. Department of Commerce 

National Technical Information Service 

5301 Shawnee Rd. 

Springfield, Virginia 22312 

Phone: (703) 605-6000, 1(800)553-6847 

Fax: (703) 605-6900 

Website: http://www.ntis.gov/

CITATION 

McDonough, M. and J.H. Cowan, Jr. 2013. Short-term movement, home range, and behavior of 

red snapper around petroleum platforms in the northern Gulf of Mexico, as determined by 

high resolution acoustic telemetry. U.S. Dept. of the Interior, Bureau of Ocean Energy 

Management, Gulf of Mexico OCS Region, New Orleans, LA. OCS Study 2013-0123. 71 

pp. 

ABOUT THE COVER 

Sunset image of platforms in the Gulf of Mexico south of Port Fourchon, Louisiana. Image 

by James H. Cowan, Jr., Louisiana State University. 
v

ACKNOWLEDGMENTS

This project was funded through a grant provided by the Bureau of Ocean Energy 

Management (BOEM) through the Coastal Marine Institute at Louisiana State University. We 

thank Dave Nieland, Michael McDonough (Sr.), Aaron Adamack, Kirsten Simonsen, Dave 

Wells, Kevin Boswell, Michelle Zapp, Shaye Sable, Kim de Mutsert, Andy Fischer and Brian 

Roth for help with field work, access to platforms and helicopter rides. Thanks also to Dr. Mark 

Benfield. Thanks most to Chevron and Texaco for allowing access to the “Circle” and the 

opportunity to use these platforms for this research. Finally, we thank the BOEM reviewers for 

their constructive criticism of this manuscript.
vii

ABSTRACT

Understanding the behavior, including the movement, of red snapper (Lutjanus 

campechanus) around and among the many oil and gas platforms in the northern Gulf of Mexico 

(GOM) is crucial to the management of this commercially and recreationally important species.

What role oil and gas production platforms play in the attraction vs. production continuum for 

red snapper is unknown, but it is certain these large structures have a role at some life-history 

stage. The VRAP acoustic telemetry system was used to track red snapper around two platforms 

in the GOM in 2005 and 2006. Fish detections per hour generally decreased over the course of 

each experiment, and detections also fluctuated with strong periodicity. Spectral analysis 

revealed that red snapper had a 24-hour periodicity to their movements. The probability of fish 

presence likewise fluctuated within a day, perhaps indicating foraging away from the platforms.

Red snapper in this study showed much lower site fidelity than in previous studies of red snapper 

on artificial reefs. These results appear to support the hypothesis that platforms function largely 

as attracting devices.
ix

TABLE OF CONTENTS

LIST OF FIGURES .....................................................................................................................................xi

LIST OF TABLES.....................................................................................................................................xiii

1.0 INTRODUCTION...............................................................................................................................1

2.0 METHODS .........................................................................................................................................3

3.0 RESULTS .........................................................................................................................................13

4.0 DISCUSSION ...................................................................................................................................35

REFERENCES ...........................................................................................................................................39

APPENDIX: REMAINING FISH PLOTS.................................................................................................43
xi

LIST OF FIGURES

Figure 1a. Map of the study area showing the position of “the Circle” (in red) in the Gulf of 

Mexico. ....................................................................................................................... 4

Figure 1b. Map of the study area showing the positions of platforms ST-135 M, ST-134 S, 

and ST-151 Y within the Circle.................................................................................. 4

Figure 2a Plots of the buoy deployment in 2005 ........................................................................ 5

Figure 2b. Plots of the buoy deployment in 2006. ....................................................................... 5

Figure 3a) Plot of the dx-corrections for fish positions in May 2005. ......................................... 8

Figure 3b) Plot of the dy-corrections for fish positions in May 2005. ......................................... 8

Figure 4a) Plot of the dx-corrections for fish positions in August 2005...................................... 9

Figure 4b. Plot of the dy-corrections for fish positions in August 2005...................................... 9

Figure 5a) Plot of the dx-correction for fish positions in 2006.................................................. 10

Figure 5b). Plot of the dy-corrections for fish positions in 2006. ............................................... 10

Figure 6a) Fish “presences” for 2006 — the hours of the study each fish registered a 

detection.................................................................................................................... 11

Figure 6b) Fish “presences” for 2005—the hours of the study each fish registered a 

detection.................................................................................................................... 11

Figure 7. Plot of all fish for which positions were calculated in 2006..................................... 16

Figure 8. Plot of all fish for which positions were calculated in 2005..................................... 16

Figure 9. Plot of fish #213........................................................................................................ 17

Figure 10. Plot of fish #1120...................................................................................................... 17

Figure 11. Plot of fish #1101; fish #1101 was not present when we redeployed in August...... 18

Figure 12. Plot of fish #1104; fish #1104 was present when we redeployed in August............ 18

Figure 13. The number of fish detected per hour in 2006.......................................................... 19

Figure 14a) The number of fish detected per hour in May 2005 ................................................. 20

Figure 14b).The number of fish detected per hour in a) May and b) August 2005...................... 20

Figure 15. Periodogram from the spectral analysis on fish detections per hour in a) 2006; 

b) May 2005; and c) August 2005. ........................................................................... 21

Figure 16. Values predicted from the regression on average hourly detections per day of 

stationary tags in 2006. The data are log-transformed.............................................. 22

Figure 17. Values predicted from the regression on average hourly detections per day of 

stationary tags in 2005. The data are log-transformed.............................................. 22

Figure 18. Average hourly detections per day of tagged fish in 2006; triangles are the 

original data, and squares are the detrended data. All data are log-transformed...... 23
xii

Figure 19. Average hourly detections per day of tagged fish in 2005; triangles are the 

original data, and squares are the detrended data. All data are log-transformed...... 23

Figure 20. The number of fish detected per hour of day in 2006, with a LOESS regression 

fit to display diel trends. ........................................................................................... 24

Figure 21. The number of fish detected per hour of day in May 2005, with a LOESS 

regression fit to display diel trends........................................................................... 25

Figure 22. The number of fish detected per hour of day in August 2005, with a LOESS 

regression fit to display diel trends........................................................................... 25

Figure 23. Mean probability of presence, across all fish and both years, by moon phase. 

Error bars indicate one standard error....................................................................... 26

Figure 24. Probability of presence, across all fish, by hour of day............................................ 26

Figure 25. Probability of presence, as calculated by the survival analysis. ............................... 27

Figure 26. Feather plots showing the consecutive movements of fish during the 2006 

experiment. Each vector represents a movement between consecutive points. ....... 28

Figure 27. Feather plots showing the consecutive movements of fish during the 2005 

experiment. Each vector represents a movement between consecutive points ........ 29

Figure 28. Mean distance from the platform for all fish by time of day in 2006. Letters 

indicate significant differences. ................................................................................ 30

Figure 29. Mean distance from the platform for all fish by time of day in 2006. Letters 

indicate significant differences. ................................................................................ 30

Figure 30. Interval, the time between two recorded positions, compared with the speed of 

movement between those two positions. .................................................................. 31

Figure 31. Speed, in body lengths per second, for hour of day for 2006. The mean value for 

each hour is superimposed........................................................................................ 31

Figure 32. Speed, in body lengths per second, for hour of day for 2005. The mean value for 

each hour is superimposed........................................................................................ 32

Figure 33. Mean depth, in meters, for each hour of the study in 2006. ..................................... 32

Figure 34. Mean depth, in meters, for each hour of the study in May 2005.............................. 33

Figure 35. Mean depth, in meters, for each hour of the study in August 2005.......................... 33

Figure 36. Periodogram from the spectral analysis on all depths per hour of study in 2006. 

