A GUIDE TO MONITORING REEF FISH IN THE

NATIONAL PARK SERVICE’S

SOUTH FLORIDA / CARIBBEAN NETWORK

NOAA Technical Memorandum NOS NCCOS 39 
Mention of trade names or commercial products does not constitute endorsement or 

recommendation for their use by the United States government. 

Citation for this Report 

Menza, C., J. Ault, J. Beets, J. Bohnsack, C. Caldow, J. Christensen, A. Friedlander, C. Jeffrey, M. 

Kendall, J. Luo, M. Monaco, S. Smith and K. Woody. 2006. A Guide to Monitoring Reef Fish in 

the National Park Service’s South Florida / Caribbean Network. NOAA Technical Memorandum 

NOS NCCOS 39. 169 pp. 

ii 
A Guide to Monitoring Reef Fish in the National Park 

Service’s South Florida / Caribbean Network 

Charles W. Menza1, Jerald S. Ault2, Jim Beets3, James A. Bohnsack4, 

Chris Caldow1

, John Christensen1, Alan Friedlander1, Chris Jeffrey1, Matt 

Kendall1

, Jiangang Luo2, Mark Monaco1, Steven G. Smith2 and Kimberly 

Woody1 

1

 National Oceanic and Atmospheric Administration, Biogeography Team 

2

 University of Miami, Rosentiel School of Marine Science and Atmospheric Science 

3

 University of Hawaii in Hilo 

4

 National Marine Fisheries Service, Southeast Fisheries Science Center 

Center for Coastal Monitoring and Assessment (CCMA)

NOAA/NOS/NCCOS

1305 East West Highway (SSMC-IV, N/SCI-1)

Silver Spring, MD 20910

NOAA Technical Memorandum NOS NCCOS 39

August 2006

United States Department of National Oceanic and National Ocean Service 

Commerce Atmospheric Administration 

Donald L. Evans Conrad C. Lautenbacher, Jr. Jack Dunnigan 

Secretary Administrator Assistant Administrator 

iii 
Executive Summary

Reef fishes are conspicuous and essential components of coral reef ecosystems and 

economies of southern Florida and the United States Virgin Islands (USVI). Throughout Florida 

and the USVI, reef fish are under threat from a variety of anthropogenic and natural stressors 

including overfishing, habitat loss, and environmental changes. 

The South Florida / Caribbean Network (SFCN), a unit of the National Park Service (NPS), 

is charged with monitoring reef fishes, among other natural and cultural resources, within six parks 

in the South Florida - Caribbean region (Biscayne National Park, BISC; Buck Island Reef National 

Monument, BUIS; Dry Tortugas National Park, DRTO; Everglades National Park, EVER; Salt 

River Bay National Historic Park and Ecological Preserve, SARI; Virgin Islands National Park, 

VIIS). Monitoring data is intended for park managers who are and will continue to be asked to 

make decisions to balance environmental protection, fishery sustainability and park use by visitors. 

The range and complexity of the issues outlined above, and the need for NPS to invest in a strategy 

of monitoring, modeling, and management to ensure the sustainability of its precious assets, will 

require strategic investment in long-term, high-precision, multispecies reef fish data that increases 

inherent system knowledge and reduces uncertainty. 

The goal of this guide is to provide the framework for park managers and researchers to 

create or enhance a reef fish monitoring program within areas monitored by the SFCN. The 

framework is expected to be applicable to other areas as well, including the Florida Keys National 

Marine Sanctuary and Virgin Islands Coral Reef National Monument. The favored approach is 

characterized by an iterative process of data collection, dataset integration, sampling design 

analysis, and population and community assessment that evaluates resource risks associated with 

management policies. Using this model, a monitoring program can adapt its survey methods to 

increase accuracy and precision of survey estimates as new information becomes available, and 

adapt to the evolving needs and broadening responsibilities of park management. 

To conduct reef fish population and community assessments, monitoring programs must 

collect abundance and size-frequency distribution data for distinct fish taxa. Concurrently collected 

data on benthic habitat and water quality are desirable as well, and can be assimilated in a survey 

design to improve survey performance. 

The method of measurement should establish a constant search area for a sample unit (e.g. 

transect, fixed radius cylinder) and obtain an accurate representation of the reef fish community 

within the sample unit, tempered by the time required to obtain the sample. The choice of 

iv 
measurement method depends on the species or species-complex, life history stages, and habitat 

chosen for sampling. 

Underwater visual census methods are ideal for assessing reef fishes in the Florida Keys 

(e.g. DRTO) and Virgin Islands (e.g. BUIS, SARI, VIIS) because of prevailing good visibility, 

rugose habitats, and management concerns requiring the use of non-destructive census methods. 

The most well known visual census methods are the belt transect and the stationary visual census. 

Alternative methods must be used to census fish in turbid (e.g. BISC) or deep environments or 

when nighttime surveys are required. 

A primary consideration of a monitoring program is to delineate the target population which 

will be monitored. For reef fish, this can be done by selecting an ecosystem area to be surveyed. For 

most monitoring purposes a sample of the population is used to infer the status of the population. 

There are many ways to select a sample from a population, but the more information available about 

a population, the easier it is to devise a selection method which provides accurate and precise 

survey estimates. A simple random sampling design is appropriate for situations where there is no 

spatial structure in the variance of investigated fish metrics or little information is available. Since 

fish metrics are typically heterogeneous, a stratified random sampling design will sample a fish 

population more effectively. Maps of environmental covariates, such as benthic habitat, at the 

appropriate spatial scales and spatial extent can be used to effectively divide the sampled population 

into strata. 

The main goal of sample surveys is to obtain accurate, high-precision estimates of 

population and community metrics at a minimum of cost. The objective of sample design analysis is 

to determine the appropriate number of samples required to achieve enough precision in population 

and community metrics (e.g., species numbers-at-size, species composition) to understand 

ecological processes and to make management decisions. Iterative analysis of candidate survey 

design performance can be used to refine survey estimates and reduce sampling cost. 

The range and types of statistical analyses that will be performed to assess the status and 

dynamics of reef fish populations and communities in National Parks depends on the specific 

management questions and resource goals to be addressed. These analyses utilize the range of 

fundamental survey data outlined above and recommended for collection in monitoring programs. 

These survey data are then used to generate multiple metrics for individual species, speciescomplexes or life history stages to assess status and trends of reef fish over time and in relation to 

specific sustainability metrics. 

v 
A single standardized monitoring protocol is not advocated because of the variability in 

ecological condition, size, management capability, and available data among SFCN park units. This 

guide is meant to serve only as a framework. Three reef fish monitoring program case studies are 

provided which build upon the presented framework using park-specific data sets, management 

concerns, and local partnerships. 

vi 
Table of Contents

1 BACKGROUND AND OBJECTIVES.........................................................................................................................1

1.1 RATIONALE ................................................................................................................................................................1

1.2 MANAGEMENT DOMAIN.............................................................................................................................................2

1.3 OBJECTIVES ...............................................................................................................................................................4

1.4 DOCUMENT ORGANIZATION.......................................................................................................................................6

2 DATA TO BE COLLECTED AND METHODS OF MEASUREMENT 7

2.1 DATA TO BE COLLECTED............................................................................................................................................7

2.2 METHODS OF MEASUREMENT ....................................................................................................................................7

2.2.1 Visual Census Methods ......................................................................................................................................8

2.2.2 Non-Visual Census Methods 8

2.2.3 Measurement Biases...........................................................................................................................................9

3 POPULATION TO BE SAMPLED AND SELECTION OF THE SAMPLE.........................................................10

3.1 POPULATION TO BE SAMPLED ..................................................................................................................................10

3.2 SELECTION OF THE SAMPLE .....................................................................................................................................10

4 CANDIDATE SAMPLING DESIGN ANALYSIS....................................................................................................14

4.1 BASIC CONCEPTS OF SAMPLING THEORY AND DESIGNS ..........................................................................................14

4.2 SAMPLING DESIGN PERFORMANCE MEASURES ........................................................................................................17

4.3 COMPOSITE SAMPLING DESIGNS ..............................................................................................................................20

4.4 ITERATIVE LEARNING ..............................................................................................................................................21

5 POPULATION AND COMMUNITY ASSESSMENTS...........................................................................................22

5.1 SPECIES AND COMMUNITY METRICS........................................................................................................................22

5.1.1 Frequency of Occurrence.................................................................................................................................22

5.1.2 Diversity Indices...............................................................................................................................................22

5.1.3 Relative and Absolute Population Abundance or Biomass ..............................................................................23

5.1.4 Population Size-Structure.................................................................................................................................24

5.2 ASSESSING CHANGES TO REEF FISH METRICS .........................................................................................................25

5.3 POPULATION MORTALITY RATE ASSESSMENTS USING SIZE-STRUCTURE................................................................26

5.4 POPULATION BIOMASS ASSESSMENTS IN RELATION TO SUSTAINABILITY BENCHMARKS ........................................26

6 CASE STUDY FOREWORD......................................................................................................................................28

7 ACKNOWLEDGMENTS ...........................................................................................................................................31

8 REFERENCES.............................................................................................................................................................32

9 CASE STUDIES 

MONITORING REEF FISH IN BUCK ISLAND REEF NATIONAL MONUMENT, VIRGIN

ISLANDS NATIONAL PARK AND VIRGIN ISLANDS CORAL REEF NATIONAL MONUMENT

2001-2005 ...........................................................................................................................................................39

MONITORING REEF FISH ASSEMBLAGES INSIDE VIRGIN ISLANDS NATIONAL PARK AND

AROUND ST. JOHN, USVI 1988-2000 ..........................................................................................................79

ASSESSMENT OF CORAL REEF FISHES IN DRY TORTUGAS NATIONAL PARK 1999-2004 ....115

vii 

.................................................................

..............................................................................................................................
1 Background and Objectives 

1.1 Rationale 

Reef fishes are conspicuous and essential components of coral reef ecosystems and 

economies of southern Florida (Johns et al. 2001; Ault et al. 2005a) and the United States Virgin 

Islands (USVI) (Hinkey et al. 1994; Mateo 1999, 2001). More than 500 species of reef fish 

including sharks, eels, flounders, gobies, puffers, groupers, parrotfishes, snappers, jacks, and 

damselfishes can be found in the reef ecosystems of the Florida Keys and USVI (Longley and 

Hildebrand 1941; Bohlke and Chaplin 1968; Clavijo et al. 1980; Munro 1983; Robbins and Ray 

1986; Smith-Vaniz et al. 1995; Randall 1996; Bohnsack et al. 1999; Humann and DeLoach 2002). 

Throughout Florida and the USVI, coral reef ecosystems are under threat from a variety of 

anthropogenic and natural stressors, including overfishing, habitat loss, and environmental changes. 

Over the past several decades, public use and conflicts over fishery resources in the two regions 

have increased sharply (Appeldoorn et al. 1992; Bohnsack et al. 1994; Leeworthy and Vanasse 

1999), while catches from historically productive fishery stocks, especially the snapper-grouper 

complex, have declined (Allen and Tashiro 1976; Bohnsack et al. 1994; Ault et al. 1997, 1998, 

2001, 2005a). In the Florida Keys, recent quantitative assessments of the multispecies reef fish 

community have shown that exploitation levels are very high, that many stocks are "overfished", 

and that overfishing has been clearly evident since the late 1970's (Ault et al. 1998, 2001, 2002, 

2005ab). Throughout the region, there is evidence of loss of grouper spawning aggregations, 

reduced catch per unit effort, changed species composition of landings, and lower mean sizes and 

abundances of several assemblages (de Graaf and Moore 1987; Appeldoorn et al. 1992; Bohnsack 

et al. 1994; Ault et al. 1998, 2005b; Beets 1996ab; Garrison et al. 1998; Beets and Friedlander 

1999; Beets & Rogers 2002; Beets and Muehlstein 2003). The impacts to the community are of 

great concern because fishing has depleted top trophic levels, shifted community structure and 

reduced the length and complexity of food webs that affect fishery resilience and prospects for their 

sustainability (Pauly et al. 2002; Ault et al. 2006). Several species, for example Goliath Grouper 

(Epinephelus itajara), Nassau Grouper (Epinephelus striatus) and Queen Conch (Strombus gigas), 

have been so depleted that they are now protected (Sadovy and Eklund 1999, Caribbean Fishery 

Management Council 1996). 

The reef fish community has also been affected by a series of other anthropogenic and 

natural stressors. Coastal development influences reef fish through a plethora of negative impacts 

1 
on reef fish habitats (Rogers and Beets 2001). Fish habitats are altered by dredging, boating, fishing 

gears, wetlands reclamation, beach renourishment, mangrove removal, and sea defense construction 

(Rogers et al. 1988; Rogers 1991; Beets 1996a; Lindeman and Snyder 1999; Rogers and Beets 

2001; Wilber et al. 2003; Mumby et al. 2004; Chiappone et al. 2005). Additional consequences of 

development include changes to water quality from pollution, sedimentation, nutrient loading, 

freshwater inflows, and regional scale hydrodynamics (McIvor et al. 1994; MacDonald et al. 1997; 

Serafy et al. 1997; Mannoni 1999; Nemeth and Sladek-Nowlis 2001; Rogers and Beets 2001; 

Cowie-Haskell and Delaney 2003). These impacts may be exacerbated by intensification of 

hurricane activity and climate variability (Rogers et al. 1982; Hughes 1994; Edmunds 2002; 

Gardner et al. 2005; Pandolfi et al. 2005). 

As coastal populations, tourism and fishing pressure continue to increase, park managers are 

being asked to make decisions to balance environmental protection, fishery sustainability, and 

visitor resource use. Balancing these conflicting uses is a complex issue, and requires an ecosystembased management (EBM) approach. EBM considers knowledge and uncertainties in biotic, abiotic, 

and human components of the whole ecosystem in an attempt to balance societal objectives 

(Christensen et al. 1996; Larkin 1996; Schramm and Hubert 1996; Pikitch et al. 2004). These 

broad-spectrum objectives are set within a framework created by the NPS’s universal mission to 

protect cultural and natural resources (Organic Act of 1916, USC title 16). 

1.2 Management Domain 

The National Park Service (NPS) through the South Florida / Caribbean Network (SFCN) is 

one of many administrative entities monitoring reef fishes and coral reef ecosystems in southern 

Florida and USVI. The SFCN is composed of four managed areas in southern Florida (Big Cypress 

National Park, BICY; Biscayne National Preserve, BISC; Dry Tortugas National Park, DRTO; 

Everglades National Park, EVER) and three in the USVI (Buck Island Reef National Monument, 

BUIS; Salt River Bay National Historic Park and Ecological Preserve, SARI; Virgin Islands 

National Park, VIIS) (Figure 1). Six of these areas are inhabited by reef fishes (BICY is excluded). 

The Virgin Islands Coral Reef National Monument (VICR) is not currently part of the SFCN, but is 

managed by the NPS and shares many ecological and management characteristics with VIIS, BUIS, 

and SARI. Information on location, date of establishment, cultural and natural resources, marine 

habitat area, and management zoning for each park is given on the NPS website (www.nps.gov). 

Site characterizations of fisheries resources and habitats have been carried out for 

2 
Figure 1: Map of the Caribbean region showing parks and monuments that are part of the National 

Park Service’s South Florida / Caribbean Network. 

3
BISC (Ault et al. 2001) and DRTO (Schmidt et al. 1999; Ault et al. 2002), but only habitats for 

SARI (Kendall et al. 2005). 

1.3 Objectives 

The range and complexity of the issues outlined above, and the need for NPS to invest in a 

strategy of monitoring, modeling, and management to ensure the sustainability of its precious 

assets, will require strategic investment in long-term, high-precision, multispecies reef fish data that 

benefits EBM by increasing inherent system knowledge and reducing uncertainty. Objectives to 

meet the goals of park-specific EBM are to: 

• Assess condition and changes in reef fish community diversity and its composition 

• Assess condition and changes in the sustainability status of reef fishes under 

exploitation 

• Assess anthropogenic impacts (e.g., fishing, sedimentation, pollution, etc.) on 

community dynamics 

• Assess effectiveness of management strategies such as Marine Protected Areas 

(MPAs), fishing regulations, land and water uses, etc. 

• Determine biological and physical processes that govern health and sustainability of 

reef fish resources 

These objectives may be tailored by local resources and interested stakeholders as outlined 

in each park’s General Management Plan, Resource Management Plan and/or Fisheries 

Management Plan. These plans set the management philosophy and direction for planning horizons 

up to 15-20 years in the future and are amended accordingly. 

There is a clear need to link monitoring programs to meet management goals and objectives. 

Monitoring programs are comprised of an iterative process of data collection, dataset integration, 

design analysis, and population and community assessment that evaluates resource risks associated 

with management policies. Figure 2 illustrates a conceptual model of an ideal reef fish monitoring 

program. The model is adaptive in two respects. First, as new information becomes available, the 

monitoring survey design used to collect data and assimilate data is tailored to increase accuracy 

and precision. Second, the monitoring process can adapt to the evolving needs and broadening 

responsibilities of EBM and NPS management plans. Adaptation need not be instantaneous or final. 

A reevaluation of the management objectives every 3-, 5- or 10-years can be sufficient to adapt to 

new management policies, shifted resource conditions, and a compilation of new data. 

4 
Figure 2: A conceptual model of an ideal reef fish monitoring program. The model illustrates the 

iterative process of data collection, dataset integration, design analysis, and population and 

community assessment that evaluates resource risks associated with management policies. Feedback 

loops critical to the iterative process are shown. 

5 
1.4 Document Organization 

This guide mainly addresses the first four components of the iterative monitoring program 

outlined in Figure 2. These components are organized in the following sections of this document: 

1. Background and Objectives 

2. Data to be Collected and Methods of Measurement 

3. Population to be Sampled and Selection of the Sample 

4. Candidate Sampling Design Analysis 

5. Population and Community Assessments 

In addition, three case studies are provided that illustrate existing monitoring programs 

within managed areas of the SFCN. The case studies are described further in Section 6. 

1. Case Study A: Reef Fish Monitoring in Virgin Islands National Park and Buck Island 

National Monument, 2001-2005 (VIIS, BUIS) 

2. Case Study B: Monitoring Reef Fish Assemblages inside Virgin Islands National 

Park and around St. John, US Virgin Islands, 1988-2000 (VIIS) 

3. Case Study C: Assessment of Coral Reef Fishery Resources in Dry Tortugas 

National Park, 1999-2004 (DRTO) 

6 
2 Data to be Collected and Methods of Measurement 

2.1 Data to be Collected 

To conduct reef fish population and community assessments, monitoring programs must 

collect several types of fundamental data on reef fishes: 

• Identification to the lowest possible taxonomic classification (preferably to the 

species level) of each individual 

• Abundance and size-frequency distribution of each taxa 

Concurrently collected data on benthic habitat and water quality are desirable as well and 

can be assimilated in a survey design to improve survey performance. Complementary data often 

include: 

• Depth 

• Substrate composition 

• Benthic floral/faunal composition 

• Vertical rugosity 

• Benthic habitat type (based on strata of a stratified sampling design) 

• Temperature 

• Salinity 

2.2 Methods of Measurement 

The method of measurement chosen for a survey should establish a consistent search area 

for a sample unit (e.g. transect, fixed radius cylinder) and obtain an accurate representation of the 

reef fish community within the sample unit, tempered by the time required to obtain the sample. 

Consistent search areas ensure comparability among sampling units and equal (or known) 

probability of sample unit selection. Search methods based solely on time are not recommended if 

sample units are defined by area, because they invalidate the equal selection property that is 

fundamental to using probability-based sampling designs. 

The choice of the sampling method depends on the species or species-complex, life history 

stages, and habitat chosen for sampling. These choices are governed by the monitoring program 

goals. The following sections outline methods that take these considerations into account. 

7 
2.2.1 Visual Census Methods 

Underwater visual census methods are ideal for assessing reef fishes in the Florida Keys and 

Virgin Islands because of prevailing good visibility, rugose habitats, and management concerns 

requiring the use of non-destructive assessment methods. The most well known visual methods are 

the stationary visual census and the belt transect. The stationary visual census samples all reef fish 

within an imaginary cylinder of fixed radius (Bohnsack and Bannerot, 1986). Belt transects sample 

all fish within a rectangle of fixed width and length (Brock, 1954). Belt transects may be more 

appropriate when sites are characterized by low visibility, highly rugose habitats, or adjacent to 

mangroves because they place the diver closer to fish. In addition, the belt transect may be more 

effective in sampling small, cryptic species (e.g. Chaenposidae, Gobiidae, Labrisomidae). Case 

study A collects reef fish data using the belt transect method. Stationary visual census methods may 

minimize measurement bias attributed to diver movements and is superior for counting pelagics 

(e.g. Carangidae, Scombridae, Clupidae). Examples of the stationary visual census are provided in 

case studies B and C. The choice of a visual census method should maximize effectiveness 

(minimize bias) of the census given potential fish species and habitats among sample units. Some 

logistical factors that could improve survey performance (more samples per unit time) and reduce 

diver fatigue are use of Nitrox SCUBA and “live-boating” (boat driver remains on non-anchored 

vessel) at dive sites. 

Alternative visual census methods which do not employ SCUBA diving and thus are free of 

SCUBA’s depth constraints utilize underwater video cameras, but they are plagued by biases 

associated with species selectivity and inconsistent census area. Underwater stereo-video (Harvey 

and Shortis 1996) and baited video cameras (Willis and Babcock 2000) are two examples. 

2.2.2 Non-Visual Census Methods 

In general, statistical issues of capture efficiency and size-selectivity (e.g., MacLennan 

1992; Gunderson 1993) are minimized using visual census methods; thus, they are typically 

preferred for coral reef ecosystems. However, not all fish species or individuals in the sampled area 

will be detected by visual methods. Alternative methods may be required in cases where: visibility 

is occluded by turbid waters or densely vegetated habitats; night-time sampling is most effective for 

target species; cryptic species are targeted; or depths exceed operational diving limits. In these 

situations, fish may be sampled more effectively using gear or poisons (ichthyocides) to capture 

fish. Two classes of gear have been used to sample reef fishes: active gear, such as trawls and seines 

8 
that are towed or pulled to capture fish within a sample unit (e.g., Robblee and DiDomenico 1991; 

Sedberry and Carter 1993; Serafy et al. 1997; Ault et al. 1999); and passive gear, such as gill nets, 

traps, and pots that are assumed to sample a fixed unit area over a given unit time (e.g., Collins and 

Sedberry 1991; Hickford and Schiel 1995; Pratt and Fox 2001; Watson and Munro 2004). 

Morphologically or behaviorally cryptic species will likely be underestimated by visual 

census methods (Smith-Vaniz et al. in press), and active and passive gear. Cryptic species can be 

quantified more-effectively using ichthyocides (e.g. rotenone) (Smith-Vaniz et al. in press; 

Ackerman and Bellwood 2000), but ichthyocides negatively impact the studied assemblage. 

2.2.3 Measurement Biases 

A sample will rarely provide an absolutely accurate measurement, but the mean of many 

unbiased samples will tend towards the mean of the population. A measurement bias occurs when 

the measurement process affects the measurements in such a way that the sample mean does not 

tend towards the population mean, but rather another (sometimes unknown) value. Common causes 

of measurement bias include inconsistent survey effort, diver behavior, species detectability, 

observer experience/training, and fish density. The primary reason fishery-independent data are 

sought for monitoring programs is because fishery data are plagued by measurement biases 

associated with differences in catch per unit effort. Thompson and Mapstone (1997) demonstrate 

observer bias can be considerable in underwater visual censuses and provide guidance for observer 

training to ameliorate its impact. 

In general, it is assumed that the method of measurement chosen has accounted for issues in 

selectivity and minimizes the probability of non-detection of a species if it is present in the 

sampling area. Selectivity is defined as the probability that an individual will be detected (or 

retained) by the sampling method (gear) given that it is vulnerable (Gunderson 1993). While there is 

always the possibility that a species will not be detected at a site, despite being present, for most 

species and gear types this non-sighting probability diminishes with increasing animal size, thus 

demanding strategic choice of the methods of measurement. There are well-known statistical 

methods for correcting for size selectivity by gear or method in sampling surveys (e.g., Pope et al. 

1975; Gunderson 1993). In addition, MacKenzie et al. (2002), Azuma et al. (1990) and Tyre et al. 

(2003) give some insights into correcting frequency of occurrence data when there are unequal 

selection probabilities amongst species. 

9 
3 Population to be Sampled and Selection of the Sample 

3.1 Population to be Sampled 

A primary consideration of a monitoring program is to delineate the target population which 

will be monitored. For reef fish, this can be done by selecting an ecosystem area to be surveyed. 

This surveyed area in which ecological processes occur and management decisions will be made is 

known as the survey domain. 

A distinction must be made between the target population (the population about which 

information is desired) and the sampled population. These populations should coincide, but 

sometimes due to practicality or convenience the sampled population is a restricted part of the target 

population. It is important to note that if a difference among these populations exists, conclusions 

drawn from the sample only apply to the sampled population (Cochran 1977). 

Population and community assessments correspond only to fish populations within the 

survey domain, thus the selection and accurate demarcation of the domain is essential for 

meaningful fish management decisions. Although management efforts in the SFCN are directed 

towards the areas within park boundaries, the reef fish assemblages in these parks depend on and 

interact with surrounding areas over a much larger spatial-scale. To comprehensively monitor and 

manage the reef fish inside the parks and assess effectiveness of NPS management strategies, the 

survey domain should incorporate areas outside park boundaries as well. 

3.2 Selection of the Sample 

There are many ways to select a sample, but the more information available about a 

population, the easier it is to devise a selection method which provides accurate and precise survey 

estimates. Simple random sampling (SRS) is the simplest and most fundamental probability-based 

survey design allowing inferences to be made from sample units to the sampled population. In the 

previous section, we defined the sample unit for a given sampling method to have a known constant 

area. The complete list of all non-overlapping, independent sample units comprises the sampling 

frame. The SRS design considers all sample units in the sampling frame equal (i.e. all sample units 

have the same probability of being selected) and thus is appropriate for situations where there is no 

spatial structure in the variance of investigated metrics. Fish populations and communities are rarely 

homogenous in nature. More often, the principal metrics of reef fish show strong association with 

benthic habitats, depths, salinity, and other environmental covariates (Ault et al. 1999; Kendall et 

al. 2003, 2004) and thus are heterogeneous. 

10 
A survey design will sample a population more effectively if the corresponding variations in 

the spatial structure of variance within the survey domain is known and taken advantage of. A 

stratified random sampling design (StRS) uses the variance structure to select sample units more 

efficiently from the sampling frame. A StRS design may divide the survey domain into regions of 

relatively homogenous variance called strata and by sampling more intensively in highly-variable 

strata, a StRS can achieve better results than a SRS using the same sample size. 

For any given metric the best criteria to use when constructing strata is the variance structure 

of the metric itself. As this knowledge is unknown (or else sampling would not be need), the next 

best criteria are variables that are highly correlated with the metric of interest. Maps of 

environmental covariates at the appropriate spatial scales and spatial extent are ideal, since the 

sampling frame is situated in a spatial framework. 

The benthic habitat maps of shallow-water (depth 0 - 20 m) areas by FMRI (1988), Kendall 

et al. (2001, 2005) and Franklin et al. (2003) are exemplary maps of a covariate for BISC, BUIS, 

DRTO, SARI and VIIS. These maps classify benthic habitats that are strongly correlated with 

principal reef fish population and community metrics. Ault et al. (1999, 2005a) and case studies A 

and C have shown parsing the survey domain according to benthic habitat types will dramatically 

improve sampling efficiency compared to a SRS. 

To effectively parse the survey domain into strata requires an analysis of covariance among 

reef fish metrics and environmental variables (e.g. benthic habitat, salinity, depth). A broad 

assortment of variance analysis techniques such as plots of stratum standard deviations against 

stratum means, Analysis of Variance (ANOVA), generalized linear models, and resampling 

methods can be used to investigate the variance structure of metrics. Figure 3 is a plot of the sample 

standard deviation of snapper (Family: Lutjanidae) against the corresponding average sample 

density among 10 distinct benthic habitat types around BUIS. The graph shows that snappers were 

not homogeneously distributed throughout the survey domain, and that stratification according to 

benthic habitat can be effective in partitioning the domain into areas with differing variances. 

Figure 3 also indicates that a stratification scheme employing benthic habitat type can be simplified 

by merging relatively similar benthic habitat types (e.g. linear reef and aggregated patch reef) into a 

single stratum. 

11 
0.0 0.5 1.0 1.5 2.0

Fish Density (#/m2)

0

1

2

3

4

5

St Dev Fish Density

Seagrass

Colonized Pavement

Colonized Bedrock

Colonized Pavementwith Sand Channels

Scattered Coral/Rockin Unconsolidated Sediment Sand

Macroalgae

Patch Reef(Individual)

Patch Reef(Aggregated)

LinearReef

Figure 3: A plot of the sample standard deviation of snapper (Family: Lutjanidae) against the 

corresponding average sample density among 10 distinct benthic habitat types around BUIS. In 

conjunction with a map of benthic habitat types, the information in this plot may be used to develop 

an efficient survey design. 

12 
Another feature of Figure 3 is the strong relationship between stratum mean density and 

standard deviation. This is a common phenomenon in surveys of marine animal populations (e.g., 

Ault et al. 1999, 2003). Thus, a stratification scheme that effectively partitions the domain with 

respect to variance of animal density may also effectively partition the domain into areas of 

differing mean densities. 

Commonly, a monitoring program will initially use a SRS design because of a scarcity of 

fish and covariate data needed to ascertain spatial relationships. As data are gathered and covariance 

analyses are performed, more efficient survey designs such as a StRS can be adopted. The 

exploration of covariance after sampling using strata differing from those actually implemented 

requires poststratification analysis on domains of study. Ault et al. (1999) use poststratification as a 

comparative stratification scheme analysis tool for pink shrimp in Biscayne Bay. Cochran (1977) 

describes the process of poststratification and corresponding computations for both SRS and StRS 

designs. 

Although statistical techniques such as ANOVA can reveal trends and regions of relatively 

homogenous variance in the survey domain, they cannot be used for hypothesis tests, unless the 

underlying population data structure in each stratum is known and the data conforms to test-specific 

assumptions (e.g., homogeneity of variance, normality, independence, etc.). Goodness-of-fit tests 

(D’Agostino and Stephens 1986) identify suitable distributions and consequently the most 

appropriate tests to use if hypothesis tests are required. In some cases, applying a transformation 

modifies the data structure to one assumed by a particular statistical technique. Commonly used 

transformations are listed in Box and Cox (1964), Sokal and Rohlff (1995), and Zar (1999). 

13 
4 Candidate Sampling Design Analysis

The main goal of statistical sampling surveys is to obtain accurate, high-precision estimates 

of population and community metrics at relatively low cost. The statistical estimation methods of 

survey design presume that the population of interest is finite and inhabits a finite spatial domain; 

consequently, these methods are well suited for application to reef fish populations and 

communities at ecosystem scales appropriate for resource management. Principles of statistical 

survey design are outlined in Kish (1967), Cochran (1977, 1983), Williams (1978), Yates (1981), 

Kalton (1983), Kalton and Anderson (1986), Thompson and Seber (1996), and Lohr (1999). 

The objective of sample design analysis is to determine the appropriate number of samples 

to be taken to achieve a certain level of precision for detecting change in population and community 

metrics (e.g., species numbers-at-size, species composition) used to understand ecological processes 

and to make management decisions. The specification of a degree of precision desired is an 

important step in sample surveys and is the responsibility of the park managers and researchers who 

use monitoring data. 

4.1 Basic Concepts of Sampling Theory and Designs 

As discussed in section 3.2, populations of coral reef fishes within an ecosystem-scale 

sampling domain are usually heterogeneously distributed in space rather than homogeneously 

distributed. In this situation, a StRS design that effectively partitions the domain into distinct strata 

which are internally homogenous will usually outperform other types of sampling designs (e.g., 

simple random, systematic, etc.). Basic concepts of StRS designs are illustrated using two 

population metrics, fish density Y (number of individuals per unit area) and fish abundance 

(total number of individuals). The concepts can be applied to SRS designs as well by taking the 

number of strata (L) equal to one. Observations of density yi for a given species are the number of 

individuals observed or captured in a standard sample unit i, (e.g., a belt transect of 100 m2). An Y

estimate of the mean density in stratum h ( Y h ) is given by 

1

h n

hi

i

h

h

y

y

n

=

=

!

(4.1)

where nh is the number of units sampled in stratum h and yhi is the density in stratum h and sample 

unit i. An estimate of the stratum variance ( 

2

h S

) is given by 

14 
( )

2

2 1

1

h n

hi h

i

h

h

y y

s

n

=

!

=

!

"

(4.2)

The estimated stratum variance is used to estimate the variance of mean density in stratum h, 

2

var 1 h h

h

h h

n s

y N n

! " # $ = & % ' ( ) * +

(4.3)

where Nh is the total possible sample units in stratum h. The quantity !

!

"

#

$

$

%

&

'

h

h

N

n 1 in equation (4.3) is 

termed the finite population correction (FPC), where 

h

h

N

n

is the sampling fraction, or the proportion 

of the domain of stratum h that is actually sampled. Note in equation (4.3) that increasing sample 

size nh reduces the variance of the estimate of mean density in two ways, first by reducing the 

quantity 

2

h

h

s

n

 and second by reducing the FPC. In practice the FPC can be ignored whenever the 

sampling fraction is less than 5% (Cochran 1977). The resulting equations are simpler, but variance 

estimates are higher. 

