NOAA Technical Report NMFS 41 July 1986

A Stationary Visual Census Technique

for Quantitatively Assessing

Community Structure

of Coral Reef Fishes

James A. Bohnsack

Scott P. Bannerot

U.S. DEPARTMENT OF COMMERCE

National Oceanic and Atmospheric Administration

National Marine Fisheries Service
NOAA TECHNICAL REPORT NMFS

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1. Synopsis of biological data on the Blue Crab, Callinectes sapidus Rathbun, by

Mark R. Millikin and Austin B. Williams. March 1984, 39 p.

2. Development of hexagrammids (Pisces: Scorpaeniformes) in the Northeastern

Pacific Ocean, by Arthur W. Kendall, Jr., and Beverly Vinter. March 1984,44 p.

3. Configurations and relative efficiencies ofshrimp trawls employed in southeastern

United States waters, by John W. Watson, Jr., Ian K. Workman, Charles W. Taylor,

and Anthony F. Serra. March 1984, 12 p.

4. Management of northern fur seals on the Pribilof Islands, Alaska, 1786-1981, by

Alton Y. Roppel. April 1984, 26 p.

5. Net phytoplankton and zooplankton in the New York Bight, January 1976 to

February 1978, with comments on the effects of wind, Gulf Stream eddies, and slope

water intrusions, by Daniel E. Smith and Jack W. Jossi. May 1984, 41 p.

6. Ichthyoplankton survey of the estuarine and inshore waters of the Florida Everglades, May 1971 to February 1972, by L. Alan Collins, and John H. Finucane. July

1984, 75 p.

7. The feeding ecology ofsome zooplankters that are important prey items of larval

fish, by Jefferson T. Turner. July 1984, 28 p.

8. Proceedings of the International Workshop on Age Determination of Oceanic

Pelagic Fishes: Tunas, Billfishes, and Sharks, by Eric D. Prince (convener and editor),

and Lynn M. Pulos (editor). December 1983, 211 p.

9. Sampling statistics in the Atlantic menhaden fishery, by Alexander J. Chester.

August 1984, 16 p.

10. Proceedings of the Seventh U.S.-Japan Meeting on Aquaculture, Marine Finfish

Culture, Tokyo, Japan, October 34, 1978, by Carl 1. Sindermann (editor). August 1984,

31 p.

11. Taxonomy of North American fish Eimeriidae, by Steve 1. Upton, David W.

Reduker, William L. Current, and Donald W. Duszynski. August 1984, 18 p.

12. Soviet-American Cooperative Research on Marine Mammals. Volume I-Pinnipeds, by Francis H. Fay, and Gennadii A. Fedoseev (editors). September 1984, 104 p.

13. Guidelines for reducing porpoise mortality in tuna purse seining, by James M.

Coe, David B. Holts. and Richard W. Butler. September 1984, 16 p.

14. Synopsis of biological data on shortnose sturgeon, kipenser brevirostrum LeSueur

1818, by Michael J. Dadswell, Bruce D. Taubert, Thomas S. Squiers, Donald Marchette, and Jack Buckley. October 1984, 45 p.

15. Chaetognatha of the Caribbean sea and adjacent areas, by Harding B. Michel.

October 1984, 33 p.

16. Proceedings ofthe Ninth and Tenth U.S.-Japan Meetings on Aquaculture, by Carl

1. Sindermann (editor). November 1984, 92 p.

17. Identification and estimation of size from the beaks of 18 species of cephalopods

from the Pacific Ocean, by Gary A. Wolff. November 1984, 50 p.

18. A temporal and spatial study of invertebrate communities associated with hardbottom habitats in the South Atlantic Bight, by E. L. Wenner, P. Hinde, D. M. Knott,

and R. F. Van Dolah. November 1984, 104 p.

19. Synopsis of biological data on spottail finfish, Diplodus holbrooki (Pisces:

Sparidae), by George H. Darcy. January 1985, 11 p.

20. Ichthyoplankton of the Continental Shelf near Kodiak Island, Alaska, by Arthur

W. Kendall, Jr., and Jean R. Dunn. January 1985, 89 p.

21. Annotated bibliography on hypoxia and its effects on marine life, with emphasis

on the Gulf of Mexico, by Maurice L. Renaud. February 1985, 9 p.

22. Congrid eels of the eastern Pacific and key to their Leptocephali, by Solomon

N. Raju. February 1985, 19 p.

23. Synopsis of biological data on the pinfish, Lagodoll rhomboides (Pisces:Sparidae),

by George H. Darcy. February 1985, 32 p.

24. Temperature conditions in the cold pool 1977-81: A comparison between southern

New England and New York transects, by Steven K. Cook. February 1985, 22 p.

25. Parasitology and pathology of marine organisms of the world ocean, by William

J. Hargis, Jr. (editor). March 1985, 135 p.

26. Synopsis of biological data on the sand perch, Diplectrum formosum (Pisces:

Serranidae), by George H. Darcy. March 1985, 21 p.

27. Proceedings of the Eleventh U.S.-Japan Meeting on Aquaculture, Salmon Enhancement, Tokyo, Japan, October 19-20, 1982, by Carl 1. Sindermann (editor). March 1985,

102 p.

28. Review of geographical stocks of tropical dolphins (Stellella spp. and Delphillus

delphis) in the eastern Pacific, by William F. Perrin, Michael D. Scott, G. Jay Walker,

and Virginia L. Casso March 1985, 28 p.

29. Prevalence, intensity, longevity, and persistence of Allisakis sp. larvae and Laeistorhyllchus telluis metacestodes in San Francisco striped bass, by Mike Moser, Judy

A. Sakanari, Carol A. Reilly, and Jeannette Whipple. April 1985, 4 p.

30. Synopsis of biological data on the pink shrimp, Palldalus borealis Kr6yer, 1838,

by Sandra E. Shumway, Herbert C. Perkins, Daniel F. Schick, and Alden P. Stickney.

May 1985, 57 p.

31. Shark catches from selected fisheries off the U.S. east coast, by Emory D. Anderson, John G. Casey, John 1. Hoey, and W. N. Witzel!. July 1985, 22 p.

32. Nutrient Distributions for Georges Bank and adjacent waters in 1979, by A. F.

1. DraxJer, A. Matte, R. Waldhauer, and 1. E. O'Reilly. July 1985, 34 p.

33. Marine flora and fauna of the Northeastern United States. Echinodermata:

Echinoidea, by D. Keith Serafy and F. Julian Fell. September 1985, 27 p.

34. Additions to a revision of the shark genus Carcharhillus: Synonymy of ApriollodOli and HypOpriOIl, and description of a new species of Carcharhillus (Carcharhinidae),

by 1. A. F. Garrick. November 1985, 26 p.

35. Synoptic review of the literature on the Southern oyster drill Thais haemastoma

floridana, by Philip A. Butler. November 1985, 9 p.
NOAA Technical Report NMFS 41

A Stationary Visual Census Technique

for Quantitatively Assessing

Community Structure

of Coral Reef Fishes

James A. Bohnsack

Scott P. Bannerot

July 1986

u.s. DEPARTMENT OF COMMERCE

Malcolm Baldrige, Secretary

National Oceanic and Atmostpheric Administration

Anthony J. Calio, Administrator

National Marine Fisheries Service

William G. Gordon, Assistant Administrator for Fisheries
The National Marine Fisheries Service (NMFS) does not approve, recommend

or endorse any proprietary product or proprietary material mentioned in this

publication. No reference shall be made to NMFS, or to this publication furnished by NMFS, in any advertising or sales promotion which would indicate

or imply that NMFS approves, recommends or endorses any proprietary

product or proprietary material mentioned herein, or which has as its purpose

an intent to cause directly or indirectly the advertised product to be used or

purchased because of this NMFS publication.

'--------------------------

ii
Contents

Abstract 1

Introduction 1

Methods

Stationary sampling methodology 2

Method evaluation 2

Experimental methods 2

Descriptive methods 4

Results and Discussion

General comments on stationary sampling methodology 4

Experimental evaluations of influencing factors 4

Habitat 4

Sampling duration 5

Sampling radius 5

Visibility 8

Sources of variation 8

Accuracy and precision 10

Statistical description of collected data 10

Descriptive community parameters 10

Dispersion patterns 13

Adequate sample size 14

Conclusions 14

Acknowledgments 14

Literature cited 15

11l

A Stationary Visual

Census Technique for

Quantitatively Assessing

Community Structure

of Coral Reef Fishes

JAMES A. BOHNSACK

Southeast Fisheries Center Miami Laboratory,

National Marine Fisheries Service, NOAA,

75 Virginia Beach Drive

Miami, FL 33149

SCOTT P. BANNEROT

Division of Biology and Living Resources,

Rosensteil School of Marine and Atmospheric Science,

University of Miami,

4600 Rickenbacker Causeway, Miami, FL 33149

ABSTRACT

A new method is described and evaluated for visually sampling reef fISh community structure in environments with highly diverse and abundant reef fISh

populations. The method is based on censuses of reef fIShes taken within a cylinder

of 7.5 m radius by a diver at randomly selected, stationary points. The method

provides quantitative data on frequency of occnrrence, fish length, abundance,

and community composition, and is simple, fast, objective, and repeatable. Species

are accumulated rapidly for listing purposes, and large numbers ofsamples are

easily obtained for statistical treatment. The method provides an alternative to

traditional visual sampling methods.