Hours are on the x-axis, and the y-axis shows the power of the periodicity. .......... 34

Figure 37. Periodogram from the spectral analysis on all depths per hour of study in May 

2005. Hours are on the x-axis, and the y-axis shows the power of the periodicity. . 34

Figure 38. Periodogram from the spectral analysis on all depths per hour of study in August 

2005. Hours are on the x-axis, and the y-axis shows the power of the periodicity. . 35
xiii

LIST OF TABLES

Table 1. Independent variables used in the logistic regression, and their units and 

descriptions. .............................................................................................................. 12

Table 2. The year of capture, identification code (ID) of the tag implanted, the standard 

length, capture sight and release sight of each individual implanted for the 

study.......................................................................................................................... 14
1

1.0 INTRODUCTION

The Magnuson-Stevens Fishery Conservation and Management Act, the 1996 revision and 

reauthorization of the 1976 Public Law 94-265 (Act), require that fishery management plans 

include an identification and description of essential fish habitat (EFH), adverse impacts on EFH 

(including the effects from fishing), and actions to conserve and enhance EFH (Gallaway et al., 

1999).

One of the most pressing federal fisheries management concerns in the Gulf of Mexico 

(GOM) region is the overfished status of red snapper Lutjanus campechanus (Schmitten, 1999).

National Marine Fisheries Service (NMFS) data collection provides evidence that the primary 

cause of overfishing on red snapper is bycatch of 0–1 year class red snapper by shrimp trawls 

(Schirripa and Legault, 1999). As such, the conservation and enhancement of red snapper EFH 

may act to reduce the impacts of shrimp trawl mortality on juvenile red snapper (Schmitten, 

1999).

Red snapper are demersal reef-associated fish that are distributed along the continental shelf 

throughout the GOM. Red snapper spend most of their first year over sand and mud bottom on 

the shrimping grounds in the northern GOM, after which they move offshore to reef 

environments (Patterson et al., 1998). Adult snapper are found in deeper offshore waters near 

coral, rocks, banks, outcrops, and manmade submerged objects, such as oil and gas platforms 

and artificial reefs (Workman and Foster, 1994). 

The way in which different life stages of red snapper use these various habitats as they grow, 

e.g. for feeding, protection, or both, is unknown. Information on the 0–1 year class of red 

snapper is sparse, especially at first settlement (Szedlmayer and Conti, 1999). However, it has 

been demonstrated that, as red snapper grow, they show strong preferences for natural and 

artificial habitat with vertical relief. 

In the waters of the northern GOM there are over 4,000 functioning oil and gas platforms 

(Stanley and Wilson, 1998). Since the first platform was installed, fishers and scientists have 

been aware of their associated nekton assemblages (Stanley and Wilson, 1997). They act as 

artificial reefs by providing habitat that potentially increases the growth and survival of the 

individuals, by affording shelter for protection from predation and spawning substrate, and by 

acting as a visual attractant for organisms not otherwise dependent on hard bottom (Gallaway et 

al., 1981). Oil and gas platforms differ from most natural habitats and from traditional artificial 

reefs in that their vertical profile extends throughout the water column into the photic zone 

(Stanley and Wilson, 1991). Increased habitat quality on, or immediately around, oil and gas 

platforms is believed to be derived from increased in situ food production associated with 

encrustation by fouling organisms and also by any amount of fortification of the bottom for 

support of the structure.

Artificial reefs, such as oil and gas platforms, may be useful tools for fishery managers if 

they increase production, but many researchers question whether they are a positive influence on 

reef stock dynamics. There have been doubts about whether they produce or attract fish. The 

resolution to this question is essential to the management of reef fish stocks. If they indeed 

constitute EFH for reef fish, then they can be considered as viable management tools. If they are 

simply attracting fish to the area, they may simply be promoting overfishing. Currently, the Gulf 

Management Council does not include oil and gas platforms, or any other artificial reef habitat, 

in their treatment of EFH.
2

Understanding the movement and behavior of red snapper (Lutjanus campechanus) around 

and among the many oil and gas platforms in the northern GOM is crucial to the management of 

this important commercial and recreational species. It has long been known that artificial reefs,

such as oil platforms, are good fishing localities. Several scientific studies have confirmed that 

platforms are common sites for fish aggregation (Seaman et al., 1989; Stanley, 1994; Love et al., 

1999; 2000; Jennings et al., 2001) and that red snapper comprise a significant percentage of these 

communities (Stanley and Wilson, 1997; Nieland and Wilson, 2002; Wilson and Nieland, 2004).

Platforms have also been implicated in the partial recovery of some fish stocks; reasons for this 

include increased food production and refuge from predators around platforms. However, they 

may simply make red snapper more vulnerable to fishing. Knowledge of how fish use platforms 

is relatively scarce.

Estimates of abundance can show only that red snapper do associate with platforms, not why.

Acoustic telemetry enables researchers to investigate site fidelity and observe temporal and 

spatial patterns in red snapper movement and behavior. Site fidelity can be a proxy for the 

suitability of a platform as red snapper habitat: if the platform does provide some benefit, the 

hypothesis is that red snapper would not risk predation and the energy costs involved with 

seeking new habitat (Werner and Anholt, 1993). Investigating red snapper movement temporally 

can reveal if snapper exhibit any patterns, such as diel periodicity and diurnal/nocturnal or 

crepuscular movements. Temporal patterns often correspond to feeding behavior (Hobson, 1965; 

Helfman, 1986) and may give insight into whether platforms play some role in feeding by red 

snapper. Whether red snapper do or do not exhibit strong site fidelity to platforms, and do or do 

not gain nutrition directly from food webs dependent upon platforms, also contributes to the 

resolution of the attraction vs. production debate (Bohnsack, 1989).

Bohnsack (1989) proposed that the varying hypotheses about why artificial reefs hold such 

high numbers of fish could be summarized by two mechanisms: attraction and production.

Artificial reefs may merely attract high numbers of fish; this mechanism would be likely to 

dominate for species that are reef-associated, recruitment-limited, and exhibit low site fidelity.

Artificial reefs may actually increase the fitness of and produce biomass for some species, 

particularly those that are reef-dependent, habitat-limited, and show high site fidelity.