Given that

h y

 represents the stratum mean number of animals per sample unit, it follows 

that stratum abundance is estimated by multiplying mean density by the total number of sampling 

units, 

h h h y = N y (4.4)

Variance of

h y is estimated in a similar manner, [ ] 2

var var h h h y = N ! y " # $ (4.5)

Note that controlling the variance of stratum mean density (equation 4.3) in turn controls the 

variance of stratum abundance (equation 4.5). 

A beneficial property of sampling design theory is that stratum estimates of population 

means (equation 4.1), totals (equation 4.4), and their associated variances (equations 4.3 and 4.5) 

are unbiased (i.e., accurate) provided that sampling is done in a random manner (Cochran 1977). 

The randomization procedures employed in case studies A, B, and C provide practical approaches. 

Sample units within a stratum were uniquely identified with respect to geographical location in a 

GIS. The units were then assigned a number from 1 to Nh. Specific units to be sampled within a 

15 
stratum (totaling nh) were selected from the complete list of Nh units using a random number 

procedure based on the discrete uniform probability distribution, which assigns equal selection 

probability to each sample unit. The procedure was repeated for each stratum in the sampling 

domain. 

Domain-wide estimates of population means and totals are computed from the individual 

stratum estimates taken from samples. Mean density for the stratified survey domain is obtained by 

summing the weighted averages of sample strata means, 

1

L

st h h

h

y W y

=

= ! (4.6)

where L is the number of strata, and strata weighting factors (Wh) are given by 

1

h h

h L

h

h

N N W

N N

=

= =

!

(4.7)

where N is the total number of possible sample units in all strata. The weighting factor 

Wh

represents the proportion of the overall survey domain (or sampling frame) contained within 

stratum h. In a SRS design 1 Wh = . 

The variance of

st y

 is estiamted as 

2

1

var var

L

st h h

h

y W y

=

! " = ! " # $ % # $ (4.8)

Domain-wide population abundance

st y and associated variance var[yst] are obtained by summing 

equations (4.4) and (4.5), respectively, over all strata, 

and 1

L

st h

h

y y

=

= !

[ ] [ ]

1

var var

L

st h

h

y y

=

= !

(4.9)

(4.10)

An important point to remember about a StRS design is that the variance of the domain-wide mean 

or total depends on the estimates of stratum variance. If a heterogeneously distributed population 

were divided into strata such that all strata were homogenous (i.e. 1

var 0

L

h

h

y

=

! " = % # $

), then population 

estimates would be made without error. Consequently, the basic objective of stratification is to 

16 
partition the sampling domain into sectors of homogenous variance for population metrics such as 

animal density. Section 3.2 describes stratification techniques. 

Developing a StRS design in practice requires both a scheme for stratifying the sampling 

domain and a scheme for allocating sample units among strata. There are two allocation schemes 

commonly used for StRS designs. The first is proportional allocation, in which sample units are 

allocated among strata according to stratum size, 

h h n = n!W

(4.11)

where n is the total sample size for the survey. The second scheme is Neyman or optimal allocation 

in which sample units are allocated according to both stratum size and the strata standard deviations 

of a considered population metric (e.g. density), 

h h

h

h h

h

W s

n n

W s

! " # $ = %

# $ # $ & ' ( (4.12)

Under this strategy, larger and more variable strata will receive more sampling effort, and vice versa 

for smaller, less variable strata. 

Neyman allocation, in concert with an effective stratification scheme, can substantially 

reduce the variance of domain-wide population estimates (e.g., equation 4.8) compared to a simple 

random sampling (SRS) design of similar sample size (Cochran 1977). In contrast, reductions in 

estimate variance (i.e., increases in the precision of estimates) may not be achieved for a 

proportional allocation scheme. In theory, a SRS design with a sufficiently large sample size will be 

equivalent to a StRS design employing proportional allocation with respect to domain-wide 

estimates of population means (e.g., equation 4.6) and variances (e.g., equation 4.8). However, 

when sampling heterogeneous populations such as reef fishes in practice, a StRS design with 

proportional allocation will at least ensure that all strata will be sampled and thus provide a guard 

against bias in domain-wide estimates of population means and totals as discussed above. This will 

especially be true for surveys with relatively modest sample sizes. 

4.2 Sampling Design Performance Measures 

Performance of sampling designs involves the trade-offs between survey costs (usually 

measured by sample sizes) and the precision of population estimates. Several performance measures 

can be computed to evaluate the efficacy of sampling designs. The most basic and perhaps most 

familiar performance measure is the standard error (SE) of an estimate, computed by taking the 

17 
square root of the variance of an estimate. For the case of mean density, the standard error is given 

by 

var

st st SE ! y " = ! y " # $ # $

(4.13)

The standard error can be used to compare the performance among design types (eq. 3.1). A relative 

measure of precision is the coefficient of variation (CV) of mean density, 

st

st

st

SE y

CV y

y

! " # $ ! " = # $ (4.14)

in which the standard error is expressed as a proportion (or percentage) of the mean. A key 

performance measure is n*, the estimated sample size required to achieve a specified variance in a 

future survey. Computation of n* (presumed optimal allocation) is carried out for mean density 

using 

2

2

*

1

h h

h

h h

h

w s

n

V w s

N

! " # $ % & =

+

'

'

where N is the total sample units in the domain and 

V

(4.15)

 is the desired variance. A convenient way to 

express the desired variance is ( )

2

V CV st st= ! y "# y $ % (4.16)

using a target CV of domain-wide mean density. 

Alternatively, if the performance measure is a margin of error (d) as used in confidence 

intervals then 

2

d V

t

! " = # $ % & (4.17)

where t is the normal deviate corresponding to the probability that the error will exceed d. This error 

is commonly referred to as Type I error. Case study A uses a performance measure which subsumes 

both Type I and Type II error rates in computations of n*. 

Aspects of design performance are illustrated in Figure 4, which shows performance data 

for estimates of black grouper density from StRS surveys in the Florida Keys coral reef ecosystem, 

including Biscayne National Park, during 1994-2002. Habitat-based stratification and visual 

sampling methods for these surveys were similar to those described in Case Study C (Ault et al. 

18 
Figure 4: Survey design performance data for annual estimates of black grouper density in the 

Florida Keys coral reef ecosystem, including Biscayne National Park, during 1994-2002. Annual 

estimates are compared to the relationship of CV versus n for a stratified by benthic habitat-based 

survey design (StRS) and a simple random survey design (SRS). Habitat-based stratification and 

visual sampling methods for surveys were similar to those described in Ault et al. (2001). 

19 
2001). The CV-n relationship (solid line) for the StRS design was estimated using equations (4.15) 

and (4.16). The CV-n curve shows that gains in precision (i.e., decreases in CV) occur as n 

increases (i.e., as the sampling budget increases), but the gains are not limitless. For the case of 

black grouper, increasing n from 50 to 200 would be expected to result in a substantial decrease in 

CV, but increasing n from 700 to 800 would be expected to result in almost no appreciable decrease 

in CV. 

A standard benchmark for performance of StRS designs is to compare these results with 

those obtained for a simple random sampling (SRS) design. The difference between a SRS design 

and alternate sampling design is known as the design effect. It is typically described as the ratio of 

the variance from the more complex design to the variance from a SRS design with the same sample 

units. The CV-n curve for a SRS design for black grouper was estimated by considering the whole 

survey domain as a single stratum. Comparing the CV-n curves for the SRS and StRS designs 

highlights the potential for achieving gains in precision through stratification of the domain by 

variables that account for spatial heterogeneity in density. Estimates of n*, the value for n in CV-n 

curves, presume that sample units are allocated among strata according to a Neyman scheme. The 

CV-n curve for the StRS design thus represents a kind of minimum bound of CV that could be 

achieved in practice for a given n, because it presumes that samples are allocated on the basis of 

stratum size as well as stratum variance. The vertical distance between the actual CVs for black 

grouper density (point values by survey year) and the corresponding potential CV (CV-n curve) 

represents the gain in precision that could be achieved by more effective allocation of samples 

among strata. For the Florida Keys surveys, formal procedures of stratification, allocation, and 

randomization were instituted in 1999. The example of Figure 4 thus shows that achieving high 

precision is not simply a matter of cost, but rather the combination of effective stratification and 

allocation along with total sample size. 

4.3 Composite Sampling Designs 

Surveys for reef fishes will usually entail multiple target species, multiple species life stages 

(e.g., juvenile, adult, exploited), and multiple metrics (e.g., population abundance, community 

diversity). It is likely that a sampling design that performs well for one case may not perform well 

for other cases, requiring some sort of compromise. Obtaining a compromise from a constrained set 

of metrics will prove less challenging than for numerous metrics. A sensible initial step is to reduce 

the number of metrics to a set deemed most important and representative of other metrics. 

20 
A practical strategy for design development in this situation is illustrated in Ault et al. 

(1999) where analysis of density-habitat relationships showed that the spatial distribution of pink 

shrimp differed with respect to life stage (juvenile and subadult). The iterative design analysis 

process outlined above was applied to each life stage, and then a ‘composite’ stratification and 

allocation scheme was formulated that performed reasonably well for both life stages, although a 

more efficient StRS design could have been implemented for either juvenile or subadult pink 

shrimp. Cochran (1977) describes a process to determine a composite allocation scheme for 

correlated metrics when a single stratification scheme is used. The compromise is taken from the 

average of optimum stratum allocations among metrics. Alternative, but computationally-intensive 

optimization techniques are given by Chatterjee (1967), Kokan and Khan (1967), Bethel (1989) and 

Rahim and Currie (1993). 

4.4 Iterative Learning 

Development of efficient (high precision, low cost) sampling designs for marine animal 

populations in practice usually occurs through an iterative learning process of design formulation, 

sampling, and performance analysis that leads to improved design formulation, sampling, and so on. 

The study by Ault et al. (1999) provides a detailed application of this iterative process to develop an 

efficient StRS design for a roller-frame trawl survey targeting pink shrimp in BISC. The main steps 

of the iterative learning process were as follows: 

1) Pilot surveys were conducted in different seasons to obtain information on the temporal 

and spatial dynamics of the pink shrimp population in Biscayne Bay, and also to refine field 

sampling methods (e.g., optimal tow distance, etc.). 

2) A variety of statistical methods, including some of the modeling tools described in 

Chapter 3, were used to identify key habitat variables influencing the spatial distribution of pink 

shrimp within and among seasons. 

3) Alternative stratification schemes were developed based on different combinations of 

influential habitat variables. The design performance of these alternative schemes was evaluated 

using the technique of post-stratification to identify the most efficient StRS design for a future 

survey. 

4) The refined StRS design was used to conduct a new survey, and steps 2 and 3 were 

repeated to further improve the sampling design. 

21 
5 Population and Community Assessments

The range and types of statistical analyses that will be performed to assess the status and 

dynamics of reef fish populations and communities in National Parks depends on the specific 

management questions and resource goals to be addressed. These analyses, by and large, utilize the 

range of fundamental survey data (abundance, size, taxonomic identification) outlined and 

recommended for collection in survey monitoring programs in Section 2. These survey data are then 

used to generate metrics for individual species and assemblages to assess status and trends of reef 

fish communities and populations over time and in relation to specific sustainability metrics. 

5.1 Species and Community Metrics 

5.1.1 Frequency of Occurrence 

Survey data relating species frequency of occurrence, i.e., the proportion of sampled sites 

that given species are seen, constitutes a primary index of fish community dynamics. Frequency of 

occurrence makes no specific reference to the actual numbers of a species at sites, but rather that 

they were simply observed or not. The measure can be used to assess changes in species spatial 

distributions over time. 

5.1.2 Diversity Indices 

Diversity indices are measures of species composition. A large number of indices have been 

proposed to compute species diversity and these are outlined in the seminal works by Pielou (1969, 

1977), Hurlburt (1971), Margalef (1974), Peet (1974), Legendre and Legendre (1998) and 

Magurran (1988). 

Species richness is the simplest index and is purely the number of distinct species at sites or 

that are observed at all sites during a particular monitoring survey. As a fish community index, this 

statistic is a general measure of fish biodiversity. More complex diversity indices, such as the 

Shannon index (Shannon 1948), Simpson index (Simpson 1949), or Pielou’s J (Pielou 1966) 

integrate both the number of species and the proportion of individuals in each species. A survey 

with many species equally represented by the same number of individuals will have a higher 

diversity index than a survey with fewer species or with an unequal distribution of individuals 

amongst species. 

22 
5.1.3 Relative and Absolute Population Abundance or Biomass 

Relative abundance (i.e., density 

i

i

a

N

!

, or numbers of animals observed or captured per unit 

sample area) in a sample unit (i) is computed as the total number of individuals ( 

Ns

) of species s 

within a given sample area ( 

i a

). This quantity can be configured to represent either a relative index, 

or it can be related to the absolute average population size, Nabs, by 

i

abs

i

C N N qN

f a

= = = (5.1)

where C is the number of fish observed (or captured) within the unit sample area, fis the nominal 

unit of effort (here it equals the area searched for 1 unit sample), q is the fraction of the population 

seen per unit sample, and

N

 is the average population size at the time of sampling. In this simple 

example, it is assumed that the design is proportional to the population (i.e., simple random sample) 

where all sample units have equal sampling probabilities. In a stratified survey, each of the various 

strata must be computed individually and weighted as discussed in section 4.1. Relative abundance 

has been used extensively in fisheries to characterize changes in fish population sizes for status and 

trends in stock assessments (Quinn and Deriso 1999, Haddon 2001, Gulland 1983) and in reef 

ecosystems (e.g., Bell 1983; Alcala 1988; Cole et al. 1990; Polunin and Roberts 1993; Dufour et al. 

1995; Russ and Acala 1996; Friedlander and Parrish 1998 and Nagelkerken et al. 2000). 

The total average population size (e.g., mid-year average) consisting of ages a at time t 

would be ! =

a"

ac

N(t) N(a,t)da

(5.2)

where 

c a = minimum age at first observation (or capture) and

! a = oldest age in the population. 

Population biomass is an integrated measure of the total mass (W, weight) of living biotic 

matter (both somatic and reproductive) for given ages at a given time. The most common procedure 

to estimate population biomass is to determine the relative density or abundance at a sample site, 

and then use species-specific allometric growth relationships to convert observations of length-atage to weight-at-age for each individual fish (Bohnsack and Harper 1988; Ault et al. 1998, 2005b; 

Froese and Pauly, 2005). The allometric relationship between weight and length is 

W (a,t) L(a,t)

! ="

(5.3)

23 
where W(a,t) is the weight of a fish at age a and time t, L(a,t) is the length of a fish at age a and 

time t and α and β are coefficients of the allometric relationship. Consequently, population biomass, 

B , can be calculated for a given species with ( ) ( , ) ( , ) ( , ) ( , )

a a

ac ac

B t N a t W a t da N a t L a t da

! !

" = = $# % ( ( & ' (5.4)

where parameters are as defined before. This can be done for s species in the reef fish community 

and added together for an assemblage estimate. 

5.1.4 Population Size-Structure 

Size-structure, as derived from the sampled population, is a distributional statistic that 

reflects the interactions of the population-dynamic processes of individual growth, mortality and 

recruitment among all sizes and ages of fish in a population (Quinn and Deriso, 1999; Haddon 

2001). Park managers should be interested in the status and trends of population size-structure 

because it provides an integrated metric of what has happened and what will happen to a fish 

assemblage. 

Size-structure, in and of itself, is a complex measure to quantify without the aide of some 

summary statistic that characterizes the distribution. Ault et al. (1998, 2005b) have shown average 

length of the exploited part of a population ( 

L

) can be a robust indicator of community response to 

exploitation. This statistic is the principal stock assessment indicator variable to quantify population 

status (Beverton and Holt 1957; Ricker 1963; Pauly and Morgan 1987; Ault and Ehrhardt 1991; 

Ehrhardt and Ault 1992; Kerr and Dickie 2001). The statistic L is a metabolic based indicator that 

reflects fishing mortality, because exploitation removes large individuals and species from the 

community (Ault et al 2002; Gislason and Rice 1998; Pauly et al. 1998; Kerr and Dickie 2001). 

Theoretically, L at a given instant is expressed as 

( ) ( , ) ( , )

( )

( ) ( , )

a

ac

a

ac

F t N a t L a t da

L t

F t N a t da

!

!

=

"

"

(5.5)

where c a = minimum age at first capture or observation, ! a = oldest age in the stock or population, 

N(a,t) = abundance for age class a, L(a,t) = length-at-age, and F(t) = instantaneous fishing mortality 

rate at time t. In practice, because age is unknown, L is calculated between lengths corresponding 

24 
to length at first capture and largest fish observed in the population. Case Study C describes the 

methods involved in calculating

L

 and estimating fishing mortality rates as input into calculations 

of sustainability benchmarks for the reef fish community in DRTO. 

5.2 Assessing Changes to Reef Fish Metrics 

An advantageous property of statistical sampling theory is that survey design estimates, such 

as stratum density (equation 4.1) and the variance of stratum density (equation 4.3), do not require 

knowledge of the underlying probability distribution (e.g., normal, gamma, etc.) of the respective 

population metric, (e.g., observations of stratum animal density yhj) (Cochran 1977). A second 

property, based on large sample theory, is that survey design estimates (e.g., population means and 

totals) are normally distributed due to the central limit theorem if sample size is large. These 

properties facilitate the analysis of survey design estimates among times or areas (e.g. monitoring 

density over time, assessing MPA effectiveness). 

A simple, straightforward approach to performing statistical tests for differences among 

survey estimates for a particular time or area is via inspection of confidence intervals (CI). If the 

sample is relatively large (n > 100) and has a Normal distribution, the survey mean will lie within a 

CI bounded by yst ± t

! k ,df SE (yst) (5.6)

with a probability of α, the Type I error rate, and where t is the critical value of Student’s tdistribution, and degrees of freedom df = nh-1. Most commonly used reef metrics (see section 5.1) 

do not posses a Normal distribution which means the Type I error rate will not equal α. Cochran 

(1977) states α will be very close to what is expected if 

2

1 n > 25!G (5.7)

where 

2 G1 is Fisher’s measure of skewness. If a sample is too small and the population is heavily 

skewed, transforming the data (e.g. ) may help. 

CIs can be used to test the hypothesis that samples were drawn from the same population, as 

is done to assess temporal change or determine MPA effectiveness. Cochran (1977), Sokal and 

Rohlff (1995), and Zar (1999) describe methods using CIs to test for differences among means. 

A comparison of multiple CIs (e.g. a time series) requires a Bonferoni adjustment to α. The 

Bonferoni adjustment is necessary because the true Type I error rate of simultaneous multiple tests 

is not α, as it should be for a single test. 

25 

[ ] [ ]

2

log 1 , st st y + y
A useful relationship stemming from equation (5.6) is that the 95% CI for a population 

metric is approximately twice the CV, because the value of t for α = 0.05 and df > 20 is 

approximately 2. Thus, for example, a StRS survey that provides a domain-wide estimate of 

abundance with a CV of 15% would be able to statistically detect a minimum change of 30% in 

population abundance between survey time periods (with a Type I error rate = 0.05). 

5.3 Population Mortality Rate Assessments Using Size-Structure 

Exploitation (or other) effects from fishing mortality could be specifically assessed by 

bounding the integral for Equation (5.5) to reflect the ages/sizes affected. For example, a minimum 

size limit 

L

c

would constrain the solution to consider the average size ( 

L

) between 

L

c

and 

L!

, the 

minimum size limit and the maximum size observed in the catch (or seen in the visual samples) or 

population. In a population where fishing mortality is strictly proportional to the stock, 

L

 of fish on 

the dock would be exactly equal to the 

L

of those fish remaining in the sea, assuming that 

recruitment remained constant within a finite range of population sizes. 

Average size has been used by several analytical studies to assess the impacts of exploitation 

on reef fish populations and communities, and thus guides management decisions regarding policies 

to achieve sustainability of reef fish resources (Williamson et al. 2004; Ault et al. 1997, 2005a, 

2005b, 2006; Nemeth 2005) [see Case Studies]. 

For the case where no fishing occurs, equation (5.5) in combination with fisheryindependent data, could be used to compute the natural mortality rate M, or the life-time expectation 

of survivorship (i.e., average maximum age in the population). 

5.4 Population Biomass Assessments in Relation to Sustainability 

Benchmarks 

An important measure of stock reproductive potential is population spawning biomass. One 

which is used more frequently in fishery management is spawning stock biomass (SSB). SSB is 

expressed as 

( ) ( , ) ( , )

a

ac

SSB t N a t W a t da

!

= "

(5.10)

where is the minimum age (or size) of sexual maturity. Case study C provides an example of 

using the SSB and the derived spawning potential (SPR) ratio to assess fishery management in 

DRTO. SPR is a management benchmark that measures the stock’s current reproductive potential to 

26 

m a
produce optimal yields on a sustainable basis. It is simply the ratio of spawning stock biomass from 

exploited and unexploited populations. Estimated SPRs can be compared to U.S. Federal standards 

which define 30% SPR as the overfishing threshold at which the stock is no longer sustainable at 

the current exploitation levels. 

27 
6 Case Study Foreword

The preceding five sections provide a framework for generating a standardized monitoring 

protocol for use in SFCN park units. The framework outlines useful methods for a monitoring 

program, but is not a single standardized monitoring protocol for all park units. The variability in 

ecological condition, size, management capability, expertise, and available data sets among park 

units implies different parks will have distinct management objectives and logistical constraints. 

Monitoring programs must be crafted for individual park units considering specific monitoring 

needs and abilities. One size of monitoring program does not fit all park units. 

Three reef fish monitoring case studies are presented which build upon the presented 

monitoring framework using park-specific data sets, management concerns, and local partnerships. 

The case studies are offered to provide persons implementing a monitoring program with the 

information required to understand the pertinent: 1) management issues, 2) sampling methods, and 

3) analytical methods used in monitoring reef fish in SFCN managed areas. The case studies 

employ distinct methodologies because they reflect differences among park needs and abilities. The 

case studies are similar, but utlitize different measurement methods, sampling designs, and 

analyses. These differences among case studies are summarized in Table 1. 

Case study A implements a stratified random sampling design in BUIS and VIIS. Regional 

benthic habitat maps are used to increase survey design performance. Field work is undertaken by 

the NOAA Biogeography Team in cooperation with NPS. Design performance, temporal changes, 

and MPA effectiveness for several fish assemblages are investigated using survey data from 2001-

2005. This case study is a good example of effectively utilizing a moderate amount of resources 

(e.g. multiple boats, dive teams) to obtain precise metrics of the community and several 

assemblages of special management concern. 

Case study B uses the stationary visual census technique to sample at multiple, permanent, 

high-diversity coral reef reference sites around and in VIIS. This strategy effectively makes use of 

few resources to monitor constrained areas with high precision. Field work is conducted by the 

University of Hawaii in Hilo and NOAA Biogeography Team. Data collceted from 1988-2000 are 

analyzed for MPA effectiveness and trends. 

Case study C employs a two-stage stratified random sampling design to sample over hard 

bottoms in DRTO. Surveys are conducted by the University of Miami in cooperation with the 

NOAA Southeast Fisheries Science Center. A regional benthic habitat map and distinct 

management zones are used to stratify the large survey domain. Analyses of survey design 

28 
performance, sustainability status of exploited fish species, and MPA performance using 1999-2004 

data are provided. This case study shows the effective use of a live-aboard dive vessel, multiple 

dive teams, and cluster sampling to efficiently survey a very large area (320 km2) and obtain precise 

metrics for fishery assessments. 

29 
Table 1: Summary of differences in (A) measurement methods, (B) sampling designs and (C) data 

analyses among case studies. 

(A) Method of Measurement 

Approach Reason(s) Case Study - Section 

Belt transect 

(1) Increase sighting frequency of 

small fish, and 

(2) Some sites characterized by 

moderate visibility (>2 m), rugose 

benthic structure, or adjacent to 

mangrove prop roots 

A-3.3 

Stationary visual census 

(1) Decrease measurement bias 

due to diver movement, and 

(2) Majority of sites have good 

visibility (>7.5 m ) 

B-3.4, C-2.3 

(B) Sampling Design 

Approach Reason(s) Case Study - Section 

Stratified random sampling design. 

Survey domain encompasses whole 

park and surrounding areas. Strata 

classified according to a covariate 

benthic habitat map. 

(1) Increase survey estimate 

precision and reduce sampling cost 

compared to SRS 

(2) Obtain representative samples 

(3) Survey/Make inferences to 

whole fish community in park 

(4) Obtain estimates of specific 

areas in survey domain (e.g. MPA) 

A-3.2 

Stratified random sampling design. 

Survey Domain encompasses only 

reference sites. 

Concentrate few resources into 

understanding references sites very 

well 

B-3.2 

Multi-stage stratified random 

sampling design. Strata classified 

according to benthic habitat map 

(1) Same as first, and 

(2) Cluster sample units according 

to mapped covariate 

(3) Sample over a large area 

(4) Increase precision by applying 

finite population correction on first 

stage of sampling 

C-2.2 

(C) Analysis of Data 

Approach Reason(s) Case Study - Section 

Analysis of differences in survey 

estimates among years and areas 

using confidence intervals. 

Assessment of change in survey 

estimates among years or among 

areas 

A-5.3, B-4.5, C-3, C-5 

Analysis of size structure, average 

size, and spawning stock biomass 

Assessment of population mortality 

rates and biomass sustainability 

benchmarks 

C-4 

Analysis of trend in survey 

estimates using Generalized Linear 

Model 

Assessment of long-term linear 

trends in survey estimates among 

years 

B-4.5, B-4.6 

30 
7 Acknowledgments

This work was funded by interagency agreement 03E0FL20900010 from the United States 

Geological Survey’s Center for Coastal and Watershed Studies. 

This guide was made possible only through the tireless efforts of persons in the University 

of Miami, University of Hawaii, National Ocean Service, United States Geological Survey, 

National Marine Fisheries Service and the staff of the South Florida Caribbean Network. We 

appreciate their help with the preliminary meetings used to outline this guide, their written 

contributions, and their patient editing. 

31 
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precision and reducing bias in biological surveys: Estimating false-negative error rates. 

Ecological Applications 13(6): 1790-1801. 

Watson M. and J.L. Munro. 2004. Settlement and recruitment of coral reef fishes in moderately 

exploited and overexploited Caribbean ecosystems: Implications for marine protected areas. 

Fisheries Research 69 (3): 415-425. 

Wilber D.H., D.G. Clarke, G.L. Ray and M. Burlas. 2003. Response of surf zone fish to beach 

nourishment operations on the northern coast of New Jersey, USA. Marine Ecology-Progress 

Series 250: 231-246. 

Williams, B. 1978. A Sampler on Sampling. John Wiley, New York. 254 pp. 

Williamson, D.H., G.R. Russ and A.M. Ayling. 2004. No-take marine reserves increase abundance 

and biomass of reef fish on inshore fringing reefs of the Great Barrier Reef. Environmental 

conservation 31(2):149-159. 

Willis, T.J., and R.C Babcock. 2000. A baited underwater video system for the determination of 

relative density of carnivorous reef fish. Marine and Freshwater Research, 51: 755-763. 

Yates, F. 1981. Sampling Methods for censuses and surveys, 3rd Ed. Charles Griffin & Co., 

London. 

Zar, J.H.1999. Biostatistical analysis. 4th Ed., Prentice-Hall, New Jersey. 663 pp. 

38 
Case Study A: Monitoring Reef Fish in Buck Island Reef 

National Monument, Virgin Islands National Park and Virgin 

Islands Coral Reef National Monument 2001-2005 

C. Menza, , C. Caldow, J. Christensen, , C. Jeffrey, M. Kendall, K. Woody and M. Monaco 

National Oceanic & Atmospheric Administration 

National Ocean Service 

National Centers for Coastal Ocean Science 

Center for Coastal Monitoring & Assessment 

Biogeography Team 

1305 East-West Hwy 

Silver Spring, MD 20910 

charles.menza@noaa.gov 

39 
Table of Contents for Case Study A

A-1 SUMMARY ..............................................................................................................................................................41

A-2 BACKGROUND INFORMATION........................................................................................................................41

A-3 SURVEY DESIGN...................................................................................................................................................43

A-3.1 POPULATION TO BE SAMPLED..............................................................................................................................43

A-3.2 SAMPLING DESIGN...............................................................................................................................................45

A-3.3 METHODS OF MEASUREMENT ..............................................................................................................................47

A-4 DATA STORAGE AND DISTRIBUTION............................................................................................................53

A-5 ANALYSES ..............................................................................................................................................................54

A-5.1 FISH ASSEMBLAGE DIFFERENCES ACROSS MULTIPLE BENTHIC HABITAT TYPES.................................................54

A-5.2 OPTIMIZING THE SAMPLING STRATEGY ...............................................................................................................58

A-5.3 OBSERVATIONS OF THE REEF FISH COMMUNITY AND SELECTED ASSEMBLAGES DURING 2001-2005 .................67

A-5.4 COMPARISON OF THE REEF FISH COMMUNITY AND SELECTED ASSEMBLAGES INSIDE AND OUTSIDE MPAS .......71

A-6 CONCLUSIONS ......................................................................................................................................................72

A-7 REFERENCES.........................................................................................................................................................77

40 
A-1 Summary 

This case study describes the survey methods and analyses used by the National Oceanic 

and Atmospheric Administration’s (NOAA) Center for Coastal Monitoring and Assessment 

(CCMA) Biogeography Team (BT) to monitor reef fish in Buck Island Reef National Monument 

(BUIS), Virgin Islands Coral Reef National Monument (VICR), and Virgin Islands National Park 

(VIIS). These areas are administered by the National Park Service. Two of these areas, VIIS and 

BUIS, are part of the South Florida / Caribbean Network (SFCN), which has been given the charge 

of generating long-term reef fish monitoring protocols. 

The survey methods provide precise, fishery-independent, minimally-biased, multispecies 

and size-structured survey data, which is needed to comprehensively assess fish populations and 

communities. The analyses described herein are applied to: (1) Determine long-term changes in reef 

fish community structure, using measures of biodiversity, abundance and biomass; (2) Determine 

long-term changes in abundance, biomass and mean-size of selected economically and ecologically 

important assemblages; and (3) Compare long-term changes in fish community structure and the 

abundance, biomass and mean-size of selected assemblages between areas inside and outside of 

Marine Protected Areas (MPAs). 

A-2 Background Information 

In 1998, the Caribbean Fisheries Management Council approached the BT to assist in the 

delineation of Essential Fish Habitat (EFH) in Puerto Rico and the Unites States Virgin Islands 

(USVI). Over the next three years, nearshore benthic marine habitat maps were developed and in 

2000 BT developed a reef fish sampling protocol to gather fish data and begin determining the 

relationships between fish and benthic habitats. Other agencies and academic partners including but 

not limited to the University of Puerto Rico, USVI Department of Planning and Natural Resources, 

United States Geological Survey, and the NPS became interested in expanding on the EFH work. 

The result was the emergence of BT’s Coral Reef Ecosystem Monitoring Program (CREMP), which 

focused around the areas of (1) St. John, USVI, (2) Buck Island, St. Croix, USVI, and (3) La 

Parguera, Puerto Rico (see Figure A-1). 

The areas monitored in the USVI by the BT and which are the focus of this study include 

VIIS, BUIS and VICR. Two of these managed areas, VICR and BUIS, offer reef fish a high level of 

protection because all extractive uses and anchoring (except for limited exceptions) are prohibited 

41 
Figure A-1: A map of the northern Caribbean region showing Biogeography Team study areas in 

Puerto Rico and the US Virgin Islands. 

42
within park boundaries. One of the goals of this case study is to assess the effectiveness of 

management/protection in these two protected parks. 

CREMP is comprised of an iterative process of data collection, assimilation, design analysis, 

and population and community assessment. As data are gathered they are analyzed and results are 

used to refine survey methods, as needed. Data are also provided to park managers to interface with 

park management policies. A conceptual model, shown in Figure A-2, details the organization of 

methods which make up the CREMP strategy. 

A central principle of CREMP is to concurrently characterize the reef fish community, 

benthic habitat, water quality, and a selection of benthic organisms requested by park managers 

(e.g. conch, lobster, urchins) at each sample unit. The concurrent data provide the ability to detect 

relationships among data at fine spatial scales. Relationships can then be integrated into an 

ecosystems-based management approach to reef fish management and serve the federal directive of 

better understanding essential reef fish habitats (NOAA 1996). Although measurements of benthic 

habitat, water quality, and supplemental benthic data are important components of CREMP, the 

methods discussed herein focus on the methods used to collect and analyze reef fish data. All 

methods are described online at NOAA (2006a). 

CREMP was created and is maintained to explicitly satisfy three monitoring goals: 

1. Determine long-term changes in reef fish community structure using measures of 

biodiversity, abundance and biomass; 

2. Determine long-term changes in abundance, biomass, and mean-size of selected 

economically and ecologically important fish assemblages and species; and 

3. Compare long-term changes in fish community structure and the abundance, 

biomass, and mean-size of important fish assemblages and species between areas 

inside and outside of Marine Protected Areas (MPA). 