Observations showed that there were no significant differences in total

numbers ofspecies or individuals censused when visibility ranged between 8 and

30 m. The reefs and habitats sampled were significant sources of variation in

number of species and individuals censused, but the diver was not a significant

influence. Community similarity indices were influenced significantly by the

specific sampling site and the reef sampled, but were not significantly affected

by the habitat or diver.

INTRODUCTION _

Interest in visual and video methods for censusing reef fishes has

greatly increased in recent years because ofthe inadequacy ofsome

traditional sampling techniques and the need for reliable, nondestructive, fishery-independent sampling methods. Various methods and

problems of visual sampling have been reviewed recently by Russell

et aJ. (1978), Sale (1980), Sale and Douglas (1981), DeMartini and

Roberts (1982), and Sale and Sharp (1983). The main objectives

in conducting reef fish censuses are to:

1) compare fish populations between reefs and other habitats, and

2) quantitatively monitor reef fish composition and relative or

absolute abundance over time.

Censusing fishes is difficult in coral reef environments because

of the structural complexity of the habitat and the mobility, diversity, and abundance of reef fishes (Russell et aI. 1978). Total counts

for reef fishes are usually possible only on small patch reefs where

populations are small enough to count in an hour or less. Counting

all individuals is impossible on larger reefs with more diverse and

abundant fish populations. At present, no census methods have been

universally accepted for censusing reef fishes on large reefs.

DeMartini and Roberts (1982) and Sale and Sharp (1983) provide reviews of common visual census methods. We found that the

most commonly used methods, belt-transect and rapid visual census techniques, suffered from several problems which rendered them

inadequate for sampling coral reef fish community structure. Six

of the most important problems were:

1) No method allowed a diver to simultaneously collect adequate

data on species composition, abundance, frequency of occurrence,

and biomass from reefs with diverse and abundant fish populations;

2) some methods attempted to deal with complexity by dealing

only with a small group or "core species" and eliminated much

of the visible fauna from consideration;

3) methods requiring extensive use of transect lines were unacceptably cumbersome, time consuming, or simply impossible to

use on some local reefs due to complex habitat features, governmental regulations, or accidental interference from other divers;

4) few methods were adequate for sampling small restricted areas,

such as certain reef microhabitats and areas damaged from ship

groundings;

5) some methods could not deal quantitatively with different

habitat types and habitat heterogeneity (patchiness) characteristic

of Caribbean reefs; and

6) all methods suffered from several recognized or unrecognized

biases that were likely to significantly affect census results. Some

biases have been documented, but most have been either ignored

or superficially investigated (Sale 1980; Sale and Douglas 1981;

Sale and Sharp 1983).

We attempted to overcome these and other sampling difficulties

by developing and testing a new visual census technique, using stationary divers to quantitatively sample fishes in diverse coral reef

communities. Here we describe the method, evaluate it under field

conditions, and report statistical characteristics of collected data.

In a separate paper (in prep.) we qualitatively evaluate advantages

and disadvantages of the method compared to commonly used

transect and rapid-search methods based on experience and available

data.

This study has three objectives:

I) Describe the stationary sampling method developed to meet

specitic sampling criteria;

2) test the sampling methodology to examine intrinsic and extrinsic factors that could influence the numerical values of collected data;
3) describe statistical characteristics of collected data so that appropriate sampling strategies and analytical methods can be used

in future studies.

METHODS _

Stationary Sampling Methodology

A restricted stationary sampling method (SS) was developed to provide quantitative data on reef fish community structure based on

the following criteria:

I) All observable species should be included in each census on

the assumption that no basis was available for a priori excluding

species prior to collecting data;

2) the method should require minimum setup time and equipment manipulation;

3) diving time should be used as efficiently as possible for collecting data;

4) data should be able to generate estimates ofspecies composition, abundance, frequency of occurrence, and biomass;

5) the method should minimize experimental, observer, and

behavioral bias; and

6) sampling should include larger economically and ecologically important species which frequently avoid divers (Bohnsack 1982).

The sampling methodology is described in detail below.

The selected stationary sampling technique was based on censuses taken at randomly selected points using open-circuit SCUBA

(Fig. I). The distance between sampling points was based on a

number of swimming kicks determined from a table of random

numbers. Homogeneous habitats were sampled using a predetermined pattern of random directions and distances. Stratified habitats

were sampled at random points while progressing at an angle across

the gradient. To avoid decompression problems, the sampling progression usually went from deeper to shallower water.

At each sampling point we initially recorded all species observed

in 5 min within an imaginary cylinder extending from the surface

to the bottom within a radius of 7.5 m (24 ft) from the observer.

Average visibility was generally greater than 12 m. The sampling

radius was estimated by tape measure.

Each sample began by facing seaward and listing all species

observed in the pre-set radius within the field of view. New sectors or field of view were scanned and new species listed by rotating

in one direction. This was continued and new species were listed

as observed for 5 minutes. No statistical data on the observed species

were recorded at this time with one exception: a few species in

moving schools were counted when first observed in the sampling

cylinder. From experience we knew that these particular species

were unlikely to remain in the sampling area. By counting schools

when first observed, we avoided counting the same individuals more

than once in case the school reappeared, and we obtained an average

abundance for highly mobile species in multiple schools.

Next we recorded statistical data for the species listed in the initial5-min sampling period; all other observed species were ignored.

The estimated number of individuals and the minimum, maximum,

and mean estimated length for each observed species were recorded

using the methodology described below. We always worked

systematically up the list from the bottom to avoid overlooking a

species and to avoid bias caused by a tendency to count each species

when it is particularly conspicuous or abundant. Thus, actual counts

for particular species were made at random times even though

delayed after the initial observation. For many species, only a few

2

individuals appeared within the sampling radius during the mitial

5-min listing period. These individuals were easily remembered and

data were recorded from memory. Species that were always present in the sampling radius were counted, one at a time, after the

5-min listing period by starting at one point and rotating 360° until

the entire area was scanned. When large schools were present, it

was sometimes necessary to count by 1O's, 20's, 50's, or even loo's.

Fish fork lengths in cm were estimated by comparing fishes to

a ruler attached perpendicular to the far end of a 1-m rod held out

from the diver (Fig. 2). This device helped avoid underwater

magnification problems in estimating fish sizes.

We found the organization of the data sheet and procedures for

recording data were critical to the success of the method. Time was

kept to the nearest second with the aid of an underwater stopwatch

attached to the top of an aluminum clipboard. Data were recorded

on plasticized paper (Fig. 3). Scientific names were abbreviated

by using the first three letters ofthe genus and the first four letters

ofthe specific name. After all fish census data were recorded, data

on depth and bottom features within the sampling radius were taken.

Method Evaluation

The SS methodology was evaluated by experimental and descriptive methods. We experimentally evaluated the influence of variations of habitat, field conditions, and the sampling protocol on the

SS method. A statistical description of one reef fish community was

also made to help evaluate the SS method and to aid other investigators in designing sampling strategies for specific research

questions. To simplify the discussion, details of specific analysis

methods are described with appropriate results.

Experimental Methods-Various factors that could affect SS data

were examined. The applicability of the SS method in various

habitats was examined using different habitats found in Looe Key

National Marine Sanctuary (lat. 24°32'N, long. 81 °24'W), Florida,

U.S.A. Habitats were compared using mean number ofspecies and

mean number of individuals as dependent variables. Effects of

sampling duration, radius, and visibility were examined using the

Looe Key Reef forereef. The forereef zone had the most complex

topography (Shinn et al. 1981) which, in our opinion, was an ideal

location to evaluate a sampling method because it was the most difficult environment to sample. The effect ofsample duration on the

number ofspecies and individuals censused was evaluated at several

randomly selected sites in July 1979. Data were collected from

7.5-m radius samples while noting I-min intervals. The effect of

different sampling radii was evaluated on 3 August 1980 at one

randomly selected site using 5-min samples collected in random

order with radii of 1,2,3,4.5,6,7.5, and 9 m. Effects ofvisibility on collected data were analyzed for samples taken from 1980

to 1985 by regressing number of species and individuals censused

against estimated visibility. Visibility was the estimated distance

at which a diver could be seen.

An experiment was conducted to examine sources of variation

between sites, habitats, reefs, and divers. Two divers (JAB and SPB)

collected SS data from the forereef of three different reefs: Looe

Key Reef (LKR) and Molasses Reef (MR, lat. 25°01'N, long.