The attraction versus production hypothesis attempts to explain these phenomena at a 

population level. The Resource Mosaic Hypothesis may explain how attraction versus production 

may be operating at the local scale, i.e., at an individual reef or a collection of reefs. Lindberg et 

al., (1990) concluded that reef-associated species feeding upon non-reef-associated benthic 

dwelling prey create areas of intense prey depletion around the reefs they inhabit. These feeding 

halos force these predators to search beyond feeding halos to find available prey. Further, they 

hypothesized that spacing would be important in this dynamic—reefs that were close together 

would have overlapping halos and more intense prey depletion. The more intense the prey 

depletion, the higher the energetic cost of searching for available prey; this higher energetic cost 

would decrease site fidelity, abundance, and growth. Researchers have studied whether the 

resource mosaic hypothesis might apply to red snapper associated with artificial and natural 

reefs. Strelchek et al., (2005) found that red snapper biomass and CPUE both decreased with 

increasing density of reefs. McCawley and Cowan (2007) found that red snapper do indeed feed 

upon non reef-associated prey surrounding both natural and artificial reefs. Westmeyer et al., 

(2007) concluded that red snapper were exhibiting very low site fidelity to platforms in the GOM 

(<1% yr-1). From these studies it may be surmised that red snapper are exhibiting behavior 

consistent with the resource mosaic hypothesis, however, that behavior has never been observed.
3 

A major goal of this study was to see if we could observe behavior that would be consistent with 

red snapper moving away from oil platforms to feed. Because the resource mosaic hypothesis 

has implications for the spacing of reefs, managers of artificial reef programs might be interested 

to know whether the existence of foraging halos should inform their decisions about where to 

place artificial reefs. 

Telemetry can collect data that gives more information than just where fish are. Because the 

date and time of both positions and detections are collected, researchers can use these data to 

look at patterns. For example, if fish are moving away from reefs, telemetry can show when 

these movements are occurring. If fish are leaving the coverage area entirely, telemetric data can 

also show where fish are and where they are not in the coverage area. 

2.0 METHODS 

Two telemetry experiments were performed on platforms in the GOM. Red snapper were 

collected aboard a charter fishing vessel during the summers of 2005 and 2006. Collections were 

performed at a complex of petroleum production platforms known as “the Circle” because they 

surround a salt dome, a common geological formation in southern Louisiana from which oil is 

extracted. The Circle (28° 39.402 N, 090° 14.126 W) is located about 50 km off Louisiana in the 

northern GOM (Figure 1a and Figure 1b). All platforms in the Circle are owned and operated by 

Chevron/Texaco. Although the fish used in this study were caught at several platforms, all fish 

were released at the platforms around which the Vemco radio acoustic positioning (VRAP) 

system was deployed. In 2005, the VRAP was deployed around ST 135-M; in 2006, it was 

deployed around ST 134-S (Figure 1a and Figure 1b). Platform 135-M stands at the southwestern 

end of the Circle in 45 m of water. Platform 134-S stands at the northern end in 37 m of water. 

The combined platforms in the Circle have a 2.5 km radius. 

Chevron/Texaco personnel at the Circle platforms have historically been willing to allow 

researchers access to study sites via helicopter. For this experiment, a site was also provided to 

house the VRAP’s base station, where power and existing radio communication infrastructure 

were available. 

The VRAP system was deployed around one platform each year to track the movements of 

red snapper around the structures (Figure 2a and Figure 2b). The VRAP system is composed of 

three receivers that detect and record transmitters with independent hydrophones. 

 
4

Figure 1a. Map of the study area showing the position of “the Circle” 

(in red) in the Gulf of Mexico.

Figure 1b. Map of the study area showing the positions of platforms ST135 M, ST-134 S, and ST-151 Y within the Circle. 
5

Figure 2a Plots of the buoy deployment in 2005

Figure 2b. Plots of the buoy deployment in 2006.
6

Each transmitter has a unique code and emits a ping at random intervals, so that VRAP can 

identify and track multiple individuals. Random intervals minimize simultaneous transmissions 

from multiple transmitters-VRAP cannot record more than one transmission at a time. All 

transmitters had battery lives (approximately one year) that exceeded the length of the study, 

which was designed to study short-term movement and site fidelity. Some of the transmitters 

also were equipped with depth sensors (six in 2005, and nine in 2006), and communicated depth 

during transmission.

A base station kept on ST-151 Y (Figure 1b) in both years initialized the receivers for 

recording and uploaded data at user-defined intervals. When all three receivers recorded a 

transmission, the base station calculated a position for that fish. The base station also recorded 

“unresolved tags”-transmitters detected by two or fewer receivers. This feature allows VRAP to 

detect fish presence, even when it is unable to calculate position. The base station then recorded 

the positions or unresolved tags, along with the date and time of transmission, for each fish.

Stationary tags were also deployed within the array for two purposes (Figures 2a and b).

Because the buoys were moored and moved with the currents, we needed a way to subtract this 

movement from the fish positions. Stationary tags also allow estimation of error in detectability.

When fish are not detected by the system, it is possibly because they are not present in the array 

or the system is unable to detect them.

Because stationary tags are always present in the array, any loss in detectability will be due 

to the limitations of the system, hydrographic features in the area, battery loss in either the tags 

or the hydrophones, or other factors that could all be considered system-wide error. The effects 

of those factors can be estimated by trends in detections of stationary tags.

Each experiment was intended to last two weeks during the spring/summer, after cold fronts 

were no longer a threat, and before hurricane season was likely to affect the GOM. In 2005, the

VRAP was deployed for five days in May (26–30) before one of the receivers was struck and 

damaged by a supply boat. Re-deployment was not possible until August, when it was possible 

to get another seven days of data (6–12 August). In 2006 it was possible to get a continuous twoweek dataset (17–30 May). The study length was determined because two weeks is the effective 

battery life of the buoys. Long-term movements or behavior of red snapper were not the goal of 

the study

Red snapper were collected for tracking with hook and line. Each fish was checked for 

visible signs of catastrophic decompression: bulging eyes, external hemorrhaging, or everted 

stomachs (Rummer and Bennett, 2005), then placed into a pre-surgery holding tank after their air 

bladder was vented. The fish remained in the pre-surgery tank until they were able to swim 

upright on their own. At that point, fish were determined to be suitable for transmitter 

implantation. Fish were brought to level four anesthesia in a 325 mg l-1 solution of MS-222. A 

small incision was made just dorsal to the ventral midline, between the pelvic fins and the anus, 

with a sterile, disposable scalpel. An ultrasonic transmitter (Vemco, Ltd.) was inserted (a Floy® 

anchor tag (or visual identification in case of recapture) into the incision, and closed the incision 

with two catgut sutures and an acrylic adhesive (Krazy Glue®). Fish were transferred to a postsurgery recovery tank and held until they again were able to swim upright. To estimate acute 

mortality, fish were monitored for ability to orient (Patterson et al., 2001).

To analyze the data, fish positions were plotted. To account for movement of the buoys, the 

mean position of each stationary tag was calculated. The difference between each position of a 

stationary tag and its mean position in the x-direction (dx) and in the y-direction (dy) was then 

calculated. Because the VRAP system cannot detect more than one tag at a time, the time at 
7

which a stationary tag was detected was never the same as when a fish was detected. For this 

reason, dx and dy were interpolated over the times during which fish positions were recorded and 

these numbers were used to correct the fish positions in both the x- and y-direction (Figures 3–

5).