The goals reflect management objectives as determined by park managers, NPS 

management plans, and local stakeholder needs. 

A-3 Survey Design 

A-3.1 Population to Be Sampled 

Although management efforts by the NPS are directed towards the VIIS, VICR, and BUIS, 

reef fishes in these parks depend on and interact with surrounding areas over a much larger spatial 

scale. To comprehensively monitor and manage reef fishes inside NPS parks, and determine the 

43 
Figure A-2: A conceptual model of the Biogeography Team Coral Reef Ecosystem Monitoring 

Program (CREMP). Note the feedback loops. 

44
effectiveness of management regimes, samples were taken from areas outside park boundaries as 

well. The survey domains, which define the populations to be sampled in Buck Island and St. John 

study areas, were chosen to include as much area as possible adjacent to targeted managed areas 

within logistical constraints. A compilation of bathymetric data from the National Geophysical Data 

Center, Geophysical Data System (NGDC 2006) and multibeam data collected by BT using a hullmounted Simrad EM 1002 multibeam sonar (NOAA 2006b) was used to determine the geographical 

areas within safe repetitive diving limits (i.e. 30 m). The populations to be sampled are shown by 

the spatial limits of strata in Figures A-3A and A-3B. 

A-3.2 Sampling Design 

A stratified random sampling design was used to maximize the amount of information 

attained at a minimum of cost and allow rigorous inferences to the entire study area or internal 

domains designated by strata. The survey domain was partitioned into strata based on three spatial 

variables: benthic habitat, management zone, and geographic zone. Benthic habitat types and 

geographic zones were incorporated because they were covariates of reef fish and could increase 

sampling design efficiency. NPS managed area boundaries were used to compare different 

management regimes and satisfy the data requirements of the third CREMP objective. 

The survey domain was divided into “hard” and “soft” benthic habitats as an effective 

compromise between parsimony and the results of an analysis of variance used to evaluate the 

covariance among representative population parameters and distinct benthic habitats. This analysis 

is described in Section A-5.1. Hard benthic habitat included areas characterized by bedrock, 

pavement, and coral reefs. Soft bottom habitats included those areas characterized by sand, 

seagrass, or macroalgae. Benthic habitat maps and a hierachical classification system produced by 

NOS (Kendall 2001) for the nearshore waters around the USVI were used as a basis for analysis and 

habitat designations. 

The extent of classified area in the NOS maps corresponded to the majority of areas within 

safe repetitive diving limits, however a long reef complex south of St. John within diving limits was 

not classified. This complex, known as the mid-shelf reef (MSR), was classified according to 

previous dive surveys as a hard bottom habitat. 

NPS managed areas were incorporated as a second spatial stratification level and were 

designated as: Inside BUIS and Outside BUIS for the Buck Island study area; and Inside VICR, 

Inside VIIS and Outside VIIS/VICR for the St. John study area. Areas inside BUIS and inside 

45 
(A)

Figure A-3: A map of the monitored (A) Buck Island and (B) St. John regions showing strata used 

in the stratified sampling design. Refer to Table A-1 for information on strata. 

(B)

46 
VICR offer reef fish a high level of protection, because all extractive uses and anchoring (except for 

limited exceptions) are prohibited within park boundaries. Thus, BUIS and VICR were defined as 

MPAs. Areas inside VIIS offer reef fish an intermediate level of protection, because commercial 

and recreational fishing is restricted in some areas of the park. The remaining areas outside of 

BUIS, VICR, and VIIS and inside the survey domain are open to both commercial and recreational 

fishing. 

Geographic zones were used as a third spatial stratification layer in St. John’s sampling 

design. These zones were designated as: (1) the MSR, (2) Coral Bay (CB), and the remaining areas. 

Each zone was characterized by a distinct range of depths and water quality characteristics, which 

would be expected to influence the variance structure of investigated fish community and 

population parameters (e.g. species number, abundance, percent occurrence) and sampling cost. 

Summary information for each stratum is provided in Table A-1. Figures A-3A and A-3B 

show the strata used in the Buck Island and St. John study areas, respectively. Sample units were 

selected for each survey by randomly choosing geographic coordinates within each stratum. A 

geographic information system (GIS) in the ArcGIS 9 environment (ESRI, inc.) was used to select 

sample units. Analyses of variance of several community and assemblage metrics were used to 

estimate sample size requirements and resolve the optimal sample allocation configuration among 

strata. A description of these analyses and corresponding results are provided in Section A-5.2. 

A-3.3 Methods of Measurement 

Reef fish data were collected using non-destructive, in situ, fishery-independent visual belt 

transects (adapted from Brock 1954) and stationary point counts (Bohnsack and Bannerot 1986). 

These methods required SCUBA gear (or snorkeling gear for shallow sites) and thus the maximum 

depth of samples was constrained to 30 m. Cumulative bottom time (time underwater) and the 

desire for monotonically decreasing (in depth) dive profiles are also constraints when using SCUBA 

and factors which require planning before going into the field. BT used NITROX gas to increase 

bottom time, planned which sites were sampled before each day of diving, and used multiple 

motorboats with independent dive teams to efficiently gather the maximum number of samples 

possible. 

The average number of dives each dive team was asked to complete each day was 

determined as a function of the number of dive teams, days available for diving, and sample site 

depths. Normally bottom time per dive lasted between 20-40 minutes depending on benthic 

47 
Strata Code 

Benthic 

Habitat MPA Area (km2) 

BUIHO Hard No 13.8 

BUIHI Hard Yes 18.5 

BUISO Soft No 12.3 

BUISI Soft Yes 5.40 

Table A-1: Strata and corresponding variables used to define strata for the (A) Buck Island and (B) 

St. John region sampling frames. 

(A) 

(B) 

Strata Code 

Benthic 

Habitat MPA Geographic Zone 

Area 

(km2) 

STJHOREST Hard No Outside CB and MSR 13.8 

STJHOCB Hard No CB 1.71 

STJHOMSR Hard No MSR 1.51 

STJHICB Hard Yes CB 0.86 

STJHIMSR Hard Yes MSR 1.07 

STJSOREST Soft No Outside CB and MSR 15.8 

STJSOCB Soft No CB 1.81 

STJSICB Soft Yes CB 0.91 

48 
substrate complexity, depth, and number of divers. Due to transport time between sample sites and 

time to set up and change tanks each dive required about 40-60 minutes. Typically, between 5 and 

10 samples were collected by each dive team each day. A map showing sample locations and 

depths, such as the example shown in Figure A-4, was a useful tool for planning. 

Divers were taken to sample units by small to medium sized motorboats using a handheld 

GPS unit. Boat drivers, which were usually divers as well, were required to successfully complete 

the requirements of the National Park Service’s Motorboat Operator Certification Course. To 

conform to legal mandates all diving was done using NOAA/NPS scientific diver regulations 

(NOAA, 2006c) and standard diving procedures (buddy teams). All divers were certified as NOAA 

scientific divers and possessed letters of reciprocity to work in NPS managed areas. All belt divers 

were trained to recognize the distinguishing marks and morpholgies of different species and to 

accurately measure lengths underwater. Divers were encouraged to calibrate underwater length 

estimates before each survey with a ruler. 

Although two methods were used to collect reef fish data, CREMP concentrated its efforts to 

collect and analyze reef fish data using the belt transect methodology, because of its ability to 

effectively sample multiple habitats, ease of use, and ability to work in relatively low visibility. 

Stationary point counts were used to allow standardized comparisons of CREMP data with 

historical datasets and current sampling programs that implement point counts. Important 

complements to the belt transect survey were the benthic habitat and water quality assessments at 

each sample site. These surveys provide important information to optimize future sampling designs 

and modeling studies and provide the requisite information for an ecosystem-based management 

approach. Additional data may be collected (e.g. conch, lobster, long-spined urchin, coral bleaching 

surveys) depending on suggestions by scientists and the needs of park managers. Only the belt 

transect method is described and only data collected using belt transects are analyzed herein. 

The belt transect used by BT to collect reef fish data had a constant area (25 m long and 4 m 

wide, total 100 m2), and a constant survey time (15 min). Area and time were held constant to 

reduce biases associated with species detectability among habitat types and ensure the probability of 

sample unit selection was known. 

Once on site an anchor or weighted buoy was deployed to mark the location of the sample 

unit. The belt transect diver and at least one other diver were deployed and descended to the 

seafloor. A typical diving team was represented by three divers: one performed the belt transect, 

49 
Figure A-4: Example of sample position map used in the field. 

50
another the stationary point count, and the third the benthic habitat assessment. Whenever rough sea 

conditions were present, the boat driver/diver would stay on the boat. If three divers were not 

available for the suite of surveys, one diver would perform the belt transect and stationary point 

count and the second diver would perform the habitat assessment. 

If a dive site’s habitat characteristics were different than the corresponding stratum 

characteristics (i.e. a dive site was located in a strata characterized by soft benthic habitat, but the 

habitat within the surveys was characterized by hard habitat) the surveys were still completed, but 

the discrepancy was noted. If a dive site could not be reached because of obstacles, surveys were 

performed as close as safely possible to the unreachable original dive location and new geographic 

coordinates were recorded. 

Once just above the seafloor, the belt transect diver fastened the end of a tape measure to the 

anchor or weighted buoy line or adjacent substrate, taking care not to damage fragile habitat. Then 

the diver proceeded away from the location where the tape was fastened in a random bearing. The 

diver gathered data for all fishes within two meters of either side of the transect line and all fishes 

towards and up to the estimated transect ending (25 m). No data were collected from the area 

behind the diver’s position. The tape measure was unreeled as the diver swam forward and not 

before to minimize fish behavioral biases. Swimming speed was maintained constant such that the 

entire length of the transect was traversed in 15 minutes regardless of substrate type or complexity. 

At this relatively slow speed, all habitat types, including complex reefs were comprehensively 

surveyed. The diver recorded fish seen in holes, under ledges and in the water column but took care 

not to alter the habitat in any manner by lifting or moving structures. To identify, enumerate, or 

locate new individuals the diver was allowed to move off the centerline of the transect, but always 

remained within 2 m of the transect centerline. 

As the diver progressed along the transect each new observed species (or lowest identifiable 

taxonomic level) was recorded and the number of individuals per species (or lowest identifiable 

taxonomic level) was tallied in 5 cm size class increments up to 35 cm using visual estimation of 

fork length. All data were entered into a standardized form (Figure A-5). If an individual fish was 

greater than 35 cm, then an estimate of the actual fork length was recorded. To decrease the total 

time spent writing, four letter codes were used to itemize the distinct species. The code consisted of 

the first two letters of the genus name followed by the first two letters of the species name (e.g. 

STPL = Stegastes planifrons). In the rare case that two species have the same four-letter code, 

letters are added from the species name until a difference occurs. 

51 
Figure A-5: Example of fish survey data entry form. 

52
A-4 Data Storage and Distribution 

Once data were collected in the field, each diver was responsible for entering his/her data 

into the Coral Reef Ecosystem Assessment and Monitoring (CREAM) database. This online 

database was created and is maintained by BT to store reef ecosystem monitoring data and serve it 

to the public. Belt transect, point count, and benthic habitat assessment data are available. All data 

are associated with a unique dive site ID and possess geographic coordinates to facilitate mapping. 

The database went online in 2005 and provides data from field studies in the USVI since 2001 and 

southwestern Puerto Rico since 2000. The CREAM database is publicly available on the World 

Wide Web (NOAA 2006d). 

Researchers, coastal managers, and interested members of the public can download the 

entire dataset, or can query the database to provide specialized reports for specific species 

assemblages. The database is capable of returning biomass, richness, abundance, and/or the 

Shannon diversity index metrics. In addition to the survey data, a second querying function was 

developed to serve both fish and habitat photos that have been collected over time. All data include 

metadata which conform to all government standards required for data dissemination. The database 

is maintained on a SQLServer 2000 server, and queried by an ASP.Net web interface developed by 

BT. The database structure is relational, maintaining data integrity through the use of Primary and 

Foreign key constraints. 

Data served by the CREAM database have been used by numerous institutions and include 

the following projects: (1) Investigations of reef fish habitat utilization patterns (Monaco et al. 

2001; Christensen et al. 2003; Monaco et al. 2003; Kendall et al. 2003; Kendall et al. 2004; Pittman 

et al., in review); (2) Assessment of marine reserve design (Monaco et al 2001; Monaco et al. 

2006); (3) Validation of benthic habitat maps of Puerto Rico and the US Virgin Islands (NOAA 

2001); (4) Analysis of zonation designations within the East End Marine Park of St. Croix (Ocean 

Conservancy); (5) Comparison with cryptic fish inventories of BUIS (USGS, University of Miami 

and NOVA Southeastern University); (6) Site characterization of BUIS (NOAA and NPS); (7) 

Evaluation of sites to observe the effects of coral bleaching on coral communities in VICR (NASA, 

University of the Virgin Islands); and (8) Analysis of the boundary design for BUIS and VICR 

(NOAA, NPS, and the University of Hawaii). 

53 
A-5 Analyses 

A-5.1 Fish Assemblage Differences across Multiple Benthic Habitat 

Types 

Fish data collected during 2001-2005 using belt transects were used to evaluate the 

covariance among representative population parameters and distinct benthic habitats. The goal of 

this analysis was to determine regions of homogenous variance so that these regions can be 

incorporated as strata in a stratified random sampling design. By parsing the survey domain into 

internally homogenous strata the precision of population parameter estimates can be drastically 

increased, which in turn drives down sampling costs. 

The survey domain was initially divided among 12 distinct habitat types in each study area 

according to the benthic habitat types defined by Kendall et al. (2001). The MSR area was included 

as a 13th habitat type when examining the data from St. John. The covariance of a set of 

representative fish assemblages (listed in Table A-2) and habitat types were examined using 

nonparametric analysis of variance (ANOVA; Kruskal-Wallis test, p<0.0001) and plots of density 

(fish per unit area) against standard deviation of density among habitat types. Species richness 

(number of species) was also examined. 

The spatial distributions of the representative fish assemblages were heterogeneous. 

Nonparametric analysis of variance indicated that habitat type explained some of the variance in all 

of the tested community and assemblage metrics (Table A-3). In Buck Island the highest species 

richness and assemblage densities were typically found in linear reef, aggregated patch reef, and 

individual patch reef habitats and the lowest in sand, seagrass, and scattered coral/rock in sand 

habitats (Figure A-6A). In St. John the highest species richness and assemblage densities were not 

found to be as consistently associated with habitat types as around Buck Island. Species richness, 

mean density of the community (all species) and mean density of groupers were highest in MSR 

sites, but snappers and piscivores were conspicuously absent (Figure A-6B). Aggregated patch 

reefs, individual patch reefs, and colonized bedrock habitats also possessed high densities for some 

of the tested assemblages, but this pattern was not consistent across assemblages (Figure A-6B). As 

in the Buck Island study area, sand and seagrass habitats were associated with low assemblage 

densities and species richness. The macroalgae habitat was omitted from the Buck Island analysis 

because too few samples were available (n=4). Samples collected in macroalgae habitat in St. John 

showed many similarities to samples collected in sand and seagrass habitats. 

54 
2 !0.05,10

2 !0.05,10

Table A-2: The list of representative reef fish assemblages, corresponding component species and 

aliases, and metrics used to optimize the Buck Island and St. John region monitoring programs. 

Monitored Assemblages Species in Assemblage 

Community - All Species All observed species 

Groupers All species in the genera Mycteroperca, Cephalopholis and Epinephelus 

Snappers All species in the genus Lutjanidae 

Herbivores All species listed as herbivores (H) in FISHBASE (Froese and Pauly 2005) 

Piscivores All species listed as piscivores (P) in FISHBASE (Froese and Pauly 2005) 

Cephalopholis fulvus Cephalopholis fulvus 

Stegastes planifrons Stegastes planifrons 

Table A-3: The results from a nonparametric analysis of variance (Kruskal-Wallis test) for species 

richness, community density, and assemblage densities among 12 habitat types in the (A) Buck 

Island and (B) St. John study areas. 

(A) Buck Island 

Community or 

Assemblage (Metric) 

Kruskal-Wallis H P [H]< 

Species Richness 494.89 <0.0001 

All Species (Density) 394.21 <0.0001 

Groupers (Density) 393.24 <0.0001 

Snappers (Density) 81.12 <0.0001 

Herbivores (Density) 452.96 <0.0001 

Piscivores (Density) 24.36 0.0113 

(B) St. John 

Community or 

Assemblage (Metric) 

Kruskal-Wallis H P [H]< 

Species Richness 318.14 <0.0001 

All Species (Density) 256.01 <0.0001 

Groupers (Density) 168.73 <0.0001 

Snappers (Density) 31.65 0.0016 

Herbivores (Density) 299.47 <0.0001 

Piscivores (Density) 22.37 0.0335 

55 
(A) Buck Island 

0 5 10 15 20

Mean

0.00

2.00

4.00

6.00

8.00

10.00

Standard Deviation

1 2

3

4

6

7

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Buck Island: Species Richness

0 50 100 150 200 250

Mean

20.00

120.00

220.00

320.00

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1

3 2

4

6

7

9

10

11

Buck Island: Density (#/100 m 2) of All Species

0 1 2 3 4 5

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Buck Island: Density (#/100 m 2) of Selected Grouper Species

1

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Buck Island: Density (#/100 m 2 ) of Snapper Species

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Buck Island: Density (#/100 m 2 ) of Herbivores

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00

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10.00

15.00

20.00

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1

2

3

47

10

11

Buck Island: Density (#/100 m 2) of Piscivores

Figure A-6: Plots of density (fish per unit area) or species richness against variance among distinct

habitat types in the (A) Buck Island and (B) St. John study areas. Benthic habitat types are defined 

as: 1-Colonized Bedrock, 2-Colonized Pavement, 3-Colonized Pavement with Sand Channels, 4-

Linear Reef, 5-Macroalgae, 6-Aggregated Patch Reefs, 7- Individual Patch Reefs, 9-Sand, 10-

Scattered Coral/Rock in Unconsolidated Sediment, 11-Seagrass and 12-MSR. 

56
(B) St John 

5 10 15 20 25

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St. John: Species Richness

0 100 200 300 400 500

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St. John: Density (#/100 m 2) of Snapper Species

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10

St. John: Density (#/100 m 2) of Piscivores

Figure A-6: (cont.) 

57
As an effective compromise between parsimony and reduction in the variance of estimated 

population parameters, two mutually exclusive strata were selected to stratify the survey domain in 

both study areas: hard and soft bottom habitats. Hard bottom habitats subsumed colonized bedrock, 

colonized pavement, colonized pavement with sand channels, linear reef, aggregated and individual 

patch reefs, scattered coral/rock in sand, and the MSR habitats. Soft bottom habitats included sand, 

seagrass, and macroalgae habitats. 

A-5.2 Optimizing the Sampling Strategy 

To ensure the survey design was as efficient as possible, sample size requirements and 

sample allocations among strata were examined for a set of community and population metrics 

(listed in Table A-2). Sample size requirements were computed using a modified verision of the 

methods described by Cochran (1977) for a stratified random sampling design. The desired variance 

was expressed as 

( )

( )

2

/ 2 (1)

st d y

V

Z! Z"

# $ %

= & ' & + ' ( )

(1)

where

d

 is the desired precision of the survey mean, 

st y

is the survey mean, Z! / 2is the Normal 

deviate corresponding to the allowable probability of Type I error, and Z!is the Normal deviate of 

the allowable probability of Type II error. The probabilities of error refer to the statistical errors 

involved in a confidence interval test to compare two surveys. The Normal deviates were set such 

that the confidence interval test would have a 5% probability of Type I error (deviate=1.96; reject 

the null when null is true) and a 20% probability of Type II error (deviate =1.64; do not reject the 

null when null is false) when surveys are compared and the null hypothesis is that the surveys come 

from the same population. 

Two different analysis endpoints were used to determine sample size requirements. The first 

endpoint was an assessment of reef fish communities and populations in either the BUIS or VIIS 

among years. Hence, only data from samples collected within BUIS or VIIS were used. The second 

endpoint was an assessment of the difference between reef fish communities and populations inside 

and outside MPAs among years. To assess the difference, data from samples collected within VICR 

and BUIS strata, and in adjacent strata (Buck Island: BUISHO, St. John: CBO and MSRO) were 

used. The difference was the domain estimate inside an MPA minus the domain estimate outside an 

58 
MPA. The variance of the difference was the sum of variances inside and outside a MPA according 

to Cochran (1977). 

The estimates of stratum means and variances were taken from 2001-2005 CREMP belt 

transect data and were weighted according to the sample size of each corresponding survey. Metrics 

which were heavily skewed so that 

2

1 n < 25!G

(where 

G1

is Fisher;s skewness coefficient) were 

log[X+1] transformed, because confidence intervals would have been inappropriate (Cochran 

1977). 

Not all potential stratum estimates were included in sample size computations because 

monitoring began in different strata in each region at separate times. There was a desire to increase 

sampling design efficiency and some assemblages were very rare in some strata. Consequently, (1) 

strata characterized by soft bottom benthic habitat were omitted for assemblage and species metrics; 

(2) strata characterized by soft bottom benthic habitat were omitted from survey estimates of the 

difference among protected and unprotected domains; (3) the MSR strata was not used to define 

survey estimates describing community structure in St John; and (4) data from 2001 and 2002 was 

only used for survey estimates for VIIS in St. John because the MSR was added in 2003 and BUIS 

expanded greatly in 2003. Tables A-4 and A-5 shows the average survey means, standard errors, 

and sample size requirements for all community and population metrics. 

Ideally desired levels of sampling precision and statistical power are used to set the most 

efficient sample size for a sampling design. Unfortunately, this process was not feasible for 

CREMP, because sample size was set by fiscal constraints (Buck Island n=120; St John n=170). 

Given these constraints many metrics in Tables A-4 and A-5 require more samples than are 

feasible. Although there was little control over the total sample size, there was flexibility over 

sample allocation among strata and allocation has a powerful influence on sampling design 

efficiency when multiple measurements are being taken and analyzed. To define the optimum 

allocation scheme for all measurements, the allocation of samples using the Neyman allocation 

scheme for all metrics was examined and a single allocation scheme which maximized the number 

of metrics that fulfilled sample size requirements was determined. The single, all-inclusive optimum 

allocation scheme is shown in Table A-6. 

59 
Table A-4: Estimates of principle reef fish community and assemblage metrics in (A) BUIS and (B) 

VIIS. Data taken from 2001-2005 belt transects. 

(A) BUIS 

Community or Assemblage 

(Metric) 

Average Estimates of 

Raw Survey Data 

Average Estimates of 

Transformed Survey 

Data 

Sample Size 

Requirements 

Mean SE CV Mean SE CV 

Species Richness 

Species Diversity 

Community Density 

Community Biomass 

15.96 

1.82 

158.17 

2211.09 

0.73 

0.06 

18.63 

302.56 

0.05 

0.03 

0.10 

0.13 

96 

50 

639 

863 

Grouper (Freq. of occurrence) 

Grouper (Density) 

Grouper (Biomass) 

Grouper (Mean Size) 

0.45 

1.55 

164.64 

19.61 

0.05 

0.28 

44.51 

0.8 

0.11 

0.18 

0.25 

0.04 

0.26 

1.05 

1.29 

0.03 

0.12 

0.02 

0.13 

0.11 

0.01 

570 

613 

602 

11 

Snapper (Freq. of occurrence) 

Snapper (Density) 

Snapper (Biomass) 

Snapper (Mean Size) 

0.3175 

1.01 

86.5 

17.09 

0.05 

15.56 

33.71 

1.6 

0.17 

0.29 

0.37 

0.09 

0.16 

0.61 

1.17 

0.03 

0.11 

0.06 

0.2 

0.19 

0.05 

1143 

1620 

1499 

121 

Herbivore (Freq. of occurrence) 

Herbivore (Density) 

Herbivore (Biomass) 

Herbivore (Mean Size) 

0.90 

52.71 

686.56 

402.4 

0.02 

5.63 

88.201 

72.02 

0.03 

0.10 

0.12 

0.17 

1.42 

2.2 

2.04 

0.04 

0.06 

0.07 

0.03 

0.03 

0.03 

22 

36 

34 

54 

Piscivore (Freq. of occurrence) 

Piscivore (Density) 

Piscivore (Biomass) 

Piscivore (Mean Size) 

0.52 

2.54 

432.4 

120.16 

0.05 

0.86 

151.29 

42.41 

0.11 

0.29 

0.32 

0.28 

0.27 

1.25 

1.65 

0.04 

0.14 

0.08 

0.14 

0.11 

0.05 

422 

1011 

578 

108 

CEFU (Freq. of occurrence) 

CEFU (Density) 

CEFU (Biomass) 

CEFU (Mean Size) 

0.36 

1.14 

96.8 

19.16 

0.05 

0.24 

21.23 

0.8 

0.13 

0.21 

0.21 

0.04 

0.2 

0.82 

1.29 

0.03 

0.11 

0.02 

0.16 

0.14 

0.01 

889 

1037 

829 

11 

STPL (Freq. of occurrence) 

STPL (Density) 

STPL (Biomass) 

STPL (Mean Size) 

0.21 

1.44 

5.17 

5.55 

0.05 

0.49 

1.61 

0.66 

0.22 

0.34 

0.31 

0.12 

0.15 

0.26 

0.78 

0.03 

0.05 

0.04 

0.24 

0.23 

0.06 

2631 

1843 

1704 

121 

60 
Table A-4: cont. 

(B) VIIS 

Community or Assemblage 

(Metric) 

Average Estimates of 

Raw Survey Data 

Average Estimates of 

Transformed Survey 

Data 

Sample Size 

Requirements 

Mean SE CV Mean SE CV 

Species Richness 

Species Diversity 

Community Density 

Community Biomass 

14.33 

1.6 

172.66 

1318.8 

0.64 

0.07 

37.19 

236.2 

0.05 

0.04 

0.19 

0.18 

92 

88 

2139 

1479 

Grouper (Freq. of occurrence) 

Grouper (Density) 

Grouper (Biomass) 

Grouper (Mean Size) 

0.31 

0.70 

75.28 

19.43 

0.04 

0.13 

17.01 

1.06 

0.13 

0.19 

0.25 

0.05 

0.14 

0.69 

2.97 

0.02 

0.08 

0.05 

0.15 

0.14 

0.02 

768 

941 

620 

13 

Snapper (Freq. of occurrence) 

Snapper (Density) 

Snapper (Biomass) 

Snapper (Mean Size) 

0.46 

2.59 

116.13 

14.95 

0.05 

0.72 

54.72 

2.00 

0.12 

0.25 

0.46 

0.13 

0.28 

0.56 

1.01 

0.04 

0.10 

0.04 

0.15 

0.18 

0.05 

545 

941 

1470 

72 

Herbivore (Freq. of occurrence) 

Herbivore (Density) 

Herbivore (Biomass) 

Herbivore (Mean Size) 

0.88 

41.99 

441.84 

392.4 

0.03 

4.47 

74.04 

76.72 

0.04 

0.10 

0.18 

0.16 

1.21 

1.67 

2.02 

0.05 

0.07 

0.07 

0.04 

0.05 

0.03 

54 

79 

81 

55 

Piscivore (Freq. of occurrence) 

Piscivore (Density) 

Piscivore (Biomass) 

Piscivore (Mean Size) 

0.56 

1.85 

195.77 

16.62 

0.04 

0.58 

97.66 

2.12 

0.16 

0.31 

0.45 

0.12 

0.21 

0.75 

1.12 

0.03 

0.12 

0.06 

0.18 

0.16 

0.06 

235 

941 

1180 

132 

CEFU (Freq. of occurrence) 

CEFU (Density) 

CEFU (Biomass) 

CEFU (Mean Size) 

0.11 

0.19 

16.53 

17.34 

0.03 

0.07 

6.9 

2.14 

0.34 

0.39 

0.48 

0.13 

0.04 

0.21 

1.22 

0.01 

0.06 

0.06 

0.36 

0.36 

0.06 

3429 

2881 

3763 

112 

STPL (Freq. of occurrence) 

STPL (Density) 

STPL (Biomass) 

STPL (Mean Size) 

0.20 

2.26 

3.2 

5.34 

0.04 

0.87 

0.94 

0.42 

0.20 

0.39 

0.30 

0.07 

0.16 

0.21 

0.78 

0.04 

0.04 

0.02 

0.25 

0.23 

0.04 

1844 

2881 

1673 

30 

61 
Table A-5: Estimates of the difference in principle reef fish community and assemblage metrics between reef fish (A) inside and outside 

BUIS and (B) inside and outside VICR. Date taken from 2003-2005 belt transects. 

(A) BUIS 

Inside BUIS Outside BUIS 

Community or Assemblage 

(Metric) 

Estimates from Raw 

Survey Data 

Mean SE CV 

Estimates from 

Transformed 

Survey Data 

Mean SE CV 

Estimates from Raw 

Survey Data 

Mean SE CV 

Estimates from 

Transformed 

Survey Data 

Mean SE CV 

Sample 

Size 

Requirements 

Species Richness 

Species Diversity 

Community Density 

Community Biomass 

19.11 

2.08 

192.67 

2593.13 

0.91 

0.08 

23.88 

373.10 

0.04 

0.04 

0.11 

0.14 

18.16 

2.16 

146.72 

2575.41 

0.94 

0.07 

13.87 

516.19 

0.05 

0.03 

0.80 

0.18 

123 

48 

411 

1851 

Grouper (Freq. of occurrence) 

Grouper (Density) 

Grouper (Biomass) 

Grouper (Mean Size) 

0.56 

2.27 

226.81 

18.71 

0.08 

0.49 

69.00 

0.98 

0.14 

0.2 

0.29 

0.05 

0.36 

1.47 

1.27 

0.05 

0.19 

0.02 

0.15 

0.13 

0.02 

0.52 

1.70 

235.06 

23.35 

0.09 

0.43 

58.51 

1.16 

0.17 

0.25 

0.24 

0.05 

0.29 

1.33 

1.37 

0.05 

0.23 

0.02 

0.20 

0.24 

0.01 

1380 

1370 

1378 

20 

Snapper (Freq. of occurrence) 

Snapper (Density) 

Snapper (Biomass) 

Snapper (Mean Size) 

0.32 

0.98 

102.14 

18.10 

0.07 

0.32 

43.42 

2.23 

0.25 

0.34 

0.43 

0.12 

0.17 

0.73 

1.21 

0.04 

0.17 

0.07 

0.26 

0.25 

0.06 

0.37 

1.33 

110.21 

19.14 

0.08 

0.56 

59.68 

2.71 

0.23 

0.39 

0.49 

0.14 

0.20 

0.76 

1.22 

0.05 

0.19 

0.10 

0.27 

0.25 

0.08 

2155 

2881 

2881 

Herbivore (Freq. of occurrence) 

Herbivore (Density) 

Herbivore (Biomass) 

Herbivore (Mean Size) 

1.00 

73.18 

1101.27 

574.32 

0.00 

9.71 

171.11 

121.02 

0.00 

0.13 

0.15 

0.20 

1.73 

2.76 

2.44 

0.06 

0.10 

0.10 

0.04 

0.03 

0.04 

1.00 

52.59 

761.12 

264.06 

0.00 

4.72 

130.45 

42.13 

0.00 

0.09 

0.15 

0.15 

1.67 

2.65 

2.19 

0.03 

0.09 

0.09 

0.02 

0.03 

0.04 

1 

14 

53 

77 

62 
(A) cont. 