80 0

23'W), Florida, and Carrie Bow Cay Reef (CBC, lat. 16°48'N,

long. 88°05'W), Belize, Central America. Divers sampled paired

sites by sequentially alternating between the two sites until each

had been sampled twice by each diver. On each reef, half the sites

selected were atop spur formations (spur habitat) and half were
Figure I.-Aerial view of Looe Key Reef forereef showing approximate areas

included in representative, randomly selected, stationary samples.

Figure 2.-A stationary diver collecting data. A ruler attached to the end of a

meter stick was used to reduce magnification errors in estimating fish lengths.

Figure 3.-A sample data collection sheet. Species codes are listed on the left as

observed. The wavey line below the species codes is used to mark the end ofspecies

observed in 5 min. "N" is the total number of individuals censused per species.

Mean, minimum, and maximum fork lengths are directly recorded for most

species. For some species, individual lengths are recorded and additions for total

individuals and calculations of mean lengths are done later in the laboratory. The

fIShing index is the number of pieces of loose fishing gear noted in the sampled

area. The sketch of features in the sampling area shows the diver's location in

the middle. Percent cover is the estimated surface area viewed by the diver.

Fishing Index :2...

Depth If IYJ

--ifCA'P' /' 6'aNl. ~.J '

I Jf' \

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Bottom Confi~u,..atjon Percent Cover

LENGTH

"';n;murr Maximum

(J 1:3

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f").. /b

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;<8 3/

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Loc,,;oo LoDe Key Foreree r- Oate l/w/8'f Visibi I j ty IS'"

Temperatu. e ;1',s· C

Time started /0;05

Time ended /0: 17

Spec i es Codes Mean

A b~ S<l.lI2 /5 1/

Tha- bda. 5. 5

Pom f'iJ.rf /32 'f

ila / macLI 0, 5,

Hu ft"ro 92 /'f

Aca coer /5 1'1-/

Pom pUq

SC~ ero, 1"- 6

Spa VI r/ 'fA

Hae 15 5-------f"/av -+__--------

301, Spur ForfY)~f;dn

15"7. ",.d,u,,! ru 66/e

107. liaof'or~ fJ./""f'l

19: A, cerv"orn,s

3% llJonhrfrea. IIw:{

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3
between spur formations (groove habitat). Specific site locations

within each habitat were selected randomly. To get worst-case

estimates of variance, divers estimated the sampling radius and made

no attempt to compare results or specific procedures during the

experiment.

Numbers of species and individuals were analyzed by 3-way

analysis of variance using SPSS (Nie et al. 1975) with diver, habitat,

and reef as the independent variables. Similarity between divers

was examined further by correlating cumulative abundance estimates

for each species with data from all three reefs.

Similarity coefficients (Bray-Curtis Index) were calculated and

analyzed for all pairwise sample comparisons in the above experiment from the two reefs with the most data, MR and LKR (Brower

and Zar 1977). Data from CBC were not used because of a lack

of replicate samples. Similarity coefficients, PS, were defined as:

s

PSij = L min (pj) n=l

where min (pj) is the lowest proportion of individuals in samples

i and j, and s is the total number ofspecies in samples i and J. The

similarity coefficient has a minimum value of 0.00, where the two

samples have no species in common, and a maximum value of 1.00,

where both samples have the same species in common and the same

numbers of individuals for each species. Bloom (1981) found this

index most accurately reflected true similarity. For clarity, details

on analysis methods for similarity coefficients are provided with

the results.

Descriptive Methods-Statistical characteristics of an observed reef

fish community were described for the LKR forereefbecause it was

representative of complex reef environments and it was the most

intensively sampled reef. Patterns of abundance, frequency of occurrence, size, and dispersion were described. An evaluation of

adequate sample size was made based on performance curves of

cumulative species and Spearman rank correlation coefficients (Zar

1974).

RESULTS AND DISCUSSION _

General Comments on Stationary Sampling Methodology

The SS method was evaluated under a variety of field conditions

and found to be extremely effective for censusing reef fishes. Stationary sampling is similar to traditional quadrat sampling in that

censusing is restricted to a small increment of space and time. It

differs in this study in that the observer remained in the middle

of a circular quadrat. Stationary sampling is similar to strip transect

sampling only in that it is the shortest possible transect (i.e., one

where the observer does not move). Instead of censusing continuously over a strip transect, censusing with the SS method is accomplished by accumulating a series of independent samples.

Data on species composition, frequency of occurrence, abundance, and average fish length were collected simultaneously. We

found that a stationary diver could easily record data and keep track

of events that a moving diver would find difficult or impossible.

Experience with other divers showed that the methods were easily

learned and reliable data could be obtained after minimal training.

As with any visual sampling method, divers must be experienced

with the local fauna. Equipment required was minimal and no time

was wasted in preparation prior to collecting data. For example,

the effort, expense, and time required to deploy transect lines and

4

make up data sheets were avoided.

Dive time was used efficiently and large numbers ofsamples were

accumulated rapidly for statistical analysis. Depending on depth

and reef complexity, we collected four to seven samples and approached 2 hours bottom time per standard 72 ft3 SCUBA

cylinder. Long bottom times were possible because a stationary diver

consumed much less air than a swimming diver. This is an important consideration, especially at remote sites. We averaged 9 samples/diver/day (minimum 6, maximum 12). The maximum number

ofsamples collected per day was limited by cold endurance in winter

and mental fatigue in the summer.

A major attribute of the SS method is that very small areas can

be censused. Thus, sampling can easily be restricted to one zone

or habitat. This is particularly useful for sampling specific microhabitats or small sites, such as damaged reef areas. Stratified sampling designs can be used where each sample must be in a particular

habitat. Statistical problems caused by lengthy transects crossing

different habitat patches or zones are eliminated. The effects of bottom heterogeneity can be examined for randomly collected samples

by multiple regression techniques. If necessary, the same sampling

point locations can be found again for repeated sampling.

Like other visually oriented sampling methods, the SS method

is not suitable for use in heavy surge, strong currents, deep depths,

and very poor visibility. Although a diver could conceivably be

attached to an anchor in strong currents, to do so in strong wave

surge could result in an embolism. Decompression problems limit

the usefulness of the method at deep depths. We did not attempt

to sample sites deeper than 20 m for this reason.

Unlike other methods that census only a few target species or

color forms, a diver must be able to visually distinguish all species

potentially present. The availability of good identification guides

for many regions reduces the problem of species identification.

However, we found that behavioral information was also important.

The described data sheet and protocol for its use were designed

to avoid bias and to prevent counting individuals more than once.

Preprinted data sheets with listed species names were tried but abandoned, mainly because divers wasted a lot of time looking for the

proper line to record data. Preprinted data sheets also tended to

bias observers by reminding them to look for particular species.

The proper position on each diver's data sheet (Fig. 3) was conveniently marked with a thumb. Much of the data could be recorded

without actually looking at the data sheet so that more time was

spent searching the sampling area. With preprinted data sheets, efficiency was directly influenced by familiarity with a particular version of a data sheet, independent of an observer's familiarity with

the fauna, such that any changes to the species list caused confusion. Also, the .large number of species potentially present made

a standard form unmanageable. Using different lists in different

regions also created confusion and reduced recording efficiency.

Experimental Evaluations of Influencing Factors

Habitat-We found that the SS method could be used in all tested

habitats ranging from flat sand to complex, high relief, spur-andgroove formations. The average number of species and individuals

censused during a sample was roughly proportional to habitat complexity (Figs. 4, 5). In general, more time was required to census

fishes in structurally complex, versus simple, habitats. In flat sand

and sea grass habitats, a sample could usually be completed in 6

min. The complex forereef environment required the longest sampling time (average 20 min, minimum 15, maximum 32).
F'tgUre 5.-Effects ofsampling time on cumulative observed species and individuals

at randomly selected foreree~ sites. (Top) Cumulative species observed over time

from one spot. Numbers show sample size. (Bottom) Percent of individuals

represented by species counted per minute during five censuses. Data were standardized by having 100% equal the number of individuals observed in 5 min. 100%

represents 640, 381, 172, 235, and 135 individuals for each numbered sample,

respectively.

Sampling Radius-We examined the effects of sampling radius

on the number of species, number of individuals, and density of

individuals censused. The number of species censused per sample

was approximately asymptotic to the radius searched, while the

number of individuals censused was approximately a linear function of the radius searched (Fig. 6). Due to time limitations, only

one sample could be replicated; however, results are assumed to

be reliable based on the high precision obtained from replication

of the 7.5-m radius sample. Individuals were not counted in the

9-m radius sample because some small individuals could not be identified at that distance.

On a theoretical basis, a wide search diameter should be much

more effective at detecting species than a short search diameter based

on search theory (Cox 1983). A small increase in search width will

initially result in a large increase in the probability of detecting a

target species. However, there eventually comes a period ofsaturation when even a large increase in search width will have a small

effect on the probability ofspecies encounter. Results (Fig. 6) empirically support this prediction.