To determine when red snapper were present at the platform, positions and unresolved tags 

were combined. If an individual was detected by VRAP during any hour of the study fish were 

considered to be “present” during that hour. By definition, a fish would be either present each 

hour, even if detected more than once, or absent, if not detected at all. The fish identification 

codes (ID) by hour of the study were plotted by placing a “dot” on the x-axis for each hour of the 

study the fish was present (Figure 6). The number of fish detected per hour were then plotted. If 

a fish was detected at least once in an hour, it was counted once and no more for each hour of the 

study. To estimate system-wide error, we modeled the average hourly detections of stationary 

tags. (The average was calculated by day of the study, so that we are looking at day 1’s average 

detections per hour vs. day 2’s vs. day 3’s, etc.). We regressed on ID, time of day (day vs. night), 

and day of study. (“Day” hours were 0600–2000, and “night” hours were the remaining hours.)

We then subtracted the values predicted by the model from the average hourly detections by day 

of study of tagged fish and calculated the slopes of the de-trended data.

To detect periodicity, we performed a spectral analysis (PROC SPECTRA, SAS Institute, 

Inc., 2007) on fish detected per hour. Spectral analysis can detect cycles in presences and 

absences. We used the spectra to determine whether there was identifiable periodicity in the 

presence/absence data on a daily or sub-daily basis. To resolve trends within a day, we also 

plotted the number of fish detected per hour by time of day (in hours) and applied a LOESS 

regression fit (PROC LOESS, SAS Institute, Inc, 2007). The LOESS regression helps visualize 

maximums and minimums in the number of detections per hour of the day. So, if the spectral 

analysis shows diel periodicity, the LOESS regression can show at what times of day more and 

fewer snapper are present.
8

Figure 3a) Plot of the dx-corrections for fish positions in May 2005.

Figure 3b) Plot of the dy-corrections for fish positions in May 2005.

-80

-60

-40

-20

0

20

40

60

0 1 2 3 4 5

dx (m)

Day of Study

-50

-40

-30

-20

-10

0

10

20

30

40

50

60

0 1 2 3 4 5

dy (m)

Day of Study
9

Figure 4a) Plot of the dx-corrections for fish positions in August 2005.

Figure 4b. Plot of the dy-corrections for fish positions in August 2005.

-100

-50

0

50

100

150

71 72 73 74 75 76 77

dx (m)

Day of Study

-150

-100

-50

0

50

100

150

200

250

300

350

71 72 73 74 75 76 77

dy (m)

Day of Study
10

Figure 5a) Plot of the dx-correction for fish positions in 2006.

Figure 5b). Plot of the dy-corrections for fish positions in 2006.

-60

-40

-20

0

20

40

60

0 2 4 6 8 10 12 14

dx (m)

Day of Study

-40

-30

-20

-10

0

10

20

30

40

50

60

0 2 4 6 8 10 12 14

dy (m)

Day of Study
11

Figure 6a) Fish “presences” for 2006 — the hours of the study each fish registered a 

detection.

Figure 6b) Fish “presences” for 2005—the hours of the study each fish registered a 

detection.
12

To further resolve periodicity, we modeled the presence and absence data with logistic 

regression (PROC REG, SAS Institute, Inc., 2007) and PROC GIMMIX (SAS Institute, Inc., 

2007). For each hour of the study, a fish was coded “1”, if it was detected by VRAP during that 

hour, and “0,” if it was not. We then regressed this binary response on the variables: day (of the 

study, starting with 1); hour of day (from 0–23) and its square, cube, and fourth power; year; 

moon phase; photo-period; wind speed; wave height; tide level and stage (WAVCS; 90° 29’, 28° 

52’; Louisiana State University). 

Logistic regression is a generalized linear model (GLM) of the form;

E(Y)= g-1

(X∙ β + α), (1)

where X is a vector of the independent variables (Table 1), and β is a vector of the fixed effect 

(independent) parameters (β is not a slope, even though it is part of a linear equation; in GLM, 

the linear function is linked to the dependent variable), and α is the intercept. The function g(∙) is 

the monotonic link function, which links the mean value of Y, E(Y), to the linear predictor, X∙ β 

+ α; g-1

(∙) is its inverse.

Table 1.

Independent variables used in the logistic regression, and their units and descriptions.

Variable Units Type Values

Day 1-14 Ordinal 2006

Day 1-5, 71-77 Ordinal 2005

Hour of day 1 hour, but (0-23)/100 

for purposes of 

regression

Ordinal 0.0, 0.01, 0. 02, . . ., 0. 23

(Hour of day)^2 (0-23/100)^2 Ordinal 0.0, 0. 001, 0. 004, . . ., 0.00529

(Hour of day)^3 (0-23/100)^3 Ordinal 0.0, 0.0001, 0. 0008, . . ., 0.00121

(Hour of day)^4 (0-23/100)^4 Ordinal 0.0, 0.00001, 0.000016, . . ., 0.000280

Year 1 year Nominal 2005, 2006

Moon-phase Quarter moon Nominal Full, last, new, first

Photoperiod Hours of daylight Continuous

Wind speed Kilometers-per-hour Continuous

Wave height Meters Continuous

Tide level Meters Continuous

Tide stage Ebb, flood Nominal

For logistic regression, the link function is the logit transform; 

Logit(Y)=(Y)/Y-1), (2)

where Y is the dependent variable and takes the values of 0 for absent and 1 for present, and 

E(Y) is the expected or mean value of the dependent variable, and gives the predicted probability 

of presence. 

The logistic regression model did not converge, so variables found to be significant (day, 

hour, hour-squared, year, photoperiod, and moon phase) then were used in PROC GLIMMIX.

The GLIMMIX procedure performs a generalized linear mixed model (GLMM), which uses both 
13

random variables and fixed effects in model building. The addition of the random variables 

allows the GLMM to account for observations that cluster—clustered observations will tend to 

be more alike than observations from other clusters because they are positively correlated.

The form of the GLMM equation is a conditional model;

E[Y|γ]= g-1

(X∙ β + Z∙ γ), (3)

The expected value of Y is conditional on γ, a vector of random effects, which is univariate 

normal with mean 0 and covariance matrix G. The univariate random effect γ adds to the GLM

the use of cluster-level terms that take the same value for observations in a cluster, but different 

values for different clusters; it adjusts the intercept at the level of the cluster, but does not modify 

the fixed effects. Simply, it produces a separate intercept for each cluster. We used this GLIMM

to specify individuals as clusters, so that individual fishes’ probability of presence was not part 

of the calculation of the fixed-effect terms. Z is a design matrix for the random effects. With only 

one random effect, it is comprised of only one column—the intercept column.

We ran different combinations of these variables in GLIMMIX to achieve the most 

parsimonious model as determined by the lowest pseudo-AIC, a measure of model fit (smaller is 

better). Pearson’s product moment correlation (PROC CORR, SAS Institute, INC., 2007) was 

used to test for correlation among variables. We ran a “survival” or time-to-event analysis 

(PROC LIFETEST, SAS Institute, Inc., 2007), which gives both probability of survival, or 

continued presence at the platform, and the mean period before permanent absence occurs. It is 

important to note that absence can be the result of emigration or mortality, including fishing 

mortality. 