Inside BUIS Outside BUIS 

Community or Assemblage 

(Metric) Estimates from Raw 

Survey Data 

Estimates from 

Transformed 

Survey Data 

Estimates from Raw 

Survey Data 

Estimates from 

Transformed 

Survey Data 

Sample 

Size 

Requirements 

Piscivore (Freq. of occurrence) 

Piscivore (Density) 

Piscivore (Biomass) 

Piscivore (Mean Size) 

Mean 

0.46 

2.08 

371.21 

24.62 

SE 

0.07 

0.90 

190.57 

3.70 

CV 

0.25 

0.36 

0.48 

0.15 

Mean 

0.25 

1.18 

1.33 

SE 

0.05 

0.20 

0.06 

CV 

0.20 

0.17 

0.04 

Mean 

0.38 

2.34 

191.97 

29.82 

SE 

0.08 

1.67 

89.31 

4.44 

CV 

0.27 

0.56 

0.45 

0.14 

Mean 

0.17 

0.80 

1.40 

SE 

0.05 

0.22 

0.05 

CV 

0.32 

0.27 

0.04 

2043 

23479 

9977 

1021 

CEFU (Freq. of occurrence) 

CEFU (Density) 

CEFU (Biomass) 

CEFU (Mean Size) 

0.46 

1.76 

143.21 

18.35 

0.08 

0.42 

37.84 

0.93 

0.18 

0.24 

0.24 

0.05 

0.29 

1.18 

1.27 

0.05 

0.18 

0.02 

0.18 

0.16 

0.02 

0.39 

1.15 

153.66 

23.01 

0.8 

0.35 

45.70 

1.20 

0.22 

0.31 

0.29 

0.05 

0.20 

0.96 

1.36 

0.05 

0.22 

0.02 

0.25 

0.22 

0.02 

19397 

2881 

2421 

10 

STPL (Freq. of occurrence) 

STPL (Density) 

STPL (Biomass) 

STPL (Mean Size) 

0.18 

1.75 

6.48 

5.78 

0.06 

0.69 

2.34 

0.85 

0.38 

0.43 

0.41 

0.15 

0.18 

0.32 

0.32 

0.05 

0.09 

0.09 

0.34 

0.33 

0.33 

0.34 

1.66 

7.24 

4.66 

0.08 

0.54 

2.74 

0.74 

0.26 

0.35 

0.37 

0.15 

0.22 

0.39 

0.72 

0.06 

0.11 

0.05 

0.29 

0.28 

0.07 

5105 

6516 

7325 

444 

63 
Table A-5: cont. 

(B) VICR 

Inside VICR Outside VICR 

Community or Assemblage 

(Metric) 

Estimates from Raw 

Survey Data 

Mean SE CV 

Estimates from 

Transformed 

Survey Data 

Mean SE CV 

Estimates from Raw 

Survey Data 

Mean SE CV 

Estimates from 

Transformed 

Survey Data 

Mean SE CV 

Sample 

Size 

Requirements 

Species Richness 

Species Diversity 

Community Density 

Community Biomass 

22.52 

2.20 

287.77 

3703.83 

0.76 

0.07 

53.37 

713.27 

0.03 

0.03 

0.19 

0.19 

2.28 

3.31 

0.05 

0.06 

0.02 

0.02 

25.27 

1.99 

428.20 

3561.00 

0.88 

0.08 

48.23 

683.90 

0.03 

0.04 

0.11 

0.17 

2.47 

3.35 

0.05 

0.05 

0.02 

0.02 

55 

74 

584 

1700 

Grouper (Freq. of occurrence) 

Grouper (Density) 

Grouper (Biomass) 

Grouper (Mean Size) 

0.76 

3.81 

351.97 

19.65 

0.05 

0.45 

124.78 

2.22 

0.07 

0.1 

0.28 

0.08 

0.52 

2.06 

2.97 

0.04 

0.17 

0.09 

0.08 

0.08 

0.02 

0.67 

3.25 

258.32 

18.26 

0.06 

0.43 

45.14 

0.85 

0.09 

0.13 

0.17 

0.04 

0.46 

1.63 

2.93 

0.03 

0.14 

0.05 

0.08 

0.08 

0.01 

369 

196 

340 

13 

Snapper (Freq. of occurrence) 

Snapper (Density) 

Snapper (Biomass) 

Snapper (Mean Size) 

0.47 

1.68 

190.11 

22.04 

0.07 

0.43 

97.21 

1.82 

0.14 

0.24 

0.53 

0.07 

0.24 

0.91 

1.33 

0.04 

0.29 

0.02 

0.17 

0.34 

0.02 

0.43 

1.48 

97.29 

24.47 

0.07 

0.43 

40.61 

3.21 

0.16 

0.29 

0.39 

0.12 

0.23 

0.84 

1.35 

0.05 

0.15 

0.04 

0.20 

0.18 

0.03 

1221 

2178 

1470 

40 

Herbivore (Freq. of occurrence) 

Herbivore (Density) 

Herbivore (Biomass) 

Herbivore (Mean Size) 

1.00 

66.94 

661.86 

7.66 

0.00 

5.08 

86.86 

0.39 

0.00 

0.07 

0.13 

0.05 

1.75 

2.70 

0.92 

0.03 

0.06 

0.01 

0.02 

0.03 

0.02 

1.00 

87.24 

721.95 

8.12 

0.00 

7.94 

71.86 

0.29 

0.00 

0.08 

0.10 

0.03 

1.86 

2.76 

0.94 

0.04 

0.04 

0.01 

0.01 

0.0.1 

0.02 

0 

21 

10 

5 

64 
(B) cont. 

Inside VICR Outside VICR 

Community or Assemblage 

(Metric) Estimates from Raw 

Survey Data 

Estimates from 

Transformed 

Survey Data 

Estimates from Raw 

Survey Data 

Estimates from 

Transformed 

Survey Data 

Sample 

Size 

Requirements 

Piscivore (Freq. of occurrence) 

Piscivore (Density) 

Piscivore (Biomass) 

Piscivore (Mean Size) 

Mean 

1.91 

332.87 

26.82 

SE 

0.86 

146.97 

5.08 

CV 

0.50 

0.64 

0.19 

Mean 

0.23 

0.91 

1.37 

SE 

0.05 

0.30 

0.07 

CV 

0.25 

0.39 

0.05 

Mean 

1.32 

608.15 

32.76 

SE 

0.37 

232.53 

4.97 

CV 

0.26 

0.38 

0.15 

Mean 

0.25 

1.34 

1.41 

SE 

0.04 

0.20 

0.06 

CV 

0.16 

0.15 

0.04 

3622 

6732 

1061 

CEFU (Freq. of occurrence) 

CEFU (Density) 

CEFU (Biomass) 

CEFU (Mean Size) 

0.61 

3.05 

148.83 

17.05 

0.04 

0.42 

20.65 

0.80 

0.07 

0.14 

0.14 

0.04 

0.42 

1.30 

1.24 

0.03 

0.06 

0.02 

0.09 

0.05 

0.02 

0.48 

2.08 

112.45 

16.82 

0.05 

0.41 

18.20 

0.69 

0.12 

0.19 

0.16 

0.04 

0.31 

1.07 

1.23 

0.04 

0.11 

0.01 

0.13 

0.11 

0.01 

501 

1790 

487 

3 

STPL (Freq. of occurrence) 

STPL (Density) 

STPL (Biomass) 

STPL (Mean Size) 

0.19 

2.55 

10.10 

6.63 

0.05 

1.00 

4.67 

0.60 

0.27 

0.39 

0.34 

0.08 

0.19 

0.37 

0.87 

0.05 

0.15 

0.03 

0.30 

0.33 

0.03 

0.44 

5.49 

7.08 

5.87 

0.07 

1.69 

1.80 

0.38 

0.17 

0.31 

0.25 

0.06 

0.42 

0.50 

0.82 

0.08 

0.08 

0.02 

0.19 

0.17 

0.03 

1611 

4368 

1180 

27 

65 
Table A-6: The optimal allocation of samples among all strata for (A) Buck Island and (B) St John 

study regions. The optimal allocation reflects the allocation which maximizes the number of metrics 

which can be tested to detect change at 10% confidence and 80% power. 

(A) Buck Island 

Strata ID 

BUIHO 

Unified 

Optimal Sample Size Allocation 

41 

Total 

Sample Size 

BUIHI 

BUISI 

54 

18 

121 

BUISO 8 

(B) St John 

Unified Total 

Strata ID Optimal Sample Size Allocation Sample Size 

STJHOREST 38 

STJHOCB 26 

STJHOMSR 14 

STJHICB 24 170 

STJHIMSR 17 

STJSOREST 43 

STJSOCB 5 

STJSICB 3 

66 
A-5.3 Observations of the Reef Fish Community and Selected 

Assemblages during 2001-2005 

The set of fish community and population metrics examined were: species richness (number 

of species), species diversity (Shannon diversity index), community biomass (biomass of all 

species), and community abundance (abundance of all species), assemblage frequency of 

occurrence (presence-absence), and assemblage density (number per unit area). The selection of 

analyzed assemblages were considered either directly related to the monitoring objectives or 

representative of other assemblages that could be of interest in the future (see Table A-2). 

Community metrics (e.g. richness) were examined using surveys collected over hard and soft strata 

during 2001-2005. Assemblage and species metrics were examined only in hard strata for the same 

time period. 

Averages for all metrics taken from within BUIS and VIIS are provided in Table A-4. We 

use the coefficient of variation as a standardized measure of precision. Higher precision is indicative 

of a more reliable estimate. In the context of monitoring, smaller changes can be found in a metric 

with higher precision than one with lower precision.

 Community metrics were typically very precise (CV<0.10; Table A-4). Survey estimates 

for groupers, snappers, herbivores, and piscivores were higher, but most were moderately precise 

(CV<0.20; Table A-4). Estimates of density for Stegastes planifrons and Cephalophlis fulvus were 

imprecise (0.2<CV<0.5; Table A-4). In general, metrics ranked from highest to lowest precision 

were as follows: community diversity, species richness, frequency of occurrence, and density. The 

series of assemblages ranked from highest to lowest precision were as follows: community, 

herbivore, piscivore, grouper, snapper, Cephalophlis fulvus, and Stegastes planifrons. 

Many of the metrics examined in this study were transformed to standardize variance and 

create a more symmetrical distribution. This was required to generate accurate confidence intervals. 

The majority of survey estimates from transformed data were precise (CV<0.10); however 

managers and researchers must be aware that the confidence intervals set using transformed 

variables are approximate to the back-transformed median not mean. If the mean of a metric 

changes, but the median remains the same, community shifts may not be detected. It is advisable to 

constantly monitor survey estimates from transformed and untransformed data simultaneously. 

Figures A-7 to A-9 show annual survey estimates of fish community and population metrics 

during 2001-2005 for inside BUIS and VIIS. Confidence intervals (95%) are shown to indicate 

metric variability. Confidence intervals are also used to test for differences among years. The 

67 
(A) BUIS

(B) VIIS

Figure A-7: Synoptic annual estimates of the community measures of species richness, biomass, 

species diversity, and density within (A) BUIS and (B) VIIS during 2001-2005. Error bars are 95% 

confidence intervals. 

68 
(A) BUIS

(B) VIIS

Figure A-8: Synoptic annual estimates of density for grouper, snapper, herbivore, and piscivore 

assemblages within (A) BUIS and (B) VIIS during 2001-2005. Error bars are 95% confidence 

intervals. 

69 
(A) BUIS

(B) VIIS

Figure A-9: Synoptic annual estimates of frequency of occurrence for grouper, snapper, herbivore 

and piscivore assemblages within (A) BUIS and (B) VIIS during 2001-2005. Error bars are 95% 

confidence intervals. 

70 
advantage of using confidence intervals to test for differences among years, instead of ANOVA, is 

its robustness to moderate violations of inherent assumptions, particularly homogeneity of variance 

and normality, if sample sizes are nearly equal and two-tailed hypotheses are considered (Cochran 

1947; Box 1953; Srivastana 1958; Posten et al. 1982). In addition, confidence intervals are easily 

created from survey estimates of the survey mean and standard error. 

The test was extended to allow multiple comparisons by incorporating a sequential 

Bonferroni correction. The Bonferroni correction increased the length of the confidence interval 

each time a survey comparison was made. Sequential comparisons were made of survey estimates 

in order of decreasing difference in means. Note: the confidence intervals shown in Figures A-7 to 

A-9 only represent intervals for the first comparison. Subsequent comparisons have larger intervals. 

Most community measures, except density, showed little change during 2001-2005. We 

found significant changes in community density in BUIS (2002>2003; 2002>2004; 2002>2005) and 

species richness in VIIS (2003>2005). The 2002 estimate of community density in BUIS had an 

abnormally large confidence interval indicating a larger variation among samples than normal. This 

large amount of variation was not seen in estimates one year later or in other assemblage metrics 

estimated for 2002 in BUIS. Interestingly, the 2002 community density estimate in VIIS was also 

abnormally large, suggesting the increase in the density of fish was a regional phenomena. 

Assemblage metrics were more variable than community metrics, but changes were found in 

grouper density (2002>2005), snapper density (2002>2004), piscviore density (2002>2005) in 

BUIS and grouper density and frequency of occurrence (2005>2004) in VIIS. Grouper, snapper, 

and piscivore estimates of density were all larger in 2002 than in other years, partly explaining high 

community density in 2002.

 In addition to searching for significant changes among synoptic estimates, estimates were 

also examined for monotonic trends. We observed that grouper, snapper, and piscivore density 

decreased monotonically from 2002-2005 in BUIS and snapper density in VIIS decreased from 

2001-2005. If density estimates can be considered random variables than the probabilities of a 

monotonic trend from 2002-2005 is 0.125 (0.53) and 2001-2005 is 0.0625 (0.54). 

A-5.4 Comparison of the Reef Fish Community and Selected 

Assemblages inside and outside MPAs 

Fish community and population metrics were compared between inside and outside of BUIS 

and VICR. The investigated time series was from 2003-2005. Data from 2001 and 2002 were not 

considered, because the survey design changed in 2002 making comparisons from before and after 

71 
2002 inconsistent. In 2002 the MSR strata was added in the St. John survey domain and the area 

sampled in BUIS was expanded to reflect monument expansion. 

A confidence interval test similar to one used in Section 5.3 was employed to compare 

metrics and test for significant differences. The center of a confidence interval was the difference in 

synoptic annual estimates (inside BUIS/VICR minus outside BUIS/VICR) and the length of the 

interval was the sum of standard errors of annual estimates as shown in Cochran (1977). Confidence 

intervals were used to test for significant differences inside and outside BUIS/VICR and for 

differences among years. Sequential Bonferroni corrections were used to assess significance. 

Results are shown in Figures A-10 to A-12. Significant differences between inside and 

outside BUIS/VICR can be readily observed if a 95% confidence interval does not include zero. 

The confidence intervals provided in Figures A-10 to A-12 are only valid for the first comparison, 

because the confidence intervals must be widened according to the Bonferroni correction for 

subsequent comparisons. 

Significant differences in community biomass (2003 IN>OUT) in BUIS and number of 

species (2004 OUT>IN) and community density (2003 OUT>IN) in VICR were observed. These 

community differences were temporary suggesting the compared areas are relatively similar. 

Piscivore frequency of occurrence in BUIS and piscivore density, herbivore density in VICR were 

all significantly different in 2004 (OUT>IN). The only metric found to be significantly higher in an 

MPA was grouper density in VICR (2005 IN>OUT). No significant changes among years were 

detected using confidence intervals. Trends were not examined because the available time series 

was too short. 

A-6 Conclusions 

The sampling techniques described herein provide park managers and researchers with 

reliable unbiased estimates of common reef fish metrics. The methods used, including the belt 

transect, stratified random sampling design, calculation of survey estimates and confidence interval 

tests, are simple, efficient and require few materials and little training. 

The high precision of most community metrics were sufficient to allow small changes in the 

reef fish community to be detected (10% change with 80% power). The lower precision of 

assemblage metrics require either more samples to be collected or a larger detection threshold (i.e. 

20% or 30% change in metrics instead of 10%). High assemblage CV values were due to low 

sighting frequency. If sighting frequency could be increased, precision would increase as well. 

72 
(A) BUIS

(B) VICR

Figure A-10: Synoptic annual estimates of the difference in community measures between inside 

and outside (A) BUIS and (B) VICR during 2003-2005. Error bars are 95% confidence intervals. 

73 
(A) BUIS

(B) VICR

Figure A-11: Synoptic annual estimates of the difference in frequency of occurrence for four 

assemblages between inside and outside of (A) BUIS and (B) VICR during 2003-2005. Error bars 

are 95% confidence intervals. 

74 
(A) BUIS

(B) VICR

Figure A-12: Synoptic annual estimates of the difference in density for four assemblages between 

inside and outside of (A) BUIS and (B) VICR during 2003-2005. Error bars are 95% confidence 

intervals. 

75 
Stratifying the survey domain according to sighting frequency is a strategy which may increase 

precision. An alternative is to examine only survey domains where sighting frequency is high, but 

managers would be unaware of changes in other domains. 

The ability to detect covariance among common reef fish populations and benthic habitat 

types using simple plots of standard survey estimates proved a valuable tool. The division of survey 

domains into hard and soft bottom benthic habitats greatly increased the precision of metrics and 

hence decreased sampling costs. In future work, CREMP will examine the affect of dividing 

populations into juvenile and adult life stages. Corresponding survey estimates would provide 

managers insight into population dynamics and recruitment and would remove the effects of 

variable recruitment from adult estimates. 

Observations of the community and assemblage metrics did not show consistent increasing 

or decreasing patterns. These observations do not mean the surveys were not part of an increasing or 

decreasing trend. Only five years of data were analyzed; many more years of data are needed before 

signals reflecting long-term change in reef fish populations and communities can be extracted from 

the noise included within a monitoring data set. The survey methods described herein are attempts 

decrease the noise as much as possible. 

76 
A-7 References 

Bohnsack, J. A., and S.P. Bannerot. 1986. A stationary visual census technique for quantitatively 

assessing community structure of coral reef fishes. Department of Commerce, NOAA Technical 

Report NMFS 41. 15 pp 

Brock, V. F. 1954. A preliminary report on a method of estimating reef fish populations. Journal of 

Wildlife Management 18: 299-308 

Box, G.E.P. 1953. Non-normality and tests on variances. Biometrika 40: 318-335. 

Christensen, J.D., C. Jeffrey, M.E. Monaco, C. Caldow, M. Kendall and R. Appeldoorn. 2003. 

Cross-shelf habitat utilization patterns of reef fishes in southwestern Puerto Rico. Gulf and 

Caribbean Research 14: 9-27. 

Cochran, W.G. 1947. Some consequences when the assumptions for analysis of variance are not 

satisfied. Biometrics 3: 22-38. 

Cochran, W.G. 1977. Sampling Techniques, 3rd edition, Wiley. 428 pp. 

Kendall, M.S., M.E. Monaco, K.R. Buja, J.D. Christensen, C.R. Kruer, and M. Finkbeiner, R.A. 

Warner. 2001. (Online). Methods Used to Map the Benthic Habitats of Puerto Rico and the U.S. 

Virgin Islands URL: http://ccma.nos.noaa.gov/ecosystems/coralreef/usvi_pr_mapping.html , 

Accessed 01/24/06. Also available on U.S. National Oceanic and Atmospheric Administration. 

National Ocean Service, National Centers for Coastal Ocean Science Biogeography Program. 

2001. (CD-ROM). Benthic Habitats of Puerto Rico and the U.S. Virgin Islands. Silver Spring, 

MD: National Oceanic and Atmospheric Administration. 

Kendall, M.S., J.D. Christensen, Z. Hillis-Starr. 2003. Multi-scale data used to analyze the spatial 

distribution of French grunts, Haemulon flavolineatum, relative to hard and soft bottom in a 

benthic landscape. Environmental Biology of Fishes 66: 19-26. 

Kendall, M.S., J.D. Christensen, C. Caldow, M. Coyne, C. Jeffrey, M. Monaco, W. Morrison, Z. 

Hillis-Starr. 2004. The influence of bottom type and shelf position on biodiversity of tropical 

fish inside a recently enlarged marine reserve. Aquatic Conservation: Marine and Freshwater 

Ecosystems 14: 113-132. 

Monaco, M.E., J.D. Christensen, and S.O. Rohmann. 2001. Mapping and monitoring of U.S. coral 

reef ecosystems: the coupling of ecology, remote sensing, and GIS technology. Earth System 

Monitor. Vol.12, No. 1. 

Monaco, M.E., J.D. Christensen, A.M. Friedlander, M.S. Kendall, C. Caldow (2003) Quantifying 

habitat utilization patterns of U.S. Caribbean and Hawaii reef fish to define marine protected 

area boundaries: the coupling of GIS and ecology. In Proc. 13th Biennial Coastal Zone 

Conference, Baltimore, MD, July 13-17, 2003. NOAA/CSC/20322-CD. CD-ROM. Charleston, 

SC: NOAA Coastal Services Center. 

Monaco, M.E., A.M. Friedlander, C. Caldow, J.D Christensen, J. Beets, J.Miller, C. rogers and R. 

Boulon. 2006. Characterizing reef fish populations and habitats within and outside the US 

Virgin Islands Coral Reef National Monument: A Lesson in Marine Proteceted Area Design. 

Fisheries Management and Ecology, In Press. 

NGDC (National Geophysical Data Center). 2006. (Online). Marine Geophysics. 

http://www.ngdc.noaa.gov/mgg/geodas/geodas.html/. Accessed 2006 July 1. 

NOAA (National Oceanic and Atmospheric Administration). 1996. Magnuson-Stevens Fishery 

Management and Conservation Act amended through 11 October 1996. NOAA Technical 

Memorandum NMFS-F/SPO-23. 

77 
NOAA (National Oceanic and Atmospheric Administration). 2001. Benthic Habitats of Puerto Rico 

and the U.S. Virgin Islands. CD-ROM. Silver Spring, MD: National Oceanic and Atmospheric 

Administration. 

NOAA (National Oceanic and Atmospheric Administration). 2006a. (Online) Detailed Methods for 

Characterization and Monitoring of Coral Reef Ecosystems and Associated Biological 

Communities. http://ccmaserver.nos.noaa.gov/ecosystems/coralreef/reef_fish/protocols.html . 

Accessed 2006 July 1. 

NOAA (National Oceanic and Atmospheric Administration). 2006b. (Online) Benthic Habitat 

Mapping of Puerto Rico and the U.S. Virgin Islands. 

http://ccma.nos.noaa.gov/ecosystems/coralreef/usvi_pr_mapping.html/ . Accessed 2006 July 1. 

NOAA (National Oceanic and Atmospheric Administration). 2006c. (Online) NOAA Dive Center, 

NOAA Diving Program. http://wwwndc.noaa.gov/ . Accessed 2006 July 1. 

NOAA (National Oceanic and Atmospheric Administration). 2006d. (Online) Coral Reef 

Ecosystem and Monitoring Database. 

http://www8.nos.noaa.gov/biogeo_public/query_main.aspx/ . Accessed 2006 July 1. 

Pittman, S.J., Christensen, J.D., Caldow, C., C Menza and Monaco, M.E. (In review, submitted to 

Ecological applications). Mapping fish diversity and abundance across shallow-water marine 

landscapes in the Caribbean. 

Posten, H.O., H.C. Yeh and D.B. Owen. 1982. Robustness of the two-sample t-test under violations 

of the homogeneity of variance assumption. Communications in Statistics: Theory and Methods 

11: 109-126. 

Srivastana, A.B.L. 1959. Effects of non-normality on the power of the analysis of variance test. 

Biometrika 46: 114-122. 

78 
Case Study B: Monitoring Reef Fish Assemblages inside Virgin 

Islands National Park and around St. John, USVI 1988-2000 

J. Beets

1

 and A. Friedlander

2 

1 Department of Marine Science 

University of Hawaii at Hilo 

200 W. Kawili St. 

Hilo, HI 96720-4091 

beets@hawaii.edu 

2 National Oceanic & Atmospheric Administration 

National Ocean Service 

National Centers for Coastal Ocean Science 

Center for Coastal Monitoring & Assessment 

Biogeography Team 

Makapu'u Point 

41-202 Kalanianaole Hwy 

Waimanalo, Hawaii 96795 

afriedlander@oceanicinstitute.org 

79 
Table of Contents for Case Study B

B-1 SUMMARY ..............................................................................................................................................................81

B-2 BACKGROUND INFORMATION ........................................................................................................................81

B-3 MONITORING STRATEGY AND METHODS...................................................................................................84

B-3.1 MONITORING OBJECTIVES ...................................................................................................................................84

B-3.2 SAMPLING DESIGN ...............................................................................................................................................84

B-3.3 SAMPLE SELECTION .............................................................................................................................................85

B-3.4 METHODS OF MEASUREMENT ..............................................................................................................................85

B-4 ANALYSES ..............................................................................................................................................................87

B-4.1 ‘POINT COUNT’ METHODS COMPARISON .............................................................................................................87

B-4.2 SAMPLE SIZE REQUIREMENT ANALYSIS...............................................................................................................88

B-4.3 OBSERVER VARIATION.......................................................................................................................................100

B-4.4 ASSESSMENT OF MARINE PROTECTION ON REEF FISH ASSEMBLAGES ...............................................................100

B-4.5 OBSERVATIONS OF REEF FISH TEMPORAL VARIATION AT REFERENCE SITES ....................................................102

B-4.6 POWER ANALYSIS OF POINT COUNT DATA AT REFERENCE SITES ......................................................................108

B-5 DISCUSSION .........................................................................................................................................................112

B-6 REFERENCES 114

80 

.......................................................................................................................................................
B-1 Summary 

This case study describes and explains the survey methods and analyses used by Jim Beets, 

Alan Friedlander, and National Park Service (NPS) / United States Geological Survey (USGS) 

collaborators in the Virgin Islands National Park (VIIS) to monitor reef fishes during 1988-2000. 

The long-term data on collected fish populations is unprecedented in duration and area of coverage 

for the Caribbean. 

Reef fish data of common reef fish metrics (e.g. species richness, abundance and biomass) 

were monitored at permanent reference sites inside VIIS and on adjacent reefs around St. John, U.S. 

Virgin Islands. The permanent reference sites were chosen by experts to fulfill monitoring 

objectives. Each site was characterized by relatively high levels of live coral cover and topographic 

complexity. 

 Throughout the monitoring program survey methods evolved and the number of permanent 

reference sites changed due to shifting logistical constraints and park management needs. This case 

study focuses on data collected annually at four permanent reference sites during 1989-2000, and 

specific data sets of greater sampling intensity to explicitly examine the effects of different 

measurement collection methods and to assess sample size requirements. Statistical analyses shown 

in this case study are used to compare different measurement collection methods, determine sample 

size requirements, assess the impact of Marine Protected Areas (MPAs), and identify trends in reef 

fish metrics. 

The purpose for this case study is to provide persons implementing a monitoring program 

with the information required to understand the pertinent management issues, sampling methods, 

and analytical methods used in monitoring reef fish in VIIS and adjacent waters around St. John. 

B-2 Background Information 

Monitoring projects were initiated by the National Park Service in the 1980s to provide 

useful data for evaluation of resources and for development of a long-term monitoring program 

(Boulon 1987). Starting in November 1988, two reef sites, Yawzi Point Reef and Cocoloba Reef, 

were sampled monthly until May 1991 (Figure B-1). The standard stationary point count census 

technique (Bohnsack and Bannerot 1986) was the method used during this period. A primary goal 

of this sampling effort was to document the variability in reef fish assemblage 

81 
Figure B-1: Map of St. John with reef habitat identified and monitoring sites labeled. Map created 

from NOAA/NOS data. 

82
characteristics based on monthly samples. The results of this monitoring were described in previous 

reports (Beets and Friedlander 1990, Beets 1993). 

Following the devastating effects of Hurricane Hugo in September 1989 (Rogers et al. 

1991), NPS initiated reef fish sampling at 18 reef sites around St. John (Figure B-1). Jim Tilmant 

(NPS) and Dr. Joe Kimmel (representing the Florida Marine Research Institute) collaborated with 

Jim Beets and Alan Friedlander (representing the U.S. Virgin Islands Division of Fish and Wildlife) 

on this project. 

One of the greatest justifications for consistent monitoring is to document the effects of 

natural events, such as the impact of hurricanes, and to attempt to differentiate natural fluctuations 

from human stresses. Storm events have devastating effects on reefs and their associated organisms. 

Storm intensity and frequency are quite variable, with low frequency of intense storms in some 

decades and several intense storms in others. Numerous storms affected the community structure of 

reefs around St. John during the monitoring period covered in this report, with some storms having 

major effects (Rogers and Beets 2001). The two largest storms passing St. John, Hurricane Hugo 

(1989) and Hurricane Marilyn (1995), devastated some reefs and had less influence on others 

(Rogers et al. 1991, 1997). Monitoring data allow for more critical assessment of these large 

disturbances and the differential effects of storms. 

The survey methods and number of permanent reference sites changed during 1989-2000 

due to shifting logistical constraints and park management needs. Initially, the NPS decision was to 

use a modified visual census technique, which was developed and used in Dry Tortugas National 

Park in 1987 (Kimmel 1992). Monitoring at the sites established in 1989 (originally 18 reef sites 

were selected with a few omitted and added among years) continued once per year during June/July, 

using the modified method, until 1994 (Figure B-1; Friedlander et al. 1999, Beets and Friedlander 

2003). 

In 1995, the standard stationary visual census technique (Bohnsack and Bannerot 1986) was 

employed to continue long-term monitoring at four established monitoring sites. These sites were 

selected based on similar reef characteristics, including depth, general reef topography (platform 

reefs with steeper edge habitat), spatial complexity, substrate characteristics, and coral cover. 

83 
B-3 Monitoring Strategy and Methods 

B-3.1 Monitoring Objectives 

The objectives of this monitoring project were to: 

1. Establish a baseline of information on reef fish assemblages around St. John, 

2. Conduct sustained monitoring on representative high-diversity reefs, 

3. Collect data on reefs with known and potential environmental degradation, 

4. Compare fish assemblages among selected reefs, and 

5. Determine trends in reef fish assemblages over time. 

To satisfy these objectives, standard reef fish assemblage metrics, including species 

richness, abundance, biomass, and diversity (see Table B-1 for descriptions), were analyzed and 

monitored. In addition, trophic groups, including benthic herbivores (dominated by damselfishes), 

mobile herbivores (dominated by parrotfishes and surgeonfishes), higher-order predators 

(dominated by groupers and snappers), and other predators (represented by numerous families) were 

investigated. 

B-3.2 Sampling Design 

To satisfy monitoring objectives, sampling was conducted at permanent reference sites with 

relatively high levels of live coral cover and topographic complexity (compared to reefs with low 

percent cover and relief) and a depth range of 1-15 m. Although monitoring was initially conducted 

at 18 sites (1989-1994), only four sites (Yawzi Point Reef, Tektite Reef, Newfound Bay West Reef, 

and Haulover Bay West Reef) have been monitored continuously during 1989-2000 (except in 

1990). The four sites monitored since 1989 are the focus of this case study. 

From past visual surveys, these reefs were known to have the highest species richness and 

biomass of reefs around St. John. The monitoring sites were located on lower forereef habitat of 

these fringing reefs. At Newfound Bay West Reef an additional zone (upper forereef – ‘Acropora 

zone’) was also monitored. All four reefs are of similar percent coral cover (10%-30%, greatly 

altered by coral diseases and storm damage in recent years, as documented for Yawzi Point reef), 

physical structure, and reef morphology. All reefs have a gently sloping reef platform (1-15 m) and 

a ‘wall-like’ edge, which extends sharply from the platform break to the sand zone (15-20 m). The 

edge zone has high topographic complexity, with numerous small to large holes. Tektite Reef is the 

84 
most developed and extensive of the four reefs, with spur and groove formations and impressive 

deeper forereef zone. Yawzi Point Reef has suffered the greatest impact from storm damage. 

Haulover Bay West Reef has a relatively narrow, but impressive, coral zone with a high density of 

large colonies of Montastrea annularis. Newfound Bay West Reef has a less developed reef 

platform, but well-developed edge structure. Haulover Bay Reef West and Newfound Bay Reef 

West had impressive upper forereef zones of Acropora palmata, which were devastated by the 

combination of white-band disease and storm damage in the 1970s and 1980s. 

B-3.3 Sample Selection 

Multiple samples were collected at each reference site to gather estimates of desired fish 

metrics (see Table B-1 and Section B-3.1), because a complete census of each reef was not 

possible. Since 1989, a minimum of 18 samples were scheduled for each permanent reference site 

based on preliminary sample size analysis. 

Sample units were restricted to reef habitat. Sampling was usually conducted from the reefsand interface to the middle portion of the reef platform, which was normally dominated by 

Montastrea annularis on lower forereef sites. Samples were divided between two reef zones (reef 

edge and reef platform), because data from previous years (<1989) showed differences in biomass 

and richness between these two zones. Reef sections for sampling were determined by divers 

(usually 2-4 divers) prior to sampling to avoid overlap. Reef edge was identified as a steep slope 

which extended from the forereef-sand interface to a change in slope, and reef platform was 

identified as the gradual slope from the edge or the sand interface to the next shallower zone, e.g., 

upper forereef [Acropora zone]). 

Sample units were haphazardly selected using random kicks as described by Bohnsack and 

Bannerot (1986). If a haphazard sample unit occupied less than 50% hard substrate and/or reef (i.e. 

was greater than 50% sand), the diver moved to another haphazardly selected point on the reef. 

In 1992 and 1999, sampling intensity was increased at Tektite Reef to compare point count 

methods (1992, n=20) and to estimate sample size requirements (1999, n=58). The data sets and 

corresponding analyses are discussed in Sections B-5.1 and B-5.2, respectively. 

B-3.4 Methods of Measurement 

Two ‘point count’ methods were used for monitoring reef fishes in sample units. The 

primary reef fish monitoring method used at the reference sites from 1995-2000 was the stationary 

85 
Table B-1: Fish assemblage characteristics (measured and derived) examined during long-term 

monitoring of reef fishes in VIIS and around St. John in 1988. 

Assemblage characteristics 

Species richness – number of species per sample 

Abundance – number of individuals per sample 

Biomass (g) – (derived metric) weight of all individuals in the sample - Biomass estimates for analysis were 

derived from calculated live wet weight. Live wet weight (W) was derived from the visually 

estimated mean fork length (FL) for each size class for each species using the relation W = 

a(FL)

b

. Values of the fitting parameters a and b for each species were derived from Bohnsack et 

al. (1986) and the FishBase web site (http://fishbase.org/). For species not in these databases, 

estimates from available literature on the species or congeners were used. Biomass of all fishes 

recorded in all censuses was obtained by multiplying the mean live wet weight for each size 

class for each species by the total number of individuals observed in that size class. 

Diversity – (derived metric) Shannon-Weiner Diversity Index - H’=S (pi ln pi), where pi is the proportion 

of all individuals counted that were of species i. 