The asymptotic function of number of species versus distance

sampled (Fig. 6) is also expected, based on the fact that the number

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;; 60

0 so :!:

... 40 0

i- 30 z

III

20 III

"- 10

Figure 4.-Mean numbers ofspecies (top) and individuals (bottom) censused per

sample in various habitats of Looe Key National Marine Sanctuary. Boxes show

95% confidence limits, vertical lines show ranges, and numbers indicate the

number of samples in each habitat. Subjective rankings of habitat complexity

from most to least complex were: forereef, buttress, live bottom, lagoon rubble

and coral, seagrass beds, and sand flats. Bohnsack et al. (in press) provides more

detailed descriptions of sampled habitats.

O+---'-----.!..---..J...-..:-~~.....::I=--..!:I!___=_:.........j

HABITAT

Sampling Duration-The number of species detected per sample

increased slowly after the initial 5 min ofsampling and varied with

habitat, with more species being found in more complex, forereef

habitats than in simpler, lagoon rubble habitats (Fig. 5, top). The

rate at which new species were observed at one site tended to level

off after 5 min of sampling effort. Species observed after the initial 5-min sampling period usually represented only one or a few

individuals, so that additional sampling time was a negligible contribution to the cumulative number of individuals. Doubling the

sampling time to 10 min only added I % to 3% more individuals

in five test samples (Fig. 5, bottom).

Five minutes was selected as the standard sampling time for listing

species present, because it was considered the minimum period adequate to carefully scan the sampling area in complex habitats and

because longer periods increased the bias toward detecting highly

mobile species. Longer time intervals also increased confusion in

distinguishing between individuals within the sampling area and

those that were continually moving in and out of the sampling area.

40.

35

160

37 18 30

w II

u

w

n.

20 ...

0 35

3 16

z 10

FOREREEF INSHORE

ZONE LIVE

BOTTOM

BUTTRESS OFFSHORE 1400 ZONE SAND

LAGOON SEAGRASS 1200 RUBBLE

& CORAL BLDS

1000 OFFSHORE LAGOON

LIVE SAND

BOTTOM

800 LAGODII

I NSHDRE SEAGRASS

SAND BEDS

... 600 0

400 >:

1- 20e

5
Figure 6.-Effects of sampling radius on the censused number of species (top)

and individuals (bottom) in S-min samples at the same site. Individuals were not

counted in the 9-m sample because some individuals could not be identified with

certainty.

of observed species is generally a logarithmic function ofthe number

of individuals sampled (MacArthur and Wilson 1967). However,

in theory the expected number of individuals censused should be

proportional to the area sampled and should increase as a function

of the square of the sampling radius. The fact that it did not is explained by the fact that all individuals were not observed and that

detection is less likely at greater distances (Sale and Sharp 1983).

To further investigate this relationship, we examined the effects

of sampling radii on density.

The effects of sampling radius on density estimates were investigated by calculating density indices for the 15 species occurring

at five or more radii. Density indices were obtained by dividing

observed number of individuals by the basal area of each respective sampling cylinder. Density indices were plotted against

sampling radius for each species (Fig. 7). Absolute density (individuals/m2) was considered the l-m intercept of linear regressions made from the linear portions of each curve (Sale and Sharp

1983). A density correction factor was calculated for each species

so that when multiplied by the 7.5-m density index, the absolute

density would be obtained (Sale and Sharp 1983). Calculated correction factors ranged between 1.85 and 7.79 (Fig. 7).

Density indices for 14 of the 15 species were inversely related

to the length of the sampling radius (Fig. 7). The remaining species

(Scarus croicensis) showed no clear density pattern, probably

because it occurred by chance in infrequent and highly mobile

schools. Regressions of density indices versus sampling radii were

4 6

DISTANCE (M)

30

25

In

w 20 Uw

In

u. 15

0

a:

w

a:l 10 ::l!

::>

z

5

0

0

240

200

In

...

<l:

5 160

:>

Q

:!: 140

u.

0

a:

w 80 a:l

::l!

::>

z 60

0

0

2

2

4 6 8

/

..../.

8

6

approximately linear if data from l-m and 2-m radii were ignored

(Fig. 7). Results for the 14 species suggested that samples taken

at radii of 2 m or less may give an unacceptably biased view of

community structure. Ten of the 14 species showed very low density indices at sampling radii of 1 m or 2 m. The most parsimonious

explanation for these low observed values is that these species

avoided approaching the observer. However, these low densities

could be artifacts ofthe small area sampled using short radii. Four

of the 14 species showed a curvilinear, negative exponential relationship with high density indices observed from the shortest radii.

However, these density estimates at short radii would probably be

unrealistically high if extrapolated over large areas. A school of

Haemulon aurolineatum happened to swim through the l-m sampling area during the census. The high densities at short radii for

the three remaining species were most likely the result of a high

proportion of sand substrate, their preferred habitat, in these

samples. Although these species could have been attracted to the

diver, this is unlikely based on our knowledge of their normal

behavior. We occasionally observed some wrasses (particularly

Halichoeres) initially attracted to the disturbed area at the feet of

the diver. However, by the time they were counted after the initial

5-min sampling period, they usually had returned to what appeared

to be their ambient density.

These results show that abundance values collected using the SS

method are indices of abundance and not absolute abundance

estimates. Ideally, observed density should not change with sampling radius if fishes are uniformly distributed and the habitat is

uniform. Obviously, not every individual of every species was seen.

Density indices declined with longer sampling radii because

individuals further away from an observer were less likely to be

detected. Individual size, behavior, coloration, and physical bottom

features within the sampling area could have had the effect of hiding

some individuals from the viewer.

The possibility that detection was related to mean species size

was examined by correlating calculated correction factors with mean

size using the Spearman rank order correlation coefficient (Zar

1974). Correction factors were not correlated with average species

size (p >0.05). This lack of correlation indicated that additional

factors besides size influenced observed abundances and the detectability of different species.

Abundance data can be calibrated with other sampling statistics

such as fishery landings or catch per unit effort. Also, correction

factors can be applied to estimate absolute abundance and density,

as discussed previously. However, absolute measures are not

necessary in most comparative studies assuming that biases are consistent for each specie&. This should be especially true when samples

are collected in the same manner from similar habitats. We did not

examine the possibility that our observed density correction factors were unique to the sampling site and could vary greatly from

site to site. We therefore recommend caution in applying correction factors between habitats.

Based on the above results and theoretical considerations, a radius

of 7.5 m (24 ft) was chosen as the standard sampling radius. This

distance maximized the number ofspecies and individuals that could

be conveniently censused in a reasonable time. It allowed observation ofsmall cryptic species, as well as large shy species, that were

often present but avoided closely approaching a diver. The latter

group was especially important to sample because it included many

of the larger commercially and ecologically important species.

Although desirable, the use of a tape measure to estimate the

sampling radius was not always necessary. The sampling radius

could be accurately estimated to 0.5 m with practice and with only
.:S

.!s

io

toO

7~O

C 1.98

I 0.28/m'

M 0.014/m'

C 5.53

I 1.0/m'

M 0.181/m'

S~S

S:S

s!s

. .

Haemulon aurolineatum

Coryphopterus dicrus

C 7.79

I 0.90/m'

M 0.011/m'

Halichoeres bivattatus

C 7.08

I 0.28/m'

M 0.40/m'

.:0 S:S 7:0 .:s

Pomacentrus variabilis

1~0 2!S

Coryphopterus glaucofraerum

C 6.37

I 0.45/m'

M 0.071/m,

o.

...... 2 o. l!O 2:S 4:0 5:5 7:0 .:5

Gnatholepis thompsoni

C 5.45

I 0.20/m'

M= 0.037/m,

o.

l~O 2:• 4:0 5:. T.O .:S

croicensis

3.89

0.22/m'

0.056/m'

O.

4:0 5:1 7:0 .:1

3.

o.

• '.S

'.S

t.0

S:s

SI.S

•••••~. = 0.107/m' ....