To examine spatial patterns, we calculated the distance moved in the x and y direction (dx 

and dy, respectively) between successive points. We plotted dx and dy on feather plots, so that 

the angle and distance of successive movements could be visualized. We then calculated a mean 

angle for each fish (Zar, 1998), which gives the mean of all the angles of movement, and a radius 

which can be used as a measure of dispersion about the mean angle (the longer the radius, the 

less the dispersion). We also calculated distance from the platform for each position of a red 

snapper, after which we calculated and plotted mean distance from the platform by hour-ofstudy, and hour-of-day.

We also calculated speed between positions by dividing distance moved over the interval (in 

seconds) between positions. When it became clear that the vast majority of the intervals were too 

large to permit an accurate estimate of speed, we plotted interval vs. speed to see if there was 

asymptote that could provide a maximum time interval as a threshold. Speeds below this 

threshold were multiplied by body length (m, SL) to calculate mean speed in body length 

s

-1 by hour-of-study and hour-of-day.

For those tags that gave depth, we calculated and plotted mean depth by both hour-of-study 

and hour-of-day. We also performed spectral analysis (PROC SPECTRA, SAS Institute, Inc., 

2007) on the all depths for each hour of the study.

3.0 RESULTS

In 2005, we captured and implanted 16 red snapper. In 2006, we captured and implanted 20 

red snapper. Table 2 gives information about the captured fish.
14

Table 2.

The year of capture, identification code (ID) of the tag implanted, the standard length, capture sight and 

release sight of each individual implanted for the study.

Year ID Std Length (mm) Capture 

sight Release sight

2005 1101 356 ST-67 B ST-135 M

2005 1100 368 ST-67 B ST-135 M

2005 1102 279 ST-67 B ST-135 M

2005 1103 279 ST-67 B ST-135 M

2005 1104 318 ST-128 X ST-135 M

2005 1105 338 ST-128 X ST-135 M

2005 1106 483 ST-135 M ST-135 M

2005 1107 279 ST-135 M ST-135 M

2005 1109 300 ST-135 M ST-135 M

2005 1110 356 ST-135 M ST-135 M

2005 119 292 ST-135 M ST-135 M

2005 120 279 ST-135 M ST-135 M

2005 121 318 ST-135 M ST-135 M

2005 122 401 ST-135 M ST-135 M

2005 123 318 ST-128 X ST-135 M

2005 124 318 ST-128 X ST-135 M

2006 1113 410 ST-135 V ST-134 S

2006 1114 445 ST-128 R ST-134 S

2006 1115 451 ST-128 R ST-134 S

2006 1116 480 ST-128 R ST-134 S

2006 1117 420 ST-128 R ST-134 S

2006 1118 360 ST 134 W ST-134 S

2006 1119 472 ST-128 R ST-134 S

2006 1120 410 ST-128 R ST-134 S

2006 1121 554 ST-135 V ST-134 S

2006 1122 423 ST-135 V ST-134 S

2006 210 460 ST 134 W ST-134 S

2006 211 395 ST 134 W ST-134 S

2006 212 405 ST-135 M ST-134 S

2006 213 432 ST-135 M ST-134 S

2006 214 415 ST-135 M ST-134 S

2006 215 470 ST-135 M ST-134 S

2006 216 404 ST-135 M ST-134 S

2006 218 482 ST-135 M ST-134 S

2006 219 476 ST-135 V ST-134 S

2006 1123 430 not recorded ST-134 S
15

All red snapper implanted with a transmitter were able to swim down and were assumed to 

have survived surgery. The positions recorded by VRAP appear to cluster largely around the 

platform for each fish in each year over the experimental time interval (Figures 7–12). We report 

the 2006 results first because the study period was not interrupted. We then report the 2005 

results, because they often complement or support the 2006 study.

In 2006, the VRAP system was able to detect 14 of the 20 red snapper implanted with 

transmitters; and the number of individuals detected decreased over the study period (Figure 6).

Six left immediately or were consumed by a migratory predator that had left the study area 

(before being tracked by VRAP); another two left after two days, and another two after eight 

days. Ten stayed near the platform for at least twelve days, but only five remained at the end of 

the 14-day study. One fish was detected only once (as an unresolved tag) during the two weeks 

of tracking; we strongly suspect that this was a ghost tag. The VRAP system is designed such 

that if two transmissions occur simultaneously, it will ignore both. However, sometimes it 

translates this “collision” of transmissions as a coded tag, a ghost tag. Often the ghost tag has an 

unknown ID, and is easily identified as a ghost tag. Rarely, the ghost tag will have the ID of a

one of transmitters used. In this case, transmitter 210 was not detected on day 1, but was detected 

subsequently, and just once, on day 10. For this reason, we believe this record is a ghost tag.

In 2005, VRAP detected 15 of 16 individuals; two of those left on day 1 of the study (Figure 

6). Another fish left during day 2, and all others remained until the experiment was interrupted 

(day 5). When we redeployed on 23 August, there were five fish still at the platform; all of these 

fish remained until the end of the study (12 August).
16

Figure 7. Plot of all fish for which positions were calculated in 2006.

Figure 8. Plot of all fish for which positions were calculated in 2005.
17

Figure 9. Plot of fish #213.

Figure 10. Plot of fish #1120.
18

Figure 11. Plot of fish #1101; fish #1101 was not present when we redeployed 

in August.

Figure 12. Plot of fish #1104; fish #1104 was present when we redeployed in 

August.
19 

Plotting the number of fish detected per hour-of-study revealed that detections appeared to 

decrease through time in both years, (most apparent in 2006, Figures 13 and 14). In addition, the 

number of detections varied periodically at smaller temporal scales (Figures 13 and 14b). 

Spectral analysis revealed peaks at 23.8 hours in 2006, 20.4 hours in May 2005, and 23.2 hours 

in August 2005 (Figure 15), strongly indicating diel periodicity in detections, and by inference 

red snapper behavior. The August 2005 plot also shows a less prominent peak at 11.6 hours, 

indicating semi-diel periodicity. 

Figure 13. The number of fish detected per hour in 2006. 

 

0

2

4

6

8

10

12

14

0 48 72 96 120 144 168 192 216 240 264 288 312

Hour of Study

Number of Fish Detected
20

Figure 14a) The number of fish detected per hour in May 2005

Figure 14b). The number of fish detected per hour in a) May and b) August 2005.

0

2

4

6

8

10

12

14

0 20 40 60 80 100 120

Hour of Study

Number of Fish Detected

0

1

2

3

4

5

6

1640 1660 1680 1700 1720 1740 1760 1780 1800 1820

Hour of Study

Number of Fish Detected
21

a)

b)

c)

Figure 15. Periodogram from the spectral analysis 

on fish detections per hour in a) 2006; 

b) May 2005; and c) August 2005.