Benthic Herbivores - dominated by damselfishes 

Mobile Herbivores - dominated by parrotfishes and surgeonfishes 

Higher-order Carnivores - dominated by groupers and snappers 

Other Carnivores - represented by numerous families 

86 
visual census technique (‘stationary point count’ - SPC) described by Bohnsack and Bannerot 

(1986). A single sample is conducted by a diver who settles just above the reef substrate at a 

haphazardly selected point. During the point count, all fish species observed are listed within a 7.5 

m radius cylinder (area: 176.7 m2) for 5 min. Numbers and sizes of fishes of each species (estimated 

fork length placed in separate size classes) are added following the 5 min listing period. Habitat 

within the cylinder is briefly described, including substrate type, estimated coral cover, dominant 

benthic organisms, relative topographic complexity, depth, and location on the reef. 

A modified ‘point count’ technique, developed by J. Kimmel and J. Tilmant for fish 

monitoring in Dry Tortugas National Park (Kimmel 1992), was used in Virgin Islands National Park 

from 1989 to 1994. This modification used a 5-m radius cylinder (area: 78.5 m2) and 15 min time 

interval with the last 5 min of the 15 min total used to search and enumerate species and individuals 

by swimming throughout the cylinder. Thus, this method was a ‘plot count’ instead of a ‘point 

count’, as described by Bohnsack and Bannerot (1986). The decision to return to the standard 

stationary visual census technique (Bohnsack and Bannerot 1986) in 1995 from the modified 

technique was made in order to standardize with investigators working elsewhere in the Caribbean 

(especially J. Bohnsack and colleagues working in the Florida Keys National Marine Sanctuary and 

Dry Tortugas National Park). 

B-4 Analyses 

B-4.1 ‘Point count’ Methods Comparison 

Methods are refined, modified, or changed as long-term monitoring progresses and as 

management questions change. To ensure that long-term monitoring data are suitable for analysis of 

trends and change, methods must be consistently applied and compared. Method changes must be 

tested, validated, compared, and calibrated. The change in methods of measurement used in VIIS, 

from a 10 m/15 min count to a 15 m/5 min count, between 1994-1995 required a methods 

comparison. 

In 1992, a study was conducted to compare the two ‘point count’ methods and to evaluate 

the need for correction factors. The methods comparison study was conducted on July 16, 1992 on 

Tektite Reef, the monitoring site with the consistently greatest species richness and fish abundance. 

Five plots on the reef of similar topographic complexity, coral cover, and depth were selected and 

marked for sampling. Each plot was located between 10-12 m water depth, approximately 25 m 

apart. Transect lines (15 m) were laid within each plot. Four experienced fish counters conducted 

87 
one sample using each method within each plot. Method, site, and time were randomly assigned. 

This sampling design allowed for paired comparisons between methods for each diver at a given 

location. 

Analysis using paired t-tests for the data obtained with the two methods yielded significant 

differences for species richness and diversity, but not for number of individuals or biomass (Table 

B-2A). P-values were sequentially Bonferroni adjusted according to Rice (1989). Analysis by 

trophic group resulted in significant differences for benthic and mobile herbivores, but none for 

higher-order carnivores or other carnivores (Table B-2B). None of the eleven most abundant 

species were observed to have significant differences between methods (Table B-2C). Results of 

Detrended Correspondence Analysis did not show large differences between methods, but, 

interestingly, demonstrated considerable observer variation (Figure B-2). 

Since analysis of the two methods yielded differences in parameter values, application of 

correction factors for some parameters would be appropriate for analyzing trends. Parameter values 

from samples at the same plot using both methods were regressed to obtain linear equations. 

Regression coefficients were low for all assemblage parameters and trophic groups (Table B-3). 

Except for diversity, regression analysis did not yield significant relationships for assemblage 

characteristics or trophic groups; therefore, strong relationships between the two methods were not 

observed. 

B-4.2 Sample Size Requirement Analysis 

In July 1999, an oversampling effort (n = 58 samples) was conducted on Tektite Reef (ca. 

13,500 m2) in order to conduct a sample size analysis using an optimization technique developed by 

Bros and Cowell (1987), and provide a complete coverage for one reef among microhabitats and 

depths. Analyses of the later data were previously reported (Friedlander et al. 1999). 

A species cumulation curve was used to examine the relationship between the cumulative 

number of species and number of samples at Tektite Reef (Figure B-3). The cumulative number of 

species reached an asymptote at 22 samples. The minimum sample size (n = 18) accounted for 96% 

of the total number of species sampled at Tektite Reef. 

A technique developed by Bros and Cowell (1987) was used to determine the number of 

samples required to determine a significant difference in the means of two independent samples. 

The number of species, number of individuals, and biomass collected in samples were investigated. 

A range of differences between means (10% - 50% of the mean) were examined. A Lotus macro 

88 
3

7 8

1 1

3 8

5 4

7 0

2

(A) Statistics for assemblage characteristics 

Assemblage 15m 10m Mean Std Error Corr. t-Ratio Prob > |t| Seq. Bon. 

characteristics Difference 

Richness 22.35 28.90 -6.55 1.1573 -0.1072 -5.6597 <0.0001 Sig. 

Abundance 181.45 200.00 -18.55 20.33 -0.0750 -0.9124 0.37 ns 

Biomass (g) 6170.28 6930.84 -760.56 1381.95 0.058 -0.5503 0.58 ns 

Diversity 2.14 2.43 -0.29 0.0597 0.583 -4.8494 0.000 Sig 

(B) Statistics for trophic group numerical abundances. Data ln(x)-transformed. 

Trophic group 15m 10m Mean Std Error Corr. t-Ratio Prob > |t| Seq. Bon. 

Diff. 

Benthic 3.4 3.8 -0.352 0.1159 0.463 -3.354 0.006 Sig. 

Herbivores 

Mobile 2.6 3.0 -0.403 0.1497 0.343 -2.692 0.014 Sig. 

Herbivores

Higher-order 1.1 1.5 -0.369 0.1839 0.219 -2.009 0.059 ns 

Carnivores

Other 4.7 4.8 -0.041 0.1501 -0.0279 -0.270 0.790 ns 

Carnivores 

(C) Statistics on abundance data for 11 numerically abundant species. 

Species 15m 10m Mean Std N Corr. t-Ratio Prob Seq. 

Diff. Error > |t| Bon. 

Chromis cyanea 65.0 58.9 6.15 7.93 20 0.64 0.78 0.45 ns 

Stegastes planifrons 18.5 28.9 -10.42 3.42 19 0.75 -3.05 0.01 ns 

Chromis multilineatus 10.3 13.1 -2.84 3.22 19 0.24 -0.88 0.39 ns 

Clepticus parrai 17.8 13.3 4.50 11.41 12 -0.39 0.39 0.70 ns 

Stegastes partitus 10.6 8.2 2.37 2.4 19 0.40 0.99 0.34 ns 

Holocentrus rufus 8.7 7.8 0.84 2.33 19 0.48 0.36 0.72 ns 

Halichoeres garnoti 5.5 7.7 -2.20 1.14 20 0.17 -1.92 0.07 ns 

Haemulon flavolineatum 6.6 7.1 -0.47 1.00 19 0.89 -0.48 0.64 ns 

Thalassoma bifasciatum 5.7 6.1 -0.35 1.17 20 0.21 -0.30 0.77 ns 

Stegastes variabilis 3.8 7.3 -3.53 1.22 19 0.89 -2.88 0.01 ns 

Scarus croicensis 3.2 6.5 -3.32 1.33 19 0.3 -2.49 0.02 ns 

Table B-2: Comparison of assemblage characteristics, trophic groups, and selected species between 

two visual census methods (15m/5 min and 10m/15 min) conducted at Tektite Reef, VIIS, on 16 

July 1992. N = 20. The greatly abundant masked goby, Coryphopterus personatus, was not included 

in analysis. A table-wise sequential Bonferroni (Seq. Bon.) adjustment of p values was made to 

control for the overall type-I error rate for each dependent variable (pi α/(1 + k – i, Rice 1989). 

89 

!
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0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200

JB

AF

JK

JB

GG

AXIS 2 

AXIS 1 

Figure B-2: Results of detrended correspondence analysis (DCA) of fish count data (4 observers; 

40 samples) conducted at Tektite Reef for comparison of point count methods. Polygons denote 

each observer. Large circles are 10m/15min censuses; small circles represent 15m/5min censuses. 

Masked goby, Coryphopterus personatus, was not included in analysis. 

90 
Table B-3: Results of least-squares linear regression analyses for fish assemblage characteristics for 

post Hurricane Hugo (1989-1994) and post Hurricane Marilyn (1996-2000) time periods. Number 

of individuals and biomass were ln(x) transformed for statistical analyses. 

Assemblage Time period Least-squares regression model R2 F P 

characteristic 

Species 1989-1994 Species = -2032.193 + 1.0337838 x 0.310 7.730 0.011 

(YR) 

1996-2000 Species = -496.3675 + 0.26025 x (YR) 0.026 0.483 0.496 

Individuals 1989-1994 ln (individuals) = -203.965 + 0.105 x 

(YR) 

0.337 9.136 0.007 

1996-2000 ln (individuals) = -104.2545 + 0.05475 x 0.060 1.103 0.307 

(YR) 

Biomass 1989-1994 ln (biomass) = -38.59824 + 0.0202027 x 0.330 9.040 0.008 

(YR) 

1996-2000 ln (biomass) = -20.3435 + 0.011 x (YR) 0.061 1.180 0.292 

91 
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12151821

24

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5457

Number of samples

Cumulative number of species

Figure B-3: Species cumulation curve showing the relationship between the cumulative number of 

species and the number of samples at Tektite Reef (Friedlander et al. 1999). 

92
program written by Doug Harper of the NMFS/SEFSC/Miami Laboratory was used to conduct 

these analyses. 

For number of species and number of individuals, standard error of the mean begins to level 

off and converge at approximately 11-13 samples (Figure B-4). Biomass had a much higher degree 

of variability and does not level off and converge until 15 to 20 samples. All analysis for number of 

individuals and biomass excluded masked gobies (Coryphopterus personatus) because they were 

ubiquitous and their large numbers (1000’s) masked trends in the remainder of the fish assemblage. 

The estimated number of samples needed to detect various levels of change varied greatly among 

the three parameters (mean abundance of species, individuals, and biomass). Figure B-5 provides 

estimates of the number of samples needed to detect various levels of change in the mean 

abundance of species, individuals, and biomass (Friedlander et al. 1999) at Tektite Reef. Using a 

Type I error rate of 0.10, the number of samples needed to detect changes decreases rapidly with 

only a slight decline in precision. Less than two samples are required to detect a 20% change in 

number of species per survey (Figure B-5A) while more than 12 are required to detect a 20% 

change in number of individuals (Figure B-5B). Again, biomass is highly variable with 

approximately 140 samples needed to detect a 20% change (Figure B-5C). Using a Type I error 

rate of 0.20 substantially decreases the number of samples needed to detect change in the 10% to 

20% range of precision. Less than two samples are required to detect a 15% change in number of 

species per survey (Figure B-5A) while 7.7 censuses are required to detect a 20% change in number 

of individuals (Figure B-5B). Again, biomass is highly variable with 84 samples needed to detect a 

20% change (Figure B-5C). 

For comparison with analyses of sample size at Tektite reef, sample size analyses were 

conducted among the remaining reference sites collected in 1999 as well (Figure B-6). Sample 

sizes used for analysis were the same for all reefs (n = 18). Similar results were obtained among 

reefs for each parameter. For species richness, 5-11 samples were required to reach an asymptote 

for standard error (Figure B-6A). Abundance required a greater number of samples among sites (7-

14; Figure B-6B). Biomass required large sample size to adequately reduce standard error (11-22; 

Figure B-6C). Differences in estimated sample sizes were apparent among reefs, with reefs with 

greater parameter values generally requiring larger sample sizes. 

For the four reference sites examined, two samples were needed to detect a 20% change in 

number of species at an alpha of 0.1 for any given site (Figure B-7A). The number of samples 

needed to detect a 20% change (at alpha = 0.1) in the number of individuals ranged from 8 samples 

93 
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MEAN SEM

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A. Number of Species

Number of Samples

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48

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Standard Error

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B. Number of Individuals

C. Biomass (kg)

Figure B-4: Sample size optimization of data from sample size analysis project at Tektite Reef, July 

1999, for number of species, number of individuals, and biomass. Relationship between standard 

error of the mean (SEM) and sample size. Monte Carlo simulation procedure for sample size 

optimization described by Bros and Cowell (1987). (Friedlander et al. 1999) 

94 
0

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10% 15% 20% 25% 30% 35% 40% 45% 50%

Alpha = 0.1

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Accuracy of mean (+/-)

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Figure B-5: Estimated number of samples needed to detect changes in the mean of data from 

sample size analysis project at Tektite Reef, July 1999. N = 58, α = 0.10 and 0.20. (Friedlander et 

al. 1999) 

95 
(A)

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Standard Error

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MEAN SEM

HIGH SEM

LOW SEM

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

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Standard Error

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A. HAULOVER WEST B. NEWFOUND

C. TEKTITE D. YAWZI POINT

(B) 

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Standard Error

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Standard Error

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A. HAULOVER WEST B. NEWFOUND

C. TEKTITE D. YAWZI POINT

Figure B-6: Results of sample size optimization for (A) species richness, (B) number of fishes, and 

(C) biomass among the four reference sites, St. John, U.S. Virgin Islands. Results based on data 

collected in 1999. (Beets and Friedlander 2003) 

96 
(C)

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Standard Error

0

4

8

12

MEAN SEM

HIGH SEM

LOW SEM

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Standard Error

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A. HAULOVER WEST B. NEWFOUND

C. TEKTITE D. YAWZI POINT

Figure B-6: cont. 

97
(A)

(B) 

Number of Samples Number of Samples Number of Samples Number of Samples 

Figure B-7: Estimated number of samples needed to detect changes in the mean for (A) species 

richness, (B) number of fishes, and (C) biomass among reference sites. N = 18 (except for Tektite: n 

= 58); α = 0.10 and 0.20. (Beets and Friedlander 2003). 

98 
Number of Samples Number of Samples (C)

Figure B-7C: cont. 

99
at Haulover West Reef to 15 at Yawzi Point (Figure B-7B). The high variance associated with total 

fish assemblage biomass resulted in sample sizes ranging from 41 at Newfound Bay to 140 at 

Tektite Reef in order to detect an accuracy within 20% of the mean (alpha = 0.1, Figure B-7C). 

B-4.3 Observer Variation 

Since observers conducted censuses in different microhabitats (Edge vs. platform) an 

analysis of variance (ANOVA) was used to examine if there were any differences in assemblage 

structure among microhabitats or observers. A Two-Way ANOVA was conducted with habitat 

(edge and platform) and observers (AF, JB, and JM) as fixed factors in the ANOVA. Biomass was 

ln(x+1) transformed. An unbalanced sample design precluded analysis of interaction terms. 

For number of species, a Two-Way ANOVA with both factors fixed showed no significant 

difference between habitats, but a significant difference among observers was observed (Table B4). Mean species counts per census were significantly greater for AF compared to JB and JM (P < 

0.05). The power for the 2 way Model I ANOVA was calculated using eq. 12.43 in Zar (1999). 

Power is very low for detecting differences in habitat but extremely high for observer differences. 

This is primarily due to the ratio of the Mean Squares for each factor compared to the Residual or 

Error Mean Square. Therefore, there exists a very large probability of committing a Type II error 

(>99%) in trying to detect a difference in habitats. Number of species per census was not 

significantly different between JB and JM (P > 0.05). Two potential reasons for observer 

differences are that: 1) observers may sample different microhabitats, and 2) observer sampling 

bias, i.e., the inclusion or exclusion of small cryptic benthic species (e.g. gobies and blennies). 

For number of individuals, a Two-Way ANOVA showed no significant difference between 

habitats or among observers (Table B-4). Mean biomass estimates were not significantly different 

among observers (P > 0.5) but were significantly higher on the edge compared to the platform 

habitat (P < 0.05; Table B-4). 

B-4.4 Assessment of Marine Protection on Reef Fish Assemblages 

To compare fish assemblage characteristics inside and outside Virgin Islands National Park, 

data was analyzed from the period during which numerous reef sites were monitored (n = 18, 1989-

1994). The selected sites were in reef habitat with greater topographic complexity than surrounding 

colonized pavement. Sites that did not have an analog reef either inside or outside the park were 

excluded from this analysis. Reef sites located inside the park that were used in analyses included: 

Hawksnest Bay Upper, Haulover Bay West, Fish Bay East, Yawzi Point Reef, and Tektite 

100 
(A) 

Assemblage Habitat Observer 

Metric 

Species Richness 0.442 <0.001 

Abundance 0.620 0.011 

Biomass <0.001 0.241 

(B) 

AF JB JM 

30.5 (2.7) 26.2 (2.5) 23.9 (3.0) 

Table B-4: (A) Results from two-way ANOVAs to investigate the effects of habitat and observers 

on three assemblage metrics. Habitats are edge and platform (DF=1). Observers were Alan 

Friedlander (AF), Jim Beets (JB), and Jeff Miller (DF=2). (B) Mean and variance values used in a 

pair-wise multiple comparison Bonferroni corrected t-test procedure. Underlined means are not 

significantly different at α = 0.05. (Friedlander et al. 1999) 

101 
Reef (refer to Figure B-1). Reef sites outside the park included Haulover Bay East, Newfound Bay 

West, Newfound Bay Upper, and Fish Bay West. There were no significant differences in number 

of species (P > 0.05) or fish biomass (P > 0.05) between sites inside and outside the park (Table B5). The total number of individuals was significantly greater (P = 0.002) at sites inside VIIS 

compared with sites outside VIIS. This is likely owing to the greater proportion of ‘edge habitat’, 

with greater topographic complexity, sampled inside the park (Haulover West, Yawzi Point, and 

Tektite Reef) and the associated presence of large schools of planktivores at these sites. 

B-4.5 Observations of Reef Fish Temporal Variation at Reference Sites 

Much temporal variation was observed for assemblage characteristics among the four 

reference sites (Figure B-8). As expected, variation in means for abundance and biomass was 

greater than for species richness. Generally, the sites with greater mean values (e.g., Tektite) 

showed greater temporal variation than the site with the lowest mean values (Haulover West). 

Comparison of mean values of assemblage characteristics between all reef fishes (juveniles and 

adults) and adults demonstrated that the adult component of the assemblage had much lower 

temporal variability, particularly for abundance (Figure B-9). This was readily apparent in 

comparison of standard deviation estimates for assemblage characteristics between all reef fishes 

and adults for data from Tektite Reef (Figure B-10). Standard deviation estimates were 

significantly smaller for adult fishes for all assemblage characteristics (t-tests values, p < 0.05). 

Since the adult components of the reef fish assemblages are less variable, they provide less ‘noise’ 

in analysis. Juveniles of many species are also more difficult to detect, especially from a stationary 

point, which is also a potential source of variability. 

The most apparent temporal signal was due to the influence of large storm events. The 

Virgin Islands have been greatly influenced by numerous large storms since 1988. Data were 

separated into two periods (1989-1994 and 1996-2000), representing the post-storm recovery 

periods following the two major storms affecting St. John during the period of analysis (Hurricane 

Hugo, Sept. 1989; Hurricane Marilyn, Sept. 1995). Since data for 1995 were collected just prior to 

Hurricane Marilyn, those data were excluded from analysis. Simple least-squares linear regressions 

were conducted on the five years of data following each storm event. All of the assemblage 

characteristics analyzed (species richness, abundance, and biomass) showed statistically significant 

increases during the five-year period following Hurricane Hugo (1989) (Figure B-11). While 

species, number of individuals, and biomass all trended upward following Hurricane Marilyn 

102 
Table B-5: Comparisons of fish assemblage characteristics inside and outside of Virgin Islands 

National Park using 15min/5min point count data from 1989 to 1994. Values in parentheses are 

standard error of the mean using pooled variances. Number of individuals did not meet the 

parametric assumption of homogeneity of variances so a Mann-Whitney Rank Sum Test was used 

in place of the parametric Student’s t-test. Statistical values of pooled data: t = Student t-test, U = 

Mann Whitney Rank Sum test. 

Assemblage 

characteristics 

Inside 

VIIS 

Outside 

VIIS 

Statistical 

Value 

P 

value 

Species Richness 

Number of individuals 

Biomass (kg) 

30.9 (0.70) 30.2 (0.80) 

228.6 (7.5) 188.2 (8.6) 

9.2 (0.7) 8.1 (0.6) 

t = 0.714 

U = 9.51 

t = 1.12 

0.476 

0.002 

0.23 

103 
Sampling Year

1989 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Biomass (kg/sample)

0

5

10

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20

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30

Species Richness (S)

0

16

20

24

28

32

Number per sample

0

50

100

150

200

250

300

350

HAULOVER WEST

NEWFOUND

TEKTITE

YAWZI POINT

Figure B-8: Comparison of fish assemblage characteristics among the four reference reefs sites 

around St. John, US Virgin Islands. The break between 1994 and 1995 marks methods change. 

Arrows mark two major hurricanes. (Beets and Friedlander 2003) 

104
1989 1991 1992 1993 1994 1996 1997 1998 1999 2000 N

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1989 1991 1992 1993 1994 1996 1997 1998 1999 2000

Number of fish per sample

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Sampling Year

1989 1991 1992 1993 1994 1996 1997 1998 1999 2000

B

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Sampling Year

1989 1991 1992 1993 1994 1996 1997 1998 1999 2000

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1989 1991 1992 1993 1994 1996 1997 1998 1999 2000

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Total Assemblage

1989 1991 1992 1993 1994 1996 1997 1998 1999 2000 N

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A. Species Richness

C. Biomass

Adults

B. Abundance

HAULOVER WEST

NEWFOUND

TEKTITE

YAWZI POINT

Figure B-9: Comparison of trends of assemblage characteristic values (a. species richness, b. 

abundance, and c. biomass) between all reef fishes (Total Assemblage - juveniles and adults) and 

Adults (juveniles excluded) for the four reference sites at St. John, US Virgin Islands. Major storm 

events marked the beginning of each of the two periods shown in each graph (1989 – Hurricane 

Hugo, 1995 – Hurricane Marilyn). The change in methods occurred in 1995 (data not presented for 

1995). (Beets and Friedlander 2003) 

105 
1989 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 N

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1989 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Number of fish per sample

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Sampling Year

1989 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

B

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Sampling Year

1989 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

B

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1989 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

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Total Assemblage

1989 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 N

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A. Species Richness

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Adults

B. Abundance

Average Standard Deviation

1989-1994: 4.52

1989-1994: 246.52

1989-1994: 102.26

1989-1994: 4.30 1995-2000: 3.63 1995-2000: 3.66

1995-2000: 250.12

1995-2000: 106.04

1989-1994: 172.79

1989-1994: 81.95

1995-2000: 172.28

1995-2000: 88.76

Figure B-10: Comparison of variance estimates (standard deviation) of assemblage characteristics 

(a. species richness, b. abundance, and c. biomass) between all reef fishes (Total Assemblage -

juveniles and adults) and Adults (juveniles excluded) for Tektite Reef, St. John, US Virgin Islands. 

Average standard deviation is presented for all reef fishes and adults for both sampling periods. The 

change in methods, which occurred in 1995, marked the separation in sampling periods. (Beets and 

Friedlander 2003) 

106 
Sampling Year

1989 1991 1992 1993 1994 1996 1997 1998 1999 2000

Number of species per sample

15

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35

Sampling Year

1989 1991 1992 1993 1994 1996 1997 1998 1999 2000

ln

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1989 1991 1992 1993 1994 1996 1997 1998 1999 2000

ln

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er of fis

h

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a

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ple

4.0

4.5

5.0

5.5

6.0

r

2 = 0.250 r

2 = 0.026

r

2 = 0.291 r

2 = 0.058

r

2 = 0.332 r

2 = 0.0595

A. Species richness

B. Abundance

C. Biomass

Figure B-11: Trends in assemblage characteristics during the five-year periods following the two 

major storms which affected St. John (Hurricane Hugo, Sept. 1989; Hurricane Marilyn, Sept. 1995). 

Average values for each of the four reference sites are represented by circles for each year. 

Regression lines and coefficients were obtained from linear regression analysis. Data for 1995 was 

excluded from these analyses. 

107 
(1995), none of these trends were significant for the five year period following the storm (Figure B11). Large storms, which passed near the Virgin Islands in 1998 and 1999, may have had a great 

negative impact on reef fish assemblage recovery, as lower values in assemblage characteristics 

were noted for 2000 (Figure B-11). 

Trophic group trends were similar to overall assemblage characteristic trends, with large 

differences in abundance among groups (Figure B-12). Generally, no strong trends were apparent 

for any trophic group, although the influence of method change and large storm events were 

apparent. Trends within families presented a finer-scale perspective than did trophic groups. For 

example, surgeonfishes (Acanthuridae) and goatfishes (Mullidae) demonstrated no strong trends, 

although differences were apparent among reference sites (Figure B-13). Abundance values of 

gobies (Gobiidae) and angelfishes (Pomacanthidae) were apparently influenced by method change, 

with lower abundance values in both taxa following the change. Abundance values of groupers 

(Serranidae) and parrotfishes (Scaridae) were apparently strongly influenced by both method 

change and storm events. Method change and storm events were clearly confounded and difficult to 

assess for many groups. 

B-4.6 Power Analysis of Point Count Data at Reference Sites 

Power analysis of monitoring data can be used to determine the duration of a monitoring 

program needed to identify a given trend with a known statistical power (1-Type II error) and 

confidence (Type I error). This analysis has the same assumptions of regression analysis (e.g., 

random and independent errors, homogenous variance). If these assumptions are not satisfied, trend 

power analysis is not recommended. We show the technique here for instances where regression is 

suitable to analyze monitoring data. 

Analyses were conducted using the freeware program MONITOR from the USGS (USGS 

2005) and by a more conservative method (Skalsky 2005). The MONITOR program provided more 

liberal results than output obtained from more conservative methods. For example, power values 

using abundance data from all permanent reference sites obtained from the MONITOR program 

were 0.839 (alpha = 0.05) and 0.962 (alpha = 0.1) for a 10-year monitoring period. A more 

conservative method (Skalsky 2005) provided much smaller power values for a 10-year monitoring 

period (Table B-6). The ability to detect small levels of change (e.g. 10% change) with sufficient 

power for most parameters is improbable using the conservative method. 

108 
Sampling Year

1989 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

ln number of fish per sample

1

10

100

Large Predators

Herbivores

Mobile Invert Feeders

Sessile Invert Feeders

Zooplanktivores

Figure B-12: Abundance trends in trophic groups among the four reference sites around St. John, 

U.S. Virgin Islands, 1989-2000. Data were ln(x)-transformed. Method changed occurred in 1995. 

Black dots mark large hurricanes. (Beets and Friedlander 2003) 

109
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

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Figure B-13: Abundance trends in selected fish families among the four reference sites around St. 

John, U.S. Virgin Islands, 1989-2000. Vertical bar on x-axis marks the method change; black dots 

mark large hurricanes. 

110 
Table B-6: Results of power analysis for species richness and abundance data using stationary point 

counts collected at Tektite Reef in Virgin Islands National Park. Power values are presented for 

three detection levels (10%, 25%, and 50% declines) and for two monitoring periods (5, 10 years). 

Type I error was set at 0.10. Analysis was based on annual sampling at each site. 

Metric Decline 5 Year Monitoring 

Period 

10 Year Monitoring 

Period 

Species Richness 10% 0.238 0.273 

25% 0.411 0.569 

50% 0.773 0.945 

Abundance 10% 0.206 0.211 

25% 0.232 0.262 

50% 0.322 0.424 

111 
B-5 Discussion 

Management decisions should be made based on scientifically-defensible data collection and 

analyses, instead of perceived changes in the sample populations. Resource managers need to know 

the degree of accuracy associated with population estimates and statistical power of analyses in 

order to use monitoring data confidently. Detecting a 25% change in mean abundance may be 

adequate for some species or locations and insufficient for others. The number of samples taken is 

an important decision in any biological study because of the time and cost involved in data 

collection. These decisions are often based on practical as well as theoretical considerations. 

The sample size (N = 18) used at Tektite Reef and other reference sites in VIIS appears 

adequate to detect changes in number of species and number of individuals for the entire 

assemblage and for many trophic guilds at a 20% level. Even with a complete census at Tektite 

Reef (N = 58 sample), it would not be possible to detect changes in biomass or number of 

individuals for some trophic guilds. The established sample size (N = 18) for reference sites is 

adequate to detect changes in number and biomass for some trophic guilds, such as herbivores and 

mobile invertebrate feeders. If trends in abundance and/or biomass and abundance of large 

commercially important species (snappers/groupers) are desired, then large samples and other 

methods must be evaluated and employed. Continued stratification by edge and platform 

microhabitats seems appropriate based on differences in biomass and detrended correspondence 

analysis results for assemblage structure. 

Despite the relatively large variances associated with assemblage parameters (species 

richness and abundance) for individual sites, repeated annual sampling over several years (5-10 

years) can detect declines with sufficient power to be useful for management purposes. Increasing 

alpha levels to 0.20 would improve the ability to detect these changes and is consistent with the 

precautionary approach to management. It is far better to make a Type I error rather than a Type II 

error if the sustainability of the resources is in question. 

In order to increase the number of monitoring sites and number of samples at a broader 

spatial scale, a more intensive sampling effort will be needed. Determination of the optimal sample 

design for snapper/grouper surveys and juvenile fish surveys should be conducted if data on these 

parameters are desired. A final important point is that these results demonstrate differences due to 

observer bias. These samples were collected by researchers with many years of experience with fish 

identification, ecological methods, and, specifically, fish counting. The bias, error, and variability of 

112 
samples collected by newly trained and less experienced samplers could be quite large and could 

lead to problematic time series data. It is important to emphasize the need for adequate training, as 

well as consideration of observer bias in data analysis efforts. 

113 
B-6 References 

Beets, J. 1993. Long-term monitoring of fisheries in Virgin Islands National Park: Chapter I. 

Baseline data, 1988-1992, with emphasis on the impact of Hurricane Hugo. Virgin Islands 

National Park, Technical Report VINP 1/93. 

Beets, J. and A. Friedlander. 1990. Long-term monitoring of fisheries in the Virgin Islands National 

Park: Impact of Hurricane Hugo. Annual Project Report to Virgin Islands National Park. 20p. 

Boulon, R. 1987. Basis for long-term monitoring of fish and shellfish species in the Virgin Islands 

National Park. Biosphere Reserve Research Report No. 22. US National Park Service/VIRMC, 

66 p. 

Beets, J. and A. Friedlander. 2003. Temporal analysis of monitoring data on reef fish assemblages 

inside the Virgin Islands cNational Park and around St. John, U.S. Virgin Islands, 1988-2000. 

Final Report to the U.S. Geological Survey Caribbean Field Station, St. John, Virgin Islands. 

Bohnsack, J.A. and S.P. Bannerot. 1986. A stationary visual census technique for quantitatively 

assessing community structure of coral reef fishes. NOAA Technical Report NMFS 41, 15 p. 

Bros, W.E. and B.C. Cowell. 1987. A technique for optimizing sample size (replication). Journal of 

Experimental Marine Biology and Ecology 114:63-71. 

Friedlander, A.M., J. Beets, and J. Miller. 1999. Evaluation of a Visual Census Method for Reef 

Fishes at Tektite Reef, Virgin Islands National Park, St. John, U.S. Virgin Islands: 

Determination of Optimal Sample Size. Report submitted to U.S. Geological Survey, Biological 

Resources Division. 23 p. 

Kimmel, J. 1992. NPS coral reef assessment program: preliminary assessment of fishes in the Dry 

Tortugas, Florida. Report to the National Park Service Coral Reef Assessment Program. 4 p. 

Rice, W.R. 1989. Analyzing tables of statistical tests. Evolution 43: 223-225. 

Rogers, C.S. McLain, L. and Tobias C. 1991. Effects of Hurricane Hugo (1989) on a coral reef in 

St. John, USVI. Marine Ecology-Progress Series 78: 189-199. 

Rogers, C.S., Garisson, V. and Grober-Dunsmore, R. 1997. A fishy story about hurricanes and 

herbivory: seven years of research on a reef in St. John, US Virgin Islands. In Proceedings of 

the 8th International Coral Reef Symposium 1, ed. H. A. Lessios and I.G. Macintyre, Balboa, 

Panama: Smithsonian Tropical Institute. p. 555-560. 

Rogers, C.S. and J. Beets. 2001. Degradation of marine ecosystems and decline of fishery resources 

in marine protected areas in the US Virgin Islands. Environmental Conservation 28 (4): 312-

322. 

Skalski, J.R. 2005. Long-term monitoring: Basic study designs, estimators, and precision and power 

calculations. Report prepared for Pacific Islands Network, National Park Service. 

USGS (United States Geological Survey). 2005. Patuxent Wildlife Research Center. Monitor Web 

Site. http://www.pwrc.usgs.gov/resshow/droege3rs/salpower.htm . Accessed July 1, 2005. 

Zar, J.H.1999. Biostatistical analysis. 4th Ed., Prentice-Hall, New Jersey. 663 pp. 