C 1.85

I O.ll/m'

M 0.059/m'

Acanthurus bahianu"

5.02

O.17/m'

0.034/m'

Halichoeres garnoti

C = 7.52

I O.17/m'

M= 0.23/m'

4'.0

4~0

Pomacentrus planifrons

C 3.35

I = 0.36/m'

7'0 .:S

~~~~~~~ partitus

C 3.41

I 0.76/m'

M 0.223/m'

Microspathodon chrysurus

.........

Sparisoma 0.1'1

C = 3.19 'j I 0.09/m,

M= 0.028/m, ...... 0.6

O.

S:S 7~0 .:S r , 2:S 4:0 1.0 2.S

0.21

bif asciatum

C = 4.94

I 0.60/m ,

M= 0.12/m ,

o.

0.1.

1~O

O.

0.2'

OA2

O.

0.21

o

O.lj••••••••

O.

0.0

>

en

z

w

o

-

0.30 Halichoeres maculipinna

C 2.75

I 0.071m'

M 0.025/m'

...---- . i---:-.-=---."':C;;O-----:S;TI:;-S---;7'.;r..O;;-----..~S

SAMPLE RADIUS (M)

Figure 7.-Index of density (ind/m2

) as a function of sampling radius for species

observed at five or more sampling radii. In general, the index of density declines

with larger sampling radii. Dotted lines show regressions based on linear portions of each curve. The I-m intercept (I) was used as the best estimate of absolute

density. M is the measured index of density based on samples with a 7.5-m radius.

e is a correction factor that is multiplied times the index of density (M) to give

estimated absolute density (I). Low-density estimates for many species at 1 and

2-m radii were assumed to be caused by individuals avoiding close approach to

the observer. See text for details.

7
periodic calibration. We found that a diver stationary on the bottom could accurately estimate distance much easier than could a

moving diver. Based on our tests of different sampling radii (Fig.

6), minor errors in estimating the 7.5-m radius are unlikely to have

significant effects on the number of species and individuals censused. Comparisons of density estimates from different sampling

radii (Fig. 7) showed that values for most species were stable for

sampling radii beyond 3 m, again suggesting that calculated density

indices would be somewhat insensitive to minor errors in estimating

the sampling radius.

The sampling radius should be constant for comparative purposes.

However, a smaller sampling radius could be used in areas with

consistently poor visibility, if the areas compared were sampled

with the same radius and under the same conditions. Correction

factors could be applied to compensate for reduced visibility.

Visibility-Effects of visibility on sample data were examined by

regressing number ofspecies and individuals observed versus ambient visibility (Fig. 8). Estimated ambient visibilities during the

study varied between 4.5 and 30 m and had no significant effect

(p > 0.05) on total number of species or individuals censused.

30 2

- 2

,.· . <I> 2 . · . 20 0..

<I>

U.

'"'"

>:

::>

z

10

Table 1.-Three-way analysis of variance on the effects of different

reefs, habitats, and divers on number ofspecies and individuals censused. The distribution of 36 samples among reefs was 16 (Molasses

Reef), 12 (Looe Key), and 8 (Carrie Bow). An equal number of

samples (18) was taken in each habitat (spur and groove) by each diver

(diver 1 and 2).

Source of

Variation df SS MS F Significance

Number of species

Main effects 4 595 149 3.57 p < 0.05

Reef (R) 2 296 148 3.55 p < 0.05

Diver (D) I 64 64 1.55 ns

Habitat (H) I 235 235 5.64 P < 0.05

2-Way interactions 5 171 34 0.82 ns

RxD 2 34 17 0.40 ns

RxH 2 133 66 1.59 ns

DxH t 4 4 0.10 ns

3-Way interactions 2 53 27 0.63 ns

(RxDxH)

Explained 11 818 74 1.78 ns

Error 24 1,001 42

Total 35 1,819 52

Number of individuals

Main effects 4 1,012,476 253,119 8.15 p < 0.001

Reef (R) 2 516,146 258,073 8.31 p < 0.002

Diver (0) I 66,650 66,650 2.15 ns

Habitat (H) 1 429,680 429.680 13.84 p < 0.001

2-Way interactions 5 229,120 45,824 1.48 ns

R x D 2 51,939 25,970 0.84 ns

RxH 2 145,438 72,719 2.34 ns

D x H I 31,743 31.743 1.02 ns

3-Way interactions 2 22,522 11,261 0.36 ns

(RxDxH)

Explained 11 1,264,119 114,920 3.7 •• Error 24 744,983 31,041

Total 35 2,009,102 57,403

Sources of Variation-A 3-way analysis of variance showed that

the combined effects of reef, diver, and habitat were significant

for species richness (p <0.05) and individual abundance (p <0.01)

(Table I). Significant sources of variation for individuals were the

reef sampled (p < 0.01) and the habitat (p < 0.01), but different

divers had no significant effect (p >0.05). Significant sources of

variation for observed species richness were also the reef (p <0.05)

and habitat sampled (p < 0.05). Again, different divers had no

significant effect (p >0.05). No significant interactions (p >0.05)

between sources of variation were found for any of the parameters.

These results suggest that differences between divers was the least

important factor influencing collected data in this study.

A more detailed comparison of variation between different divers

was done by correlating cumulative abundance data obtained from

the above experiment. Abundance estimates were significantly correlated (r 2 = 0.863, p < 0.01) although regression showed that

one diver tended to provide slightly higher abundance estimates (Fig.

9). The observed slope (0.853 ± 0.0992, 95% CI) was significantly

However, only a few samples were collected at visibilities less than

8 m. We anticipate that lower visibilities would have a significant

effect at some point. Samples collected under different visibility

conditions might perhaps be compared using nonparametric

methods. We suspect, but do not show, that rank/order relationships probably would not be altered significantly for most species

even with greatly reduced visibilities.

30

30

- •

20

20

· .

.. .

VISIBILITY (M)

10

• 3

· .

o .......---Ir----r-r----,r-----.---,.----,--...J

10

2.8 ---0

'"

2.6 <I>

--' «::>

2.4 ::

'"::

-::

'" ~. 2 w'"

'">:

::>

z

2.0

1.8 0

Figure 8.-Regressions of the observed number of species (p > 0.05, top) and

individuals (p >0.05, bottom) on visibility. Regression statistics: For species, Y

= 26.2 - 0.0338 X, r 2 = 0.007; For individuals, Y = 2.31 + 0.00142 X, r 2 =

0.003.

8
1000

v • 0.3215 + 0.8529 x

'"

w,.

e 100

o

w

o

10

.LAC MAX i

• KYP SECT

sell. eRO I

SPH BARR

CLE PARR

• CAR RUBE

•

OBSERVEO

EXPECTEO

Table 2.-Four-way analysis of variance on the effects of different

reefs, habitats, sites, and divers on similarity coefficients for paired

samples. The distribution of 28 samples among reefs was 16 (Molasses

Reef), and 12 (Looe Key). An equal number ofsamples (14) was taken

in each habitat (spur and groove) by each diver (diver 1 and 2). Sec

text for details.

Source of

Variation df SS MS F Significance

Main effects 7 6,292,355 898,908 3.05 p < 0.025

Reef 2 2,676,832 1,338,416 4.54 P < 0.025

Diver 2 450.165 225,082 0.76 ns

Habitat 2 118,013 59,007 0.20 ns

Site I 953,217 953,217 3.23 p < 0.10

Explained 7 6,292,354 898,907 305 p < 0.025

Error 20 5,899,400 294,970

Total 27 12,191,755 451,546

10 100 1000

TOTAL INOIVIOUALS OBSERVEO (OlVER 1)

Figure 10.-Mean and 95% confidence limits ofsimilarity coefficients as a funclion ofsampling variables. Similarity coefficients were independently tested using

chi-square and I-way ANOVA analyses for differences within and between sites,

reefs, divers, and habitats. Results showed that actual sample site and reef

significantly influence similarity values, while habitat and diver were not significant influences. See text for details. Results from 4-way ANOVA are provided

in Table 2. Key: ** = p < 0.01, *0* = P < 0.001. ns = not significant.

similarity values for samples from the same site indicate that the

SS data reflect the actual biota present. The fact that the reefsampled

was a major influence on collected data indicates that the method

will be effective for comparing different reefs. The position of the

diver on or between spurs had a surprisingly minor effect on collected data. This is apparently because the same biota were being

censused, although from different perspectives.

Any good sampling method should reflect the biota as much as

possible and should be least affected by differences between

observers. Differences between divers was the least important factor affecting numbers of species, individuals, and similarity coefficients among the tested sources of variation in this study (Figs.

9, 10). This conclusion does not imply that inter-observer variability

is not a potentially significant factor in other studies using the SS