23.83333

0

2

4

6

8

10

12

14

16

0 24 48 72 96 120

Period (hr)

Power

20.4

25.5

0

1

2

3

4

5

6

7

0 6 12 18 24 30 36 42 48 54

Period (hr)

Power

23.1666

11.58333

3

0

2

4

6

8

10

0 12 24 36 48

Period (hr)

Power
22

Stationary tags did show a decline in detections in both years (Figures 16 and 17). There was 

a decline in the average number of fish detected for each day of the study in 2006, even after the 

de-trending (Figure 18). In 2005, it looks like there was a decline between deployments, but 

there is no apparent trend within either deployment (Figure 19).

Figure 16. Values predicted from the regression on average hourly 

detections per day of stationary tags in 2006. The data are 

log-transformed.

Figure 17. Values predicted from the regression on average hourly 

detections per day of stationary tags in 2005. The data are 

log-transformed.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Day of Study

Detections

0

0.5

1

1.5

2

2.5

1 2 3 4 5 71 72 73 74 75 76 77

Day of Study

Detecions 
23

Figure 18. Average hourly detections per day of tagged fish in 2006; 

triangles are the original data, and squares are the

detrended data. All data are log-transformed.

Figure 19. Average hourly detections per day of tagged fish in 2005; 

triangles are the original data, and squares are the detrended 

data. All data are log-transformed.

y = -0.0181x + 0.769

y = -0.0315x + 1.955

-0.5

0

0.5

1

1.5

2

2.5

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Day of Study

Detections

0

0.5

1

1.5

2

2.5

1 2 3 4 5 71 72 73 74 75 76 77

Day of Study

Detecions 
24

The LOESS regression plot from 2006 revealed highest values during nighttime hours; fewer 

detections occurred during the day (Figure 20). Similarly, the 2005 data and LOESS regressions 

indicate the number of detections were higher during nighttime hours, but lowest near dusk, 

1600–2000 hours (Figures 21 and 22). The logistic regression did not converge; but day, hour, 

hour2

, moon phase, and photoperiod were significant variables, so we used them to form the 

model for the generalized linear mixed model (GLMM). The correlation between day and moonphase was high (r= 0.77), so we did not include day in the GLMM to reduce colinearity. The

most parsimonious model, i.e., with lowest pseudo-AIC, contained the variables: hour, hour2, 

and moon-phase.

Figure 20. The number of fish detected per hour of day in 2006, with a 

LOESS regression fit to display diel trends.

0

2

4

6

8

10

12

14

0 6 12 18 24

Hour of Day

Number of Fish Detected
25

Figure 21. The number of fish detected per hour of day in May 2005, 

with a LOESS regression fit to display diel trends.

Figure 22. The number of fish detected per hour of day in August 2005, 

with a LOESS regression fit to display diel trends.

Probability of presence declined with successive moon-phases: 81% at full moon, 45% at last 

quarter, and 1% during new moons and first quarters (Figure 23).

0

2

4

6

8

10

12

14

16

0 6 12 18 24

Hour of Day

Number of Fish Detected

0

1

2

3

4

5

6

0 6 12 18 24

Hour of Day

Number of Fish Detected
26

Figure 23. Mean probability of presence, across all fish and both years, 

by moon phase. Error bars indicate one standard error.

Both experiments began during a full moon, and occurred during similar cycles despite the 

interruption in 2005. Within a day, probability of presence was similar to the LOESS regression 

results: highest overnight and declining during the day (Figure 24).

The survival analysis found that mean survival time for 2006 was 165.8 hours, or 6.9 days; 

70% of the data were “events”—fish abandoning the platform, and 30% were censored (Figure 

24).

Figure 24. Probability of presence, across all fish, by hour of day.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

full last new first

Moonphase

Probability of presence

0

0.2

0.4

0.6

0.8

1

0 6 12 18

Hour of day

Probability of Presence

Full

Last

New

First
27

In 2005, the mean survival time was 0, because the only three events were fish that had left 

immediately; all other data were censored (Figure 25).

Figure 25. Probability of presence, as calculated by the survival 

analysis.

The feather plots of 2006 data do not indicate directed (non-random) movements; however, 

fish 213, 219 and 1113 appear to have “axes” to their movements—movements appear to be 

predominantly north/south; the 2005 feather plots show movements that again appear to be 

random in direction, although fish 1109 shows a similar north/south axis (Figures 26 and 27).

However, the mean angles calculated for each fish did not confirm an “axis” of movements. The 

radii of the fish with axes were shorter (their mean angles showed more dispersion) than some of 

those for the fish without axes and fish appear to have made larger movements with time as the 

experiment progressed.

The distance from the platform results were mixed. In 2006, the fish showed a significant 

difference between day and night values (Figure 28), but this difference was quite small (3 

meters). In 2005, fish were farther from the platform at night during May but farther during the 

day in August (Figure 29). The difference during August 2005 was not significant.

Figure 30 shows that speeds calculated with intervals less than five minutes showed the 

greatest range: speeds calculated for intervals greater than that were rarely larger than 1m/sec.

Figures 31 and 32 show the speeds by hour-of-day with the mean values for each hour 

superimposed. Speeds were lower in 2006 than in 2005, but otherwise there were no observable 

patterns either within a day or throughout the study. 

Mean depth by hour-of-study is plotted in Figure 33 for 2006, in Figure 34 for May 2005, 

and in Figure 35 for August 2005. Depth, like detections, displays regular increases and 

decreases. Spectral analysis revealed a strong peak at 24.1 hours in 2006 (Figure 36); the 2005 

data had peaks at 6.0 and 20.8 hours in May (Figure 37) and some power at 11.8 hours in August 

(Figure 38).

0

0.2

0.4

0.6

0.8

1

0 500 1000 1500 2000 2500

Probability of Presence

Hour of Study

2005

2006
28

Figure 26. Feather plots showing the consecutive movements of fish during the 2006 experiment. 

Each vector represents a movement between consecutive points.
29

Figure 27. Feather plots showing the consecutive movements of fish during the 2005 experiment. 

Each vector represents a movement between consecutive points
30

Figure 28. Mean distance from the platform for all fish by time of day in 

2006. Letters indicate significant differences.

Figure 29. Mean distance from the platform for all fish by time of day in 2006. 

Letters indicate significant differences.

0

5

10

15

20

25

30

35

day night

Time of Day

Mean distance from platform (m)

a

b

0

20

40

60

80

100

120

day night day night

August August May May 

Time of day (month)

Mean distance from platform (m)

a a

b c
31

Figure 30. Interval, the time between two recorded positions, compared 

with the speed of movement between those two positions.

Figure 31. Speed, in body lengths per second, for hour of day for 

2006. The mean value for each hour is superimposed.

0

1

2

3

4

5

6

7

8

9

0.00 5.00 10.00 15.00 20.00

Interval (min)

Speed (body length/sec)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0 5 10 15 20 25

Hour of Day

Speed (body length/s)
32

Figure 32. Speed, in body lengths per second, for hour of day for 

2005. The mean value for each hour is superimposed.

Figure 33. Mean depth, in meters, for each hour of the study in 2006.

0

0.5

1

1.5

2

2.5

3

0 6 12 18 24

Hour of day

Speed (body length/s)

-45

-40

-35

-30

-25

-20

-15

-10

-5

0

0

487296

120144168192216240268292320

Hour of Study

Depth (m)
33

Figure 34. Mean depth, in meters, for each hour of the study in May 

2005.