114 
Case Study C: Assessment of Coral Reef Fishes in Dry 

Tortugas National Park 1999-2004 

Jerald S. Ault

1

, Steven G. Smith1, Jiangang Luo1, David B. McClellan2, Douglas E. Harper

2 and 

James A. Bohnsack2 

1

University of Miami 

Rosenstiel School of Marine and Atmospheric Science 

4600 Rickenbacker Causeway 

Miami, FL 33149 

jault@rsmas.miami.edu 

2

NOAA Fisheries 

Southeast Fisheries Science Center 

75 Virginia Beach Drive 

Miami, FL 33149 

115 
Table of Contents for Case Study C

C-1 INTRODUCTION..................................................................................................................................................117

C-2 FISHERY-INDEPENDENT SURVEY DESIGN ................................................................................................118

C-2.1 POPULATION TO BE SAMPLED ............................................................................................................................118

C-2.2 SAMPLING DESIGN .............................................................................................................................................118

C-2.3 METHOD OF MEASUREMENT 123

C-3 ESTIMATION OF BASELINE POPULATION METRICS 126

C-3.1 METHODS...........................................................................................................................................................126

C-3.2 RESULTS ............................................................................................................................................................127

C-3.3 CONCLUSIONS ....................................................................................................................................................132

C-4 STATUS AND TRENDS OF EXPLOITED CORAL REEF FISHES ..............................................................134

C-4.1 METHODS...........................................................................................................................................................134

C-4.2 RESULTS ............................................................................................................................................................137

C-4.3 DISCUSSION .......................................................................................................................................................141

C-5 MPA PERFORMANCE IN THE DRY TORTUGAS REGION .......................................................................146

C-5.1 METHODS...........................................................................................................................................................147

C-5.2 RESULTS ............................................................................................................................................................148

C-5.3 DISCUSSION .......................................................................................................................................................157

C-6 REFERENCES.......................................................................................................................................................160

C-7 COMPUTATIONAL FORMULAE .....................................................................................................................164

116 

..............................................................................................................................

.............................................................................
C-1 Introduction 

Dry Tortugas National Park (DRTO) is a unique tropical marine environment of national 

significance, renowned for its productive coral reef ecosystem, diverse natural resources, broad 

recreational fishing opportunities, and spectacular scenic beauty. The Dry Tortugas and the Florida 

Keys support multibillion dollar fishing and tourism industries in south Florida, including 

economically-important fisheries for pink shrimp, lobster, reef fish (snapper-grouper complex), 

kingfish, and Spanish mackerel. Also, the Tortugas region is believed to be extremely important for 

coral reefs and fisheries as a source of recruitment because of its upstream location in the Florida 

Current that facilitates advective dispersion and transport of eggs and larvae to the rest of the Keys 

(Lee and Williams, 1999; Dahlgren and Sobel, 2000; Lindeman et al., 2001; Ault et al., 2002; 

Yeung and Lee, 2002; Domeier, 2004). 

Over the past several decades, public use of and conflicts over fishery resources have 

increased sharply, while catches from historically productive snapper and grouper stocks have 

declined (Bohnsack et al. 1994; Ault et al. 1997, 1998, 2001). Management actions (e.g. 

establishing size, season, and bag limits on a number of species) implemented by the National 

Oceanic and Atmospheric Administration (NOAA) and the National Park Service (NPS) in the 

Tortugas region aim to reverse declines in important fishery and coral reef resources, including the 

establishment in 2001 of “no-take” marine protected areas in NOAA Sanctuary waters and a 

proposed research natural area (RNA) within DRTO (DOC 1996; NPS 2000; Culhane 2002). There 

is also broader scientific and management interest in developing a better understanding of marine 

reserve design and ultimate performance in rebuilding fisheries and conserving marine biodiversity 

(Bohnsack and Ault 1996; Bohnsack 1998). In addition, as the south Florida-Everglades restoration 

efforts proceed, it will be essential to have effective monitoring programs and predictive models to 

assess ecosystem changes. 

The goal of this case study was to assess baseline conditions of coral reef fishes of DRTO 

during the period 1999-2004, and to evaluate the performance of marine protected areas (MPAs) in 

the Dry Tortugas region. This study analyzed data obtained during comprehensive fisheriesindependent monitoring surveys conducted in 1999, 2000, 2002 and 2004. Data were collected 

using a stationary visual census method under a stratified random sampling design (Ault et al. 

2002). Visual survey data, and additional fishery datasets, were analyzed to evaluate the status and 

117 
trends of exploited and non-exploited species in DRTO over the 1999 to 2004 time frame. These 

analyses are organized into three main sections: 

(1) Estimation of baseline population metrics; 

(2) Sustainability status of exploited coral reef fishes; and 

(3) MPA performance in the Dry Tortugas region. 

We apply a suite of statistical and analytical methodologies previously developed to assess 

status and trends of coral reef fishes in DRTO and the Florida Keys coral reef ecosystem (Ault et al. 

2001, 2002, 2003; Meester et al. 2001, 2004; Franklin et al. 2003). A key aspect of our efforts was 

the use of fishery-independent data to calculate the average size of fish in the exploited phase for 

each stock to monitor status and trends in resources. Length-based stock assessment indicators 

developed previously can be effectively used to quantify the condition of exploited and nonexploited populations (Ault et al. 2001, 2002, 2005a, 2005b). 

This study also reports results from fisheries independent surveys in the Tortugas region to 

assess reef fish populations before and after the establishment of the Tortugas no-take marine 

reserves (NTMRs) in June 2001. To evaluate potential impacts of NTMRs and other factors on reef 

fish sustainability in the Florida Keys coral reef ecosystem, temporal changes of relatively simple 

population and community metrics (e.g., frequency of occurrence, abundance, size compositions, 

and species richness) for the Tortugas region, and within and outside NTMRs, were analyzed. 

C-2 Fishery-Independent Survey Design 

C-2.1 Population to be Sampled 

The Tortugas region is located about 113 km west of Key West, Florida, and encompasses 

approximately 1686 km2 in two principal areas: DRTO (managed by Department of the Interior, 

DOI); and Tortugas Bank (NOAA, FKNMS, Department of Commerce) (Figure C-1). The survey 

domain encompassed coral reef habitats less than 33 m deep in Tortugas Bank and DRTO. 

C-2.2 Sampling Design 

The survey domain was partitioned into habitat strata based on the degree of vertical relief 

(e.g., rugosity, complexity) and the degree of patchiness (e.g., amount of softbottom substrate 

interspersed among reef structures) of the hardbottom substrate (Figure C-2, Table C-1; Ault et al., 

2002; Franklin et al., 2003). Management zones were incorporated as a second spatial stratification 

variable, designated as follows: Tortugas Bank NTMR -- closed to all types of fishing; Tortugas 

118 
Dry Tortugas

National Park

Tortugas Bank NTMR

Tortugas Bank

Fished

FKNMS

Dry Tortugas 

National Park 

Tortugas Bank NTMR 

Tortugas Bank 

Fished 

FKNMS 

Figure C-1: Dry Tortugas region spatial management boundaries. Locations of the sampled 

primary units during a 2004 survey are shown (open pentagons). Bathymetry is denoted by light to 

dark shading (white: 0-3 m; black: > 50 m). 

119 
Pinnacles Medium Pinnacles Medium Profile Reef

Patchy Hardbottom inSand

ReefTerrace

ReefTerrace T Patch errace Patch

Reefs

Low-Relief

RockyOutcrops Spur &Groove

High-Relief

Spur &Groove

Low-Relief Hardbottom

Figure C-2: Spatial distribution of the 8 classified coral reef habitats in the Dry Tortugas region 

overlain with the 200 by 200 m primary unit sampling grid used in monitoring surveys.

120 
Habitat Degree of Degree of Domain-wide Area 

Reef Habitat Classification Code Patchiness Vertical Relief Nh Ah (km2

) 

Low-relief hardbottom LRHB Low Low 4,909 196.36

Low-relief spur & groove LRSG Moderate Low 296 11.84

Patchy hardbottom in sand PHBS High Low 913 36.52

Medium profile reef MDPR Low Moderate 194 7.76

Rocky outcrops RKOC Moderate-High Moderate 1164 46.56

Reef terrace RFTC Low High 422 16.88

High-relief spur & groove HRSG Moderate High 127 5.08

Pinnacle reef RFPN High High 57 2.28

Total 8,082 323.28 

Tortugas Bank Tortugas Bank Dry Tortugas 

Habitat Fished NTMR National Park 

Code Nh Ah (km2) Nh Ah (km2) Nh Ah (km2) 

LRHB 

LRSG 

PHBS 

MDPR 

RKOC 

RFTC 

HRSG 

RFPN 

1,108 

— 

38 

— 

134 

47 

— 

— 

44.32 

— 

1.52 

— 

5.36 

1.88 

— 

— 

1,438 

— 

35 

— 

282 

327 

— 

29 

57.52 

— 

1.40 

— 

11.28 

13.08 

— 

1.16 

2,363 

296 

840 

194 

748 

48 

127 

28 

94.52 

11.84 

33.60 

7.76 

29.92 

1.92 

5.08 

1.12 

Total 1,327 53.08 2,111 84.44 4,644 185.76 

Table C-1: (A) Habitat strata (h) characteristics and sizes in terms of primary sampling units (Nh) 

and area (Ah) for the Dry Tortugas sampling domain. (B) Habitat strata sizes for three management 

zones within the Dry Tortugas sampling domain; dashes denote habitats not found in a given 

management zone. 

(A) 

(B)

121 
Bank Fished -- open to all types of commercial and recreational fishing under regional regulations; 

and, DRTO -- open to only recreational hook-and-line fishing (Figure C-1). In the Tortugas region, 

areas open to fishing (e.g. Tortugas Bank Fished zone), allow a variety of types of legal fishing 

activities under regional management and represent the lowest level of resource protection in the 

study area. DRTO represents an intermediate level of resource protection by allowing only 

recreational angling. Commercial fishing has been prohibited since 1935 when it was established as 

a National Monument. Recreational lobster diving was prohibited in 1980. After conversion to 

DRTO in 1992, protection increased with the exclusion of headboats for recreational fishing in 

1995. The Tortugas Bank NTMR, a no-take and no anchoring reserve, represents the highest level 

of resource protection. Prior to July 1 2001, Tortugas Bank was open to fishing under Gulf of 

Mexico Fishery Management Council and Florida Fish and Wildlife Conservation Commission 

regulations. 

Our spatially-intensive study employed a two-stage stratified random survey design 

(Cochran 1977) to optimize sampling effort and to choose sampling locations. The sampling 

domain was overlain in a Geographical Information System (GIS) with a grid of 200 x 200 m cells 

(40,000 m2) which are the primary sample units. Each cell that contained reef habitat was assigned a 

unique number and randomly selected for sampling from a discrete uniform probability distribution 

to ensure that each primary unit had equal selection probability. Second-stage sample units, i.e., 

diver visual census locations, were then randomly positioned on appropriate habitat within each 

primary unit. For reef fish sampling, there are 226 non-overlapping possible 15 m diameter fish 

sampling stations possible within a given primary sample unit. Two second-stage units (denoted as 

‘diver stations’) were sampled in each primary unit. Each diver station was sampled by two 

individual divers (i.e., a buddy pair), for a total of 4 scientific dives within each primary unit under 

normal operations. Because of concerns about autocorrelation and safe diving practices, each fish 

sampling station (i.e., second-stage unit) consisted of the average of combined stationary point 

estimates from two individual divers (i.e., a “buddy pair”). 

The principal population abundance metrics were frequency of occurrence (i.e., presenceabsence), density (number per unit area), and abundance (total number). Animal density was the 

primary abundance metric used for developing and optimizing the survey design (Ault et al. 2002, 

2003). Coefficient of variation (CV) of mean density, the standard error expressed as a proportion 

of the mean value, was the principal measure used to evaluate survey design performance. Metrics 

were computed using formulae in Section C-8. 

122 
Table C-2 shows statistical sample sizes in terms of primary (n) and second-stage (nm) 

sample units by year, habitat, and management zone. A total of 4,092 scientific dives for more than 

668 hours bottom time, including 3,234 fish survey dives, were made during 1999-2004 cruises in 

the Tortugas region to assess reef fishes, coral reef benthic habitats, and spiny lobsters. 

A detailed analysis of stratification and allocation aspects of this sampling design was 

conducted during spring 2002 prior to the summer 2002 survey. A complete account of this 

sampling plan development is given in Ault et al. (2003). 

This habitat-based stratification is effective because it capitalizes on the statistical 

covariance between fish abundance and coral reef habitat types analyzed from previous surveys 

(Ault et al. 2002, Franklin et al. 2003). In addition, a number of logistical factors enabled divers to 

quickly obtain high sample size over substantial areas at relatively low costs: (1) use of a large, liveaboard dive vessel equipped with Nitrox SCUBA; (2) “live-boating” at dive sites where the vessel 

never anchored, but deployed divers at specified coordinates and picked up the free-swimming 

groups after samples are taken; (3) utilizing highly-trained professional divemasters to oversee the 

complex dive operations; and, (4) conducting the annual surveys within 2-3 weeks during periods 

(May-June) of minimum winds. 

C-2.3 Method of Measurement 

Principal population abundance metrics were collected by standard, non-destructive, in situ, 

fishery-independent visual monitoring methods by highly trained and experienced divers using open 

circuit Nitrox SCUBA. Visual methods are ideal for assessing reef fishes in the Tortugas and 

Florida Keys because of prevailing good visibility and management concerns requiring the use of 

non-destructive assessment methods. 

Reef fish data are collected by a stationary diver centered in a randomly selected circular 

plot (Bohnsack and Bannerot 1986). The circular plot method provides reliable quantitative 

estimates of species composition, abundance (density per plot), frequency of occurrence, and 

individual size composition for the reef fish community. Divers sample 15 m diameter circular plots 

(177 m2) for 5 minutes attempting to count all fish observed within each imaginary cylinder 

extending from the bottom to the limits of vertical visibility (usually the surface). Divers begin each 

sample by facing in one direction and listing all species within the field of view. When no new 

species are noted, new sectors of the cylinder are scanned by rotating in one direction for the 5 min 

period. Several complete rotations were usually made for each plot. After the initial 5 min, data are 

123 
(A) 1999 

Tortugas Bank Tortugas Bank Dry Tortugas 

Habitat Fished NTMR National Park Domain-wide 

Code n nm n nm n nm n nm 

LRHB 11 22 16 29 24 47 51 98 

LRSG — — — — 15 30 15 30 

PHBS 5 10 4 7 7 12 16 29 

MDPR — — — — 4 8 4 8 

RKOC 4 8 12 23 8 14 24 45 

RFTC 4 8 28 53 5 10 37 71 

HRSG — — — — 12 24 12 24 

RFPN — — 8 16 3 6 11 22 

Total 24 48 68 128 78 151 170 327 

(B) 2000 

Tortugas Bank Tortugas Bank Dry Tortugas 

Habitat Fished NTMR National Park Domain-wide 

Code n nm n nm n nm n nm 

LRHB 10 20 17 31 34 64 61 115 

LRSG — — — — 5 9 5 9 

PHBS 10 20 11 20 25 45 46 85 

MDPR — — — — 9 17 9 17 

RKOC 2 4 11 17 28 52 41 73 

RFTC 0 0 17 31 7 12 24 43 

HRSG — — — — 12 22 12 22 

RFPN — — 5 10 4 7 9 17 

Total 22 44 61 109 124 228 207 381 

Table C-2: Reef fish survey sample sizes in terms of primary (n) and second-stage (nm) units by 

habitat class and management zone for (A) 1999, (B) 2000, and (C) 2004. Habitat codes are defined 

in Table 1; dashes denote habitats not found in a given management zone. 

124 
(C) 2004 

Habitat 

Code 

Tortugas Bank 

Fished 

n nm 

Tortugas Bank 

NTMR 

n nm 

Dry Tortugas 

National Park 

n nm 

Domain-wide 

n nm 

LRHB 22 41 9 18 81 146 112 205 

LRSG — — — — 14 26 14 26 

PHBS 11 19 2 4 24 44 37 67 

MDPR — — — — 23 39 23 39 

RKOC 10 19 27 54 24 45 61 118 

RFTC 5 9 16 32 17 33 38 74 

HRSG — — — — 4 8 4 8 

RFPN — — 9 18 7 14 16 32 

Total 48 88 63 126 194 355 305 569 

125 
then collected on the abundance and minimum, mean, and maximum lengths for each species 

sighted. Depth, substrate composition, benthic fauna percentage cover, and vertical relief-rugosity 

characteristics of reef structure are recorded for each plot from the polar perspective of the centrally 

located observer. An all purpose tool (APT), consisting of a ruler connected perpendicularly to the 

end of a meter stick, is used as a reference device to reduce apparent magnification errors in fish 

size estimates. An innovative state-of-the-art digital laser video camera system was designed and 

deployed to increase the precision of both sizing and counting reef fish species. The technical 

methodology is being calibrated against standard divers using the visual census methods and APT 

meter sticks. In usual operations, divers periodically calibrate their sample radius estimates with the 

meter stick or fiberglass tape. Species with few individuals (e.g., Queen Angelfish Holacanthus 

ciliaris, Barracuda Sphyraena barracuda, Hogfish Lachnolaimus maximus) are counted and their 

size estimated immediately. Highly mobile species that are unlikely to remain in the area, such as 

sharks and carangids, are tabulated when first observed and then ignored. For common species (e.g., 

damselfishes, wrasses, etc.) one 360° rotation is made for each species by working back up the list 

in reverse order of recording to reduce potential bias by avoiding counting a species when they were 

particularly abundant or obvious. The time required to record each sample averaged 15-20 min 

(range 5 – 30 min), depending on the habitat. 

Benthic community assessments of hard corals, octocorals, sponges, and algae were 

strategically integrated with the reef fish sampling effort allocations to optimize the performance 

and provide maximum structural coherence of both fish and habitat surveys, and to provide a 

quantitative basis for comparison and calibration of survey efforts that improve mapping and spatial 

stratifications of the survey domain. 

C-3 Estimation of Baseline Population Metrics 

C-3.1 Methods 

During the summers of 1999 and 2000, quantitative fishery-independent survey expeditions 

assessed the multispecies coral reef fish community and associated reef habitats in the Tortugas 

region. These data were used in the development of NTMRs in the Tortugas waters of FKNMS, and 

also used to spatially delineate a no-take Research Natural Area within DRTO. Subsequent surveys 

were conducted in DRTO during summers of 2002 and 2004 prior to implementation of the NTMR 

to obtain additional baseline data on the status of reef fish populations inside and outside the 

proposed boundaries of the RNA. This section documents the basic results of fishery-independent 

126 
surveys conducted in DRTO during 1999-2004. These results provide the fundamental basis for the 

stock assessments of Section C-4 and assessment of MPA performance in Section C-5. 

Baseline population estimates were computed using samples collected within DRTO. The 

DRTO surveys divided among 2 spatial management zones (inside and outside the proposed RNA) 

(Table C-3). Since benthic habitat classification, digital mapping, and development of the DRTO 

survey design occurred concurrently with the surveys of 1999 and 2000 (Ault et al. 2002), 

abundance metrics were estimated as a composite of these two years to alleviate problems of 

misclassification of habitats and misallocation of samples among habitat strata. In this procedure, 

strata means and variance components were computed as two-year averages weighted by respective 

sample sizes in 1999 and 2000. 

C-3.2 Results 

Over the 1999-2004 period, we observed a total of 267 fish species in RVC surveys in the 

Tortugas region. Fish species richness ranged from 8 to 64 species per primary sample unit (psu) 

and, in general, was correlated with habitat class. Greatest reef fish species diversity (63 to 64 

species per psu) was found in high rugosity habitats (reef terrace and reef pinnacles), whereas 

lowest diversity (8 to 11 per psu) was found in low rugosity habitats (low-relief hard-bottom and 

patchy hard-bottom in sand), as illustrated in Figure 4.3 for the 2004 survey. 

Visual survey estimates of percent occurrence by spatial management zone are given in 

Table C-4 for principal exploited and non-target reef fish species. For most species, both exploited 

and non-target, percent occurrence was consistent over the survey time frame with values 

fluctuating within 5 to 10% domain-wide and exhibiting somewhat larger fluctuations in the two 

management zones. Among snapper-grouper complex species, moderate increasing trends in 

percent occurrence were observed for black grouper and mutton snapper, and a moderate decreasing 

trend was observed for hogfish. Nassau grouper and goliath grouper, species under fishing 

moratoria in the Gulf of Mexico and South Atlantic regions, were seen very infrequently by 

scientific divers. Among non-target species, increasing trends in percent occurrence were observed 

for foureye butterflyfish (Chaetodon capistratus), spotted goatfish (Pseudopeneus maculates), and 

purple reeffish (Chromis scotti). 

Visual survey estimates of population mean density by management zone and domain-wide 

design performance are given in Table C-5. Design performance improved, i.e., CV of mean 

density decreased, over the survey time frame for a majority of principal reef fishes (6 of 8 snapper127 
Habitat Inside RNA Outside RNA 

Code Nh Ah (km2) Nh Ah (km2) 

LRHB 1,094 43.76 1,269 50.76 

LRSG 28 1.12 268 10.72 

PHBS 429 17.16 411 16.44 

MDPR 36 1.44 158 6.32 

RKOC 355 14.20 393 15.72 

RFTC 38 1.52 10 0.40 

HRSG 26 1.04 101 4.04 

RFPN 19 0.76 9 0.36 

Total 2,025 81.00 2,619 104.76 

Table C-3: (A) Habitat strata (h) characteristics and sizes in terms of primary sampling units (Nh) 

and area (Ah) for the Dry Tortugas National Park (DRTO) sampling domain. (B) Habitat strata sizes 

for two management zones within the DRTO sampling domain. 

(A) 

(B) 

Reef Habitat Classification 

Habitat 

Code 

Degree of 

Patchiness 

Degree of 

Vertical Relief 

Domain-wide Area 

Nh Ah (km2) 

Low-relief hardbottom LRHB Low Low 2,363 94.52 

Low-relief spur & groove LRSG Moderate Low 296 11.84 

Patchy hardbottom in sand PHBS High Low 840 33.60 

Medium profile reef MDPR Low Moderate 194 7.76 

Rocky outcrops RKOC Moderate-High Moderate 748 29.92 

Reef terrace RFTC Low High 48 1.92 

High-relief spur & groove HRSG Moderate High 127 5.08 

Pinnacle reef RFPN High High 28 1.12 

Total 

4,644 185.76 

128 
Table C-4: Visual survey estimates of percent occurrence (and standard errors in parentheses) for selected fish species during three time 

periods for the Dry Tortugas National Park (DRTO) sampling domain and for two spatial management zones. 

DRTO Inside RNA Outside RNA 

Taxa 1999-2000 2002 2004 1999-2000 2002 2004 1999-2000 2002 2004 

Snapper-Grouper Complex 

Groupers (Serranidae) 

Goliath Grouper 

(Epinephelus itajara) 1.1 (1.1) 7.1 (2.7) 1.1 (0.5) 0.1 (0.1) 7.1 (4.0) 2.5 (1.2) 1.9 (1.9) 7.1 (3.6) 0.0 (0.0) 

Red Grouper 

(E. morio) 62.6 (4.2) 57.9 (4.8) 54.5 (3.7) 53.2 (7.1) 50.4 (8.4) 61.6 (5.1) 69.8 (5.1) 63.7 (5.6) 49.1 (5.2) 

Nassau Grouper 

(E. striatus) 1.5 (1.1) 1.1 (1.0) 0.5 (0.4) 2.0 (2.0) 0.2 (0.2) 0.1 (0.1) 1.0 (1.0) 1.7 (1.7) 0.8 (0.6) 

Black Grouper 

(Mycteroperca bonaci) 27.6 (3.8) 36.7 (4.7) 36.8 (3.3) 28.2 (6.6) 29.2 (6.0) 34.4 (4.9) 27.2 (4.3) 42.5 (6.8) 38.6 (4.4) 

Snappers (Lutjanidae) 

Mutton Snapper 

(Lutjanus analis) 17.1 (3.8) 26.3 (6.2) 27.4 (3.3) 16.9 (7.1) 24.3 (6.8) 26.2 (4.7) 17.2 (4.0) 27.9 (9.7) 28.3 (4.7) 

Gray Snapper 

(L. griseus) 17.9 (3.7) 14.0 (5.0) 14.2 (2.8) 22.6 (6.8) 15.2 (4.3) 16.9 (3.9) 14.3 (4.1) 13.1 (8.3) 12.2 (3.9) 

Yellowtail Snapper 

(Ocyurus chrysurus) 82.4 (3.5) 78.2 (4.1) 79.5 (2.7) 85.1 (5.2) 85.9 (5.1) 76.2 (4.3) 80.4 (4.8) 72.2 (6.0) 82.1 (3.5) 

Wrasses (Labridae) 

Hogfish 

(Lachnolaimus maximus) 52.1 (4.5) 39.4 (4.0) 41.5 (3.6) 48.2 (7.5) 42.3 (5.7) 45.1 (4.7) 55.1 (5.4) 37.2 (5.6) 38.7 (5.3) 

Grunts (Haemulidae) 

White Grunt 

(Haemulon plumieri) 86.4 (2.9) 73.2 (3.5) 76.8 (3.2) 86.1 (4.6) 71.6 (6.3) 76.7 (4.3) 86.6 (3.7) 74.4 (3.9) 76.9 (4.5) 

Bluestriped Grunt 

(H. sciurus) 5.8 (2.0) 6.6 (3.0) 8.3 (1.9) 3.0 (1.6) 5.4 (2.3) 13.4 (3.5) 8.0 (3.3) 7.5 (5.1) 4.3 (1.9) 

Non-Target Fishes 

Surgeonfishes (Acanthuridae) 

Ocean Surgeon 

(Acanthurus bahianus) 43.9 (4.9) 39.8 (4.8) 49.0 (3.9) 40.0 (8.4) 35.9 (7.1) 54.1 (5.3) 47.0 (5.8) 42.9 (6.6) 45.0 (5.6) 

Blue Tang 

(A. coeruleus) 72.3 (4.4) 75.6 (5.2) 74.9 (3.3) 78.7 (7.0) 78.1 (8.0) 78.1 (4.5) 67.4 (5.6) 73.7 (6.9) 72.5 (4.7) 

129 
Table C-4 (cont.) 

DRTO Inside RNA Outside RNA 

Taxa 1999-2000 2002 2004 1999-2000 2002 2004 1999-2000 2002 2004 

Butterflyfishes 

(Chaetodontidae) 

Foureye Butterflyfish 

(Chaetodon capistratus) 23.3 (3.6) 27.0 (6.3) 34.5 (3.0) 16.1 (4.9) 16.7 (7.7) 24.9 (3.9) 28.9 (5.2) 35.0 (9.4) 41.9 (4.4) 

Spotfin Butterflyfish 

(C. ocellatus) 50.1 (4.8) 39.7 (4.3) 47.5 (3.3) 51.3 (8.6) 41.3 (6.6) 45.4 (5.2) 49.1 (5.3) 38.4 (5.5) 49.1 (4.2) 

Goatfishes (Mullidae) 

Spotted Goatfish 

(Psuedupeneus maculatus) 47.5 (4.6) 41.5 (4.2) 65.1 (3.5) 52.1 (7.5) 38.4 (6.4) 67.7 (4.7) 43.9 (5.8) 43.8 (5.5) 63.1 (4.9) 

Angelfishes 

(Pomacanthidae) 

Blue Angelfish 

(Holocanthus bermudensis) 45.4 (4.8) 53.4 (5.1) 46.1 (3.5) 53.1 (8.6) 49.3 (8.9) 43.5 (4.3) 39.4 (5.2) 56.5 (6.0) 48.1 (5.3) 

Gray Angelfish 

(Pomacanthus arcuatus) 42.4 (4.7) 42.5 (4.5) 47.5 (3.3) 39.1 (8.3) 38.5 (7.6) 48.2 (4.2) 44.9 (5.4) 45.6 (5.4) 47.1 (4.9) 

Damselfishes 

(Pomacentridae) 

Purple Reeffish 

(Chromis scotti) 25.4 (4.4) 54.7 (5.3) 53.5 (3.5) 23.9 (7.7) 48.7 (7.6) 48.0 (5.4) 26.6 (5.0) 59.3 (7.4) 57.8 (4.5) 

Bicolor Damselfish 

(Stegastes partitus) 58.4 (4.8) 67.8 (4.4) 58.7 (3.7) 61.2 (7.9) 54.5 (6.8) 57.4 (5.0) 56.2 (6.0) 78.1 (5.7) 59.7 (5.4) 

Cocoa Damselfish 

(S. variabilis) 91.5 (2.2) 89.0 (3.5) 97.3 (0.9 ) 93.2 (2.7) 85.7 (6.2) 99.5 (0.5) 90.2 (3.4) 91.5 (3.9) 95.7 (1.6) 

Parrotfishes (Scaridae) 

Striped Parrotfish 

(Scarus iseri) 89.6 (2.8) 84.1 (3.3) 93.3 (1.8) 95.8 (3.2) 89.4 (5.3) 90.7 (3.6) 84.7 (4.3) 80.0 (4.1) 95.4 (1.8) 

Redband Parrotfish 

(Sparisoma aurofrenatum) 80.9 (4.2) 69.3 (4.9) 85.4 (2.2) 78.3 (7.4) 65.7 (6.5) 81.2 (3.8) 83.0 (4.6) 72.1 (7.0) 88.6 (2.6) 

Stoplight Parrotfish 

(Sparisoma viride) 58.1 (4.7) 53.4 (5.5) 65.4 (3.2) 59.9 (8.1) 44.7 (7.3) 67.6 (4.8) 56.7 (5.5) 60.0 (7.9) 63.8 (4.3) 

130 
Table C-5: Visual survey estimates of population mean density D (and standard errors in parentheses) for selected fish species during 

three time periods for Dry Tortugas National Park (DRTO) sampling domain and for two spatial management zones. The design 

performance measure CV (%) of mean density is also provided for the DRTO sampling domain. Density unit is number per 177 m2. 

DRTO Inside RNA Outside RNA 

1999-2000 2002 2004 1999-2000 2002 2004 1999-2000 2002 2004 

Taxa D (SE) CV D (SE) CV D (SE) CV D (SE) D (SE) D (SE) D (SE) D (SE) D (SE) 

Exploited 

Red Grouper 0.62 (0.06) 9.2 0.68 (0.08) 11.9 0.54 (0.04) 7.5 0.56 (0.09) 0.51 (0.08) 0.59 (0.06) 0.67 (0.07) 0.82 (0.13) 0.50 (0.06) 

Black Grouper 0.22 (0.03) 15.6 0.60 (0.12) 20.7 0.47 (0.06) 12.0 0.19 (0.04) 0.53 (0.19) 0.41 (0.08 0.24 (0.05) 0.66 (0.17) 0.52 (0.08) 

Mutton Snapper 0.14 (0.05) 34.8 0.34 (0.14) 39.5 0.31 (0.05) 17.5 0.18 (0.11) 0.22 (0.11) 0.30 (0.09) 0.12 (0.03) 0.44 (0.23) 0.31 (0.07) 

Gray Snapper 1.14 (0.47) 40.8 0.95 (0.36) 38.2 6.05 (4.96) 81.9 1.30 (0.78) 1.14 (0.51) 12.3 (11.4) 1.02 (0.57) 0.80 (0.51) 1.26 (0.46) 

Yellowtail Snapper 5.09 (0.64) 12.6 9.40 (2.48) 26.4 12.4 (1.41) 11.4 4.75 (0.94) 13.7 (5.43) 11.7 (2.42) 5.34 (0.88) 6.09 (1.31) 12.9 (1.67) 

Hogfish 0.60 (0.09) 14.3 0.53 (0.07) 13.5 0.45 (0.05) 11.2 0.51 (0.11) 0.61 (0.11) 0.49 (0.07) 0.67 (0.13) 0.47 (0.09) 0.43 (0.07) 

White Grunt 7.10 (1.26) 17.8 4.69 (0.82) 17.6 7.24 (1.77) 24.4 5.60 (1.66) 3.06 (0.65) 6.67 (2.31) 8.26 (1.83) 5.95 (1.37) 7.68 (2.58) 

Bluestriped Grunt 0.16 (0.10) 64.5 0.60 (0.49) 82.2 0.74 (0.40) 54.5 0.06 (0.06) 0.22 (0.11) 1.37 (0.91) 0.23 (0.18) 0.90 (0.87) 0.24 (0.11) 

Non-Target 

Ocean Surgeon 1.00 (0.18) 18.4 0.70 (0.15) 21.6 1.03 (0.13) 12.5 0.93 (0.30) 0.66 (0.25) 1.08 (0.16) 1.06 (0.23) 0.74 (0.19) 0.99 (0.19) 

Blue Tang 1.92 (0.27) 13.9 3.65 (0.38) 10.5 3.67 (0.41) 11.1 1.94 (0.38) 4.32 (0.73) 3.90 (0.40) 1.90 (0.37) 3.13 (0.38) 3.49 (0.65) 

Foureye Butterflyfish 0.33 (0.07) 20.2 0.52 (0.14) 26.7 0.49 (0.06) 12.8 0.25 (0.11) 0.34 (0.20) 0.30 (0.06) 0.40 (0.08) 0.66 (0.19) 0.64 (0.10) 

Spotfin Butterflyfish 0.64 (0.08) 12.1 0.86 (0.22) 25.3 0.64 (0.06) 9.5 0.59 (0.13) 0.99 (0.46) 0.58 (0.07) 0.67 (0.09) 0.75 (0.14) 0.68 (0.09) 

Spotted Goatfish 0.53 (0.07) 13.6 0.61 (0.08) 13.7 1.51 (0.21) 13.9 0.57 (0.10) 0.74 (0.16) 1.37 (0.22) 0.50 (0.10) 0.50 (0.08) 1.62 (0.33) 

Blue Angelfish 0.56 (0.08) 15.1 0.91 (0.15) 16.4 0.66 (0.07) 11.1 0.65 (0.15) 0.78 (0.22) 0.81 (0.13) 0.49 (0.10) 1.02 (0.21) 0.54 (0.08) 

Gray Angelfish 0.45 (0.06) 13.4 0.58 (0.08) 13.8 0.96 (0.33) 34.2 0.43 (0.11) 0.58 (0.13) 0.72 (0.11) 0.46 (0.07) 0.58 (0.10) 1.15 (0.58) 

Purple Reeffish 1.73 (0.52) 30.2 10.9 (3.63) 33.4 7.07 (1.01) 14.3 1.48 (0.85) 6.22 (2.76) 7.01 (1.41) 1.93 (0.66) 14.5 (6.08) 7.12 (1.43) 

Bicolor Damselfish 3.38 (0.62) 18.2 7.30 (1.81) 24.7 4.30 (0.45) 10.6 2.47 (0.72) 2.83 (0.71) 4.09 (0.59) 4.08 (0.94) 10.8 (3.16) 4.46 (0.67) 

Cocoa Damselfish 4.88 (0.33) 6.8 5.98 (0.82) 13.7 4.99 (0.25) 5.1 5.72 (0.61) 5.78 (0.89) 5.23 (0.31) 4.23 (0.35) 6.13 (1.28) 4.80 (0.39) 

Striped Parrotfish 10.1 (1.98) 19.5 11.7 (1.97) 16.9 11.1 (0.55) 4.9 13.0 (4.45) 14.1 (4.28) 12.1 (0.92) 7.88 (0.69) 9.79 (1.15) 10.3 (0.66) 

Redband Parrotfish 2.87 (0.58) 20.1 2.59 (0.43) 16.5 4.72 (1.62) 34.4 2.34 (0.44) 2.94 (0.78) 2.92 (0.29) 3.27 (0.96) 2.33 (0.46) 6.12 (2.87) 

Stoplight Parrotfish 1.07 (0.13) 12.4 1.33 (0.22) 16.6 1.87 (0.24) 12.8 1.26 (0.25) 1.35 (0.39) 2.34 (0.44) 0.93 (0.14) 1.31 (0.25) 1.50 (0.25) 

131 
grouper species, 9 of 13 non-target species). Among the eight snapper-grouper species analyzed, 

CVs were below 15% in 4 cases and ranged from 15 to 25% in 2 cases for the 2004 survey. High 

CVs were estimated in 2004 for gray snapper (Lutjanus griseus) and bluestriped grunt (Haemulon 

sciurus), two species with relatively low frequency of occurrence (Table C-4), i.e., high frequencies 

of zero observation samples. Among non-target species, CVs were below 15% in 11 of 13 cases. A 

CV of 15% enables statistical detection of a 30% or more change in a population metric from one 

time period to another. 