1·Way ChiANOVA Square

w

~o. «":;;

•• ••• w

....

•• ••• '"

w

w

'"

0.7 0.8

• WITHIN SITES

0.4 0.5 0.6

•

PERCENT SI MILAR lTV

MOLASSES REEF

•

•

•

SPUR ~ABrTAT

•

LOOE KEV REEF

•

BETWEEN DIVERS

orVER 1

--01....V·... ER-2,..-

0.3

--+-

BETWEEN SITES

GROOVE HABITAT

BETWEEN --HABITATS

-

BETWEEN REEFS

0.2

'"

w

>

0

....

«

ns ::

1'" nS

ns

Figure 9.-Correlation of cumulative abundance estimates for 103 species by two

divers sampling the same sampling sites (r2 = 0.963, p <0.01, 18 samples/diver).

Numerals indicate multiple data points atop one another. The observed slope

(0.853) differed significantly (p <0.05) from an expected slope of 1.0, indicating

that diver 2 provided slightly higher abundance estimates for low and moderately abundant species. Coded names show highly mobile species that occurred unpredictably in large schools. These species accounted for the greatest differences

in abundance estimates between divers. Uncoded species names can be found in

Table 3.

different (p <0.05, (-test) from a slope of 1.00 expected if perfect

agreement between divers occurred. The major differences in abundance estimates between divers tended to be for highly mobile

schooling species whose presence in samples is a chance occurrence.

Similarity coefficients were analyzed by three methods. First,

similarity coefficients were analyzed as dependent variables by

4-way ANOYA (Sokal and Rohlf 1981) using coded independent

variables representing site, diver, habitat, and reef. Codes reflected

whether the two samples were

1) taken from the same or different sites;

2) taken by diver I, diver 2, or both divers;

3) taken from Looe Key Reef, Molassas Reef, or both reefs; and

4) taken from groove habitats, spur habitats, or both habitats.

A total of378 coefficients were produced from 28 samples. Degrees

of freedom and mean squares were corrected to reflect the actual

sample size (n = 28) rather than the implied sample size (n = 378).

Because it is not clear whether similarity coefficients meet all the

assumptions of ANOYA, specifically that of being normal and independent variables, two other analyses were also done. In the second analysis, similarity coefficients were assigned to O.I-unit

categories. Frequency distributions ofsimilarity coefficients for each

parameter were then compared to the total distribution using chisquare tests. In the third analysis, each variable was independently

tested using I-way ANOYA. Because independent tests for each of

the four parameters increases the type-I error, an alpha of 0.01

was used to reject each null hypothesis in the chi-square and I-way

ANOYA analyses in order to keep the overall type-I error level less

than 5% (i.e., I - [0.99]4).

Results from all three methods (Table 2, Fig. 10) showed that

correlation coefficients were significantly influenced by the actual

site and reefsampled (p <0.05) but were not significantly influenced

by the diver or habitat (p >0.05). Many factors can influence collected SS data, including reef heterogeneity; natural variation of

individuals moving in, out, and around the sampling area;

methodological errors; and differences between divers. The high

9
4·0 lr-----------------------,

11). The approximate linear decline of ranked log 10 abundances

(Fig. 11, top) is typical of many undisturbed, highly diverse communities (Brower and Zar 1977; Hubbell 1979). Species ranked

according to frequency of occurrence (Fig. 11, center) showed a

smooth decline from a few common species to many rare species

(Fig. 11, ceuter). This pattern is also typical of highly diverse

tropical communities and implies that large numbers of samples

are probably necessary to statistically describe the rarer species.

Mean fish lengths varied by two orders of magnitude (Fig. 11,

bottom; Fig. 12) which indicates that total biomass varied greatly

80 80 '00 120

RANK By ABUNDANCE

20 40

RANK BY PERCENT FREOUENCY

180

'40

t. 120

100

"

z 80 ...

!~~I~I~,,~H~IH~~~~~~~,~ -'

z

c 80 ...

2

40

20

0

0 20 40 eo 80 100 120

RANK BY IIUN SIZE

3.5

3.0

...

u

z 2.5 c

Q

z

::>

.. 2.0 c

9 1.5

'·0

0.5

0

0

100

80

80

40

20

Accuracy and Precision-The described rigorous sampling protocol was used to improve precision, avoid bias, and to prevent

counting individuals more than once. Results presented above (Figs.

9, 10) show good precision and repeatability between and within

observers for the same sampling site. However, it is impossible

to evaluate with certainty the accuracy of the method because there

is no way to know the true abundance and distribution of any species

on a reef. Accuracy, although desirable, is not as critical when using

relative abundance comparisons or rank/order statistics, because

they are less sensitive than parametric statistics to less-than-major

inaccuracies. Nevertheless, the SS method may, have improved accuracy because many sources of observer bias (see Sale and Sharp

1983) were reduced or eliminated. For example, stationary divers

eliminated biases caused by moving divers

1) swimming at different speeds,

2) swimming at different distances from the substrate,

3) searching at different distances down a transect, and

4) looking in particular hiding places based on special personal

knowledge about the expected fauna.

In addition, a circular sampling area has the minimum border

for the area sampled. This reduces potential edge effect errors caused

by deciding whether an individual is inside or outside the sampling

area. Such errors are more likely in narrow strip transects because

the ratio of border to area sampled is much greater.

We observed, but did not quantify, that stationary sampling

reduces bias resulting from some species being attracted to or repelled by moving divers. For example, the yellowtail snapper,

Ocyurus chrysurus, usually congregated around a moving diver but

quickly lost interest in a stationary diver and returned to what appeared to be normal densities by the time they were counted. Some

shy species, such as the graysby, Epinephelus cruentatus, hid and

were often overlooked by moving divers during transect surveys.

However, they appeared to habituate to the stationary diver and

could be censused by the end of a 5-min sample. Moving divers

would probably overestimate abundance of yellowtail and underestimate abundance of graysby.

or other visual methods, especially if divers are not adequately

trained. Ideally, for comparative studies the same divers should collect data from all the sites. However, we suggest that the method

is robust, and valid comparisons can be made with results taken

by different divers. Improved precision between divers could probably be achieved by comparing data periodically, by using measured

sampling radii, and by reducing slight differences in protocols for

scanning and counting individuals.

Statistical Description of Collected Data

Detailed descriptive statistics are provided for reef fishes based on

stationary sampling data from the forereef at Looe Key Reef (Table

3, Fig. II). Knowledge ofstatistical characteristics of census data

collected from reef environments is important for evaluating the

census method, for designing future sampling strategies. and for

selecting appropriate analytic methods for answering specific

research questions. Although many studies have reported sampling

methods for examining the community structure of coral reef fishes,

few have reported assumptions or statistical characteristics of the

resulting data.

Descriptive Community Parameters-A total of 117 species were

observed in 160 random samples collected between June and

September 1983 (Table 3). Species were plotted according to ranked

abundance, frequency of occurrence, and mean fork length (Fig.

Figure 11.-Patterns oftotal abundance (top), frequency of occurrence (center),

and estimated fork lengths (bottom) for 117 species observed in 160 samples on

the forereef of Looe Key Reef in 1983. Estimated lengths show mean individual

lengths and range of minimum-ta-maximum length for each species. Details are

provided for each species in Table 3.

10
Table 3.-Summary of data collected from the forereef of Looe Key Reef between June and September 1983. Scientific names are according to Robins et at. (1980). ---_.

Mean Length (em) Total individuals Frequency Percent Variance/