Figure 35. Mean depth, in meters, for each hour of the study in August 

2005.

-45

-40

-35

-30

-25

-20

-15

-10

-5

0

0

24

487296

Hour of Study

Depth (m)

-45

-40

-35

-30

-25

-20

-15

-10

-5

0

165916801698

172017301749176717811798

Hour of Study

Depth (m)
34

Figure 36. Periodogram from the spectral analysis on all depths per hour 

of study in 2006. Hours are on the x-axis, and the y-axis 

shows the power of the periodicity.

Figure 37. Periodogram from the spectral analysis on all depths per hour 

of study in May 2005. Hours are on the x-axis, and the y-axis 

shows the power of the periodicity.

0

100

200

300

400

500

600

700

0 6 12 18 24 30 36 42 48 54 60 66

Period (hr)

Power

24.1

0

10

20

30

40

50

60

70

80

90

0 6 12 18 24 30 36

Period

Power

20.4
35

Figure 38. Periodogram from the spectral analysis on all depths per hour 

of study in August 2005. Hours are on the x-axis, and the yaxis shows the power of the periodicity.

4.0 DISCUSSION

Our study is the first to use a real-time telemetry system to describe the movement and 

behavior of red snapper. Population estimates have shown that red snapper congregate near 

platforms in large numbers (Stanley and Wilson, 1997; Nieland and Wilson, 2002; Wilson and 

Nieland, 2004); and site fidelity studies on a variety of artificial reef types and sizes have 

reported widely varying results, ranging from <25% to >60% yr -1 (Beaumariage, 1969; Fable, 

1980; Szedlmayer and Shipp, 1994; Szedlmayer, 1997; Patterson et al, 2001; Patterson et al., 

2003; Szedlmayer and Schroepfer, 2005). However, some tag-and-release studies show that oneway, long-term movements of red snapper are possible for fish that have been at large for many 

days (Szedlmayer and Shipp, 1994; Patterson et al., 2001). These results, sometimes conflicting, 

have contributed to the confusion over whether artificial reefs attract or produce red snapper, 

because of the role that site fidelity plays in some of the conceptual models defining the 

argument (e.g., Bohnsack, 1989; Lindberg et al., 1990; Strelcheck et al., 2005). 

None of the above studies, however, provides insight about how red snapper use artificial 

habitats in the short-term, and this too has relevance to the attraction/production debate. The two 

years of data collected here provide this information, and together, illuminate to some extent how 

red snapper use platforms, albeit that pattern of use varied slightly between years. In 2006, we 

were able to collect a complete two-week dataset that provides the best short-term picture. In 

2005, our experiment was interrupted. However, we were able to redeploy when a few of the 

tagged fish still were within the detection radius of the VRAP system, giving some additional 

short-term information, along with a longer-term perspective. In both years, there were 

departures on day 1 of the study; in fact, some of the fish appeared to have left before the VRAP 

was initialized. It is possible that these fish were consumed by predators during swim down, as 

we frequently observed marine mammals and fish predators in close proximity to the charter boat 

0

100

200

300

400

500

600

0 6 12 18 24 30 36

Period (hr)

Power

11.8
36

where the tagging was taking place. In 2006, we found that fish departed individually throughout 

the two weeks (Figure 2); feather plots showing that large movements were possible beginning 

from the time of release support this contention (Figure 15). In 2005, the experiment was 

interrupted when 12 individuals were still at the platform. By the time we redeployed, there were 

only 5 remaining; these fish then remained until the end of the study. The fish tagged in this 

study exhibited almost binomial behavior; fish either exhibited high site fidelity—two months or 

more—or almost none at all—leaving the study area in less than two weeks.

It is interesting to note, however, that all of the red snapper exhibited low site fidelity when 

compared to results from more traditional tagging studies, particularly considering the brevity of 

our study. The GLMM shows declining probability of presence with successive moon-phases, 

from 81% at the beginning to 1% by the end of the experiments. This result is probably spurious,

and we believe that moon phase is simply representing days-at-large. In 2005, a full moon cycle 

passed while the VRAP system was not deployed. No fish returned during this interval, despite 

the fact that the associated moon phases did. Similarly, Westmeyer et al., (2007), who collected 

presence/absence telemetry data on acoustic-tagged red snapper over a several-month period, did 

not observe cyclical patterns in site fidelity. Presumably, if fish were more likely to be present 

during a full moon (rather than at the beginning of the experiment) and less likely to be present 

during the first quarter (rather than at the end of the experiment), we should have seen a cyclical 

pattern in the data.

The “survival” analysis models the decline in probability of presence on the scale of days, 

although the true fate of the fish is undetermined. The 2006 probability of survival curve declines 

quickly (Figure 14). In fact, there is a less than 50% chance of a fish remaining at 200 hours after 

release, or about 8.3 days. The mean survival time is only 6.9 days, so the average fish either 

emigrated or was preyed upon in less than a week. Westmeyer et al., (2007) also reported low 

site fidelity to platforms, <1%, than previous studies of red snapper site fidelity to artificial reefs 

(Szedlmayer and Shipp, 1994; Szedlmayer, 1997). It is worth noting that the structures in the 

above-cited studies were smaller by several orders of magnitude than platforms. It is possible 

that the number of red snapper and other reef-associated species inhabiting platforms (Stanley 

and Wilson, 1997; Nieland and Wilson, 2002; Wilson and Nieland, 2004) could create rather 

large foraging halos of depleted prey resources (Lindberg et al., 1990; and Bortone et al., 1998), 

providing that these fishes mostly consume prey derived from the benthos, as has been described 

for red snapper at artificial and natural reefs in other locations (McCawley and Cowan, 2007; 

Wells et al., 2008). As such, this could force red snapper to make long searches for available 

prey, a behavior that is consistent with the resource mosaic hypothesis (Lindberg et al., 1990).

We hypothesize that if movement is fitness-driven, large movements to forage may become so 

energetically costly there is no incentive for fish to return. 

The number of fish detected each hour oscillated at regular intervals, in both 2006 and 

August 2005 (Figures 6 and 7); the Fourier periodogram reveals that these oscillations occurred

on roughly 24-hour periods (Figure 8). The tagged fish that remained in the study area 

consistently left and re-entered the area within the detection radius daily. Distances from the 

platform concur with the results of the detections, showing differences between day and night 

values (Figures 18 and 20). Fish moved farther away during the day, and the distances they 

moved increased with the number of days at large (Figures 17 and 19). The LOESS regression fit 

from 2006 shows that detections were higher during nighttime hours and lower during daytime 

hours (Figure 9). The 2005 data are less clear, but the August 2005 data show a similar pattern

(Figure 11). The lowest numbers of fish detected, however, are found between 1800–2100 h. The 
37

probability of presence calculated for both years confirms this pattern (Figure 22). We feel 

confident in saying that red snapper appeared to remain closer to the platforms during nighttime 

hours and dispersed during daytime hours.