Among snapper-grouper species, red grouper (Epinephelus morio) and white grunt 

(Haemulon plumierii) exhibited fairly stable domain-wide densities over the survey time period 

(Table C-5). Consistent with estimates of percent occurrence, increases in density over time were 

observed for black grouper (Mycteroperca bonaci) and mutton snapper (Lutjanus analis), and a 

slight decrease was observed for hogfish (Lachnolaimus maximus). Density also increased for 

yellowtail snapper (Ocyurus chrysurus). These trends in domain-wide density were also observed in 

the two management zones in most cases. Mean densities appeared to increase for gray snapper and 

bluestriped grunt, but estimates for these species were quite variable. Eight of 13 non-target species 

exhibited either stable or fluctuating trends in domain-wide density over time, and five species 

(Blue tang Acanthurus coeruleus, foureye butterflyfish, spotted goatfish, purple reeffish, stoplight 

parrotfish Sparisoma viride) showed increases in density from 1999-2000 to 2004. 

Further insights to time trends in population densities for snapper-grouper species are 

provided in Table C-6, which provides life stage-specific density estimates. Observed increases in 

population densities for yellowtail snapper and black grouper were also apparent for both juvenile 

and adult life stages. The increase in population density of mutton snapper seems to have occurred 

in the adult life stage only, whereas the decrease in population density for hogfish seems to have 

mainly occurred in the juvenile life stage. Interestingly, stable trends in population density for red 

grouper corresponded with an increase in adult density and a decrease in juvenile density. 

C-3.3 Conclusions 

Reef fish surveys conducted during the 1999-2004 time period provide a robust baseline for 

population abundance metrics prior to implementation of the no-take RNA in the Park. Our survey 

approach of estimating abundance for both pre-exploited and exploited life stages of reef fishes will 

facilitate separating the effects of reductions in fishing from fluctuations in recruitment in future 

132 
Table C-6: Survey estimates of mean density (and standard errors in parentheses) for juvenile, adult, and exploited life stages of 

principal snapper-grouper species during three time periods for the Dry Tortugas National Park (DRTO) sampling domain and for two 

spatial management zones. Density unit is number per 177 m2. 

DRTO Inside RNA Outside RNA

 1999-2000 2002 2004 1999-2000 2002 2004 1999-2000 2002 2004

Taxa D SE D SE D SE D SE D SE D SE D SE D SE D SE

Juveniles

Red Grouper 0.43 0.05 0.46 0.07 0.26 0.03 0.30 0.07 0.34 0.07 0.25 0.04 0.52 0.06 0.55 0.10 0.26 0.04

Black Grouper 0.20 0.03 0.46 0.11 0.33 0.04 0.19 0.04 0.43 0.17 0.28 0.06 0.21 0.04 0.48 0.14 0.37 0.06

Mutton Snapper 0.03 0.02 0.07 0.07 0.02 0.02 0.03 0.02 0.00 0.00 0.01 0.01 0.04 0.02 0.12 0.12 0.03 0.03

Gray Snapper 0.64 0.36 0.19 0.14 1.90 1.48 0.80 0.67 0.08 0.02 3.65 3.38 0.51 0.39 0.28 0.25 0.55 0.33

Yellowtail Snapper 3.87 0.56 5.12 0.85 7.41 1.02 3.68 0.82 5.38 1.35 6.71 1.70 4.02 0.77 4.91 1.08 7.96 1.24

Hogfish 0.19 0.04 0.05 0.02 0.05 0.01 0.12 0.04 0.07 0.04 0.03 0.01 0.25 0.07 0.03 0.02 0.06 0.02

White Grunt 4.90 1.09 3.33 0.67 3.73 0.77 3.35 1.41 1.61 0.34 3.69 1.22 6.11 1.59 4.65 1.16 3.76 0.98

Bluestriped Grunt 0.07 0.03 0.39 0.31 0.47 0.30 0.02 0.01 0.17 0.09 0.90 0.68 0.10 0.06 0.56 0.54 0.15 0.08

Adults

Red Grouper 0.19 0.04 0.23 0.05 0.28 0.03 0.26 0.07 0.18 0.04 0.34 0.04 0.14 0.03 0.26 0.08 0.24 0.04

Black Grouper 0.02 0.02 0.14 0.03 0.15 0.02 0.003 0.003 0.10 0.04 0.14 0.03 0.03 0.03 0.18 0.05 0.15 0.03

Mutton Snapper 0.11 0.04 0.27 0.08 0.29 0.05 0.15 0.10 0.22 0.07 0.29 0.09 0.08 0.02 0.31 0.12 0.28 0.06

Gray Snapper 0.50 0.19 0.76 0.28 4.15 3.48 0.50 0.16 1.06 0.50 8.60 7.98 0.51 0.31 0.53 0.31 0.71 0.25

Yellowtail Snapper 1.21 0.28 4.28 1.90 4.98 0.69 1.07 0.40 8.30 4.31 4.99 1.09 1.32 0.39 1.17 0.47 4.97 0.89

Hogfish 0.41 0.06 0.48 0.07 0.41 0.05 0.39 0.09 0.54 0.10 0.46 0.06 0.43 0.09 0.43 0.09 0.37 0.06

White Grunt 2.19 0.54 1.36 0.36 3.51 1.26 2.25 0.87 1.44 0.49 2.98 1.23 2.15 0.67 1.30 0.51 3.92 2.01

Bluestriped Grunt 0.09 0.07 0.21 0.19 0.26 0.12 0.04 0.03 0.04 0.03 0.48 0.25 0.13 0.13 0.34 0.33 0.10 0.08

Exploited Phase

Red Grouper 0.15 0.03 0.15 0.04 0.22 0.03 0.22 0.07 0.12 0.04 0.27 0.04 0.10 0.02 0.16 0.05 0.17 0.03

Black Grouper 0.02 0.02 0.14 0.03 0.15 0.02 0.003 0.003 0.10 0.04 0.14 0.03 0.03 0.03 0.18 0.05 0.15 0.03

Mutton Snapper 0.10 0.04 0.22 0.06 0.23 0.04 0.14 0.10 0.18 0.05 0.26 0.07 0.07 0.02 0.25 0.09 0.22 0.05

Gray Snapper 0.45 0.18 0.73 0.27 4.15 3.48 0.45 0.15 1.05 0.50 8.59 7.98 0.45 0.30 0.48 0.28 0.71 0.25

Yellowtail Snapper 0.59 0.18 1.03 0.50 1.94 0.37 0.47 0.27 2.07 1.14 1.91 0.62 0.69 0.25 0.22 0.13 1.97 0.45

Hogfish 0.21 0.04 0.29 0.06 0.30 0.03 0.22 0.05 0.38 0.10 0.35 0.06 0.20 0.05 0.21 0.07 0.25 0.04

White Grunt 2.52 0.59 1.44 0.37 4.32 1.57 2.70 0.94 1.56 0.52 4.04 1.97 2.39 0.75 1.33 0.51 4.54 2.32

Bluestriped Grunt 0.14 0.10 0.26 0.20 0.49 0.30 0.05 0.03 0.13 0.08 0.97 0.68 0.21 0.18 0.35 0.35 0.12 0.08

133 
analyses of NTMR performance. Recent efforts to refine benthic habitat maps for DRTO should 

lead to improvements in the accuracy and precision of abundance estimates in future surveys. 

C-4 Status and Trends of Exploited Coral Reef Fishes 

Our research focus has been to quantify the reef fish community response to exploitation in 

the Florida Keys (Ault et al. 1998, 2005b). The principal stock assessment indicator variable to 

quantify population status is average length ( 

L

) of the exploited part of the population, which is a 

metabolic-based indicator that is highly correlated with population size (Beverton and Holt 1957; 

Ricker 1963; Pauly and Morgan 1987; Ault and Ehrhardt 1991; Ehrhardt and Ault 1992; Kerr and 

Dickie 2001). For exploited species, Lreflects the rate of fishing mortality. Because body size is 

broadly correlated with trophic level, large individuals and species are often top predators. Biomass 

declines of these animals are usually the most marked community response to exploitation (Ault et 

al. 1998; Pauly et al. 1998; Gislason and Rice 1998; Kerr and Dickie 2001). Previous studies have 

shown that the 

L

estimator of mortality rate is unbiased under conditions of constant annual 

recruitment to the exploited stock (Ehrhardt and Ault 1992; Quinn and Deriso 1999). However, 

resulting mortality estimates may exhibit positive bias under conditions of an increasing trend in 

recruitment. In the Florida Keys, recruitment of the most severely depleted stocks may be 

increasing at present in response to a series of recent management actions for the snapper-grouper 

complex, including gear restrictions, minimum size and bag limits, and spatial closures (Ault et al. 

2005a). 

We evaluate average size as an estimator of exploitation status for Dry Tortugas and Florida 

reef fishes. Our objective is to apply the L estimator of mortality simultaneously to a suite of reef 

fish species under the same nominal fishing effort as a first-order indicator of the community 

response to exploitation. 

C-4.1 Methods 

Table C-7 provides life history parameters for Florida reef fishes taken from Ault et al. 

(1998, 2005b) and Claro et al. (2001). Natural mortality rate (M) was estimated from lifespan, 

applying the procedure of Alagaraga (1984). Total instantaneous mortality rate (Z) was estimated 

using the method of Ehrhardt and Ault (1992), which is based on length at first capture ( 

L

c ), 

maximum length in the stock ( 

L! ), and average length in the exploited stock ( L ), in conjunction 

134 
Table C-7: Life history input parameters and estimated population parameters for Florida reef fishes (see text for description of 

symbols). 

Input Parameters Estimated Parameters 

Species aλ (y) K (y-1) L∞

(mm) a0 (y) W∞(kg) 

Lc 

(mm) 

Lm 

(mm) 

Lλ

(mm) 

Lbar 

(mm) 

M 

(y-1) 

F 

(y-1) 

SPR 

(%) F/Fmsy B/Bmsy 

Groupers 

Rock Hind 12 0.167 499 -2.50 2.48 200 329 455 289.3 0.250 0.175 33.2 0.70 1.54 

Graysby 15 0.130 415 -0.94 1.14 200 165 362 234.3 0.200 0.511 24.9 2.56 0.53 

Coney 17 0.145 699 -1.08 1.50 200 185 648 – 0.176 – 

Red hind 11 0.200 471 -2.40 1.75 180 341 439 267.6 0.272 0.116 47.2 0.43 1.41 

Goliath 37 0.054 2394 -3.62 244.9 600 979 2126 1823.0 0.081 0.0 10.0 0.0 3.72 

Red 17 0.153 938 -0.10 11.9 500 434 869 584.4 0.176 0.480 23.5 2.72 0.50 

Nassau 17 0.153 938 -0.10 11.9 600 483 869 600.0 0.176 1.000 15.7 5.67 0.27 

Black 33 0.170 1306 -0.30 41.4 600 801 1301 737.1 0.091 0.624 2.0 6.87 0.05 

Yellowmouth 17 0.063 882 -9.03 15.7 500 470 710 610.0 0.176 0.0 5.0 0.0 1.82 

Gag 23 0.149 1187 -0.80 25.1 600 684 1074 713.2 0.130 0.457 15.7 3.50 0.41 

Scamp 21 0.126 1000 -1.36 19.3 500 489 940 500.0 0.143 1.000 2.0 7.01 0.02 

Tiger 26 0.110 740 -1.88 6.38 500 460 705 – 0.115 – 

Yellowfin 15 0.170 860 0.00 15.7 300 529 792 739.5 0.200 0.0 5.0 0.0 2.24 

Snappers 

Mutton 14 0.129 939 -0.74 14.1 400 276 798 502.5 0.214 0.338 33.4 1.58 0.74 

Schoolmaster 12 0.180 570 0.00 3.28 250 145 504 344.7 0.250 0.144 61.0 0.57 1.34 

Blackfin 9 0.084 730 -2.90 2.41 220 230 459 – 0.333 – 

Gray 12 0.136 722 -0.86 5.25 250 230 596 282.6 0.250 1.58 6.8 6.35 0.18 

Dog 12 0.100 854 -2.00 10.2 300 229 643 496.4 0.250 0.0 10.0 0.0 2.57 

Mahogany 10 0.097 618 -1.73 3.18 230 130 419 – 0.300 – 

Lane 10 0.097 618 -1.73 3.24 200 206 418 225.7 0.300 1.176 9.3 3.92 0.24 

Silk 9 0.092 781 -2.31 9.28 190 304 504 – 0.333 – 

Yellowtail 14 0.209 455 -0.71 1.24 250 199 433 270.6 0.214 1.865 13.2 8.71 0.27 

Vermilion 14 0.144 650 -0.24 3.40 230 273 566 – 0.214 – 

Wrasses 

Hogfish 23 0.080 913 -1.78 14.1 300 249 786 394.1 0.130 0.309 17.3 2.37 0.46 

Grunts 

Porkfish 7 0.440 397 -0.35 1.72 200 231 381 260.0 0.428 0.567 28.0 1.32 0.77 

Margate 10 0.174 753 -0.45 8.57 200 428 630 355.8 0.300 0.052 75.2 0.17 3.60 

Tomtate 11 0.091 442 -2.09 1.89 150 131 307 172.0 0.272 0.871 17.1 3.20 0.41 

French 12 0.179 294 0.00 0.57 160 177 260 197.1 0.250 0.398 27.6 1.59 0.66 

Cottonwick 9 0.320 350 -0.50 0.82 160 207 333 225.5 0.333 0.257 42.6 0.77 1.24 

White 8 0.186 512 -0.78 3.06 170 180 411 206.6 0.375 1.176 9.9 3.14 0.28 

Bluestriped 8 0.300 413 0.00 1.36 180 205 375 231.9 0.375 0.671 20.3 1.79 0.55 

135 
F M Z

L t

with the Bertalanffy growth parameters

K

 and 

L!

. Estimates of Z were computed using an 

iterative numerical algorithm (LBAR; Ault et al. 1996; FAO 2003), and annual estimates of 

fishing mortality rate ( ) were obtained by subtracting from . All input values are given in 

Table C-7. 

Theoretically, in year is expressed as 

( ) ( , ) ( , )

( )

( ) ( , )

c

c

a

a

a

a

F t N a t L a t da

L t

F t N a t da

!

!

=

"

"

(1)

where

c a

 is the minimum age at first capture, a!the oldest age in the stock, 

N(a,t)

 the 

abundance for age class 

a , L(a,t)

 the length at age, and 

F(t)

 is the instantaneous fishing 

mortality rate at time 

t

. In practice, Lis usually estimated in the length range 

L

c

" L! . Estimates 

of average length and the corresponding variances were obtained from fishery-independent 

length composition data, applying standard statistical procedures (Sokal and Rohlf 1981). Nonnormality of length observations was corrected by log-transformation. 

A numerical cohort-structured model, REEFS (Reef Fish Equilibrium Exploitation 

Fishery Simulator; Ault and Olson 1996; Ault et al. 1998) was used to conduct simulation 

analysis of uncertainty properties of F estimates based on average size, and to compute several 

fishery management reference points of stock status, or “sustainability benchmarks”, including 

yield-per-recruit (YPR), spawning potential ratio (SPR), and limit control rules. The benchmarks 

used to evaluate sustainable exploitation in terms of a limit control rule were: (F generating 

maximum sustainable yield, MSY); B

msy

 (population biomass at MSY); and SPR (spawning 

potential ratio; Mace 1997; Restrepo and Powers 1999). We define F

msy as F = M . The REEFS 

models the age-size distribution of the population from larvae to mature adults to maximum sizeage using a number of population dynamic functions to regulate birth, growth, and survivorship 

processes, including selection and harvest by the fishery (see Ault et al. 1998). 

Since population biomass

B(a,t) is the product of numbers-at-age times weight-at-age, 

yield in weight

Y

w from a species was calculated as 

( , , ) ( ) ( , ) ( ) ( , ) ( , )

c c

L L

w c

L L

Y F L t F t B L a t dL F t N L a t W L a t dL

! !

= = = " "

(2) 

136 

F

msy
Spawning stock biomass (SSB), a measure of stock reproductive potential, was obtained 

by integrating over individuals in the population between the minimum size of sexual maturity 

( 

L

m

) and the maximum size ( 

L!

( ) ( , )

m

L

L

SSB t B L a t dL

!

= "

), 

(3)

Spawning potential ratio (SPR) is a management benchmark that measures the stock’s 

current reproductive potential to produce optimal yields on a sustainable basis, 

un loited

loited

SSB

SSB

SPR

exp

exp

=

. (4) 

Estimated SPRs are compared to U.S. Federal standards which define 30% SPR as the 

overfishing threshold at which the stock is no longer sustainable at the current exploitation level. 

C-4.2 Results 

The analytical relationship between 

L

and 

F

, some aspects of uncertainty in average 

length mortality estimates, and the expected stochastic population length compositions at three 

levels of fishing mortality ( 

2001 0; ; F = F = Fmsy

F = F ) are depicted for hogfish in Figure C-3. 

Although the 95% confidence interval (CI) of 

L

is larger at F

msy

than at F2001, the corresponding 

CI of

F

 is higher at F2001owing to the non-linear relationship between 

L

and 

F

. The non-linear 

relationship also results in asymmetric CIs of F that are more pronounced at higher exploitation 

rates. The method thus has high statistical power for discerning between sustainable and 

overfished levels of F , but has low power for discerning between overfished and severely 

overfished levels of F . L mortality estimates, based on fishery-independent size composition data collected in 

2004 from Dry Tortugas region, and management benchmarks for 31 species of reef fish are 

provided in Table C-7. For some species (e.g., coney Cephalophlus fulvus, goliath grouper 

Epinephelus itajara, tiger grouper Mycteroperca tigris, yellowfin grouper Mycteroperca 

venenosa), it was difficult to obtain reliable mortality estimates due to low sample sizes. The 

theoretical relationships among various fishery sustainability decision metrics are shown for 

hogfish in Figure C-4. Estimates of SPR for Tortugas reef fishes are graphed in Figure C-5. In 

137 

L
250

300

350

400

450

500

550

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

Fishing Mortality Rate 

Average Length (mm) 

0 .00 

0 .02 

0 .04 

134174214254294334374414454494534574614654694734774 

Length (mm) 

Frequency 

F = F msy Current Exploitation 

0.00 

0.02 

0.04 

0.06 

0.08 

0.10 

0.12 

134174214254294334374414454494534574614654694734

Length (mm) 

Frequency 

F = F 2001 

250

300

350

400

450

500

550

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

Fishing Mortality Rate

Average Length (mm)

0.00

0.02

0.04

134174214254294334374414454494534574614654694734774

Length (mm)

Frequency

F = F msy Current Exploitation

0.00

0.02

0.04

0.06

0.08

0.10

0.12

134174214254294334374414454494534574614654694734

Length (mm)

Frequency

F = F 2001

F = 0 

Unexploited 

F = 0

Unexploited

134

174214254294334374414

454

494534574614

654

694

734

774

Length (mm) 

0 .06 MSY Exploitation 

774 

Length (mm)

0.06 MSY Exploitation

774

Figure C-3: Relationship of Lbar in the exploited phase and fishing mortality F for hogfish, and 

the variation in F estimates (dotted horizontal bars) resulting from variation in Lbar (dashed 

vertical bars). Insets show representative population length frequency compositions at F0, Fmsy, 

and F2001. 

138 

Frequency 0.04

0.03

0.02

0.01

0.00
Figure C-4: Theoretical relationship of the fishery sustainability decision metrics spawning 

potential ratio (SPR) and yield-per-recruit (YPR) to fishing mortality rate (F) for hogfish. Graph 

shows position of maximum sustainable yield (MSY) and Fmsy that are used to compute limit 

control rules under the precautionary approach to fishery management. 

139 
0.00

0.20

0.40

0.60

0.80

1.00

G

R

O

U

P

E

R

S

Rock HindGraysbyConeyRed HindGoliathRed

NassauBlackYellow

mouth

GagScampTigerYellowfinS

N

A

P

P

E

R

S

MuttonSchoolmasterBlackfinGrayDogMahogonyLaneSilkYellowtailVermilionHogfishG

R

U

N

T

S

PorkfishMargateTomtateFrenchCottonwickWhiteBluestriped

S

p

a

w

nin

g

P

o

t

e

n

tial R

a

tio

Groupers Snappers Grunts

Overfishing (30% SPR)

0.00

0.20

0.40

0.60

0.80

1.00

G

R

O

U

P

E

R

S

Rock HindGraysbyConeyRed HindGoliathRed

NassauBlackYellow

mouth

GagScampTigerYellowfinS

N

A

P

P

E

R

S

MuttonSchoolmasterBlackfinGrayDogMahogonyLaneSilkYellowtailVermilionHogfishG

R

U

N

T

S

PorkfishMargateTomtateFrenchCottonwickWhiteBluestriped

S

p

a

w

nin

g

P

o

t

e

n

tial R

a

tio

Groupers Snappers Grunts

Overfishing (30% SPR)

Figure C-5: Spawning potential ratio (SPR) analysis for species in the snapper-grouper complex 

from the Dry Tortugas National Park (DRTO) for the period 1999-2004. Dark bars indicate 

overfished stocks and open bars indicate stocks that are above the 30% SPR standard. 

140 
general, the majority of species in the snapper-grouper complex for which estimates could be 

made are below the 30% SPR standard. A comparison of DRTO to the broader Tortugas region 

shows that SPR estimates are fairly similar (Figure C-5). 

Values of the

F F

msy

 ratio plotted against the

B B

msy

 ratio (Figure C-6) suggest that 

most species of the snapper–grouper complex experience overfishing (F-ratio >1, B-ratio <1; 

Restrepo and Powers 1999) and have been subject to unsustainable rates of exploitation in recent 

years. Overfishing appears most severe for long-lived, slow-growing fish (cf. Table C-7). 

SPR estimates for Florida Keys reef fishes in comparison to those from Dry Tortugas are 

also shown in Figure C-7. The overall pattern of exploitation appears to be similar between the 

two regions. It appears that snapper SPRs are marginally higher in the Dry Tortugas. 

Of particular concern are the populations of long-lived fishes like black grouper, which 

live more than several decades and reach relatively large sizes (cf. Table C-7). These long-lived, 

slow growing fishes tend to be exceptionally sensitive to even modest exploitation rates, and as 

exploitation increases there is a significant truncation of the older, mature size classes in a 

process known as “juvenescense”, i.e., making the population younger through excessive fishing 

mortality (Figure C-8). 

Ecological interpretation of the plot of SPR dependent on M K (Figure C-9) suggests 

that the greater proportion of those fishes experiencing non-sustainable rates of exploitation 

(SPR < 30%) are those with relatively low M K values. Fish with M K values greater than 1.5 

could be expected to recover in sustainable population sizes in 5-15 years. However, for those 

with M K values less than 1.5, recovery for stocks with SPRs less than 30% may be expected to 

take 2-3 decades or more. 

C-4.3 Discussion 

The L method for estimating total mortality exhibits relatively robust properties for 

assessing exploitation impacts on the Florida coral reef fish community (Ehrhardt and Ault 1992; 

Quinn and Deriso 1999; Ault et al. 1998, 2001, 2002, 2005b). The use of L as an estimator of 

fishing mortality, and therefore indirectly as an indicator of exploitation, has several practical 

advantages: (1) relatively simple data requirements (i.e. accurate and precise information on the 

age-and-size relationship, length frequency compositions for the exploited stock, and estimates 

of M); (2) the method applies to both fishery-independent and -dependent data, and therefore 

141 
0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Park SPR

T

o

r

t

u

g

a

s

R

e

gio

n

S

P

R

Groupers Snappers Grunts

Red Hind

Rock Hind

Schoolmaster

French

Margate

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Park SPR

T

o

r

t

u

g

a

s

R

e

gio

n

S

P

R

Groupers Snappers Grunts

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Park SPR

T

o

r

t

u

g

a

s

R

e

gio

n

S

P

R

Groupers Snappers Grunts

Red Hind

Rock Hind

Schoolmaster

French

Margate

Figure C-6: Comparison of spawning potential ratio (SPR) for reef fish in the exploited snappergrouper complex in Dry Tortugas National Park (DRTO) versus the Tortugas Region domain 

(Park and Tortugas Bank). Perfect agreement between estimates follows the dotted line. Note the 

relative high degree of correlation between regional estimates. 

142 
0

1

2

3

4

5

6

7

8

9

0.0 0.5 1.0 1.5 2.0

B/Bmsy

F

/

F

m

s

y

grunts snappers groupers

MSY Target

Overfishing

Yellowtail

Scamp

Black

Gray

Nassau

Lane

White

Gag

Tomtate

Red

Graysby

Hogfish Bluestriped

French Porkfish

Mutton

Cottonwick

Schoolmaster

Rock Hind

Red Hind Yellowmouth

0

1

2

3

4

5

6

7

8

9

0.0 0.5 1.0 1.5 2.0

B/Bmsy

F

/

F

m

s

y

grunts snappers groupers

MSY Target

Overfishing

Yellowtail

Scamp

Black

Gray

Nassau

Lane

White

Gag

Tomtate

Red

Graysby

Hogfish Bluestriped

French Porkfish

Mutton

Cottonwick

Schoolmaster

Rock Hind

Red Hind Yellowmouth

Figure C-7: Plot of F/Fmsy ratio against B/Bmsy ratio for fishes in the snapper-grouper complex 

in the Dry Tortugas region for 2004 (blue - groupers; yellow - snappers and wrasses; green -

grunts). 

143 
Florida Keys Tortugas Region

Groupers Snappers

Grunts

0.0

0.2

0.4

0.6

0.8

1.0

R

o

ck Hind

Gra

ysby

C

one

y

R

ed Hind

G

oliath

R

ed

N

assau

Bla

ck

Y

ello

w

m

outh

G

a

g

S

c

a

mp

Tig

er

Y

ello

wfin

Mutton

S

cho

olm

aster

Bla

ckfin

Gra

y

D

o

g

M

aho

g

ony

LaneSilkY

ello

wtail

V

ermilion

H

o

gfish

P

orkfish

M

arg

ate

T

o

mtate

FrenchC

ottonwick

WhiteBluestriped

S

p

a

w

nin

g

P

ote

ntial R

atio

Florida Keys Tortugas Region

Groupers Snappers

Grunts

0.0

0.2

0.4

0.6

0.8

1.0

R

o

ck Hind

Gra

ysby

C

one

y

R

ed Hind

G

oliath

R

ed

N

assau

Bla

ck

Y

ello

w

m

outh

G

a

g

S

c

a

mp

Tig

er

Y

ello

wfin

Mutton

S

cho

olm

aster

Bla

ckfin

Gra

y

D

o

g

M

aho

g

ony

LaneSilkY

ello

wtail

V

ermilion

H

o

gfish

P

orkfish

M

arg

ate

T

o

mtate

FrenchC

ottonwick

WhiteBluestriped

S

p

a

w

nin

g

P

ote

ntial R

atio

Figure C-8: Comparative SPR analysis for exploited reef fishes between the Dry Tortugas 

region (green) and Florida Keys (orange) for the period 2000-2002. Dark bars indicate 

overfished stocks, open bars indicate stocks that are above the 30% SPR standard (blue 

horizontal line). 

144 
0.00

0.01

0.02

0.03

0.04

486 556 626 696 766 836 906 976 1046 1116 1186 1256

Length (mm)

F

r

e

q

u

e

n

c

y

0.000

0.005

0.010

0.015

0.020

486 556 626 696 766 836 906 976 1046 1116 1186 1256

Length (mm)

F

r

e

q

u

e

n

c

y

0.00

0.02

0.04

0.06

0.08

486 556 626 696 766 836 906 976 1046 1116 1186 1256

Total length (mm)

F

r

e

q

u

e

n

c

y

Lightly Exploited

F = FMSY

F = F2004

0.00

0.01

0.02

0.03

0.04

486 556 626 696 766 836 906 976 1046 1116 1186 1256

Length (mm)

F

r

e

q

u

e

n

c

y

0.000

0.005

0.010

0.015

0.020

486 556 626 696 766 836 906 976 1046 1116 1186 1256

Length (mm)

F

r

e

q

u

e

n

c

y

0.00

0.02

0.04

0.06

0.08

486 556 626 696 766 836 906 976 1046 1116 1186 1256

Total length (mm)

F

r

e

q

u

e

n

c

y

Lightly Exploited

F = FMSY

F = F2004

Figure C-9: Process of “juvenesecense” of a black grouper population when: (upper panel) 

lightly exploited; (middle panel) exploited at MSY; and, (lower panel) current exploitation level 

in the Florida Keys. 

145 
may help to resolve incongruencies that exist among individual data sets; (3) relatively simple 

computational requirements for the 

L

algorithm, because length-to-age transformation is not 

required, unlike most contemporaneous age-based mortality estimators (Quinn and Deriso 1999); 

and (4) the method might be used to generate a “community control rule” indicator of 

exploitation status, when used in the context of synoptic fishery-independent survey data. 

The principal fleet targeting the reef fish complex of approximately 50 species is 

recreational, and the principal gear is hook and line, which is relatively non-selective (Ault et al. 

2005a). Because many of the species co-occur in similar habitats, capture probability for most 

species on any given trip at any location is greater than zero. Therefore, nominal fishing effort in 

the Florida Keys affects the snapper–grouper complex as a whole, but acts differentially on 

individual species, depending on their life history characteristics. The impact of exploitation was 

more severe for the slow-growing, long-lived groupers and hogfish than for other species. 

Because of these factors, management to build sustainable fisheries may need to consider the 

entire reef-fish complex and perhaps invoke a spatial context to interventions. 

C-5 MPA Performance in the Dry Tortugas Region 

An extensive literature has touted the use of ‘no-take’ marine reserves (NTMRs -- areas 

protected from all extractive uses) as the means to reverse declining trends in tropical coral reef 

ecosystems (Polunin 1990, 2002; Roberts and Polunin 1991; DeMartini 1993; Bohnsack and 

Ault 1996; Roberts 1997; Allison et al. 1998; Guenette et al. 1998; Bohnsack et al. 2004; Gell 

and Roberts 2003; Halpern and Warner 2002, 2003; Hastings and Botsford 2003; Lubchenco et 

al. 2003; Willis et al. 2003; Hooker and Gerber 2004; Meester et al. 2001, 2004; Ault et al. 