Species abundance per sample (N = 160) frequency Mean Min. Max. mean ratio K

Abudefduf saxatilis 5174 32.3375 124 77.50 9.69 3 15 83.17 0.31730 +

Acanthurus bahianus 492 3.0750 117 73.13 11.84 3 20 12.49 0.73415 *

Acanthurus chirurgus 39 0.2438 26 16.25 18.35 [, 27 1.64 0.28730 +

Acanthurus coeruleus 263 1.6438 105 65.63 12.05 3 30 4.29 0.88935 **

Aluterus schoepfi 2 0.0125 2 1.25 23.5 16 31 154.88 10000.00000

Aluterus scriptus 6 0.0375 6 3.75 42.67 36 50 1. 71 10000.00000

Amblycirrhitus pinos 1 0.0063 1 0.63 9 9 9 2.56 2208.14351

Anisotremus surinamensis 1 0.0063 1 0.63 30 30 30 40.96 2208.14351

Anisotremus virginicus 18 0.1125 13 8.13 18.15 4 25 2.28 0.36213 +

Aulostomus maculatus 19 0.1188 17 10.63 35.59 20 50 1.21 1.11383 +

8alistes capriscus 2 0.0125 1 0.63 35 35 35 1.28 -

80dianus rufus 158 0.9875 89 55.63 21.85 9 32 1.31 2.32183 +

Calamus bajonado 23 0.1438 18 11.25 35.22 20 50 1.00 0.35476 +

Calamus calamus 15 0.0938 11 6.88 21.91 15 30 1.54 0.54051 +

Cantherhines pullus 13 0.0813 11 6.88 11.56 3 16 1.77 0.28672 +

Canthidermis sufflamen 5 0.0313 4 2.50 39.5 35 45 2.05 0.06289 +

Canthigaster rostrata 27 0.1688 20 12.50 3.58 2 5 1.52 0.30974 +

Caranx bartholomaei 22 0.1375 7 4.38 47.1 32 75 11.64 0.02269 +

Caranx ruber 453 2.8313 56 35.00 17.15 8 35 26.91 0.13562 +

Chaetodon capistratus 260 1.6250 104 65.00 8.32 1 15 1.42 10000.00000 **

Chaetodon ocellatus 79 0.4938 42 26.25 11 6 16 262.48 10000.00000 **

Chaetodon sedentarius 3 0.0188 2 1.25 12 9 15 0.85 10000.00000

Chaetodon striatus 59 0.3688 35 21.88 10.29 8 16 1.56 10000.00000 **

Chromis cyaneus 213 1.3313 72 45.00 7.08 2 14 2.70 10000.00000 **

Chromis insolatus 1 0.0063 1 0.63 2 2 2 2.56 2208.14351

Chromis multilineatus 779 4.8688 47 29.38 8.35 5 13 64.41 0.08608 +

Chromis scotti 29 0.1813 6 3.75 5.83 2 11 17.30 0.01464 +

Clepticus parrai 98 0.6125 9 5.63 11.33 8 20 23.51 0.01569 +

Coryphopterus dicrus 36 0.2250 19 11.88 2.84 2 4 2.56 0.12328 +

Coryphopterus glaucofraenum 183 1.1438 44 27.50 2.83 2 5 5.05 10000.00000 **

Coryphopterus personatus 2338 14.6125 31 19.38 2.07 1 3 407.43 0.03611 +

Oiodon hystrix 1 0.0063 1 0.63 43 43 1;3 2.56 2208.14351

Oiplectrum formosum 16 0.1000 1 0.63 3 3 3 16.00 10000.00000

Echeneis naucrates 3 0.0188 3 1.88 8.67 5 12 0.85 10000.00000

Epinephelus cruentatus 82 0.5125 69 43.13 16.24 6 28 1.12 10000.00000

Epinephelus guttatus 1 0.0063 1 0.63 23 23 23 2.56 2208.14351

Equetus acuminatus 1 0.0063 1 0.63 9 9 9 2.56 2208.14351

Equetus punctatus 1 0.0063 1 0.63 12 12 12 2.56 2208.14351

Gnatholepis thompsoni 58 0.3625 20 12.50 4.25 3 5 3.58 0.36250 **

Gobiosona oceanops 62 0.3875 31 19.38 2.39 2 3 2.64 0.22515 +

Haemulon album 27 0.1688 2 1.25 21.5 18 25 24.27 10000.00000 *

Haemulon aurolineatum 5444 34.0250 77 48.13 13.15 1 22 152.78 0.12675 +

Haemulon carbonarium 351 2.1938 19 11.88 17.94 12 27 164.10 0.02830 +

Haemulon chrysargyreum 463 2.8938 17 10.63 13 6 17 55.29 10000.00000 ** Haemulon flavolineatum 256 1.6000 109 68.13 14.65 9 20 2.25 1.43209 *

Haemulon macrostomum 52 0.3250 30 18.75 24.36 3 32 5.96 0.22267 +

Haemulon melanurum 2 0.0125 2 1.25 17 17 17 1.28 10000.0'::000

Haemulon parrai 53 0.3313 5 3.13 24 19 26 19.32 10000.00000

Haemulon plumieri 136 0.8500 64 40.00 19.32 14 24 8.30 0.39304 **

Haemulon sciurus 299 1.8688 56 35.00 21.88 3 50 18.12 0.16065 **

Halichoeres bivittatus 620 3.8750 78 48.75 5.62 3 11 23.23 0.24358 +

Halichoeres garnoti 636 3.9750 132 82.50 6.67 3 20 4.65 1.14524 +

Halichoeres maculipinna 733 4.5813 119 74.38 5.75 2 11 14.31 0.63636 **

Halichoeres poeyi 1 0.0063 1 0.63 11 11 11 2.56 2208.14351

Halichoeres radiatus 107 0.6688 54 33.75 10.87 3 45 2.39 0.53628 +

Hemipteronotus novacula 1 0.0063 1 0.63 - - - 2.56 9556.93451

Hemipteronotus splendens 1 0.0063 1 0.63 7 7 7 2.56 10000.00000

Holacanthus bermudensis 8 0.0500 7 4.38 29 25 33 1.28 0.20907 +

Holacanthus ciliaris 13 0.0813 13 8.13 21.58 10 29 0.79 10000.00000

Holacanthus tricolor 37 0.2313 27 16.88 14.08 4 25 1.73 0.69275 +

Holocentrus ascensionis 7 0.0438 6 3.75 17.17 14 20 1.46 0.14711 +

11
Table 3.-Continued. ----

Mean ___ Length (c~ ___ Total individuals F""quency Percent Variance!

Species abundance per sample (N = 160) fre'luency Mean Min. Max. mean ratio K -------

Holocentrus rufus 25 0.1563 16 10.00 15.13 13 20 1.64 0.14419 +

Hypoplectrus gemma # 3 0.0188 3 1.88 8 7 10 0.85 10000.00000

Hypoplectrus unicolor 3 0.0188 3 1.88 4 3 5 0.85 10000.00000

Inermia vittata 31 0.1938 2 1.25 8 5 11 23.87 10000.00000 * Kyphosus sectatrix 357 2.2313 29 18.13 26.15 12 38 39.27 0.05324 +

Lachnolaimus maximus 30 0.1875 24 15.00 26.73 20 35 1.37 0.55925 +

Lactophrys bicaudalis 2 0.0125 2 1.25 8.5 8 9 1.28 10000.00000

Lactophrys triqueter 4 0.0250 4 2.50 7.5 l; 14 5.76 10000.00000

Lutjanus analis 3 0.0188 3 1.88 45.7 38 60 0.85 10000.00000 +

Lutjanus apodus 129 0.8063 23 14.38 19.7 14 24 9.60 0.05795 +

Lutjanus griseus 100 0.6250 24 15.00 30.55 17 45 20.07 0.06840 +

Lutjanus mahogoni 9 0.0563 3 1.88 18.67 16 21 4.55 0.01004 +

Lutjanus synagris 254 1.5875 17 10.63 17.71 12 25 25.20 10000.00000

Malacanthus plumieri 2 0.0125 2 1.25 11 9 13 1.28 10000.00000

Malacoctenus triangulatus 3 0.0188 3 1.88 6 5 7 0.85 10000.00000

Megalops atlanticus 2 0.0125 2 1.25 137 122 152 1.28 10000.00000

Microspathodon chrysurus 787 4.9188 130 81.25 10.02 3 14 6.30 1.01558 +

Monacanthus tuckeri 2 0.0125 1 0.63 6 6 6 1.28 - MUlloidichthys martinir.us 290 1.8125 38 23.75 18.83 7 30 15.57 0.08938 +

Muraena miliaris 2 0.0125 2 1.25 30 ~c

L"> 35 1.28 10000.00000

Mycteroperca bonaci 5 0.0313 5 3.13 41.5 31 65 0.51 10000.00000

Ocyurus chrysurus 1107 6.9188 150 93.75 20.68 9 45 15.55 1.08759 ** Odontoscion dente~ 72 0.4500 28 17.50 12.32 9 16 6.97 0.12053 +

Ophioblennius atlanticus 24 0.1500 14 8.75 5.64 3 8 2.67 0.10137 +

Ophstognathus aurifrons 8 0.0500 3 1.88 5 3 7 2.88 10000.00000

Pempheris schomburgki 274 1. 7125 7 4.38 8 :; 10 157.90 0.00836 +

Pomacanthus arcuatus 51 0.3188 42 26.25 30.21 23 35 1.25 7.13701 +

Pomacanthus paru 21 0.1313 17 10.63 30.44 25 35 1.10 0.39529 +

Pomacentrus diencaeus 103 0.6438 19 11.88 8.63 5 12 7.18 10000.00000 ** Pomacentrus fuscus 596 3.7250 58 36.25 6.32 " 12 20.45 0.14125 * Pomacentrus leucostictus 14 0.0875 9 5.63 3.13 1 6 1.65 0.13835 +

Pomacentrus partitus 5694 35.5875 154 96.25 3.82 2 5 2:;.78 1.49706 ** Pomacentrus planifrons 814 5.0875 83 51.88 6.21 2 11 13.29 0.26525 * Pomacentrus variabilis 72 0.4500 20 12.50 6.31 2 11 9.10 0.06327 +

Priacantnus cruentatus 2 0.0125 2 1.25 15.5 14 17 1.28 10000.00000

Pseudupeneus maculatus 17 0.1063 7 4.38 11.29 8 14 2.41 10000.00000

Scarus coelestinus 10 0.0625 9 5.63 38.89 28 45 1.02 0.39468 +

Scarus coeruleus 48 0.3000 21 13.13 35.1 20 45 9.01 0.09504 * Scarus croicensis 560 3.5000 94 58.75 5.59 2 22 7.68 0.44184 • Scarus guacE.maia 10 0.0625 8 5.00 32.6 3 48 2.30 10000. 00000

Scarus taeniopterus 67 0.4188 46 28.75 12.37 3 28 1.87 0.71533 +

Scarus vetula 42 0.2625 24 15.00 20.61 5 34 2.19 0.19084 +

Scomberomorus cavalla 1 0.0063 1 0.63 120 120 120 2.56 2718.89497

Scomberomorus maculatus 1 0.0063 1 0.63 42 42 42 2.56 2208.14351

Serranus baldwini 2 0.0125 2 1.25 4 4 4 1.28 10000.00000

Serranus trigrinus 44 0.2750 36 22.50 6.94 3 10 0.93 1.85613 +

Sparisoma aurofrenatum 256 1.6000 99 61.88 13.75 2 25 2.25 1.14195 +

Sparisoma chrysopterum 62 0.3875 32 20.00 17.96 6 39 62.80 0.21978 +

Sparisoma rubripinne 94 0.5875 44 27.50 25.14 15 40 2.72 0.30405 .. Sparisoma viride 262 1.6375 105 65.63 19.52 2 43 2.82 1.:;2577 +

Sphoeroides spengleri 1 0.0063 1 0.63 5 5 5 2.56 2208.14351

Sphyraena barracuda 69 0.4313 39 24.38 81 39 160 3.71 0.29199 * Synodus intermedius 1 0.0063 1 0.63 9 9 9 2.56 7109.71622

Thalassoma bifasciatum 9558 59.7375 156 97.50 4.46 1 7 72.42 0.89184 +

Trachinotus falcatus 3 0.0188 3 1.88 56.7 45 75 0.85 10000.00000

Tylosurus crocodilus 2 0.0125 2 1.25 65 55 75 1.28 10000.00000

# Now considered a color form of H. unicolor.

+ fits negative binomial distribution (p > 0.05)

* reject negative binomial fit (p < 0.05) *. reject negative binomial fit (fJ < 0.01)
501 A

40 Corypterus

glaucofroenum

30

20

Ocyurus chrysurus

10 Sphyraena barracuda

0

40 Ocyurus chyrsurus 40 Acanthurus coeruleus

30 30

20 2

10

0 12 24 36 0 12 24 36

o . Spa"soma viride

15 15

10 10

5 <70 1"] 5

UUIA VI 0 171 0 0 12 24 36 0 12

LENGTH (eM)

lut janus griseus