Mean depth likewise showed regular increases and decreases; this pattern was identified as 

semi-diel or diel using spectral analysis (Figures 27 and 29). While there were no obvious 

patterns in mean depth by hour of day, 3-D plots reveal that fish are making larger movements at 

depth. It is likely that diel periodicity represents feeding behavior; many diurnal and nocturnal 

fish school during their inactive phase and disperse to forage during their active phase (Hobson, 

1965; Helfman, 1986). These facts could explain the observed patterns in fish detections—red 

snapper may be aggregating near the platform, but foraging away from the platform. This would 

be consistent with diet studies indicating that red snapper, and other reef-associated species, feed 

on non-reef-associated prey items (Sedberry and Cuellar, 1993; Lindquist et al., 1994; 

McCawley and Cowan 2007). Such behavior by red snapper appears to be consistent for fish 

inhabiting both artificial and natural reefs (McCawley and Cowan 2007; Wells et al., 2008). 

Red snapper in this study appear to have frequently moved away from the platforms and 

outside of the detection radius of VRAP (a maximum of 350 meters in 2006) on a diel basis. If 

these movements are indeed related to foraging, the resulting haloes of depleted prey are at least, 

384,845 m

2

, and could be much larger. We interpret these results to mean that foraging 

opportunities are not likely to be the primary factor that compels red snapper to aggregate at oil 

and gas platforms.

In most fishes, the necessity of foraging and the risk of being eaten while doing so is a 

constant struggle (Werner and Anholt 1993). If an individual red snapper must search a large 

area of the surrounding seafloor before finding sufficient prey, the risk of predation increases, 

thus making the choice of returning to a point of origin complicated, especially if large 

movements result in discovery of other nearby natural and artificial habitats. From the standpoint 

of Bohnsack’s (1989) conceptual model, the lack of dependence on the platforms directly for 

prey resources, coupled with low site fidelity and high fishing pressure in the western Gulf 

(SEDAR 7 2005; Cowan et al., 2010), suggests to us that it is unlikely that oil and gas platforms 

are directly responsible for increasing productivity of the red snapper stock.

It is also possible that what we have interpreted here to be low site fidelity may actually be a 

consequence of increased vulnerability to predation attributable to red snapper foraging behavior.

In a recent synthesis of available information about fish use of oil and gas platforms (VERSAR 

2008), results indicate that these very large structures likely affect red snapper much differently 

than do relatively small, low relief artificial reefs. The latter are well-studied compared to the 

much larger platforms, which may be attributable to the difficulty associated with sampling 

larger structures. That said, some clear differences are apparent: 1) low-relief reefs support a 

much less complex community of fishes than do platforms. In studies off Alabama, most of the 

biomass around small reefs was comprised by only three species, namely red snapper, gag 

grouper, and grey trigger fish (Strelcheck et al., 2005). These three species account for more than 

85% of the fish biomass observed, and red snapper alone accounted for about 75% of the 

biomass. Relatively few large piscivores were observed during visual surveys of the reefs, 

suggesting that large predators may not accumulate in large numbers around low-relief reefs on 

the inner shelf. In contrast, red snapper are abundant on platforms, but they comprise a relatively 

small fraction of total numbers and biomass of the fish assemblage (<15%, VERSAR 2008).

While speculative, we conclude that if the high vertical relief of platforms is causing these 

structures to act as fish attraction devices for large predators, such as sharks, barracuda, large 
38

jacks, king mackerel, etc., it is conceivable that increased predation mortality of red snapper who 

reside near platforms offsets any biomass accumulation that would otherwise occur in the 

absence of predators. This phenomenon could be driving low site-fidelity numbers as much as, if 

not more than, any resource-mosaic dynamics and foraging haloes.

The VRAP system is not without weaknesses, and these should be considered in future 

attempts to work with this system around large structures like platforms. One issue that must be 

addressed is the ability of VRAP to detect transmitters. The receivers are contained within 

moored buoys that do move with the tides, and were moored around an oil platform with eight

piles, which undoubtedly obstructed transmissions. Our assumption is that these obstructions did 

not vary with time, and as such would not preclude identification of diel patterns of behavior.

The VRAP system also is limited by detection radius, as all telemetry systems are affected by the 

acoustic noise at the site of deployment. Platforms 134-S and 135-M were both active during this

study and surrounded by other active production platforms—the study area is quite noisy 

acoustically, and our detection radius (150 m in 2006, 350 in 2005) no doubt was limited by this 

noise. In addition, high numbers of snapping shrimp on platforms also creates noise that can 

affect detection radius (personal observation). 

Problems related to detection radius appear to have affected other red snapper site fidelity 

estimates, although the issue has received little discussion. Szedlmayer (1997) described high 

site fidelity based on red snapper that remained within the maximum detection radius of the 

receiver, which was 1.6 km, but failed to include those that left immediately or those did not stay 

within 1.6 in subsequent calculations. In a mark and recapture study, Szedlmayer and Shipp 

(1994) considered movement less than 2 km to demonstrate high site fidelity. In contrast, 

Patterson et al., (2003) modeled decline in recaptures at experimental reefs following saturation 

tagging to estimate red snapper site fidelity. They reported more conservative (24.8–26.5% per 

year) site fidelity estimates for the same types and sizes of artificial reefs as did Szedlmayer and 

coauthors, but included fish reefs in their calculations that left immediately or that were 

recovered nearby but on other experimental reefs. Establishing the proper distance in this context 

can be difficult, but should not be arbitrary or merely based on detection radius, which, as seen 

above, can vary widely. Even examples from the literature may offer little guidance, as minimum 

distances required to indicate emigration can vary widely by species. In a study of the dispersal 

of galjoen, Attwood and Bennett (1994) concluded fish remaining within 50-kmwide transects to 

be residents. Beamish and McFarlane (1988) concluded that movements of less than 200 km to 

be exhibiting resident behavior for sablefish. Willis et al. (2001) considered fish captured within 

500 m of their original tagging site to have remained residents.

Perhaps a better approach would be to study site fidelity in the context of home range.

Researchers studying both wildlife and fish have used the home range concept to study habitat 

affinity for their species. Determining well-defined home ranges for red snapper could enlighten 

their habitat affinities and whether oil and gas platforms or other artificial reefs coincide with 

these affinities. Determining site fidelity to that home range could then be far more informative.

This study and that of Westmeyer et al., (2007) indicate that site fidelity to platforms may be 

very low, and it appears that red snapper move away from the platforms to forage. Both of these 

behaviors would be interpreted to lie on the attraction end of the spectrum as defined by 

Bohnsack (1989)

.
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APPENDIX: REMAINING FISH PLOTS

This appendix contains the plots of all fish for which there were more than 24 hours worth of 

positions.
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The Department of the Interior Mission

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

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

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

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

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

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

Department assesses our energy and mineral resources and works to ensure 

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

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

major responsibility for American Indian reservation communities and for 

people who live in island communities. 

The Bureau of Ocean Energy Management Mission 

The Bureau of Ocean Energy Management (BOEM) promotes energy 

independence, environmental protection, and economic development through 

responsible, science-based management of offshore conventional and 

renewable energy.