2002, 2005a; Mangel and Levin 2005). 

In the Florida Keys, increased fishing pressure from rapid regional human population 

growth and environmental changes associated with coastal development have raised concerns 

about fisheries sustainability and persistence of the coral reef ecosystem (Porter and Porter, 

2001; Ault et al. 2005a; Pandolfi et al. 2005). In response to declining trends in reef fishery 

catches, a series of regional federal and state management regulations were imposed, including 

recreational bag limits, minimum size limits, commercial quotas and trip limits, seasonal 

closures, gear restrictions, limited commercial entry, closed fisheries, species moratoria, game 

fish status, and restrictions on sale and possession. These regulations were implemented to 

stabilize catches, protect spawning stock biomass, and reduce fishing mortality rates. Despite the 

146 
bevy of regulations imposed in the Florida Keys, recent fishery assessments indicate that, for 

example, black grouper spawning stock biomass was less than 10% of its historical size (Ault et 

al., 2005b). 

In recent years, new ecosystem-based management measures have been enacted in the 

Florida Keys, including the 1997 implementation of a network of 23 NTMRs by the Florida 

Keys National Marine Sanctuary (FKNMS, NOAA, www.fknms.noaa.gov). In July 2001, the 

Florida Keys network was expanded to become the largest in North America with the 

implementation of two NTMRs in the Dry Tortugas region covering about 566 km2. 

C-5.1 Methods 

Our statistical analyses focused on changes between baseline years 1999 and 2000 

(before) and 2004 (after), when synoptic surveys were conducted in both Tortugas Bank and 

DRTO. Statistical analysis of change was evaluated using a community metric, species richness, 

and two population metrics: frequency of occurrence and abundance. Statistical estimation 

procedures followed Cochran (1977) for a two-stage stratified random sampling design. In these 

procedures, strata means and variances of a given metric are weighted by strata sizes, i.e., 

= !hWh Nh Nh /

, to obtain overall means and variances for either specific management zones, or 

for the entire Tortugas domain. Species richness was estimated on a primary sample unit basis 

(i.e., the number of unique species observed within a primary unit by the group of divers) to 

ensure a sufficient search area for obtaining reliable estimates. In this case, the statistical sample 

size was n, the number of sampled primary units. Both frequency of occurrence and abundance 

were estimated by species on a second-stage unit basis, the standard approach for two-stage 

designs (Cochran, 1977), where the number of second-stage units nm was the statistical sample 

size. Since benthic habitat classification, digital mapping, and development of the Tortugas 

survey design occurred concurrently with the baseline surveys of 1999 and 2000 (Ault et al. 

2002), each population and community metric was estimated as a composite of the two baseline 

years to alleviate problems of misclassification of habitats and misallocation of samples among 

habitat strata. In this procedure, strata means and variance components were computed as twoyear averages weighted by respective sample sizes in 1999 and 2000. 

Species chosen for detailed analyses reflected the range of population dynamic processes 

(growth and survivorship) for relatively abundant exploited and non-exploited components of the 

reef fish community. Statistical tests for differences among estimates of mean density, total 

147 
abundance, and mean proportion of samples for the sampling design configuration were 

conducted by inspection of confidence intervals (CI) using Bonferroni adjustments (Cochran, 

1977). Detection of change was defined as the ability to discriminate between the 95% CI of 

mean responses between the two time periods. The Bonferoni CI t-test was used because it is 

more suited to sample design statistics and does not require homogenous variance between two 

distributions to test differences in the mean responses. Changes in length compositions between 

time periods were evaluated using standard two sample Chi-square tests (Agresti 1996). The 

absolute ability to detect changes was thus determined by the precision of the survey estimates 

(e.g., standard error). 

C-5.2 Results 

For the Tortugas sampling domain, we detected no change in mean species richness 

(mean number of species per psu) between the 1999-2000 ( 

37.1± 0.7

SE) baseline and 2004 

( 

38.1± 0.5

SE), even though we could have detected a change > 1.4 species (i.e., approximately 2 

SE). We found similar results for selected taxa. For example, mean richness for species of 

exploited snappers and groupers was

7.8 ± 0.2

 for both 1999-2000 and 2004 survey periods. 

Species richness (diversity) of the snapper-grouper complex was also related to reef rugosity, in 

that it was highest on reef terrace and pinnacle habitats found on the northwestern Tortugas Bank 

and western DRTO, and also in medium profile reef in the northwestern portion of the Park 

(Figure C-10). It was lowest in low-relief hardbottom and patchy hardbottom in sand habitats. 

The relatively stable community structure shown for richness was also reflected in 

domain-wide estimates of frequency of occurrence or sighting frequency. Although there were 

minor changes in ranks between years, only four of the top 50 species (of 267 total) for the 2004 

survey were not among the top 50 species for the 1999-2000 surveys (Table C-8). The top 50 

species included 11 (of 55 total) species from the exploited snapper-grouper complex. 

Estimates of frequency of occurrence and abundance for representative species of 

principal families are given in Tables C-9 and C-10, respectively. Analyses of change between 

1999-2000 and 2004 using black grouper as an example are illustrated. Domain-wide percent 

occurrence for black grouper increased from 19.5% in 1999-2000 to 28.8% in 2004 (Table C-9; 

p<0.01), along with a concomitant abundance increase of 124% (Table C-10A; p<0.001). 

Detection of temporal change in abundance was facilitated by a decrease in the survey 

coefficient of variation (CV=SE/Mean) from 14.5% to 10.3%. The increase in domain-wide 

abundance was accompanied by a shift in the length composition between 1999-2000 and 2004 

148 
0.0

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0.2

0.3

0.4

0.5

0.6

0.7

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1.0

0.5 1.0 1.5 2.0 2.5 3.0 3.5

M/K

S

P

R

Groupers Snappers Grunts

Black

Gag

Scamp Yellowfin

Nassau

Red

Bluestriped

Porkfish

Yellowtail

Cottonwick Goliath Red Hind

Rock Hind

Graysby

Hogfish

Gray

White

Margate

Dog Yellowmouth

Tomtate

Lane

Schoolmaster

0.0

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Black

Gag

Scamp Yellowfin

Nassau

Red

Bluestriped

Porkfish

Yellowtail

Cottonwick Goliath Red Hind

Rock Hind

Graysby

Hogfish

Gray

White

Margate

Dog Yellowmouth

Tomtate

Lane

Schoolmaster

Figure C-10: Plot of estimated spawning potential ratio (SPR) dependent on M/K (natural 

mortality rate divided by the growth rate) for exploited groupers (blue), snappers and wrasses 

(yellow), and grunts (green) from the Dry Tortugas. 

149 
1

2

3

4

5

6

7

8

9

1

2

3

4

5

6

7

8

9

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2

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7

8

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4

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8

9

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2

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4

5

6

7

8

9

Table C-8: Ranked top 50 reef fish species in terms of percent occurrence for 2004 compared to 

1999-2000. Common names in bold denote species in the exploited snapper-grouper complex. 

Occurrence Rank 

Common name Scientific name Family 2004 1999-2000 Change 

Bluehead Thalassoma bifasciatum Labridae 1 = 

Striped parrotfish Scarus iseri Scaridae 2 = 

Cocoa damselfish Stegastes variabilis Pomacentridae 3 = 

Redband parrotfish Sparisoma aurofrenatum Scaridae 5 + 

Yellowhead wrasse Halichoeres garnoti Labridae 10 + 

Blue tang Acanthurus coeruleus Acanthuridae 8 + 

Bicolor damselfish Stegastes partitus Pomacentridae 11 + 

Spotted goatfish Pseudupeneus maculatus Mullidae 20 + 

White grunt Haemulon plumieri Haemulidae 4 -

Slippery dick Halichoeres bivittatus Labridae 10 7 -

Yellowtail snapper Ocyurus chrysurus Lutjanidae 1 9 -

Saucereye porgy Calamus calamus Sparidae 1 6 -

Stoplight parrotfish Sparisoma viride Scaridae 1 15 + 

Bridled goby Coryphopterus glaucofraenum Gobiidae 1 12 -

Red grouper Epinephelus morio Serranidae 1 13 -

Purple reeffish Chromis scotti Pomacentridae 1 28 + 

Ocean surgeon Acanthurus bahianus Acanthuridae 1 18 + 

Blue angelfish Holacanthus bermudensis Pomacanthidae 1 16 -

Spotfin butterflyfish Chaetodon ocellatus Chaetodontidae 1 17 -

Butter hamlet Hypoplectrus unicolor Serranidae 20 29 -

Greenblotch parrotfish Sparisoma atomarium Scaridae 2 24 + 

Masked goby Coryphopterus personatus Gobiidae 2 25 + 

Blue hamlet Hypoplectrus gemma Serranidae 2 38 + 

Yellowhead jawfish Opistognathus aurifrons Opistognathidae 2 21 -

Gray angelfish Pomacanthus arcuatus Pomacanthidae 2 23 -

Hogfish Lachnolaimus maximus Labridae 2 19 -

Foureye butterflyfish Chaetodon capistratus Chaetodontidae 2 32 + 

Clown wrasse Halichoeres maculipinna Labridae 2 26 -

Threespot damselfish Stegastes planifrons Pomacentridae 2 30 + 

Beaugregory Stegastes leucostictus Pomacentridae 30 34 + 

Harlequin bass Serranus tigrinus Serranidae 3 27 -

Saddled blenny Malacoctenus triangulatus Labrisomidae 3 14 -

Barred hamlet Hypoplectrus puella Serranidae 3 31 -

Neon goby Elacatinus oceanops Gobiidae 3 22 -

Graysby Cephalophilis cruentatus Serranidae 3 37 + 

Black grouper Mycteroperca bonaci Serranidae 3 44 + 

Blue chromis Chromis cyanea Pomacentridae 3 45 + 

Mutton snapper Lutjanus analis Lutjanidae 3 52 + 

Tobaccofish Seranus tabacarius Serranidae 3 46 + 

Bar jack Caranx ruber Carangidae 40 40 = 

Queen angelfish Holacanthus ciliaris Pomacanthidae 4 43 + 

Great barracuda Sphyraena barracuda Sphyraenidae 4 49 + 

Sharpnose puffer Canthigaster rostrata Tetraodontidae 4 39 -

Spanish hogfish Bodianus rufus Labridae 4 47 + 

Tomtate Haemulon aurolineatum Haemulidae 4 41 -

Princess parrotfish Scarus taeniopterus Scaridae 4 61 + 

Reef butterflyfish Chaetodon sedentarius Chaetodontidae 4 36 -

French grunt Haemulon flavolineatum Haemulidae 4 50 + 

Cero Scomberomorus regalis Scombridae 4 117 + 

Bucktooth parrotfish Sparisoma radians Scaridae 50 101 + 

150 
Table C-9: Domain-wide estimates of percent occurrence for representative exploited and nontarget fish species for baseline years 1999-2000 and the 2004 survey. Statistically significant 

change between baseline years and 2004: ns - not significant; * - p<0.05; ** - p<0.01; *** -

p<0.001. 

Percent Occurrence (SE) 

Taxa 1999-2000 2004 Change 

Snapper-Grouper Complex 

Groupers (Serranidae) 

Goliath grouper (Epinephelus itajara) 0.5 (0.4) 1.3 (0.5) ns 

Red grouper (E. morio) 67.0 (3.3) 62.8 (3.1) ns 

Nassau grouper (E. striatus) 1.0 (0.6) 0.3 (0.2) ns 

Black grouper (Mycteroperca bonaci) 19.5 (2.5) 28.8 (2.4) ** 

Snappers (Lutjanidae) 

Mutton snapper (Lutjanus analis) 14.8 (2.4) 25.8 (3.0) *** 

Gray snapper (L. griseus) 17.3 (2.5) 12.2 (1.5) * 

Yellowtail snapper (Ocyurus chrysurus) 74.7 (3.2) 68.1 (3.1) * 

Wrasses (Labridae) 

Hogfish (Lachnolaimus maximus) 52.8 (3.5) 42.6 (3.0) ** 

Grunts (Haemulidae) 

White grunt (Haemulon plumieri) 82.0 (2.7) 71.5 (2.7) *** 

Bluestriped grunt (H. sciurus) 6.4 (1.7) 7.7 (1.2) ns 

Non-Target Fishes 

Surgeonfishes (Acanthuridae) 

Ocean surgeon (Acanthurus bahianus) 54.9 (3.3) 60.3 (2.7) ns 

Blue tang (A. coeruleus) 76.4 (3.1) 80.9 (2.2) ns 

Butterflyfishes (Chaetodontidae) 

Foureye butterflyfish (Chaetodon capistratus) 34.0 (3.3) 42.3 (2.8) * 

Spotfin butterflyfish (C. ocellatus) 56.4 (3.4) 49.9 (3.0) ns 

Goatfishes (Mullidae) 

Spotted goatfish (Psuedupeneus maculatus) 50.7 (3.6) 71.7 (2.2) *** 

Angelfishes (Pomacanthidae) 

Blue angelfish (Holocanthus bermudensis) 57.9 (3.2) 55.9 (2.7) ns 

Gray angelfish (Pomacanthus arcuatus) 45.5 (3.3) 43.9 (2.8) ns 

Damselfishes (Pomacentridae) 

Purple reeffish (Chromis scotti) 37.2 (3.4) 62.2 (3.1) *** 

Bicolor damselfish (Stegastes partitus) 72.7 (2.9) 72.6 (2.3) ns 

Cocoa damselfish (S. variabilis) 87.7 (2.3) 90.0 (2.0) ns 

Parrotfishes (Scaridae) 

Striped parrotfish (Scarus iseri) 88.4 (2.4) 94.3 (1.3) * 

Redband parrotfish (Sparisoma aurofrenatum) 80.8 (2.9) 86.9 (1.9) * 

Stoplight parrotfish (Sparisoma viride) 59.3 (3.5) 64.5 (3.3) ns 

151 
1999-2000 2004 

Abundance CV Abundance CV 

Taxa (millions) (%) (millions) (%) Change 

Snapper-Grouper Complex 

Red grouper 1.260 6.8 1.237 6.5 -2% ns 

Black grouper 0.277 14.5 0.622 10.3 +124% *** 

Mutton snapper 0.216 21.2 0.452 13.2 +109% *** 

Gray snapper 3.714 54.3 5.155 74.0 +39% ns 

Yellowtail snapper 8.257 13.0 23.169 27.2 +181% * 

Hogfish 1.121 10.7 0.910 12.0 -19% ns 

White grunt 9.317 15.5 9.644 21.6 +4% ns 

Bluestriped grunt 0.330 47.0 0.854 42.0 +159% ns 

Non-Target Fishes 

Ocean surgeon 2.045 13.3 2.275 8.0 +11% ns 

Blue tang 3.474 9.7 5.747 7.8 +65% *** 

Foureye butterflyfish 0.960 10.8 1.083 7.5 +13% ns 

Spotfin butterflyfish 1.315 7.5 1.256 6.8 -5% ns 

Spotted goatfish 1.076 10.7 3.204 9.8 +198% *** 

Blue angelfish 1.555 8.0 1.525 6.8 -2% ns 

Gray angelfish 0.868 9.2 1.588 27.2 +83% ns 

Purple reeffish 11.518 17.8 20.219 13.0 +76% *** 

Bicolor damselfish 12.914 10.4 17.269 7.8 +34% ** 

Cocoa damselfish 7.654 5.9 7.384 4.9 -4% ns 

Striped parrotfish 16.117 18.3 22.290 10.1 +38% * 

Redband parrotfish 4.565 16.2 7.096 23.3 +56% ns 

Stoplight parrotfish 1.936 9.7 3.012 10.3 +56% *** 

Table C-10: (A) Domain-wide estimates of abundance (and associated coefficient of variation CV) and 

changes between baseline years 1999-2000 and 2004 for representative exploited and non-target fish 

species. (B) Population abundance changes between 1999-2000 and 2004 within management zones in 

the Dry Tortugas region. Statistically significant change between baseline years and 2004: ns - not 

significant; * - p<0.05; ** - p<0.01; *** - p<0.001. 

(A) 

152 
Table C-10: (cont.) 

(B) 

Taxa 

Tortugas Bank 

Fished 

Tortugas Bank 

NTMR 

Dry Tortugas 

National Park 

Snapper-Grouper Complex 

Red grouper 

Black grouper 

Mutton snapper 

Gray snapper 

Yellowtail snapper 

Hogfish 

White grunt 

-43% 

+84% 

-45% 

-96% 

-19% 

-27% 

+7% 

* 

ns 

ns 

ns 

ns 

ns 

ns 

+38% 

+120% 

+303% 

-51% 

+367% 

+6% 

+24% 

* 

* 

** 

ns 

ns 

ns 

ns 

-9% 

+128 

+142% 

+270% 

+132% 

-25% 

+2% 

ns 

*** 

*** 

ns 

*** 

ns 

ns 

Bluestriped grunt +50% ns +13% ns +242% ns 

Non-Target Fishes 

Ocean surgeon 

Blue tang 

Foureye butterflyfish 

Spotfin butterflyfish 

Spotted goatfish 

Blue angelfish 

Gray angelfish 

Purple reeffish 

Bicolor damselfish 

+2% 

+13% 

+86% 

+35% 

+133% 

-18% 

-24% 

+31% 

+6% 

ns 

ns 

* 

ns 

** 

ns 

ns 

ns 

ns 

+75% 

+28% 

-18% 

-31% 

+326% 

-20% 

+58% 

+42% 

+73% 

** 

ns 

ns 

* 

*** 

ns 

ns 

ns 

** 

-9% 

+99% 

+32% 

0% 

+175% 

+31% 

+120% 

+263% 

+17% 

ns 

*** 

ns 

ns 

*** 

* 

ns 

*** 

ns 

Cocoa damselfish -28% ns -21% ns +6% ns 

Striped parrotfish 

Redband parrotfish 

Stoplight parrotfish 

+51% 

+121% 

+9% 

ns 

*** 

ns 

+127% 

+26% 

+26% 

* 

ns 

ns 

+9% 

+56% 

+84% 

ns 

ns 

*** 

153 
towards a higher proportion of exploited-phase animals in the black grouper population (Figure C-11A; 

Chi-square p<0.001 for lengths > 30 cm). 

Abundance estimates for black grouper showed increasing trends in all three management zones 

over the survey time period, but statistically significant increases were detected only in Tortugas Bank 

NTMR and DRTO (Table C-10B). In 2004, black grouper population size structure appeared to expand 

in the Bank NTMR and Park areas, but was highly truncated above the minimum legal size in the Bank 

fished area. Changes in length compositions within management zones paralleled changes in abundance 

(Figure C-11B, Table C-10), with a higher proportion of exploited phased animals in the Bank NTMR 

(p<0.05) and Park (p<0.001). No change in length composition was detected in the Tortugas Bank 

Fished area. 

Significant increases in domain-wide occurrence and abundance were also detected for mutton 

snapper, corresponding with significant increases in abundance in the Tortugas Bank NTMR and DRTO 

management zones. In general, trends in occurrence mirrored those for abundance for species with 

relatively small population sizes. 

No change in either occurrence or abundance for red grouper was detected domain-wide; 

however, a significant decrease in abundance in the Tortugas Bank fished area and a significant increase 

in Tortugas Bank NTMR was detected. Similar to black grouper, increases in the population proportion 

of larger (older) individuals for red grouper (Figure C-10A; Chi-square p<0.001 for lengths > 30 cm) 

was noted. 

A marginal decrease in domain-wide occurrence for yellowtail snapper was detected; on the 

other hand, a domain-wide increase in abundance corresponding with a significant increase in the Park 

was also detected. Evidently, more fish were seen at fewer sites, however, the observed decline in 

percent occurrence probably had little biological significance. As a result, abundance may be a more 

indicative metric of population change. This disparity between occurrence and abundance was also 

observed for other schooling species: gray snapper, hogfish, and white grunt. 

Domain-wide occurrences of goliath grouper and Nassau grouper, two species under fishing 

moratoria, remained low over the survey period. We observed goliath grouper in one primary sampling 

unit in 1999, two units in 2000, and in 10 primary sampling units in 2004 (seven in the Park and three in 

the Bank NTMR) which is perhaps encouraging for its recovery, but was not a statistically significant 

change in frequency of occurrence. 

Among species not targeted by exploitation, domain-wide increases in both occurrence and 

abundance were detected for spotted goatfish, purple reeffish, and striped parrotfish (Scarus iseri). 

154 
(A)

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R

elativ

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y

Length (cm)

R

elativ

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y

Length (cm)

Black Grouper Red Grouper

1999 -2000

n=209

2004

n=558

1999 -2000

n=735

2004

n=702

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elativ

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elativ

e

Frequenc

y

Length (cm)

Black Grouper Red Grouper

1999 -2000

n=209

2004

n=558

1999 -2000

n=735

2004

n=702

Figure C-11: (A) Domain-wide comparisons of length compositions for black grouper (left panels) and 

red grouper (right panel) between 1999-2000 (top) and 2004 (bottom) surveys. (B) Comparison of the 3 

spatial zones for black grouper for 2004. Hatched bars are pre-exploited phase and shaded bars are 

exploited phase animals. Number of length observations are given on each panel. 

155 
(B)

0 . 0 0

0 . 0 5

0 . 1 0

0 . 1 5

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elativ

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u

e

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c

y

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Black Grouper 2004

Tortugas Bank, Fished

n = 16

Tortugas Bank, NTMR

n = 140

Dry Tortugas National Park

n = 402

0 . 0 0

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elativ

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c

y

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Black Grouper 2004

Tortugas Bank, Fished

n = 16

Tortugas Bank, NTMR

n = 140

Dry Tortugas National Park

n = 402

Figure C-11: (Cont.) 

156
On the other hand, we detected increases in domain-wide occurrence but no changes in abundance for 

foureye butterflyfish and redband parrotfish (Sparisoma aurofrenatum). For blue tang, bicolor 

damselfish (Stegastes partitus), and stoplight parrotfish, no changes were detected in domain-wide 

occurrence but we detected increases in domain-wide abundance. Domain-wide increases in spotted 

goatfish corresponded to significant increases in abundance in all three management zones. Domainwide increases in abundance of blue tang, purple reeffish, and stoplight parrotfish corresponded to 

increased abundances in the Park. Increases in domain-wide abundance of bicolor damselfish and striped 

parrotfish were accompanied by significant abundance increases in Tortugas Bank NTMR. In several 

cases, management zone changes in abundance were detected but these did not correspond to domainwide changes. 

An unexpected occurrence in the 2004 survey, based on our previous cruises, was the sighting of 

large (>2000 fish) schools of large (> 9 kg) permit (Trachinotus falcatus) at 8 primary sampling unit 

locations. The timing and schooling behavior of these mature permit suggests that these may have been 

spawning aggregations. Seven of the 8 schools were sighted on Tortugas Bank, and these were either 

inside or just outside the Bank NTMR. 

C-5.3 Discussion 

The Tortugas region represents a de facto adaptive management experiment in which three 

discrete, contiguous areas are being managed under different levels of resource protection. The 

Tortugas Bank Fished zone operates under regional fishing regulations and is less protected than DTNP 

which is open only to recreational angling. Finally, the Tortugas Bank NTMR (North Ecological 

Reserve), protected from all extraction, is the most protected. Determining the efficacy of the suite of 

management approaches is one of Florida’s most critical resource management problems and a unique 

challenge for science-based resource management. 

A number of authors have pointed out that detection of changes in population abundance and 

biomass in response to any fishery management action has often suffered from lack of rigor in the 

design of both fishery-dependent and fishery-independent surveys (e.g., Hurlbert, 1984; Stewart-Oaten 

et al., 1986; Underwood 1990, 1993; Willis et al., 2003; Hilborn et al., 2004; Sale et al., 2005). Relative 

to traditional fishery-dependent approaches, quantitative assessments of NTMRs present their own 

unique challenges because there are no catches to examine from closed areas and data must be spatiallyexplicit. In addition, data must be collected that reflects community dynamics, not just exploited species 

dynamics, to evaluate the performance of ecosystem-based management. These principles were the 

impetus for our survey sampling approach in the Tortugas region. 

157 
The fisheries-independent RVC surveys provided fairly precise estimates of species richness and 

frequency of occurrence. However, while also a precise measure, abundance was a metric more 

indicative of population change because it tracked population variability at both low and high population 

sizes. In general, our population detection limits for changes in abundance ranged between 15% to 30%, 

i.e., twice the measured CV. In some cases it was difficult to obtain precise estimates of abundance. For 

example, low sighting frequency coupled with relatively high abundance at few sites yielded high CVs 

for gray snapper. Overall, CI t-tests were found to be a conservative application of statistical methods 

because they required detection of differences in mean abundance with respect to each time period. The 

method became less robust as the size of the spatial unit (e.g., management zone, habitat type, etc.) 

decreased. 

The impacts of management actions on population biomass could take years to occur and then be 

detected (e.g., Beverton and Holt, 1957). However, signs of recovery in the Tortugas reef fish 

community over a relatively short time after implementation of NTMRs was observed. We have shown 

that metrics of the reef fish community (e.g., richness and species composition) were very stable over 

the study time period. However, for a representative suite of 21 reef fishes, increases in domain-wide 

abundance for three exploited species (black grouper, mutton snapper, yellowtail snapper) and six nontarget species (blue tang, spotted goatfish, purple reeffish, bicolor damselfish, striped parrotfish, and 

stoplight parrotfish) were detected. No decreases in domain-wide abundance were detected for any of 

the species analyzed. 

The observed contrasts where abundance changes occurred between exploited and non-target 

species suggests that spatial protection may have been an important contributing factor in region-wide 

changes. Abundance increases were detected for non-target species in all three management zones, but 

only one species, Chaetodon ocellatus, decreased, and that occurred in the Bank NTMR. For exploited 

species, significant abundance increases were confined to the Bank NTMR and DRTO, while the only 

significant abundance decrease occurred in the Bank Fished area. Moreover, significant shifts in length 

compositions towards larger animals for black grouper and red grouper were found. In addition, in the 

Bank Fished area, black grouper size frequency distributions showed continued truncation of fish larger 

than the legal minimum size limit consistent with continued fishing pressure. Similar responses to 

spatial protection have been observed in the region for heavily exploited spiny lobster and mutton 

snapper (Davis and Dodrill, 1980; Cox and Hunt, 2005; Burton et al., 2005). 

However, our results also suggest that the population increases observed in the Bank NTMR and 

Park could have been augmented by co-occurring regional fishery management actions or favorable 

environmental conditions. Increases in abundance of larger animals would also be expected in response 

158 
to traditional management measures such as bag limits and size limits. For example, minimum size 

limits for black grouper have been increased from 18” (45.7 cm) established in 1985 to 20” (50.8 cm) in 

1990, and 22” (55.9 cm) for recreational fishers and 24” (61.0 cm) for commercial fishers in 1999. The 

last regulation brought the minimum size up to the minimum size of sexual maturity (Ault et al. 2005b). 

Generally, abundance changes in non-target species would not be expected to be in direct response to 

fishery management policy. Increases in non-target species abundance suggests that the environment 

plays an important role and may have contributed to good recruitment events in recent years. Random 

variability in year class strengths or the passing of several hurricanes in the late-1990s may also have 

influenced recruitment for both exploited and non-target reef fishes. In reality, it is likely that many of 

the factors interact. 

Similar observations of fish population recovery, have been made in other coral reef ecosystems 

but usually over longer time frames (c.f., Halpern and Warner 2002; Russ et al. 2004; Alcala et al. 

2005). According to populations dynamics theory, not enough time has elapsed since implementation of 

the Tortugas NTMR to fully explain the findings. It is thus highly likely that not all the observed 

changes were a direct response to NTMR implementation. Furthermore, potential impacts on reef fish 

community dynamics are complex and may be influenced by shifts in composition, trophic cascades 

promulgated by predator-prey responses and habitat competition. The next research challenge will be to 

develop and refine methods for improved understanding of the relative contributions of NTMRs, various 

fishery management actions, community interactions, and environmental factors in terms of achieving 

the goal of building sustainable fisheries. 

As this rebuilding process and reef ecosystem responds to management actions over the next 

several decades, a continued concern will be balancing fishing with resource protection. A particular 

concern is the likely continued growth in demand from the recreational fleet and its fishing power due to 

technological improvements. Although failure to adequately control fishing mortality can have 

potentially detrimental consequences for the stocks and the economy (Steele and Hoagland 2003), 

removal of units of fishing effort once they have been established will be difficult, i.e., the “ratchet” 

effect (Ludwig et al., 1993). In the long run, a precautionary ecosystem-based approach to management 

using multiple control methods offers promise for providing fishery sustainability and persistence of the 

Florida Keys coral reef ecosystem. As noted by Stefansson and Rosenberg (2005), combining catch 

controls with large closed areas may be the most effective system of reducing risk of stock collapse 

while maintaining short and long-term economic performance and buffering uncertainty. 

159 
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163 
C-7 Computational Formulae

Symbol Definition Computational Formula 

Domain Variables 

h Stratum subscript ( 

h = 1,K,!

) 

i Primary stage sample unit subscript ( ) 

j Second-stage sample unit subscript ( ) 

A

Ah

hi a

hij a

Nh

M hi

NhM h

h n

mhi

nhmh

wh

Area of entire survey domain 

Area of stratum h 

Area of ith primary stage unit in stratum h 

Area of jth second-stage unit in primary unit i in stratum h 

Total number of primary units i in stratum h 

Total number of second-stage units j in primary unit i in stratum h 

Total number of possible second-stage units j in stratum h 

Number of sampled primary units i in stratum h 

Number of sampled second-stage units j in primary unit i in stratum h 

Total number of second-stage units j sampled in stratum h 

Stratum h weighting factor 

!

=

=

"

h 1

A Ah

!

=

=

Nh

i

h hi A a

1

1

Mhi

hi hij

j

a a

=

= !

hi

h

h

a

A

N =

hi

hi

hij

a M

a

=

!

=

h

h h

h h

h N M

N M

w

164 

Nh i = 1,K,

M hi j = 1,K,
Computational Formulae (cont.) 

Symbol Definition Computational Formula 

Animal Density 

Density (individuals per m2) at sampled second-stage unit j in primary 

unit i in stratum h Dhij

Dhi

Dh

Dst

2

1h s

2

2h s

var[Dh ]

var[Dst]

SE[Dst]

Mean density in primary unit i in stratum h 

Mean density in stratum h 

Domain-wide mean density for a stratified random survey 

Sample variance among primary units i in stratum h 

Sample variance among second-stage units j in stratum h 

Variance of mean density in stratum h 

Variance of domain-wide mean density 

Standard error of domain-wide mean density 

= !

j

hij

hi

hi D

m

D 1

= !

i

hi

h

h D

n

D 1

= !hDst whDh

( )

1

2

2

1

!

!

=

"

h

i

hi h

h

n

D D

s

( )

! !

"

"

"

#

$

%

%

%

&

'

(

(

=

i hi

i

hij hi

h

h

m

D D

n

s

1

1

2

2

2

[ ] 2

2

2

1

1 1

var h

h h

h

h

h

h

h

h

h

h

h s

n m

M

m

N

n

s

n

N

n

D

!

!

"

#

$

$

%

&

'

+

!

!

"

#

$

$

%

&

'

=

[ ]= ! [ ]

h

Dst wh Dh var var 2

SE[Dst]= var[Dst]

165 
Computational Formulae (cont.) 

Symbol Definition Computational Formula 

Frequency of Occurrence 

Proportion occurrence for second-stage unit j within primary unit i in 

phij stratum h 

phi

h p

pst

2

1h s

2

2h s

[ ] var ph

[ ] var pst

Mean proportion in primary unit i in stratum h 

Mean proportion in stratum h 

Domain-wide mean proportion for a stratified random survey 

Sample variance among primary units i in stratum h 

Sample variance among second-stage units j in stratum h 

Variance of mean proportion in stratum h 

Variance of domain-wide mean proportion 

= !

j

hij

hi

hi p

m

p

1

= !

i

hi

h

h p

n

p

1

= !hpst wh ph

( )

1

2

2

1

!

!

=

"

h

i

hi h

h

n

p p

s

( )( )!

"

# $

%

&

' (

(

)

*

+

+

,

-

'

= . hi hi

i hi

hi

h

h p p

m

m

n

s 1

1

2 1

2

[ ] 2

2

2

1

1 1

var h

h h

h

h

h

h

h

h

h

h

h s

n m

M

m

N

n

s

n

N

n

p

!

!

"

#

$

$

%

&

'

+

!

!

"

#

$

$

%

&

'

=

[ ]= ! [ ]

h

var pst wh var ph

2

166 
Computational Formulae (cont.) 

Symbol Definition Computational Formula 

Abundance 

Yh

ˆ

Yst

ˆ

var[Y

ˆ

h ]

var[Y

ˆ

st]

Abundance (number of animals) in stratum h 

Domain-wide abundance for a stratified random survey 

Variance of abundance in stratum h 

Variance of domain-wide abundance ( )( )( ) Yh = Dh NhM h Th

ˆ

= !hYst Yh

ˆ ˆ

[ ] [ ]( ) ( ) 2 2

var

ˆ

var Yh = D NhM h Th

[ ]= ! [ ]

h

Yst Yh

ˆ

var

ˆ

var

167