Figure 12.-Length/frequency histograms ofselected species

showing size distributions of individual species based on stationary sampling data. A. Mean lengths per sample for

representative small, medium, and large species; B. Comparison of minimum and maximum lengths for Ocyurus

chrysurus; C. length/frequency composition of two species

with taxonomic, morphological, and ecological similarities;

D. comparison of two similar-sized reefspecies in which one

is found on reefs at all sizes while the other recruits only as

a young adult.

between species. Clearly, community analyses based only on mean

abundance may be misleading in terms ofthe biological importance

of various species.

A major attribute of the SS method is that data are collected

simultaneously on species composition, abundance, frequency of

occurrence, and individual lengths for all visually detectable species.

Thus, data on all major community parameters can be collected

practically with this one method. Size distributions can be determined for individual species based on length data (Fig. 12). An

index of biomass could be obtained from length data for each species

by multiplying abundance estimates by weight based on empirically derived, species-specific length-weight relationships (Russell et

al. 1978). Mean length data could be used directly to compare

average stock sizes between habitats, reefs, and over time. Minimum lengths may be useful indicators of recruitment size for

sampled habitats, while maximum lengths may be useful indicators

of fishing pressure.

Abundance patterns from census data are characterized by high

variance (Table 3). The SS method relies on ability to easily obtain

large numbers of samples. Goodall (1970) noted in systems with

naturally high variance that variance can be reduced more effectively by more intensive sampling than by improved precision. Also,

for statistical purposes and for the same effort, many small samples

are usually preferable to a few large samples. In general, confidence

interval width for any parameter is narrowed with more samples.

13

Dispersion Patterns-Dispersion patterns, examined on the basis

of variance-to-mean ratios (Pielou 1969; Brower and Zar 1977),

showed that 105 out of 117 species (90 %) censused on the Looe

Key forereef were clumped, and 12 species (10%) were randomly

distributed among samples (Table 3). No species were distributed

uniformly. Among clumped species, 48 species did not differ

significantly (p >0.05) from a negative binomial distribution (Table

3). A total of 51 species had k values greater than 1000 which implies a Poisson distribution. Two species were rejecled from the

negative binomial curve fit program. The distributions of the remaining fOUl species were significantly different (p < 0.05) from

a negative binomial distribution but had low k values.

Most species exhibited clumped dispersion patterns due to schooling and the habitat heterogeneity of the fore reef environment. This

high vanance between individual samples probably reflects true

distributions on a reef in space and time. This suggests that nonparametric procedures may be most appropriate for analyzing raw

data, although transformations and combining samples will normalize data in many cases and allow use of parametric procedures,

The fact that many species fit a negative binomial distribution is

important because it implies that mean abundances may not be the

best criteria for comparing populations. Bannerot and Austin (1983)

have suggested that statistics such as "k" or the negative probability

of zero would be more sensitive measures for comparing populations with data fitting a negative binomial distribution.
Adequate Sample Size-The number ofsamples necessary for an

adequate sample was examined by plotting performance curves (Fig.

13). Increases in cumulative species slows rapidly with additional

samples (Fig. 13, top). An average of six samples included species

representing 90% ofthe total individuals censused in 160 samples.

Spearman rank correlation coefficients (Zar 1974), based on number

of species in various numbers of independent randomly selected

samples, were also plotted versus cumulative sample size. Correlations increased rapidly with sample size until leveling off around

a value of 0.8 with 20 or more lumped samples (Fig. 13, bottom).

No significant correlations were found for comparisons of one and

two samples (p >0.05) but all comparisons were significantly correlated with four or more lumped samples (p < 0.05), and very

highly correlated with eight or more samples (p < 0.001). These

results suggest that 8 to 20 samples may be sufficient for some purposes. However, adequate sample size depends on the statistical

characteristics of a specific parameter, the acceptable chance of error, and the degree ofresolution desired. The number of samples

necesssary for a study can be estimated by usual statistical procedures (see Elliott 1977; Green 1979). More detailed comments

on data analysis and community structure analysis are beyond the

scope of this paper.

CONCLUSIONS---- _

Stationary sampling is a new and valid method for sampling reef

fish community structure even in diverse environments with abundant reef fish populations. It offers a standardized means of comparing reeffish communities and reduces many of the inadequacies

oftraditional visual sampling methods. It also offers many desirable

features worth considering for reef fish sampling programs. Quantitative data are provided on frequency of occurrence, fish length,

abundance, and community composition. The method is simple,

fast, objective, repeatable, and easy to use. Species are accumulated

rapidly for listing purposes, and large numbers of samples can be

easily obtained for statistical treatment. Although we sampled for

all observable species, the method can be modified to count specific

taxa or groups ofinterest, such as commercial species, grunts, herbivores, or single species.

Despite the major advantages ofstationary sampling, the method

will not solve all sampling problems. As noted, the method is not

suitable for use under certain environmental conditions. As with

most visual methods, crevice-dwelling, cryptic, and very secretive

species are probably not effectively sampled. Extremely intense

sampling efforts would be required to detect all rare species. Finally,

the SS method was designed to evaluate community structure: it

may be inefficient, as presented, for studies concerned only with

one, or a few, species or genera.

ACKNOWLEDGMENTS _

We thank D. E. Harper, D. B. McClellan, and D. L. Sutherland

of the Southeast Fisheries Center, National Marine Fisheries Service, OAA, for contributing to the preparation of the manuscript,

and G. Beardsley, B. Bohnsack, S. Bortone, D. G. Lindquist, J.

Witzig, and an anonymous reviewer for critical comments and suggestions. Support for field operations was provided by I. Hooper

(Newfound Harbor Marine Institute), C. R. Robins (Division of

Biology and Living Resources, Rosensteil School of Marine and

Atmospheric Science, University of Miami), and Klaus Rutzler

(Smithsonian Institution). Research was supported in part by

NOAA Postdoctoral Research Awards No. NA80AADOO108 and

NA81AADOO102 from the Office of University Affairs; the

Cooperative Institute for Marine and Atmospheric Studies, University of Miami; and the Sanctuary Programs Division, Office of

Ocean and Coastal Resource Management, NOAA. Initial concepts

were developed from funding by the Lerner Fund for Marine

Research (American Museum of Natural History) and the Florida

Sea Grant College Program with support from the Office of Sea

Grant, NOAA, U.S. Department of Commerce, Grant No.

NA80AA-E-00038.

80

150

50

80

20 40

NUMBER OF SAMPLES

o

1.0

0.5

0.4

0.8

a::

w

Cl

a::

o

""

z

<C

a::

z 0.2 <C

a::

<C

w

0-

Vl _ 0,2 +----r--"'T-"----.--"'T-"----.--.,-----.--...-J

o

>--

z

w

u

....

....

w

o

u

Figure l3.-Mean cumulative species (top) and mean Spearman rank correlation coefficients (bottom) for lumped samples. Vertical lines show 95% confidence

limits. Numbers show sample sizes. Significance of Spearman rank correlation

coefficients are indicated in the bottom figure (n.s., not significant; * = p <0.05;

** = P < 0.01).

14
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15